research proposal in deep learning

• Deep Learning Research Proposal

The word deep learning is the study and analysis of deep features that are hidden in the data using some intelligent deep learning models . Recently, it turns out to be the most important research paradigm for advanced automated systems for decision-making . Deep learning is derived from machine learning technologies that learn based on hierarchical concepts . So, it is best for performing complex and long mathematical computations in deep learning .

This page describes to you the innovations of deep learning research proposals with major challenges, techniques, limitations, tools, etc.!!!

One most important thing about deep learning is the multi-layered approach . It enables the machine to construct and work the algorithms in different layers for deep analysis . Further, it also works on the principle of artificial neural networks which functions in the same human brain. Since it got inspiration from the human brain to make machines automatically understand the situation and make smart decisions accordingly.  Here, we have given you some of the important real-time applications of deep learning.

Deep Learning Project Ideas

• Natural Language Processing
• Pattern detection in Human Face
• Image Recognition and Object Detection
• Driverless UAV Control Systems
• Prediction of Weather Condition Variation
• Machine Translation for Autonomous Cars
• Medical Disorder Diagnosis and Treatment
• Traffic and Speed Control in Motorized Systems
• Voice Assistance for Dense Areas Navigation
• Altitude Control System for UAV and Satellites

Now, we can see the workflow of deep learning models . Here, we have given you the steps involved in the deep learning model. This assists you to know the general procedure of deep learning model execution . Similarly, we precisely guide you in every step of your proposed deep learning model . Further, the steps may vary based on the requirement of the handpicked deep learning project idea. Anyway, the deep learning model is intended to grab deep features of data by processing through neural networks . Then, the machine will learn and understand the sudden scenarios for controlling systems.

Process Flow of Deep Learning

• Step 1 – Load the dataset as input
• Step 2 – Extraction of features
• Step 3 – Process add-on layers for more abstract features
• Step 4 – Perform feature mapping
• Step 5 –Display the output

Although deep learning is more efficient to automatically learn features than conventional methods, it has some technical constraints. Here, we have specified only a few constraints to make you aware of current research. Beyond these primary constraints, we also handpicked more number of other constraints. To know other exciting research limitations in deep learning , approach us. We will make you understand more from top research areas.

Deep Learning Limitations

• Test Data Variation – When the test data is different from training data, then the employed deep learning technique may get failure. Further, it also does not efficiently work in a controlled environment.
• Huge Dataset – Deep learning models efficiently work on large-scale datasets than limited data

Our research team is highly proficient to handle different deep learning technologies . To present you with up-to-date information, we constantly upgrade our research knowledge in all advanced developments. So, we are good not only at handpicking research challenges but also more skilled to develop novel solutions. For your information, here we have given you some most common data handling issues with appropriate solutions. 

What are the data handling techniques?

• Variables signifies the linear combo of factors with errors
• Depends on the presence of different unobserved variables (i.e., assumption)
• Identify the correlations between existing observed variables
• If the data in a column has fixed values, then it has “0” variance.
• Further, these kinds of variables are not considered in target variables
• If there is the issue of outliers, variables, and missing values, then effective feature selection will help you to get rid out of it. 
• So, we can employ the random forest method
• Remove the unwanted features from the model
• Repeat the same process until attaining maximum  error rate
• At last, define the minimum features
• Remove one at a time and check the error rate
• If there are dependent values among data columns, then may have redundant information due to similarities.
• So, we can filter the largely correlated columns based on coefficients of correlation
• Add one at a time for high performance
• Enhance the entire model efficiency
• Addresses the possibility where data points are associated with high-dimensional space
• Select low-dimensional embedding to generate related distribution
•   Identify the missing value columns and remove them by threshold
• Present variable set is converted to a new variable set
• Also, referred to as a linear combo of new variables
• Determine the location of each point by pair-wise spaces among all points which are represented in a matrix
• Further, use standard multi-dimensional scaling (MDS) for determining low-dimensional points locations

In addition, we have also given you the broadly utilized deep learning models in current research . Here, we have classified the models into two major classifications such as discriminant models and generative models . Further, we have also specified the deep learning process with suitable techniques. If there is a complex situation, then we design new algorithms based on the project’s needs . On the whole, we find apt solutions for any sort of problem through our smart approach to problems.

Deep Learning Models

• CNN and NLP (Hybrid)
• Domain-specific
• Image conversion
• Meta-Learning

Furthermore, our developers are like to share the globally suggested deep learning software and tools . In truth, we have thorough practice on all these developing technologies. So, we are ready to fine-tuned guidance on deep learning libraries, modules, packages, toolboxes , etc. to ease your development process. By the by, we will also suggest you best-fitting software/tool for your project . We ensure you that our suggested software/tool will make your implementation process of deep learning projects techniques more simple and reliable .

Deep Learning Software and Tools

• Caffe & Caffe2
• Deep Learning 4j
• Microsoft Cognitive Toolkit

So far, we have discussed important research updates of deep learning . Now, we can see the importance of handpicking a good research topic for an impressive deep learning research proposal. In the research topic, we have to outline your research by mentioning the research problem and efficient solutions . Also, it is necessary to check the future scope of research for that particular topic.

The topic without future research direction is not meant to do research!!!

For more clarity, here we have given you a few significant tips to select a good deep learning research topic.

How to write a research paper on deep learning?

• Check whether your selected research problem is inspiring to overcome but not take more complex to solve
• Check whether your selected problem not only inspires you but also create interest among readers and followers
• Check whether your proposed research create a contribution to social developments
• Check whether your selected research problem is unique

From the above list, you can get an idea about what exactly a good research topic is. Now, we can see how a good research topic is identified.

• To recognize the best research topic, first undergo in-depth research on recent deep learning studied by referring latest reputed journal papers.
• Then, perform a review process over the collected papers to detect what are the current research limitations, which aspect not addressed yet, which is a problem is not solved effectively,   which solution is needed to improve, what the techniques are followed in recent research, etc.
• This literature review process needs more time and effort to grasp knowledge on research demands among scholars.
• If you are new to this field, then it is suggested to take the advice of field experts who recommend good and resourceful research papers.
• Majorly, the drawbacks of the existing research are proposed as a problem to provide suitable research solutions.
• Usually, it is good to work on resource-filled research areas than areas that have limited reference.
• When you find the desired research idea, then immediately check the originality of the idea. Make sure that no one is already proved your research idea.
• Since, it is better to find it in the initial stage itself to choose some other one.
• For that, the search keyword is more important because someone may already conduct the same research in a different name. So, concentrate on choosing keywords for the literature study.

How to describe your research topic?

One common error faced by beginners in research topic selection is a misunderstanding. Some researchers think topic selection means is just the title of your project. But it is not like that, you have to give detailed information about your research work on a short and crisp topic . In other words, the research topic is needed to act as an outline for your research work.

For instance: “deep learning for disease detection” is not the topic with clear information. In this, you can mention the details like type of deep learning technique, type of image and its process, type of human parts, symptoms , etc.

The modified research topic for “deep learning for disease detection” is “COVID-19 detection using automated deep learning algorithm”

 For your awareness, here we have given you some key points that need to focus on while framing research topics. To clearly define your research topic, we recommend writing some text explaining:

• Research title
• Previous research constraints
• Importance of the problem that overcomes in proposed research
• Reason of challenges in the research problem
• Outline of problem-solving possibility

To the end, now we can see different research perspectives of deep learning among the research community. In the following, we have presented you with the most demanded research topics in deep learning such as image denoising, moving object detection, and event recognition . In addition to this list, we also have a repository of recent deep learning research proposal topics, machine learning thesis topics . So, communicate with us to know the advanced research ideas of deep learning.

Research Topics in Deep Learning

• Continuous Network Monitoring and Pipeline Representation in Temporal Segment Networks
• Dynamic Image Networks and Semantic Image Networks
• Advance Non-uniform denoising verification based on FFDNet and DnCNN
• Efficient image denoising based on ResNets and CNNs
• Accurate object recognition in deep architecture using ResNeXts, Inception Nets and  Squeeze and Excitation Networks
• Improved object detection using Faster R-CNN, YOLO, Fast R-CNN, and Mask-RCNN

Overall, we are ready to support you in all significant and new research areas of deep learning . We guarantee you that we provide you novel deep learning research proposal in your interested area with writing support. Further, we also give you code development , paper writing, paper publication, and thesis writing services . So, create a bond with us to create a strong foundation for your research career in the deep learning field.

Related Pages

Services we offer.

Mathematical proof

Pseudo code

Conference Paper

Research Proposal

System Design

Literature Survey

Data Collection

Thesis Writing

Data Analysis

Rough Draft

Paper Collection

Code and Programs

Paper Writing

Course Work

Advertisement

Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions

• Review Article
• Published: 18 August 2021
• Volume 2 , article number  420 , ( 2021 )

Cite this article

• Iqbal H. Sarker   ORCID: orcid.org/0000-0003-1740-5517 1 , 2  

196k Accesses

673 Citations

24 Altmetric

Explore all metrics

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning , unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions . Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

Similar content being viewed by others

Machine learning and deep learning

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Avoid common mistakes on your manuscript.

Introduction

In the late 1980s, neural networks became a prevalent topic in the area of Machine Learning (ML) as well as Artificial Intelligence (AI), due to the invention of various efficient learning methods and network structures [ 52 ]. Multilayer perceptron networks trained by “Backpropagation” type algorithms, self-organizing maps, and radial basis function networks were such innovative methods [ 26 , 36 , 37 ]. While neural networks are successfully used in many applications, the interest in researching this topic decreased later on. After that, in 2006, “Deep Learning” (DL) was introduced by Hinton et al. [ 41 ], which was based on the concept of artificial neural network (ANN). Deep learning became a prominent topic after that, resulting in a rebirth in neural network research, hence, some times referred to as “new-generation neural networks”. This is because deep networks, when properly trained, have produced significant success in a variety of classification and regression challenges [ 52 ].

Nowadays, DL technology is considered as one of the hot topics within the area of machine learning, artificial intelligence as well as data science and analytics, due to its learning capabilities from the given data. Many corporations including Google, Microsoft, Nokia, etc., study it actively as it can provide significant results in different classification and regression problems and datasets [ 52 ]. In terms of working domain, DL is considered as a subset of ML and AI, and thus DL can be seen as an AI function that mimics the human brain’s processing of data. The worldwide popularity of “Deep learning” is increasing day by day, which is shown in our earlier paper [ 96 ] based on the historical data collected from Google trends [ 33 ]. Deep learning differs from standard machine learning in terms of efficiency as the volume of data increases, discussed briefly in Section “ Why Deep Learning in Today's Research and Applications? ”. DL technology uses multiple layers to represent the abstractions of data to build computational models. While deep learning takes a long time to train a model due to a large number of parameters, it takes a short amount of time to run during testing as compared to other machine learning algorithms [ 127 ].

While today’s Fourth Industrial Revolution (4IR or Industry 4.0) is typically focusing on technology-driven “automation, smart and intelligent systems”, DL technology, which is originated from ANN, has become one of the core technologies to achieve the goal [ 103 , 114 ]. A typical neural network is mainly composed of many simple, connected processing elements or processors called neurons, each of which generates a series of real-valued activations for the target outcome. Figure 1 shows a schematic representation of the mathematical model of an artificial neuron, i.e., processing element, highlighting input ( \(X_i\) ), weight ( w ), bias ( b ), summation function ( \(\sum\) ), activation function ( f ) and corresponding output signal ( y ). Neural network-based DL technology is now widely applied in many fields and research areas such as healthcare, sentiment analysis, natural language processing, visual recognition, business intelligence, cybersecurity, and many more that have been summarized in the latter part of this paper.

Schematic representation of the mathematical model of an artificial neuron (processing element), highlighting input ( \(X_i\) ), weight ( w ), bias ( b ), summation function ( \(\sum\) ), activation function ( f ) and output signal ( y )

Although DL models are successfully applied in various application areas, mentioned above, building an appropriate model of deep learning is a challenging task, due to the dynamic nature and variations of real-world problems and data. Moreover, DL models are typically considered as “black-box” machines that hamper the standard development of deep learning research and applications. Thus for clear understanding, in this paper, we present a structured and comprehensive view on DL techniques considering the variations in real-world problems and tasks. To achieve our goal, we briefly discuss various DL techniques and present a taxonomy by taking into account three major categories: (i) deep networks for supervised or discriminative learning that is utilized to provide a discriminative function in supervised deep learning or classification applications; (ii) deep networks for unsupervised or generative learning that are used to characterize the high-order correlation properties or features for pattern analysis or synthesis, thus can be used as preprocessing for the supervised algorithm; and (ii) deep networks for hybrid learning that is an integration of both supervised and unsupervised model and relevant others. We take into account such categories based on the nature and learning capabilities of different DL techniques and how they are used to solve problems in real-world applications [ 97 ]. Moreover, identifying key research issues and prospects including effective data representation, new algorithm design, data-driven hyper-parameter learning, and model optimization, integrating domain knowledge, adapting resource-constrained devices, etc. is one of the key targets of this study, which can lead to “Future Generation DL-Modeling”. Thus the goal of this paper is set to assist those in academia and industry as a reference guide, who want to research and develop data-driven smart and intelligent systems based on DL techniques.

The overall contribution of this paper is summarized as follows:

This article focuses on different aspects of deep learning modeling, i.e., the learning capabilities of DL techniques in different dimensions such as supervised or unsupervised tasks, to function in an automated and intelligent manner, which can play as a core technology of today’s Fourth Industrial Revolution (Industry 4.0).

We explore a variety of prominent DL techniques and present a taxonomy by taking into account the variations in deep learning tasks and how they are used for different purposes. In our taxonomy, we divide the techniques into three major categories such as deep networks for supervised or discriminative learning, unsupervised or generative learning, as well as deep networks for hybrid learning, and relevant others.

We have summarized several potential real-world application areas of deep learning, to assist developers as well as researchers in broadening their perspectives on DL techniques. Different categories of DL techniques highlighted in our taxonomy can be used to solve various issues accordingly.

Finally, we point out and discuss ten potential aspects with research directions for future generation DL modeling in terms of conducting future research and system development.

This paper is organized as follows. Section “ Why Deep Learning in Today's Research and Applications? ” motivates why deep learning is important to build data-driven intelligent systems. In Section“ Deep Learning Techniques and Applications ”, we present our DL taxonomy by taking into account the variations of deep learning tasks and how they are used in solving real-world issues and briefly discuss the techniques with summarizing the potential application areas. In Section “ Research Directions and Future Aspects ”, we discuss various research issues of deep learning-based modeling and highlight the promising topics for future research within the scope of our study. Finally, Section “ Concluding Remarks ” concludes this paper.

Why Deep Learning in Today’s Research and Applications?

The main focus of today’s Fourth Industrial Revolution (Industry 4.0) is typically technology-driven automation, smart and intelligent systems, in various application areas including smart healthcare, business intelligence, smart cities, cybersecurity intelligence, and many more [ 95 ]. Deep learning approaches have grown dramatically in terms of performance in a wide range of applications considering security technologies, particularly, as an excellent solution for uncovering complex architecture in high-dimensional data. Thus, DL techniques can play a key role in building intelligent data-driven systems according to today’s needs, because of their excellent learning capabilities from historical data. Consequently, DL can change the world as well as humans’ everyday life through its automation power and learning from experience. DL technology is therefore relevant to artificial intelligence [ 103 ], machine learning [ 97 ] and data science with advanced analytics [ 95 ] that are well-known areas in computer science, particularly, today’s intelligent computing. In the following, we first discuss regarding the position of deep learning in AI, or how DL technology is related to these areas of computing.

The Position of Deep Learning in AI

Nowadays, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are three popular terms that are sometimes used interchangeably to describe systems or software that behaves intelligently. In Fig. 2 , we illustrate the position of deep Learning, comparing with machine learning and artificial intelligence. According to Fig. 2 , DL is a part of ML as well as a part of the broad area AI. In general, AI incorporates human behavior and intelligence to machines or systems [ 103 ], while ML is the method to learn from data or experience [ 97 ], which automates analytical model building. DL also represents learning methods from data where the computation is done through multi-layer neural networks and processing. The term “Deep” in the deep learning methodology refers to the concept of multiple levels or stages through which data is processed for building a data-driven model.

An illustration of the position of deep learning (DL), comparing with machine learning (ML) and artificial intelligence (AI)

Thus, DL can be considered as one of the core technology of AI, a frontier for artificial intelligence, which can be used for building intelligent systems and automation. More importantly, it pushes AI to a new level, termed “Smarter AI”. As DL are capable of learning from data, there is a strong relation of deep learning with “Data Science” [ 95 ] as well. Typically, data science represents the entire process of finding meaning or insights in data in a particular problem domain, where DL methods can play a key role for advanced analytics and intelligent decision-making [ 104 , 106 ]. Overall, we can conclude that DL technology is capable to change the current world, particularly, in terms of a powerful computational engine and contribute to technology-driven automation, smart and intelligent systems accordingly, and meets the goal of Industry 4.0.

Understanding Various Forms of Data

As DL models learn from data, an in-depth understanding and representation of data are important to build a data-driven intelligent system in a particular application area. In the real world, data can be in various forms, which typically can be represented as below for deep learning modeling:

Sequential Data Sequential data is any kind of data where the order matters, i,e., a set of sequences. It needs to explicitly account for the sequential nature of input data while building the model. Text streams, audio fragments, video clips, time-series data, are some examples of sequential data.

Image or 2D Data A digital image is made up of a matrix, which is a rectangular array of numbers, symbols, or expressions arranged in rows and columns in a 2D array of numbers. Matrix, pixels, voxels, and bit depth are the four essential characteristics or fundamental parameters of a digital image.

Tabular Data A tabular dataset consists primarily of rows and columns. Thus tabular datasets contain data in a columnar format as in a database table. Each column (field) must have a name and each column may only contain data of the defined type. Overall, it is a logical and systematic arrangement of data in the form of rows and columns that are based on data properties or features. Deep learning models can learn efficiently on tabular data and allow us to build data-driven intelligent systems.

The above-discussed data forms are common in the real-world application areas of deep learning. Different categories of DL techniques perform differently depending on the nature and characteristics of data, discussed briefly in Section “ Deep Learning Techniques and Applications ” with a taxonomy presentation. However, in many real-world application areas, the standard machine learning techniques, particularly, logic-rule or tree-based techniques [ 93 , 101 ] perform significantly depending on the application nature. Figure 3 also shows the performance comparison of DL and ML modeling considering the amount of data. In the following, we highlight several cases, where deep learning is useful to solve real-world problems, according to our main focus in this paper.

DL Properties and Dependencies

A DL model typically follows the same processing stages as machine learning modeling. In Fig. 4 , we have shown a deep learning workflow to solve real-world problems, which consists of three processing steps, such as data understanding and preprocessing, DL model building, and training, and validation and interpretation. However, unlike the ML modeling [ 98 , 108 ], feature extraction in the DL model is automated rather than manual. K-nearest neighbor, support vector machines, decision tree, random forest, naive Bayes, linear regression, association rules, k-means clustering, are some examples of machine learning techniques that are commonly used in various application areas [ 97 ]. On the other hand, the DL model includes convolution neural network, recurrent neural network, autoencoder, deep belief network, and many more, discussed briefly with their potential application areas in Section 3 . In the following, we discuss the key properties and dependencies of DL techniques, that are needed to take into account before started working on DL modeling for real-world applications.

An illustration of the performance comparison between deep learning (DL) and other machine learning (ML) algorithms, where DL modeling from large amounts of data can increase the performance

Data Dependencies Deep learning is typically dependent on a large amount of data to build a data-driven model for a particular problem domain. The reason is that when the data volume is small, deep learning algorithms often perform poorly [ 64 ]. In such circumstances, however, the performance of the standard machine-learning algorithms will be improved if the specified rules are used [ 64 , 107 ].

Hardware Dependencies The DL algorithms require large computational operations while training a model with large datasets. As the larger the computations, the more the advantage of a GPU over a CPU, the GPU is mostly used to optimize the operations efficiently. Thus, to work properly with the deep learning training, GPU hardware is necessary. Therefore, DL relies more on high-performance machines with GPUs than standard machine learning methods [ 19 , 127 ].

Feature Engineering Process Feature engineering is the process of extracting features (characteristics, properties, and attributes) from raw data using domain knowledge. A fundamental distinction between DL and other machine-learning techniques is the attempt to extract high-level characteristics directly from data [ 22 , 97 ]. Thus, DL decreases the time and effort required to construct a feature extractor for each problem.

Model Training and Execution time In general, training a deep learning algorithm takes a long time due to a large number of parameters in the DL algorithm; thus, the model training process takes longer. For instance, the DL models can take more than one week to complete a training session, whereas training with ML algorithms takes relatively little time, only seconds to hours [ 107 , 127 ]. During testing, deep learning algorithms take extremely little time to run [ 127 ], when compared to certain machine learning methods.

Black-box Perception and Interpretability Interpretability is an important factor when comparing DL with ML. It’s difficult to explain how a deep learning result was obtained, i.e., “black-box”. On the other hand, the machine-learning algorithms, particularly, rule-based machine learning techniques [ 97 ] provide explicit logic rules (IF-THEN) for making decisions that are easily interpretable for humans. For instance, in our earlier works, we have presented several machines learning rule-based techniques [ 100 , 102 , 105 ], where the extracted rules are human-understandable and easier to interpret, update or delete according to the target applications.

The most significant distinction between deep learning and regular machine learning is how well it performs when data grows exponentially. An illustration of the performance comparison between DL and standard ML algorithms has been shown in Fig. 3 , where DL modeling can increase the performance with the amount of data. Thus, DL modeling is extremely useful when dealing with a large amount of data because of its capacity to process vast amounts of features to build an effective data-driven model. In terms of developing and training DL models, it relies on parallelized matrix and tensor operations as well as computing gradients and optimization. Several, DL libraries and resources [ 30 ] such as PyTorch [ 82 ] (with a high-level API called Lightning) and TensorFlow [ 1 ] (which also offers Keras as a high-level API) offers these core utilities including many pre-trained models, as well as many other necessary functions for implementation and DL model building.

A typical DL workflow to solve real-world problems, which consists of three sequential stages (i) data understanding and preprocessing (ii) DL model building and training (iii) validation and interpretation

Deep Learning Techniques and Applications

In this section, we go through the various types of deep neural network techniques, which typically consider several layers of information-processing stages in hierarchical structures to learn. A typical deep neural network contains multiple hidden layers including input and output layers. Figure 5 shows a general structure of a deep neural network ( \(hidden \; layer=N\) and N \(\ge\) 2) comparing with a shallow network ( \(hidden \; layer=1\) ). We also present our taxonomy on DL techniques based on how they are used to solve various problems, in this section. However, before exploring the details of the DL techniques, it’s useful to review various types of learning tasks such as (i) Supervised: a task-driven approach that uses labeled training data, (ii) Unsupervised: a data-driven process that analyzes unlabeled datasets, (iii) Semi-supervised: a hybridization of both the supervised and unsupervised methods, and (iv) Reinforcement: an environment driven approach, discussed briefly in our earlier paper [ 97 ]. Thus, to present our taxonomy, we divide DL techniques broadly into three major categories: (i) deep networks for supervised or discriminative learning; (ii) deep networks for unsupervised or generative learning; and (ii) deep networks for hybrid learning combing both and relevant others, as shown in Fig. 6 . In the following, we briefly discuss each of these techniques that can be used to solve real-world problems in various application areas according to their learning capabilities.

A general architecture of a a shallow network with one hidden layer and b a deep neural network with multiple hidden layers

A taxonomy of DL techniques, broadly divided into three major categories (i) deep networks for supervised or discriminative learning, (ii) deep networks for unsupervised or generative learning, and (ii) deep networks for hybrid learning and relevant others

Deep Networks for Supervised or Discriminative Learning

This category of DL techniques is utilized to provide a discriminative function in supervised or classification applications. Discriminative deep architectures are typically designed to give discriminative power for pattern classification by describing the posterior distributions of classes conditioned on visible data [ 21 ]. Discriminative architectures mainly include Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN or ConvNet), Recurrent Neural Networks (RNN), along with their variants. In the following, we briefly discuss these techniques.

Multi-layer Perceptron (MLP)

Multi-layer Perceptron (MLP), a supervised learning approach [ 83 ], is a type of feedforward artificial neural network (ANN). It is also known as the foundation architecture of deep neural networks (DNN) or deep learning. A typical MLP is a fully connected network that consists of an input layer that receives input data, an output layer that makes a decision or prediction about the input signal, and one or more hidden layers between these two that are considered as the network’s computational engine [ 36 , 103 ]. The output of an MLP network is determined using a variety of activation functions, also known as transfer functions, such as ReLU (Rectified Linear Unit), Tanh, Sigmoid, and Softmax [ 83 , 96 ]. To train MLP employs the most extensively used algorithm “Backpropagation” [ 36 ], a supervised learning technique, which is also known as the most basic building block of a neural network. During the training process, various optimization approaches such as Stochastic Gradient Descent (SGD), Limited Memory BFGS (L-BFGS), and Adaptive Moment Estimation (Adam) are applied. MLP requires tuning of several hyperparameters such as the number of hidden layers, neurons, and iterations, which could make solving a complicated model computationally expensive. However, through partial fit, MLP offers the advantage of learning non-linear models in real-time or online [ 83 ].

Convolutional Neural Network (CNN or ConvNet)

The Convolutional Neural Network (CNN or ConvNet) [ 65 ] is a popular discriminative deep learning architecture that learns directly from the input without the need for human feature extraction. Figure 7 shows an example of a CNN including multiple convolutions and pooling layers. As a result, the CNN enhances the design of traditional ANN like regularized MLP networks. Each layer in CNN takes into account optimum parameters for a meaningful output as well as reduces model complexity. CNN also uses a ‘dropout’ [ 30 ] that can deal with the problem of over-fitting, which may occur in a traditional network.

An example of a convolutional neural network (CNN or ConvNet) including multiple convolution and pooling layers

CNNs are specifically intended to deal with a variety of 2D shapes and are thus widely employed in visual recognition, medical image analysis, image segmentation, natural language processing, and many more [ 65 , 96 ]. The capability of automatically discovering essential features from the input without the need for human intervention makes it more powerful than a traditional network. Several variants of CNN are exist in the area that includes visual geometry group (VGG) [ 38 ], AlexNet [ 62 ], Xception [ 17 ], Inception [ 116 ], ResNet [ 39 ], etc. that can be used in various application domains according to their learning capabilities.

Recurrent Neural Network (RNN) and its Variants

A Recurrent Neural Network (RNN) is another popular neural network, which employs sequential or time-series data and feeds the output from the previous step as input to the current stage [ 27 , 74 ]. Like feedforward and CNN, recurrent networks learn from training input, however, distinguish by their “memory”, which allows them to impact current input and output through using information from previous inputs. Unlike typical DNN, which assumes that inputs and outputs are independent of one another, the output of RNN is reliant on prior elements within the sequence. However, standard recurrent networks have the issue of vanishing gradients, which makes learning long data sequences challenging. In the following, we discuss several popular variants of the recurrent network that minimizes the issues and perform well in many real-world application domains.

Long short-term memory (LSTM) This is a popular form of RNN architecture that uses special units to deal with the vanishing gradient problem, which was introduced by Hochreiter et al. [ 42 ]. A memory cell in an LSTM unit can store data for long periods and the flow of information into and out of the cell is managed by three gates. For instance, the ‘Forget Gate’ determines what information from the previous state cell will be memorized and what information will be removed that is no longer useful, while the ‘Input Gate’ determines which information should enter the cell state and the ‘Output Gate’ determines and controls the outputs. As it solves the issues of training a recurrent network, the LSTM network is considered one of the most successful RNN.

Bidirectional RNN/LSTM Bidirectional RNNs connect two hidden layers that run in opposite directions to a single output, allowing them to accept data from both the past and future. Bidirectional RNNs, unlike traditional recurrent networks, are trained to predict both positive and negative time directions at the same time. A Bidirectional LSTM, often known as a BiLSTM, is an extension of the standard LSTM that can increase model performance on sequence classification issues [ 113 ]. It is a sequence processing model comprising of two LSTMs: one takes the input forward and the other takes it backward. Bidirectional LSTM in particular is a popular choice in natural language processing tasks.

Gated recurrent units (GRUs) A Gated Recurrent Unit (GRU) is another popular variant of the recurrent network that uses gating methods to control and manage information flow between cells in the neural network, introduced by Cho et al. [ 16 ]. The GRU is like an LSTM, however, has fewer parameters, as it has a reset gate and an update gate but lacks the output gate, as shown in Fig. 8 . Thus, the key difference between a GRU and an LSTM is that a GRU has two gates (reset and update gates) whereas an LSTM has three gates (namely input, output and forget gates). The GRU’s structure enables it to capture dependencies from large sequences of data in an adaptive manner, without discarding information from earlier parts of the sequence. Thus GRU is a slightly more streamlined variant that often offers comparable performance and is significantly faster to compute [ 18 ]. Although GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets [ 18 , 34 ], both variants of RNN have proven their effectiveness while producing the outcome.

Basic structure of a gated recurrent unit (GRU) cell consisting of reset and update gates

Overall, the basic property of a recurrent network is that it has at least one feedback connection, which enables activations to loop. This allows the networks to do temporal processing and sequence learning, such as sequence recognition or reproduction, temporal association or prediction, etc. Following are some popular application areas of recurrent networks such as prediction problems, machine translation, natural language processing, text summarization, speech recognition, and many more.

Deep Networks for Generative or Unsupervised Learning

This category of DL techniques is typically used to characterize the high-order correlation properties or features for pattern analysis or synthesis, as well as the joint statistical distributions of the visible data and their associated classes [ 21 ]. The key idea of generative deep architectures is that during the learning process, precise supervisory information such as target class labels is not of concern. As a result, the methods under this category are essentially applied for unsupervised learning as the methods are typically used for feature learning or data generating and representation [ 20 , 21 ]. Thus generative modeling can be used as preprocessing for the supervised learning tasks as well, which ensures the discriminative model accuracy. Commonly used deep neural network techniques for unsupervised or generative learning are Generative Adversarial Network (GAN), Autoencoder (AE), Restricted Boltzmann Machine (RBM), Self-Organizing Map (SOM), and Deep Belief Network (DBN) along with their variants.

Generative Adversarial Network (GAN)

A Generative Adversarial Network (GAN), designed by Ian Goodfellow [ 32 ], is a type of neural network architecture for generative modeling to create new plausible samples on demand. It involves automatically discovering and learning regularities or patterns in input data so that the model may be used to generate or output new examples from the original dataset. As shown in Fig. 9 , GANs are composed of two neural networks, a generator G that creates new data having properties similar to the original data, and a discriminator D that predicts the likelihood of a subsequent sample being drawn from actual data rather than data provided by the generator. Thus in GAN modeling, both the generator and discriminator are trained to compete with each other. While the generator tries to fool and confuse the discriminator by creating more realistic data, the discriminator tries to distinguish the genuine data from the fake data generated by G .

Schematic structure of a standard generative adversarial network (GAN)

Generally, GAN network deployment is designed for unsupervised learning tasks, but it has also proven to be a better solution for semi-supervised and reinforcement learning as well depending on the task [ 3 ]. GANs are also used in state-of-the-art transfer learning research to enforce the alignment of the latent feature space [ 66 ]. Inverse models, such as Bidirectional GAN (BiGAN) [ 25 ] can also learn a mapping from data to the latent space, similar to how the standard GAN model learns a mapping from a latent space to the data distribution. The potential application areas of GAN networks are healthcare, image analysis, data augmentation, video generation, voice generation, pandemics, traffic control, cybersecurity, and many more, which are increasing rapidly. Overall, GANs have established themselves as a comprehensive domain of independent data expansion and as a solution to problems requiring a generative solution.

Auto-Encoder (AE) and Its Variants

An auto-encoder (AE) [ 31 ] is a popular unsupervised learning technique in which neural networks are used to learn representations. Typically, auto-encoders are used to work with high-dimensional data, and dimensionality reduction explains how a set of data is represented. Encoder, code, and decoder are the three parts of an autoencoder. The encoder compresses the input and generates the code, which the decoder subsequently uses to reconstruct the input. The AEs have recently been used to learn generative data models [ 69 ]. The auto-encoder is widely used in many unsupervised learning tasks, e.g., dimensionality reduction, feature extraction, efficient coding, generative modeling, denoising, anomaly or outlier detection, etc. [ 31 , 132 ]. Principal component analysis (PCA) [ 99 ], which is also used to reduce the dimensionality of huge data sets, is essentially similar to a single-layered AE with a linear activation function. Regularized autoencoders such as sparse, denoising, and contractive are useful for learning representations for later classification tasks [ 119 ], while variational autoencoders can be used as generative models [ 56 ], discussed below.

Sparse Autoencoder (SAE) A sparse autoencoder [ 73 ] has a sparsity penalty on the coding layer as a part of its training requirement. SAEs may have more hidden units than inputs, but only a small number of hidden units are permitted to be active at the same time, resulting in a sparse model. Figure 10 shows a schematic structure of a sparse autoencoder with several active units in the hidden layer. This model is thus obliged to respond to the unique statistical features of the training data following its constraints.

Denoising Autoencoder (DAE) A denoising autoencoder is a variant on the basic autoencoder that attempts to improve representation (to extract useful features) by altering the reconstruction criterion, and thus reduces the risk of learning the identity function [ 31 , 119 ]. In other words, it receives a corrupted data point as input and is trained to recover the original undistorted input as its output through minimizing the average reconstruction error over the training data, i.e, cleaning the corrupted input, or denoising. Thus, in the context of computing, DAEs can be considered as very powerful filters that can be utilized for automatic pre-processing. A denoising autoencoder, for example, could be used to automatically pre-process an image, thereby boosting its quality for recognition accuracy.

Contractive Autoencoder (CAE) The idea behind a contractive autoencoder, proposed by Rifai et al. [ 90 ], is to make the autoencoders robust of small changes in the training dataset. In its objective function, a CAE includes an explicit regularizer that forces the model to learn an encoding that is robust to small changes in input values. As a result, the learned representation’s sensitivity to the training input is reduced. While DAEs encourage the robustness of reconstruction as discussed above, CAEs encourage the robustness of representation.

Variational Autoencoder (VAE) A variational autoencoder [ 55 ] has a fundamentally unique property that distinguishes it from the classical autoencoder discussed above, which makes this so effective for generative modeling. VAEs, unlike the traditional autoencoders which map the input onto a latent vector, map the input data into the parameters of a probability distribution, such as the mean and variance of a Gaussian distribution. A VAE assumes that the source data has an underlying probability distribution and then tries to discover the distribution’s parameters. Although this approach was initially designed for unsupervised learning, its use has been demonstrated in other domains such as semi-supervised learning [ 128 ] and supervised learning [ 51 ].

Schematic structure of a sparse autoencoder (SAE) with several active units (filled circle) in the hidden layer

Although, the earlier concept of AE was typically for dimensionality reduction or feature learning mentioned above, recently, AEs have been brought to the forefront of generative modeling, even the generative adversarial network is one of the popular methods in the area. The AEs have been effectively employed in a variety of domains, including healthcare, computer vision, speech recognition, cybersecurity, natural language processing, and many more. Overall, we can conclude that auto-encoder and its variants can play a significant role as unsupervised feature learning with neural network architecture.

Kohonen Map or Self-Organizing Map (SOM)

A Self-Organizing Map (SOM) or Kohonen Map [ 59 ] is another form of unsupervised learning technique for creating a low-dimensional (usually two-dimensional) representation of a higher-dimensional data set while maintaining the topological structure of the data. SOM is also known as a neural network-based dimensionality reduction algorithm that is commonly used for clustering [ 118 ]. A SOM adapts to the topological form of a dataset by repeatedly moving its neurons closer to the data points, allowing us to visualize enormous datasets and find probable clusters. The first layer of a SOM is the input layer, and the second layer is the output layer or feature map. Unlike other neural networks that use error-correction learning, such as backpropagation with gradient descent [ 36 ], SOMs employ competitive learning, which uses a neighborhood function to retain the input space’s topological features. SOM is widely utilized in a variety of applications, including pattern identification, health or medical diagnosis, anomaly detection, and virus or worm attack detection [ 60 , 87 ]. The primary benefit of employing a SOM is that this can make high-dimensional data easier to visualize and analyze to understand the patterns. The reduction of dimensionality and grid clustering makes it easy to observe similarities in the data. As a result, SOMs can play a vital role in developing a data-driven effective model for a particular problem domain, depending on the data characteristics.

Restricted Boltzmann Machine (RBM)

A Restricted Boltzmann Machine (RBM) [ 75 ] is also a generative stochastic neural network capable of learning a probability distribution across its inputs. Boltzmann machines typically consist of visible and hidden nodes and each node is connected to every other node, which helps us understand irregularities by learning how the system works in normal circumstances. RBMs are a subset of Boltzmann machines that have a limit on the number of connections between the visible and hidden layers [ 77 ]. This restriction permits training algorithms like the gradient-based contrastive divergence algorithm to be more efficient than those for Boltzmann machines in general [ 41 ]. RBMs have found applications in dimensionality reduction, classification, regression, collaborative filtering, feature learning, topic modeling, and many others. In the area of deep learning modeling, they can be trained either supervised or unsupervised, depending on the task. Overall, the RBMs can recognize patterns in data automatically and develop probabilistic or stochastic models, which are utilized for feature selection or extraction, as well as forming a deep belief network.

Deep Belief Network (DBN)

A Deep Belief Network (DBN) [ 40 ] is a multi-layer generative graphical model of stacking several individual unsupervised networks such as AEs or RBMs, that use each network’s hidden layer as the input for the next layer, i.e, connected sequentially. Thus, we can divide a DBN into (i) AE-DBN which is known as stacked AE, and (ii) RBM-DBN that is known as stacked RBM, where AE-DBN is composed of autoencoders and RBM-DBN is composed of restricted Boltzmann machines, discussed earlier. The ultimate goal is to develop a faster-unsupervised training technique for each sub-network that depends on contrastive divergence [ 41 ]. DBN can capture a hierarchical representation of input data based on its deep structure. The primary idea behind DBN is to train unsupervised feed-forward neural networks with unlabeled data before fine-tuning the network with labeled input. One of the most important advantages of DBN, as opposed to typical shallow learning networks, is that it permits the detection of deep patterns, which allows for reasoning abilities and the capture of the deep difference between normal and erroneous data [ 89 ]. A continuous DBN is simply an extension of a standard DBN that allows a continuous range of decimals instead of binary data. Overall, the DBN model can play a key role in a wide range of high-dimensional data applications due to its strong feature extraction and classification capabilities and become one of the significant topics in the field of neural networks.

In summary, the generative learning techniques discussed above typically allow us to generate a new representation of data through exploratory analysis. As a result, these deep generative networks can be utilized as preprocessing for supervised or discriminative learning tasks, as well as ensuring model accuracy, where unsupervised representation learning can allow for improved classifier generalization.

Deep Networks for Hybrid Learning and Other Approaches

In addition to the above-discussed deep learning categories, hybrid deep networks and several other approaches such as deep transfer learning (DTL) and deep reinforcement learning (DRL) are popular, which are discussed in the following.

Hybrid Deep Neural Networks

Generative models are adaptable, with the capacity to learn from both labeled and unlabeled data. Discriminative models, on the other hand, are unable to learn from unlabeled data yet outperform their generative counterparts in supervised tasks. A framework for training both deep generative and discriminative models simultaneously can enjoy the benefits of both models, which motivates hybrid networks.

Hybrid deep learning models are typically composed of multiple (two or more) deep basic learning models, where the basic model is a discriminative or generative deep learning model discussed earlier. Based on the integration of different basic generative or discriminative models, the below three categories of hybrid deep learning models might be useful for solving real-world problems. These are as follows:

Hybrid \(Model\_1\) : An integration of different generative or discriminative models to extract more meaningful and robust features. Examples could be CNN+LSTM, AE+GAN, and so on.

Hybrid \(Model\_2\) : An integration of generative model followed by a discriminative model. Examples could be DBN+MLP, GAN+CNN, AE+CNN, and so on.

Hybrid \(Model\_3\) : An integration of generative or discriminative model followed by a non-deep learning classifier. Examples could be AE+SVM, CNN+SVM, and so on.

Thus, in a broad sense, we can conclude that hybrid models can be either classification-focused or non-classification depending on the target use. However, most of the hybrid learning-related studies in the area of deep learning are classification-focused or supervised learning tasks, summarized in Table 1 . The unsupervised generative models with meaningful representations are employed to enhance the discriminative models. The generative models with useful representation can provide more informative and low-dimensional features for discrimination, and they can also enable to enhance the training data quality and quantity, providing additional information for classification.

Deep Transfer Learning (DTL)

Transfer Learning is a technique for effectively using previously learned model knowledge to solve a new task with minimum training or fine-tuning. In comparison to typical machine learning techniques [ 97 ], DL takes a large amount of training data. As a result, the need for a substantial volume of labeled data is a significant barrier to address some essential domain-specific tasks, particularly, in the medical sector, where creating large-scale, high-quality annotated medical or health datasets is both difficult and costly. Furthermore, the standard DL model demands a lot of computational resources, such as a GPU-enabled server, even though researchers are working hard to improve it. As a result, Deep Transfer Learning (DTL), a DL-based transfer learning method, might be helpful to address this issue. Figure 11 shows a general structure of the transfer learning process, where knowledge from the pre-trained model is transferred into a new DL model. It’s especially popular in deep learning right now since it allows to train deep neural networks with very little data [ 126 ].

A general structure of transfer learning process, where knowledge from pre-trained model is transferred into new DL model

Transfer learning is a two-stage approach for training a DL model that consists of a pre-training step and a fine-tuning step in which the model is trained on the target task. Since deep neural networks have gained popularity in a variety of fields, a large number of DTL methods have been presented, making it crucial to categorize and summarize them. Based on the techniques used in the literature, DTL can be classified into four categories [ 117 ]. These are (i) instances-based deep transfer learning that utilizes instances in source domain by appropriate weight, (ii) mapping-based deep transfer learning that maps instances from two domains into a new data space with better similarity, (iii) network-based deep transfer learning that reuses the partial of network pre-trained in the source domain, and (iv) adversarial based deep transfer learning that uses adversarial technology to find transferable features that both suitable for two domains. Due to its high effectiveness and practicality, adversarial-based deep transfer learning has exploded in popularity in recent years. Transfer learning can also be classified into inductive, transductive, and unsupervised transfer learning depending on the circumstances between the source and target domains and activities [ 81 ]. While most current research focuses on supervised learning, how deep neural networks can transfer knowledge in unsupervised or semi-supervised learning may gain further interest in the future. DTL techniques are useful in a variety of fields including natural language processing, sentiment classification, visual recognition, speech recognition, spam filtering, and relevant others.

Deep Reinforcement Learning (DRL)

Reinforcement learning takes a different approach to solving the sequential decision-making problem than other approaches we have discussed so far. The concepts of an environment and an agent are often introduced first in reinforcement learning. The agent can perform a series of actions in the environment, each of which has an impact on the environment’s state and can result in possible rewards (feedback) - “positive” for good sequences of actions that result in a “good” state, and “negative” for bad sequences of actions that result in a “bad” state. The purpose of reinforcement learning is to learn good action sequences through interaction with the environment, typically referred to as a policy.

Schematic structure of deep reinforcement learning (DRL) highlighting a deep neural network

Deep reinforcement learning (DRL or deep RL) [ 9 ] integrates neural networks with a reinforcement learning architecture to allow the agents to learn the appropriate actions in a virtual environment, as shown in Fig. 12 . In the area of reinforcement learning, model-based RL is based on learning a transition model that enables for modeling of the environment without interacting with it directly, whereas model-free RL methods learn directly from interactions with the environment. Q-learning is a popular model-free RL technique for determining the best action-selection policy for any (finite) Markov Decision Process (MDP) [ 86 , 97 ]. MDP is a mathematical framework for modeling decisions based on state, action, and rewards [ 86 ]. In addition, Deep Q-Networks, Double DQN, Bi-directional Learning, Monte Carlo Control, etc. are used in the area [ 50 , 97 ]. In DRL methods it incorporates DL models, e.g. Deep Neural Networks (DNN), based on MDP principle [ 71 ], as policy and/or value function approximators. CNN for example can be used as a component of RL agents to learn directly from raw, high-dimensional visual inputs. In the real world, DRL-based solutions can be used in several application areas including robotics, video games, natural language processing, computer vision, and relevant others.

Several potential real-world application areas of deep learning

Deep Learning Application Summary

During the past few years, deep learning has been successfully applied to numerous problems in many application areas. These include natural language processing, sentiment analysis, cybersecurity, business, virtual assistants, visual recognition, healthcare, robotics, and many more. In Fig. 13 , we have summarized several potential real-world application areas of deep learning. Various deep learning techniques according to our presented taxonomy in Fig. 6 that includes discriminative learning, generative learning, as well as hybrid models, discussed earlier, are employed in these application areas. In Table 1 , we have also summarized various deep learning tasks and techniques that are used to solve the relevant tasks in several real-world applications areas. Overall, from Fig. 13 and Table 1 , we can conclude that the future prospects of deep learning modeling in real-world application areas are huge and there are lots of scopes to work. In the next section, we also summarize the research issues in deep learning modeling and point out the potential aspects for future generation DL modeling.

Research Directions and Future Aspects

While existing methods have established a solid foundation for deep learning systems and research, this section outlines the below ten potential future research directions based on our study.

Automation in Data Annotation According to the existing literature, discussed in Section 3 , most of the deep learning models are trained through publicly available datasets that are annotated. However, to build a system for a new problem domain or recent data-driven system, raw data from relevant sources are needed to collect. Thus, data annotation, e.g., categorization, tagging, or labeling of a large amount of raw data, is important for building discriminative deep learning models or supervised tasks, which is challenging. A technique with the capability of automatic and dynamic data annotation, rather than manual annotation or hiring annotators, particularly, for large datasets, could be more effective for supervised learning as well as minimizing human effort. Therefore, a more in-depth investigation of data collection and annotation methods, or designing an unsupervised learning-based solution could be one of the primary research directions in the area of deep learning modeling.

Data Preparation for Ensuring Data Quality As discussed earlier throughout the paper, the deep learning algorithms highly impact data quality, and availability for training, and consequently on the resultant model for a particular problem domain. Thus, deep learning models may become worthless or yield decreased accuracy if the data is bad, such as data sparsity, non-representative, poor-quality, ambiguous values, noise, data imbalance, irrelevant features, data inconsistency, insufficient quantity, and so on for training. Consequently, such issues in data can lead to poor processing and inaccurate findings, which is a major problem while discovering insights from data. Thus deep learning models also need to adapt to such rising issues in data, to capture approximated information from observations. Therefore, effective data pre-processing techniques are needed to design according to the nature of the data problem and characteristics, to handling such emerging challenges, which could be another research direction in the area.

Black-box Perception and Proper DL/ML Algorithm Selection In general, it’s difficult to explain how a deep learning result is obtained or how they get the ultimate decisions for a particular model. Although DL models achieve significant performance while learning from large datasets, as discussed in Section 2 , this “black-box” perception of DL modeling typically represents weak statistical interpretability that could be a major issue in the area. On the other hand, ML algorithms, particularly, rule-based machine learning techniques provide explicit logic rules (IF-THEN) for making decisions that are easier to interpret, update or delete according to the target applications [ 97 , 100 , 105 ]. If the wrong learning algorithm is chosen, unanticipated results may occur, resulting in a loss of effort as well as the model’s efficacy and accuracy. Thus by taking into account the performance, complexity, model accuracy, and applicability, selecting an appropriate model for the target application is challenging, and in-depth analysis is needed for better understanding and decision making.

Deep Networks for Supervised or Discriminative Learning: According to our designed taxonomy of deep learning techniques, as shown in Fig. 6 , discriminative architectures mainly include MLP, CNN, and RNN, along with their variants that are applied widely in various application domains. However, designing new techniques or their variants of such discriminative techniques by taking into account model optimization, accuracy, and applicability, according to the target real-world application and the nature of the data, could be a novel contribution, which can also be considered as a major future aspect in the area of supervised or discriminative learning.

Deep Networks for Unsupervised or Generative Learning As discussed in Section 3 , unsupervised learning or generative deep learning modeling is one of the major tasks in the area, as it allows us to characterize the high-order correlation properties or features in data, or generating a new representation of data through exploratory analysis. Moreover, unlike supervised learning [ 97 ], it does not require labeled data due to its capability to derive insights directly from the data as well as data-driven decision making. Consequently, it thus can be used as preprocessing for supervised learning or discriminative modeling as well as semi-supervised learning tasks, which ensure learning accuracy and model efficiency. According to our designed taxonomy of deep learning techniques, as shown in Fig. 6 , generative techniques mainly include GAN, AE, SOM, RBM, DBN, and their variants. Thus, designing new techniques or their variants for an effective data modeling or representation according to the target real-world application could be a novel contribution, which can also be considered as a major future aspect in the area of unsupervised or generative learning.

Hybrid/Ensemble Modeling and Uncertainty Handling According to our designed taxonomy of DL techniques, as shown in Fig 6 , this is considered as another major category in deep learning tasks. As hybrid modeling enjoys the benefits of both generative and discriminative learning, an effective hybridization can outperform others in terms of performance as well as uncertainty handling in high-risk applications. In Section 3 , we have summarized various types of hybridization, e.g., AE+CNN/SVM. Since a group of neural networks is trained with distinct parameters or with separate sub-sampling training datasets, hybridization or ensembles of such techniques, i.e., DL with DL/ML, can play a key role in the area. Thus designing effective blended discriminative and generative models accordingly rather than naive method, could be an important research opportunity to solve various real-world issues including semi-supervised learning tasks and model uncertainty.

Dynamism in Selecting Threshold/ Hyper-parameters Values, and Network Structures with Computational Efficiency In general, the relationship among performance, model complexity, and computational requirements is a key issue in deep learning modeling and applications. A combination of algorithmic advancements with improved accuracy as well as maintaining computational efficiency, i.e., achieving the maximum throughput while consuming the least amount of resources, without significant information loss, can lead to a breakthrough in the effectiveness of deep learning modeling in future real-world applications. The concept of incremental approaches or recency-based learning [ 100 ] might be effective in several cases depending on the nature of target applications. Moreover, assuming the network structures with a static number of nodes and layers, hyper-parameters values or threshold settings, or selecting them by the trial-and-error process may not be effective in many cases, as it can be changed due to the changes in data. Thus, a data-driven approach to select them dynamically could be more effective while building a deep learning model in terms of both performance and real-world applicability. Such type of data-driven automation can lead to future generation deep learning modeling with additional intelligence, which could be a significant future aspect in the area as well as an important research direction to contribute.

Lightweight Deep Learning Modeling for Next-Generation Smart Devices and Applications: In recent years, the Internet of Things (IoT) consisting of billions of intelligent and communicating things and mobile communications technologies have become popular to detect and gather human and environmental information (e.g. geo-information, weather data, bio-data, human behaviors, and so on) for a variety of intelligent services and applications. Every day, these ubiquitous smart things or devices generate large amounts of data, requiring rapid data processing on a variety of smart mobile devices [ 72 ]. Deep learning technologies can be incorporate to discover underlying properties and to effectively handle such large amounts of sensor data for a variety of IoT applications including health monitoring and disease analysis, smart cities, traffic flow prediction, and monitoring, smart transportation, manufacture inspection, fault assessment, smart industry or Industry 4.0, and many more. Although deep learning techniques discussed in Section 3 are considered as powerful tools for processing big data, lightweight modeling is important for resource-constrained devices, due to their high computational cost and considerable memory overhead. Thus several techniques such as optimization, simplification, compression, pruning, generalization, important feature extraction, etc. might be helpful in several cases. Therefore, constructing the lightweight deep learning techniques based on a baseline network architecture to adapt the DL model for next-generation mobile, IoT, or resource-constrained devices and applications, could be considered as a significant future aspect in the area.

Incorporating Domain Knowledge into Deep Learning Modeling Domain knowledge, as opposed to general knowledge or domain-independent knowledge, is knowledge of a specific, specialized topic or field. For instance, in terms of natural language processing, the properties of the English language typically differ from other languages like Bengali, Arabic, French, etc. Thus integrating domain-based constraints into the deep learning model could produce better results for such particular purpose. For instance, a task-specific feature extractor considering domain knowledge in smart manufacturing for fault diagnosis can resolve the issues in traditional deep-learning-based methods [ 28 ]. Similarly, domain knowledge in medical image analysis [ 58 ], financial sentiment analysis [ 49 ], cybersecurity analytics [ 94 , 103 ] as well as conceptual data model in which semantic information, (i.e., meaningful for a system, rather than merely correlational) [ 45 , 121 , 131 ] is included, can play a vital role in the area. Transfer learning could be an effective way to get started on a new challenge with domain knowledge. Moreover, contextual information such as spatial, temporal, social, environmental contexts [ 92 , 104 , 108 ] can also play an important role to incorporate context-aware computing with domain knowledge for smart decision making as well as building adaptive and intelligent context-aware systems. Therefore understanding domain knowledge and effectively incorporating them into the deep learning model could be another research direction.

Designing General Deep Learning Framework for Target Application Domains One promising research direction for deep learning-based solutions is to develop a general framework that can handle data diversity, dimensions, stimulation types, etc. The general framework would require two key capabilities: the attention mechanism that focuses on the most valuable parts of input signals, and the ability to capture latent feature that enables the framework to capture the distinctive and informative features. Attention models have been a popular research topic because of their intuition, versatility, and interpretability, and employed in various application areas like computer vision, natural language processing, text or image classification, sentiment analysis, recommender systems, user profiling, etc [ 13 , 80 ]. Attention mechanism can be implemented based on learning algorithms such as reinforcement learning that is capable of finding the most useful part through a policy search [ 133 , 134 ]. Similarly, CNN can be integrated with suitable attention mechanisms to form a general classification framework, where CNN can be used as a feature learning tool for capturing features in various levels and ranges. Thus, designing a general deep learning framework considering attention as well as a latent feature for target application domains could be another area to contribute.

To summarize, deep learning is a fairly open topic to which academics can contribute by developing new methods or improving existing methods to handle the above-mentioned concerns and tackle real-world problems in a variety of application areas. This can also help the researchers conduct a thorough analysis of the application’s hidden and unexpected challenges to produce more reliable and realistic outcomes. Overall, we can conclude that addressing the above-mentioned issues and contributing to proposing effective and efficient techniques could lead to “Future Generation DL” modeling as well as more intelligent and automated applications.

Concluding Remarks

In this article, we have presented a structured and comprehensive view of deep learning technology, which is considered a core part of artificial intelligence as well as data science. It starts with a history of artificial neural networks and moves to recent deep learning techniques and breakthroughs in different applications. Then, the key algorithms in this area, as well as deep neural network modeling in various dimensions are explored. For this, we have also presented a taxonomy considering the variations of deep learning tasks and how they are used for different purposes. In our comprehensive study, we have taken into account not only the deep networks for supervised or discriminative learning but also the deep networks for unsupervised or generative learning, and hybrid learning that can be used to solve a variety of real-world issues according to the nature of problems.

Deep learning, unlike traditional machine learning and data mining algorithms, can produce extremely high-level data representations from enormous amounts of raw data. As a result, it has provided an excellent solution to a variety of real-world problems. A successful deep learning technique must possess the relevant data-driven modeling depending on the characteristics of raw data. The sophisticated learning algorithms then need to be trained through the collected data and knowledge related to the target application before the system can assist with intelligent decision-making. Deep learning has shown to be useful in a wide range of applications and research areas such as healthcare, sentiment analysis, visual recognition, business intelligence, cybersecurity, and many more that are summarized in the paper.

Finally, we have summarized and discussed the challenges faced and the potential research directions, and future aspects in the area. Although deep learning is considered a black-box solution for many applications due to its poor reasoning and interpretability, addressing the challenges or future aspects that are identified could lead to future generation deep learning modeling and smarter systems. This can also help the researchers for in-depth analysis to produce more reliable and realistic outcomes. Overall, we believe that our study on neural networks and deep learning-based advanced analytics points in a promising path and can be utilized as a reference guide for future research and implementations in relevant application domains by both academic and industry professionals.

Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin Ma, Ghemawat S, Irving G, Isard M, et al. Tensorflow: a system for large-scale machine learning. In: 12th { USENIX } Symposium on operating systems design and implementation ({ OSDI } 16), 2016; p. 265–283.

Abdel-Basset M, Hawash H, Chakrabortty RK, Ryan M. Energy-net: a deep learning approach for smart energy management in iot-based smart cities. IEEE Internet of Things J. 2021.

Aggarwal A, Mittal M, Battineni G. Generative adversarial network: an overview of theory and applications. Int J Inf Manag Data Insights. 2021; p. 100004.

Al-Qatf M, Lasheng Y, Al-Habib M, Al-Sabahi K. Deep learning approach combining sparse autoencoder with svm for network intrusion detection. IEEE Access. 2018;6:52843–56.

Article   Google Scholar  

Ale L, Sheta A, Li L, Wang Y, Zhang N. Deep learning based plant disease detection for smart agriculture. In: 2019 IEEE Globecom Workshops (GC Wkshps), 2019; p. 1–6. IEEE.

Amarbayasgalan T, Lee JY, Kim KR, Ryu KH. Deep autoencoder based neural networks for coronary heart disease risk prediction. In: Heterogeneous data management, polystores, and analytics for healthcare. Springer; 2019. p. 237–48.

Anuradha J, et al. Big data based stock trend prediction using deep cnn with reinforcement-lstm model. Int J Syst Assur Eng Manag. 2021; p. 1–11.

Aqib M, Mehmood R, Albeshri A, Alzahrani A. Disaster management in smart cities by forecasting traffic plan using deep learning and gpus. In: International Conference on smart cities, infrastructure, technologies and applications. Springer; 2017. p. 139–54.

Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA. Deep reinforcement learning: a brief survey. IEEE Signal Process Mag. 2017;34(6):26–38.

Aslan MF, Unlersen MF, Sabanci K, Durdu A. Cnn-based transfer learning-bilstm network: a novel approach for covid-19 infection detection. Appl Soft Comput. 2021;98:106912.

Bu F, Wang X. A smart agriculture iot system based on deep reinforcement learning. Futur Gener Comput Syst. 2019;99:500–7.

Chang W-J, Chen L-B, Hsu C-H, Lin C-P, Yang T-C. A deep learning-based intelligent medicine recognition system for chronic patients. IEEE Access. 2019;7:44441–58.

Chaudhari S, Mithal V, Polatkan Gu, Ramanath R. An attentive survey of attention models. arXiv preprint arXiv:1904.02874, 2019.

Chaudhuri N, Gupta G, Vamsi V, Bose I. On the platform but will they buy? predicting customers’ purchase behavior using deep learning. Decis Support Syst. 2021; p. 113622.

Chen D, Wawrzynski P, Lv Z. Cyber security in smart cities: a review of deep learning-based applications and case studies. Sustain Cities Soc. 2020; p. 102655.

Cho K, Van MB, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014.

Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 2017; p. 1251–258.

Chung J, Gulcehre C, Cho KH, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014.

Coelho IM, Coelho VN, da Eduardo J, Luz S, Ochi LS, Guimarães FG, Rios E. A gpu deep learning metaheuristic based model for time series forecasting. Appl Energy. 2017;201:412–8.

Da'u A, Salim N. Recommendation system based on deep learning methods: a systematic review and new directions. Artif Intel Rev. 2020;53(4):2709–48.

Deng L. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inf Process. 2014; p. 3.

Deng L, Dong Yu. Deep learning: methods and applications. Found Trends Signal Process. 2014;7(3–4):197–387.

Article   MathSciNet   MATH   Google Scholar  

Deng S, Li R, Jin Y, He H. Cnn-based feature cross and classifier for loan default prediction. In: 2020 International Conference on image, video processing and artificial intelligence, volume 11584, page 115841K. International Society for Optics and Photonics, 2020.

Dhyani M, Kumar R. An intelligent chatbot using deep learning with bidirectional rnn and attention model. Mater Today Proc. 2021;34:817–24.

Donahue J, Krähenbühl P, Darrell T. Adversarial feature learning. arXiv preprint arXiv:1605.09782, 2016.

Du K-L, Swamy MNS. Neural networks and statistical learning. Berlin: Springer Science & Business Media; 2013.

MATH   Google Scholar  

Dupond S. A thorough review on the current advance of neural network structures. Annu Rev Control. 2019;14:200–30.

Google Scholar  

Feng J, Yao Y, Lu S, Liu Y. Domain knowledge-based deep-broad learning framework for fault diagnosis. IEEE Trans Ind Electron. 2020;68(4):3454–64.

Garg S, Kaur K, Kumar N, Rodrigues JJPC. Hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in sdn: a social multimedia perspective. IEEE Trans Multimed. 2019;21(3):566–78.

Géron A. Hands-on machine learning with Scikit-Learn, Keras. In: and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media; 2019.

Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning, vol. 1. Cambridge: MIT Press; 2016.

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Advances in neural information processing systems. 2014; p. 2672–680.

Google trends. 2021. https://trends.google.com/trends/ .

Gruber N, Jockisch A. Are gru cells more specific and lstm cells more sensitive in motive classification of text? Front Artif Intell. 2020;3:40.

Gu B, Ge R, Chen Y, Luo L, Coatrieux G. Automatic and robust object detection in x-ray baggage inspection using deep convolutional neural networks. IEEE Trans Ind Electron. 2020.

Han J, Pei J, Kamber M. Data mining: concepts and techniques. Amsterdam: Elsevier; 2011.

Haykin S. Neural networks and learning machines, 3/E. London: Pearson Education; 2010.

He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell. 2015;37(9):1904–16.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 2016; p. 770–78.

Hinton GE. Deep belief networks. Scholarpedia. 2009;4(5):5947.

Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–54.

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.

Huang C-J, Kuo P-H. A deep cnn-lstm model for particulate matter (pm2. 5) forecasting in smart cities. Sensors. 2018;18(7):2220.

Huang H-H, Fukuda M, Nishida T. Toward rnn based micro non-verbal behavior generation for virtual listener agents. In: International Conference on human-computer interaction, 2019; p. 53–63. Springer.

Hulsebos M, Hu K, Bakker M, Zgraggen E, Satyanarayan A, Kraska T, Demiralp Ça, Hidalgo C. Sherlock: a deep learning approach to semantic data type detection. In: Proceedings of the 25th ACM SIGKDD International Conference on knowledge discovery & data mining, 2019; p. 1500–508.

Imamverdiyev Y, Abdullayeva F. Deep learning method for denial of service attack detection based on restricted Boltzmann machine. Big Data. 2018;6(2):159–69.

Islam MZ, Islam MM, Asraf A. A combined deep cnn-lstm network for the detection of novel coronavirus (covid-19) using x-ray images. Inf Med Unlock. 2020;20:100412.

Ismail WN, Hassan MM, Alsalamah HA, Fortino G. Cnn-based health model for regular health factors analysis in internet-of-medical things environment. IEEE. Access. 2020;8:52541–9.

Jangid H, Singhal S, Shah RR, Zimmermann R. Aspect-based financial sentiment analysis using deep learning. In: Companion Proceedings of the The Web Conference 2018, 2018; p. 1961–966.

Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: a survey. J Artif Intell Res. 1996;4:237–85.

Kameoka H, Li L, Inoue S, Makino S. Supervised determined source separation with multichannel variational autoencoder. Neural Comput. 2019;31(9):1891–914.

Karhunen J, Raiko T, Cho KH. Unsupervised deep learning: a short review. In: Advances in independent component analysis and learning machines. 2015; p. 125–42.

Kawde P, Verma GK. Deep belief network based affect recognition from physiological signals. In: 2017 4th IEEE Uttar Pradesh Section International Conference on electrical, computer and electronics (UPCON), 2017; p. 587–92. IEEE.

Kim J-Y, Seok-Jun B, Cho S-B. Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. Inf Sci. 2018;460:83–102.

Kingma DP, Welling M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.

Kingma DP, Welling M. An introduction to variational autoencoders. arXiv preprint arXiv:1906.02691, 2019.

Kiran PKR, Bhasker B. Dnnrec: a novel deep learning based hybrid recommender system. Expert Syst Appl. 2020.

Kloenne M, Niehaus S, Lampe L, Merola A, Reinelt J, Roeder I, Scherf N. Domain-specific cues improve robustness of deep learning-based segmentation of ct volumes. Sci Rep. 2020;10(1):1–9.

Kohonen T. The self-organizing map. Proc IEEE. 1990;78(9):1464–80.

Kohonen T. Essentials of the self-organizing map. Neural Netw. 2013;37:52–65.

Kök İ, Şimşek MU, Özdemir S. A deep learning model for air quality prediction in smart cities. In: 2017 IEEE International Conference on Big Data (Big Data), 2017; p. 1983–990. IEEE.

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 2012; p. 1097–105.

Latif S, Rana R, Younis S, Qadir J, Epps J. Transfer learning for improving speech emotion classification accuracy. arXiv preprint arXiv:1801.06353, 2018.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324.

Li B, François-Lavet V, Doan T, Pineau J. Domain adversarial reinforcement learning. arXiv preprint arXiv:2102.07097, 2021.

Li T-HS, Kuo P-H, Tsai T-N, Luan P-C. Cnn and lstm based facial expression analysis model for a humanoid robot. IEEE Access. 2019;7:93998–4011.

Liu C, Cao Y, Luo Y, Chen G, Vokkarane V, Yunsheng M, Chen S, Hou P. A new deep learning-based food recognition system for dietary assessment on an edge computing service infrastructure. IEEE Trans Serv Comput. 2017;11(2):249–61.

Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE. A survey of deep neural network architectures and their applications. Neurocomputing. 2017;234:11–26.

López AU, Mateo F, Navío-Marco J, Martínez-Martínez JM, Gómez-Sanchís J, Vila-Francés J, Serrano-López AJ. Analysis of computer user behavior, security incidents and fraud using self-organizing maps. Comput Secur. 2019;83:38–51.

Lopez-Martin M, Carro B, Sanchez-Esguevillas A. Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Syst Appl. 2020;141:112963.

Ma X, Yao T, Menglan H, Dong Y, Liu W, Wang F, Liu J. A survey on deep learning empowered iot applications. IEEE Access. 2019;7:181721–32.

Makhzani A, Frey B. K-sparse autoencoders. arXiv preprint arXiv:1312.5663, 2013.

Mandic D, Chambers J. Recurrent neural networks for prediction: learning algorithms, architectures and stability. Hoboken: Wiley; 2001.

Book   Google Scholar  

Marlin B, Swersky K, Chen B, Freitas N. Inductive principles for restricted boltzmann machine learning. In: Proceedings of the Thirteenth International Conference on artificial intelligence and statistics, p. 509–16. JMLR Workshop and Conference Proceedings, 2010.

Masud M, Muhammad G, Alhumyani H, Alshamrani SS, Cheikhrouhou O, Ibrahim S, Hossain MS. Deep learning-based intelligent face recognition in iot-cloud environment. Comput Commun. 2020;152:215–22.

Memisevic R, Hinton GE. Learning to represent spatial transformations with factored higher-order boltzmann machines. Neural Comput. 2010;22(6):1473–92.

Article   MATH   Google Scholar  

Minaee S, Azimi E, Abdolrashidi AA. Deep-sentiment: sentiment analysis using ensemble of cnn and bi-lstm models. arXiv preprint arXiv:1904.04206, 2019.

Naeem M, Paragliola G, Coronato A. A reinforcement learning and deep learning based intelligent system for the support of impaired patients in home treatment. Expert Syst Appl. 2021;168:114285.

Niu Z, Zhong G, Hui Yu. A review on the attention mechanism of deep learning. Neurocomputing. 2021;452:48–62.

Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2009;22(10):1345–59.

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al. Pytorch: An imperative style, high-performance deep learning library. Adv Neural Inf Process Syst. 2019;32:8026–37.

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.

MathSciNet   MATH   Google Scholar  

Pi Y, Nath ND, Behzadan AH. Convolutional neural networks for object detection in aerial imagery for disaster response and recovery. Adv Eng Inf. 2020;43:101009.

Piccialli F, Giampaolo F, Prezioso E, Crisci D, Cuomo S. Predictive analytics for smart parking: A deep learning approach in forecasting of iot data. ACM Trans Internet Technol (TOIT). 2021;21(3):1–21.

Puterman ML. Markov decision processes: discrete stochastic dynamic programming. Hoboken: Wiley; 2014.

Qu X, Lin Y, Kai G, Linru M, Meng S, Mingxing K, Mu L, editors. A survey on the development of self-organizing maps for unsupervised intrusion detection. Mob Netw Appl. 2019; p. 1–22.

Rahman MW, Tashfia SS, Islam R, Hasan MM, Sultan SI, Mia S, Rahman MM. The architectural design of smart blind assistant using iot with deep learning paradigm. Internet of Things. 2021;13:100344.

Ren J, Green M, Huang X. From traditional to deep learning: fault diagnosis for autonomous vehicles. In: Learning control. Elsevier. 2021; p. 205–19.

Rifai S, Vincent P, Muller X, Glorot X, Bengio Y. Contractive auto-encoders: Explicit invariance during feature extraction. In: Icml, 2011.

Rosa RL, Schwartz GM, Ruggiero WV, Rodríguez DZ. A knowledge-based recommendation system that includes sentiment analysis and deep learning. IEEE Trans Ind Inf. 2018;15(4):2124–35.

Sarker IH. Context-aware rule learning from smartphone data: survey, challenges and future directions. J Big Data. 2019;6(1):1–25.

Article   MathSciNet   Google Scholar  

Sarker IH. A machine learning based robust prediction model for real-life mobile phone data. Internet of Things. 2019;5:180–93.

Sarker IH. Cyberlearning: effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet of Things. 2021;14:100393.

Sarker IH. Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective. SN Comput Sci. 2021.

Sarker IH. Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective. SN Computer. Science. 2021;2(3):1–16.

MathSciNet   Google Scholar  

Sarker IH. Machine learning: Algorithms, real-world applications and research directions. SN Computer. Science. 2021;2(3):1–21.

Sarker IH, Abushark YB, Alsolami F, Khan AI. Intrudtree: a machine learning based cyber security intrusion detection model. Symmetry. 2020;12(5):754.

Sarker IH, Abushark YB, Khan AI. Contextpca: Predicting context-aware smartphone apps usage based on machine learning techniques. Symmetry. 2020;12(4):499.

Sarker IH, Colman A, Han J. Recencyminer: mining recency-based personalized behavior from contextual smartphone data. J Big Data. 2019;6(1):1–21.

Sarker IH, Colman A, Han J, Khan AI, Abushark YB, Salah K. Behavdt: a behavioral decision tree learning to build user-centric context-aware predictive model. Mob Netw Appl. 2020;25(3):1151–61.

Sarker IH, Colman A, Kabir MA, Han J. Individualized time-series segmentation for mining mobile phone user behavior. Comput J. 2018;61(3):349–68.

Sarker IH, Furhad MH, Nowrozy R. Ai-driven cybersecurity: an overview, security intelligence modeling and research directions. SN Computer. Science. 2021;2(3):1–18.

Sarker IH, Hoque MM, Uddin MK. Mobile data science and intelligent apps: concepts, ai-based modeling and research directions. Mob Netw Appl. 2021;26(1):285–303.

Sarker IH, Kayes ASM. Abc-ruleminer: User behavioral rule-based machine learning method for context-aware intelligent services. J Netw Comput Appl. 2020;168:102762.

Sarker IH, Kayes ASM, Badsha S, Alqahtani H, Watters P, Ng A. Cybersecurity data science: an overview from machine learning perspective. J Big data. 2020;7(1):1–29.

Sarker IH, Kayes ASM, Watters P. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. J Big Data. 2019;6(1):1–28.

Sarker IH, Salah K. Appspred: predicting context-aware smartphone apps using random forest learning. Internet of Things. 2019;8:100106.

Satt A, Rozenberg S, Hoory R. Efficient emotion recognition from speech using deep learning on spectrograms. In: Interspeec, 2017; p. 1089–1093.

Sevakula RK, Singh V, Verma NK, Kumar C, Cui Y. Transfer learning for molecular cancer classification using deep neural networks. IEEE/ACM Trans Comput Biol Bioinf. 2018;16(6):2089–100.

Sujay Narumanchi H, Ananya Pramod Kompalli Shankar A, Devashish CK. Deep learning based large scale visual recommendation and search for e-commerce. arXiv preprint arXiv:1703.02344, 2017.

Shao X, Kim CS. Multi-step short-term power consumption forecasting using multi-channel lstm with time location considering customer behavior. IEEE Access. 2020;8:125263–73.

Siami-Namini S, Tavakoli N, Namin AS. The performance of lstm and bilstm in forecasting time series. In: 2019 IEEE International Conference on Big Data (Big Data), 2019; p. 3285–292. IEEE.

Ślusarczyk B. Industry 4.0: are we ready? Pol J Manag Stud. 2018; p. 17

Sumathi P, Subramanian R, Karthikeyan VV, Karthik S. Soil monitoring and evaluation system using edl-asqe: enhanced deep learning model for ioi smart agriculture network. Int J Commun Syst. 2021; p. e4859.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 2015; p. 1–9.

Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. A survey on deep transfer learning. In: International Conference on artificial neural networks, 2018; p. 270–279. Springer.

Vesanto J, Alhoniemi E. Clustering of the self-organizing map. IEEE Trans Neural Netw. 2000;11(3):586–600.

Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A, Bottou L. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res. 2010;11(12).

Wang J, Liang-Chih Yu, Robert Lai K, Zhang X. Tree-structured regional cnn-lstm model for dimensional sentiment analysis. IEEE/ACM Trans Audio Speech Lang Process. 2019;28:581–91.

Wang S, Wan J, Li D, Liu C. Knowledge reasoning with semantic data for real-time data processing in smart factory. Sensors. 2018;18(2):471.

Wang W, Zhao M, Wang J. Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network. J Ambient Intell Humaniz Comput. 2019;10(8):3035–43.

Wang X, Liu J, Qiu T, Chaoxu M, Chen C, Zhou P. A real-time collision prediction mechanism with deep learning for intelligent transportation system. IEEE Trans Veh Technol. 2020;69(9):9497–508.

Wang Y, Huang M, Zhu X, Zhao L. Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on empirical methods in natural language processing, 2016; p. 606–615.

Wei P, Li Y, Zhang Z, Tao H, Li Z, Liu D. An optimization method for intrusion detection classification model based on deep belief network. IEEE Access. 2019;7:87593–605.

Weiss K, Khoshgoftaar TM, Wang DD. A survey of transfer learning. J Big data. 2016;3(1):9.

Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, Gao M, Hou H, Wang C. Machine learning and deep learning methods for cybersecurity. Ieee access. 2018;6:35365–81.

Xu W, Sun H, Deng C, Tan Y. Variational autoencoder for semi-supervised text classification. In: Thirty-First AAAI Conference on artificial intelligence, 2017.

Xue Q, Chuah MC. New attacks on rnn based healthcare learning system and their detections. Smart Health. 2018;9:144–57.

Yousefi-Azar M, Hamey L. Text summarization using unsupervised deep learning. Expert Syst Appl. 2017;68:93–105.

Yuan X, Shi J, Gu L. A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Syst Appl. 2020;p. 114417.

Zhang G, Liu Y, Jin X. A survey of autoencoder-based recommender systems. Front Comput Sci. 2020;14(2):430–50.

Zhang X, Yao L, Huang C, Wang S, Tan M, Long Gu, Wang C. Multi-modality sensor data classification with selective attention. arXiv preprint arXiv:1804.05493, 2018.

Zhang X, Yao L, Wang X, Monaghan J, Mcalpine D, Zhang Y. A survey on deep learning based brain computer interface: recent advances and new frontiers. arXiv preprint arXiv:1905.04149, 2019; p. 66.

Zhang Y, Zhang P, Yan Y. Attention-based lstm with multi-task learning for distant speech recognition. In: Interspeech, 2017; p. 3857–861.

Download references

Author information

Authors and affiliations.

Swinburne University of Technology, Melbourne, VIC, 3122, Australia

Iqbal H. Sarker

Chittagong University of Engineering & Technology, Chittagong, 4349, Bangladesh

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Iqbal H. Sarker .

Ethics declarations

Conflict of interest.

The author declares no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K. N. and M. Shivakumar.

Rights and permissions

Reprints and permissions

About this article

Sarker, I.H. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN COMPUT. SCI. 2 , 420 (2021). https://doi.org/10.1007/s42979-021-00815-1

Download citation

Received : 29 May 2021

Accepted : 07 August 2021

Published : 18 August 2021

DOI : https://doi.org/10.1007/s42979-021-00815-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Deep learning
• Artificial neural network
• Artificial intelligence
• Discriminative learning
• Generative learning
• Hybrid learning
• Intelligent systems
• Find a journal
• Publish with us
• Track your research

• Warning : Invalid argument supplied for foreach() in /home/customer/www/opendatascience.com/public_html/wp-includes/nav-menu.php on line 95 Warning : array_merge(): Expected parameter 2 to be an array, null given in /home/customer/www/opendatascience.com/public_html/wp-includes/nav-menu.php on line 102
• ODSC EUROPE
• AI+ Training
• Speak at ODSC

• Data Analytics
• Data Engineering
• Data Visualization
• Deep Learning
• Generative AI
• Machine Learning
• NLP and LLMs
• Business & Use Cases
• Career Advice
• Write for us
• ODSC Community Slack Channel
• Upcoming Webinars

Best Deep Learning Research of 2021 So Far

Deep Learning Modeling Research posted by Daniel Gutierrez, ODSC August 2, 2021 Daniel Gutierrez, ODSC

The discipline of AI most often mentioned these days is deep learning (DL) along with its many incarnations implemented with deep neural networks. DL also is a rapidly accelerating area of research with papers being published at a fast clip by research teams from around the globe.

I enjoy keeping a pulse on deep learning research and so far in 2021 research innovations have propagated at a quick pace. Some of the top topical areas for deep learning research are: causality, explainability/interpretability, transformers, NLP, GPT, language models, GANs, deep learning for tabular data, and many others.

In this article, we’ll take a brief tour of my top picks for deep learning research  (in no particular order) of papers that I found to be particularly compelling. I’m pretty attached to this leading-edge research. I’m known to carry a thick folder of recent research papers around in my backpack and consume all the great developments when I have a spare moment. Enjoy! 

Check out my previous lists: Best Machine Learning Research of 2021 So Far , Best of Deep Reinforcement Learning Research of 2019 , Most Influential NLP Research of 2019 , and Most Influential Deep Learning Research of 2019 . 

Cause and Effect: Concept-based Explanation of Neural Networks

In many scenarios, human decisions are explained based on some high-level concepts. This paper takes a step in the interpretability of neural networks by examining their internal representation or neuron’s activations against concepts. A concept is characterized by a set of samples that have specific features in common. A framework is proposed to check the existence of a causal relationship between a concept (or its negation) and task classes. While the previous methods focus on the importance of a concept to a task class, the paper goes further and introduces four measures to quantitatively determine the order of causality. Through experiments, the effectiveness of the proposed method is demonstrated in explaining the relationship between a concept and the predictive behavior of a neural network.

Pretrained Language Models for Text Generation: A Survey

Text generation has become one of the most important yet challenging tasks in natural language processing (NLP). The resurgence of deep learning has greatly advanced this field by neural generation models, especially the paradigm of pretrained language models (PLMs). This paper presents an overview of the major advances achieved in the topic of PLMs for text generation. As the preliminaries, the paper presents the general task definition and briefly describes the mainstream architectures of PLMs for text generation. As the core content, the deep learning research paper discusses how to adapt existing PLMs to model different input data and satisfy special properties in the generated text. 

A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLP

Building a dialogue system that can communicate naturally with humans is a challenging yet interesting problem of agent-based computing. The rapid growth in this area is usually hindered by the long-standing problem of data scarcity as these systems are expected to learn syntax, grammar, decision making, and reasoning from insufficient amounts of task-specific data sets. The recently introduced pre-trained language models have the potential to address the issue of data scarcity and bring considerable advantages by generating contextualized word embeddings. These models are considered counterparts of ImageNet in NLP and have demonstrated the ability to capture different facets of language such as hierarchical relations, long-term dependency, and sentiment. This short survey paper discusses the recent progress made in the field of pre-trained language models. 

TrustyAI Explainability Toolkit

AI is becoming increasingly more popular and can be found in workplaces and homes around the world. However, how do we ensure trust in these systems? Regulation changes such as the GDPR mean that users have a right to understand how their data has been processed as well as saved. Therefore if, for example, you are denied a loan you have the right to ask why. This can be hard if the method for working this out uses “black box” machine learning techniques such as neural networks. TrustyAI is a new initiative which looks into explainable artificial intelligence (XAI) solutions to address trustworthiness in ML as well as decision services landscapes. This deep learning research paper looks at how TrustyAI can support trust in decision services and predictive models. The paper investigates techniques such as LIME, SHAP and counterfactuals, benchmarking both LIME and counterfactual techniques against existing implementations. 

Generative Adversarial Network: Some Analytical Perspectives

Ever since its debut, generative adversarial networks (GANs) have attracted tremendous amount of attention. Over the past years, different variations of GANs models have been developed and tailored to different applications in practice. Meanwhile, some issues regarding the performance and training of GANs have been noticed and investigated from various theoretical perspectives. This paper starts from an introduction of GANs from an analytical perspective, then moves onto the training of GANs via SDE approximations and finally discusses some applications of GANs in computing high dimensional MFGs as well as tackling mathematical finance problems.

PyTorch Tabular: A Framework for Deep Learning with Tabular Data

In spite of showing unreasonable effectiveness in modalities like Text and Image, deep learning has always lagged gradient boosting in tabular data – both in popularity and performance. But recently there have been newer models created specifically for tabular data, which is pushing the performance bar. But popularity is still a challenge because there is no easy, ready-to-use library like scikit-learn for deep learning. PyTorch Tabular is a new deep learning library which makes working with deep learning and tabular data easy and fast. It is a library built on top of PyTorch and PyTorch Lightning and works on Pandas dataframes directly. Many SOTA models like NODE and TabNet are already integrated and implemented in the library with a unified API. PyTorch Tabular is designed to be easily extensible for researchers, simple for practitioners, and robust in industrial deployments.

A Survey of Quantization Methods for Efficient Neural Network Inference

As soon as abstract mathematical computations were adapted to computation on digital computers, the problem of efficient representation, manipulation, and communication of the numerical values in those computations arose. Strongly related to the problem of numerical representation is the problem of quantization : in what manner should a set of continuous real-valued numbers be distributed over a fixed discrete set of numbers to minimize the number of bits required and also to maximize the accuracy of the attendant computations? This perennial problem of quantization is particularly relevant whenever memory and/or computational resources are severely restricted, and it has come to the forefront in recent years due to the remarkable performance of Neural Network models in computer vision, natural language processing, and related areas. Moving from floating-point representations to low-precision fixed integer values represented in four bits or less holds the potential to reduce the memory footprint and latency by a factor of 16x; and, in fact, reductions of 4x to 8x are often realized in practice in these applications. Thus, it is not surprising that quantization has emerged recently as an important and very active sub-area of research in the efficient implementation of computations associated with Neural Networks. This paper surveys approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the advantages/disadvantages of current methods. 

How to decay your learning rate

Complex learning rate schedules have become an integral part of deep learning. This research finds empirically that common fine-tuned schedules decay the learning rate after the weight norm bounces. This leads to the proposal of ABEL : an automatic scheduler which decays the learning rate by keeping track of the weight norm. ABEL’s performance matches that of tuned schedules and is more robust with respect to its parameters. Through extensive experiments in vision, NLP, and RL, it is shown that if the weight norm does not bounce, it is possible to simplify schedules even further with no loss in performance. In such cases, a complex schedule has similar performance to a constant learning rate with a decay at the end of training.

GPT Understands, Too

While GPTs with traditional fine-tuning fail to achieve strong results on natural language understanding (NLU), this paper shows that GPTs can be better than or comparable to similar-sized BERTs on NLU tasks with a novel method P-tuning — which employs trainable continuous prompt embeddings. On the knowledge probing (LAMA) benchmark, the best GPT recovers 64% (P@1) of world knowledge without any additional text provided during test time, which substantially improves the previous best by 20+ percentage points. On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning. Importantly, it is found that P-tuning also improves BERTs’ performance in both few-shot and supervised settings while largely reducing the need for prompt engineering. Consequently, P-tuning outperforms the state-of-the-art approaches on the few-shot SuperGlue benchmark.

Understanding Robustness of Transformers for Image Classification

Deep Convolutional Neural Networks (CNNs) have long been the architecture of choice for computer vision tasks. Recently, Transformer-based architectures like Vision Transformer (ViT) have matched or even surpassed ResNets for image classification. However, details of the Transformer architecture — such as the use of non-overlapping patches — lead one to wonder whether these networks are as robust. This paper performs an extensive study of a variety of different measures of robustness of ViT models and compare the findings to ResNet baselines. Investigated is robustness to input perturbations as well as robustness to model perturbations. The paper finds that when pre-trained with a sufficient amount of data, ViT models are at least as robust as the ResNet counterparts on a broad range of perturbations. Also found is that Transformers are robust to the removal of almost any single layer, and that while activations from later layers are highly correlated with each other, they nevertheless play an important role in classification.

Improving DeepFake Detection Using Dynamic Face Augmentation

The creation of altered and manipulated faces has become more common due to the improvement of DeepFake generation methods. Simultaneously, we have seen the development of detection models for differentiating between a manipulated and original face from image or video content. We have observed that most publicly available DeepFake detection datasets have limited variations, where a single face is used in many videos, resulting in an oversampled training dataset. Due to this, deep neural networks tend to overfit to the facial features instead of learning to detect manipulation features of DeepFake content. As a result, most detection architectures perform poorly when tested on unseen data. This paper provides a quantitative analysis to investigate this problem and present a solution to prevent model overfitting due to the high volume of samples generated from a small number of actors.

An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks

Edge TPUs are a domain of accelerators for low-power, edge devices and are widely used in various Google products such as Coral and Pixel devices. This paper first discusses the major microarchitectural details of Edge TPUs. This is followed by an extensive evaluation of three classes of Edge TPUs, covering different computing ecosystems that are either currently deployed in Google products or are the product pipeline. Building upon this extensive study, the paper discusses critical and interpretable microarchitectural insights about the studied classes of Edge TPUs. Mainly discussed is how Edge TPU accelerators perform across CNNs with different structures. Finally, the paper presents ongoing efforts in developing high-accuracy learned machine learning models to estimate the major performance metrics of accelerators such as latency and energy consumption. These learned models enable significantly faster (in the order of milliseconds) evaluations of accelerators as an alternative to time-consuming cycle-accurate simulators and establish an exciting opportunity for rapid hard-ware/software co-design.

Attention Models for Point Clouds in Deep Learning: A Survey

Recently, the advancement of 3D point clouds in deep learning has attracted intensive research in different application domains such as computer vision and robotic tasks. However, creating feature representation of robust, discriminative from unordered and irregular point clouds is challenging. The goal of this paper is to provide a comprehensive overview of the point clouds feature representation which uses attention models. More than 75+ key contributions in the recent three years are summarized in this survey, including the 3D objective detection, 3D semantic segmentation, 3D pose estimation, point clouds completion etc. Also provided are: a detailed characterization of (i) the role of attention mechanisms, (ii) the usability of attention models into different tasks, and (iii) the development trend of key technology.

Constrained Optimization for Training Deep Neural Networks Under Class Imbalance

Deep neural networks (DNNs) are notorious for making more mistakes for the classes that have substantially fewer samples than the others during training. Such class imbalance is ubiquitous in clinical applications and very crucial to handle because the classes with fewer samples most often correspond to critical cases (e.g., cancer) where misclassifications can have severe consequences. Not to miss such cases, binary classifiers need to be operated at high True Positive Rates (TPR) by setting a higher threshold but this comes at the cost of very high False Positive Rates (FPR) for problems with class imbalance. Existing methods for learning under class imbalance most often do not take this into account. This paper argues that prediction accuracy should be improved by emphasizing reducing FPRs at high TPRs for problems where misclassification of the positive samples are associated with higher cost. To this end, it’s posed the training of a DNN for binary classification as a constrained optimization problem and introduce a novel constraint that can be used with existing loss functions to enforce maximal area under the ROC curve (AUC). The resulting constrained optimization problem is solved using an Augmented Lagrangian method (ALM), where the constraint emphasizes reduction of FPR at high TPR. Results demonstrate that the proposed method almost always improves the loss functions it is used with by attaining lower FPR at high TPR and higher or equal AUC.

Deep Convolutional Neural Networks with Unitary Weights

While normalizations aim to fix the exploding and vanishing gradient problem in deep neural networks, they have drawbacks in speed or accuracy because of their dependency on the data set statistics. This paper is a comprehensive study of a novel method based on unitary synaptic weights derived from Lie Group to construct intrinsically stable neural systems. Here it’s shown that unitary convolutional neural networks deliver up to 32% faster inference speeds while maintaining competitive prediction accuracy. Unlike prior arts restricted to square synaptic weights, the paper expands the unitary networks to weights of any size and dimension.

TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up

The recent explosive interest with transformers has suggested their potential to become powerful “universal” models for computer vision tasks, such as classification, detection, and segmentation. An important question is how much further transformers can go – are they ready to take some more notoriously difficult vision tasks, e.g., generative adversarial networks (GANs)? Driven by that curiosity, this paper conducts the first pilot study in building a GAN completely free of convolutions, using only pure transformer-based architectures. The proposed vanilla GAN architecture, dubbed TransGAN , consists of a memory-friendly transformer-based generator that progressively increases feature resolution while decreasing embedding dimension, and a patch-level discriminator that is also transformer-based. TransGAN is seen to notably benefit from data augmentations (more than standard GANs), a multi-task co-training strategy for the generator, and a locally initialized self-attention that emphasizes the neighborhood smoothness of natural images. Equipped with those findings, TransGAN can effectively scale up with bigger models and high-resolution image datasets. Specifically, the architecture achieves highly competitive performance compared to current state-of-the-art GANs based on convolutional backbones. The GitHub repo associated with this paper can be found HERE .

Deep Learning for Scene Classification: A Survey

Scene classification , aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision. The rise of large-scale datasets, which constitute a dense sampling of diverse real-world scenes, and the renaissance of deep learning techniques, which learn powerful feature representations directly from big raw data, have been bringing remarkable progress in the field of scene representation and classification. To help researchers master needed advances in this field, the goal of this paper is to provide a comprehensive survey of recent achievements in scene classification using deep learning. More than 260 major publications are included in this survey covering different aspects of scene classification, including challenges, benchmark datasets, taxonomy, and quantitative performance comparisons of the reviewed methods. In retrospect of what has been achieved so far, this paper is concluded with a list of promising research opportunities.

Introducing and assessing the explainable AI (XAI) method: SIDU

Explainable Artificial Intelligence (XAI) has in recent years become a well-suited framework to generate human-understandable explanations of black box models. This paper presents a novel XAI visual explanation algorithm denoted SIDU that can effectively localize entire object regions responsible for prediction. The paper analyzes its robustness and effectiveness through various computational and human subject experiments. In particular, the SIDU algorithm is assessed using three different types of evaluations (Application, Human and Functionally-Grounded) to demonstrate its superior performance. The robustness of SIDU is further studied in presence of adversarial attack on black box models to better understand its performance.

Evolving Reinforcement Learning Algorithms

This paper proposes a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. The method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, the method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, two learned algorithms are highlighted which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behavior shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods.

RepVGG: Making VGG-style ConvNets Great Again

VGG-style ConvNets, although now considered a classic architecture, were attractive due to their simplicity. In contrast, ResNets have become popular due to their high accuracy but are more difficult to customize and display undesired inference drawbacks. To address these issues, Ding et al. propose RepVGG – the return of the VGG! 

RepVGG is an efficient and simple architecture using plain VGG-style ConvNets. It decouples the inference-time and training-time architecture through a structural re-parameterization technique. The researchers report favorable speed-accuracy tradeoff compared to state-of-the-art models, such as EfficientNet and RegNet. RepVGG achieves 80% top-1 accuracy on ImageNet and is benchmarked as being 83% faster than ResNet-50. This research is part of a broader effort to build more efficient models using simpler architectures and operations. The GitHub repo associated with this paper can be found HERE .

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) defies this and instead selects different parameters for each incoming example. The result is a sparsely-activated model — with outrageous numbers of parameters — but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs and training instability — this paper addresses these with the Switch Transformer . The Google Brain researchers simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. The proposed training techniques help wrangle the instabilities and it is shown that large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. They design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings to measure gains over the mT5-Base version across all 101 languages. Finally, the paper advances the current scale of language models by pre-training up to trillion parameter models on the “Colossal Clean Crawled Corpus” and achieve a 4x speedup over the T5-XXL model. The GitHub repo associated with this paper can be found HERE . 

How to Learn More about Deep Learning Research

At our upcoming event this November 16th-18th in San Francisco,  ODSC West 2021 will feature a plethora of talks, workshops, and training sessions on deep learning and deep learning research. You can register now for 60% off all ticket types  before the discount drops to 40% in a few weeks. Some  highlighted sessions on deep learning  include:

Sessions on Deep Learning and Deep Learning Research:

• GANs: Theory and Practice, Image Synthesis With GANs Using TensorFlow: Ajay Baranwal | Center Director | Center for Deep Learning in Electronic Manufacturing, Inc
• Machine Learning With Graphs: Going Beyond Tabular Data: Dr. Clair J. Sullivan | Data Science Advocate | Neo4j
• Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2.0: Oliver Zeigermann | Software Developer | embarc Software Consulting GmbH
• Get Started with Time-Series Forecasting using the Google Cloud AI Platform: Karl Weinmeister | Developer Relations Engineering Manager | Google

Sessions on Machine Learning:

• Towards More Energy-Efficient Neural Networks? Use Your Brain!: Olaf de Leeuw | Data Scientist | Dataworkz
• Practical MLOps: Automation Journey: Evgenii Vinogradov, PhD | Head of DHW Development | YooMoney
• Applications of Modern Survival Modeling with Python: Brian Kent, PhD | Data Scientist | Founder The Crosstab Kite
• Using Change Detection Algorithms for Detecting Anomalous Behavior in Large Systems: Veena Mendiratta, PhD | Adjunct Faculty, Network Reliability and Analytics Researcher | Northwestern University

Sessions on MLOps:

• Tuning Hyperparameters with Reproducible Experiments: Milecia McGregor | Senior Software Engineer | Iterative
• MLOps… From Model to Production: Filipa Peleja, PhD | Lead Data Scientist | Levi Strauss & Co
• Operationalization of Models Developed and Deployed in Heterogeneous Platforms: Sourav Mazumder | Data Scientist, Thought Leader, AI & ML Operationalization Leader | IBM
• Develop and Deploy a Machine Learning Pipeline in 45 Minutes with Ploomber: Eduardo Blancas | Data Scientist | Fidelity Investment

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

Report States OpenAI Will Release Search Engine to Compete Against Google

AI and Data Science News posted by ODSC Team May 9, 2024 According to a new report, OpenAI has its sights on Google’s search engine dominance as the...

TikTok Implements new AI Content Labeling System

AI and Data Science News posted by ODSC Team May 9, 2024 Embattled social media platform, TikTok announced on Thursday that it will start labeling content created using...

Apple is Developing AI Chips in Data Centers According to Report

AI and Data Science News posted by ODSC Team May 8, 2024 According to a report from the Wall Street Journal, Apple is developing specialized chips aimed at...

• Office Hours

One of CS230's main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well-qualified to start machine learning or AI research. The final project is intended to start you in these directions.

Instructors

Time and Location

Wednesday 9:30AM-11:20AM Zoom

Getting Started

Project starter package.

The teaching team has put together a

• github repository with project code examples, including a computer vision and a natural language processing example (both in Tensorflow and Pytorch).
• A series of posts to help you familiarize yourself with the project code examples, get ideas on how to structure your deep learning project code, and to setup AWS. The code examples posted are optional and are only meant to help you with your final project. The code can be reused in your projects, but the examples presented are not complex enough to meet the expectations of a quarterly project.
• A sheet of resources to get started with project ideas in several topics

Project Topics

This quarter in CS230, you will learn about a wide range of deep learning applications. Part of the learning will be online, during in-class lectures and when completing assignments, but you will really experience hands-on work in your final project. We would like you to choose wisely a project that fits your interests. One that would be both motivating and technically challenging.

Most students do one of three kinds of projects:

• Application project. This is by far the most common: Pick an application that interests you, and explore how best to apply learning algorithms to solve it.
• Algorithmic project. Pick a problem or family of problems, and develop a new learning algorithm, or a novel variant of an existing algorithm, to solve it.
• Theoretical project. Prove some interesting/non-trivial properties of a new or an existing learning algorithm. (This is often quite difficult, and so very few, if any, projects will be purely theoretical.) Some projects will also combine elements of applications and algorithms.

Many fantastic class projects come from students picking either an application area that they’re interested in, or picking some subfield of machine learning that they want to explore more. So, pick something that you can get excited and passionate about! Be brave rather than timid, and do feel free to propose ambitious things that you’re excited about. (Just be sure to ask us for help if you’re uncertain how to best get started.) Alternatively, if you’re already working on a research or industry project that deep learning might apply to, then you may already have a great project idea.

Project Hints

A very good CS230 project will be a publishable or nearly-publishable piece of work. Each year, some number of students continue working on their projects after completing CS230, submitting their work to a conferences or journals. Thus, for inspiration, you might also look at some recent deep learning research papers. Two of the main machine learning conferences are ICML and NeurIPS . You may also want to look at class projects from previous years of CS230 ( Fall 2017 , Winter 2018 , Spring 2018 , Fall 2018 ) and other machine learning/deep learning classes ( CS229 , CS229A , CS221 , CS224N , CS231N ) is a good way to get ideas. Finally, we crowdsourced and curated a list of ideas that you can view here , and an older one here , and a (requires Stanford login).

Once you have identified a topic of interest, it can be useful to look up existing research on relevant topics by searching related keywords on an academic search engine such as: http://scholar.google.com . Another important aspect of designing your project is to identify one or several datasets suitable for your topic of interest. If that data needs considerable pre-processing to suit your task, or that you intend to collect the needed data yourself, keep in mind that this is only one part of the expected project work, but can often take considerable time. We still expect a solid methodology and discussion of results, so pace your project accordingly.

Notes on a few specific types of projects:

• Computation power. Amazon Web Services is sponsoring the CS230 projects by providing you with GPU credits to run your experiments! We will update regarding how to retrieve your GPU credits. Alternatively Google Cloud and Microsoft Azure offer free academic units which you can apply to.
• Preprocessed datasets. While we don’t want you to have to spend much time collecting raw data, the process of inspecting and visualizing the data, trying out different types of preprocessing, and doing error analysis is often an important part of machine learning. Hence if you choose to use preprepared datasets (e.g. from Kaggle, the UCI machine learning repository, etc.) we encourage you to do some data exploration and analysis to get familiar with the problem.
• Replicating results. Replicating the results in a paper can be a good way to learn. However, we ask that instead of just replicating a paper, also try using the technique on another application, or do some analysis of how each component of the model contributes to final performance.

Project Deliverables

This section contains the detailed instructions for the different parts of your project.

Groups: The project is done in groups of 1-3 people; teams are formed by students.

Submission: We will be using Gradescope for submission of all four parts of the final project. We’ll announce when submissions are open for each part. You should submit on Gradescope as a group: that is, for each part, please make one submission for your entire project group and tag your team members.

Evaluation: We will not be disclosing the breakdown of the 40% that the final project is worth amongst the different parts, but the video and final report will combine to be the majority of the grade. Attendance and participation during your TA meetings will also be considered. Projects will be evaluated based on:

• The technical quality of the work. (I.e., Does the technical material make sense? Are the things tried reasonable? Are the proposed algorithms or applications clever and interesting? Do the authors convey novel insight about the problem and/or algorithms?)
• Significance. (Did the authors choose an interesting or a “real” problem to work on, or only a small “toy” problem? Is this work likely to be useful and/or have impact?)
• The novelty of the work. (Is this project applying a common technique to a well-studied problem, or is the problem or method relatively unexplored?)

In order to highlight these components, it is important you present a solid discussion regarding the learnings from the development of your method, and summarizing how your work compares to existing approaches.

Deadline: April 19, Wednesday 11:59 PM

First, make sure to submit the following Google form so that we can match you to a TA mentor. In the form you will have to provide your project title, team members and relevant research area(s).

In the project proposal, you’ll pick a project idea to work on early and receive feedback from the TAs. If your proposed project will be done jointly with a different class’ project, you should obtain approval from the other instructor and approval from us. Please come to the project office hours to discuss with us if you would like to do a joint project. You should submit your proposals on Gradescope. All students should already be added to the course page on Gradescope via your SUNet IDs. If you are not, please create a private post on Ed and we will give you access to Gradescope.

In the proposal, below your project title, include the project category. The category can be one of:

• Computer Vision
• Natural Language Processing
• Generative Modeling
• Speech Recognition
• Reinforcement Learning
• Others (Please specify!)

Your project proposal should include the following information:

• What is the problem that you will be investigating? Why is it interesting?
• What are the challenges of this project?
• What dataset are you using? How do you plan to collect it?
• What method or algorithm are you proposing? If there are existing implementations, will you use them and how? How do you plan to improve or modify such implementations?
• What reading will you examine to provide context and background? If relevant, what papers do you refer to?
• How will you evaluate your results? Qualitatively, what kind of results do you expect (e.g. plots or figures)? Quantitatively, what kind of analysis will you use to evaluate and/or compare your results (e.g. what performance metrics or statistical tests)?

Presenting pointers to one relevant dataset and one example of prior research on the topic are a valuable (optional) addition. We link one past example of a good project proposal here and a latex template .

Deadline: May 19, Friday 11:59 PM

The milestone will help you make sure you’re on track, and should describe what you’ve accomplished so far, and very briefly say what else you plan to do. You should write it as if it’s an “early draft” of what will turn into your final project. You can write it as if you’re writing the first few pages of your final project report, so that you can re-use most of the milestone text in your final report. Please write the milestone (and final report) keeping in mind that the intended audience is Profs. Ng and Katanforoosh and the TAs. Thus, for example, you should not spend two pages explaining what logistic regression is. Your milestone should include the full names of all your team members and state the full title of your project. Note: We will expect your final writeup to be on the same topic as your milestone. In order to help you the most, we expect you to submit your running code. Your code should contain a baseline model for your application. Along with your baseline model, you are welcome to submit additional parts of your code such as data pre-processing, data augmentation, accuracy matric(s), and/or other models you have tried. Please clean your code before submitting, comment on it, and cite any resources you used. Please do not submit your dataset . However, you may include a few samples of your data in the report if you wish.

Submission Deadline: June 7, Wednesday 11:59 PM (No late days allowed)

Your video is required to be a 3-4 minute summary of your work. There is a hard limit of 4 minutes, and TAs will not watch a video beyond the 4 minute mark. Include diagrams, figures and charts to illustrate the highlights of your work. The video needs to be visually appealing, but also illustrate technical details of your project.

If possible, try to come up with creative visualizations of your project. These could include:

• System diagrams
• More detailed examples of data that don’t fit in the space of your report
• Live demonstrations for end-to-end systems

We recommend searching for conference presentation sessions (AAAI, Neurips, ECCV, ICML, ICLR etc) and following those formats.

You can find a sample video from a previous iteration of the class here

Final Report

Deadline: June 7, Wednesday 11:59 PM (No late days allowed)

The final report should contain a comprehensive account of your project. We expect the report to be thorough, yet concise. Broadly, we will be looking for the following:

• Good motivation for the project and an explanation of the problem statement
• A description of the data
• Any hyperparameter and architecture choices that were explored
• Presentation of results
• Analysis of results
• Any insights and discussions relevant to the project

After the class, we will post all the final writeups online so that you can read about each other’s work. If you do not want your write-up to be posted online, then please create a private Piazza post.

Office Address

• #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
• [email protected]
• +91- 81240 01111

Social List

Trending topics for phd research proposal in machine learning.

Machine learning techniques have prompted at the forefront over the last few years due to the advent of big data. Machine learning is a precise subfield of artificial intelligence (AI) that seeks to analyze the massive data chunks and facilitate the system to learn the data automatically without the explicit support of programming. The machine learning algorithms attempt to reveal the fine-grained patterns from the unprecedented data under multiple perspectives and to build an accurate prediction model as never before. For the purpose of learning, the machine learning algorithm is sub-categorized into four broad groups include supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. Whenever the new unseen data is fed as input to the machine learning algorithm, it automatically learns and predicts the forthcoming by exploiting the previous experience over time. Machine learning is continually liberating its potency in a broad range of applications, including the Internet of Things (IoT), computer vision, natural language processing, speech processing, online recommendation system, cyber security, neuroscience, prediction analytics, fraud detection, and so on.

• Guidelines for Preparing a Phd Research Proposal

Latest Research Proposal Ideas in Machine Learning

• Research Proposal on Attention-based Image Segmentation
• Research Proposal on Representation learning for multi-modal data
• Research Proposal on Machine Learning methods for Heart Disease Prediction
• Research Proposal on Graph Pooling and Graph Unpooling
• Research Proposal on Multi-modal and cross-domain feature fusion
• Research Proposal on Machine Learning for Multimedia classification
• Research Proposal on Neural Machine Translation with reinforcement learning
• Research Proposal on Multi-view and Multi-modal Fusion for Semi-Supervised Learning
• Research Proposal on Stock Market Prediction using Machine Learning
• Research Proposal on Adaptive radiotherapy using deep learning
• Research Proposal on Adversarial Natural Language Processing
• Research Proposal on Deep Recurrent Neural Networks
• Research Proposal on Automated image analysis and diagnosis in radiology using deep learning
• Research Proposal on Scalable and fault-tolerant data stream processing
• Research Proposal on Deep Belief Networks
• Research Proposal on Bayesian optimization for deep learning hyperparameters
• Research Proposal on Cross-domain opinion mining
• Research Proposal on Restricted Boltzmann Machines
• Research Proposal on Quantum generative models
• Research Proposal on Deep Generative Models using Belief Networks
• Research Proposal on Deep Neural Networks for Speech Recognition
• Research Proposal on Transfer learning across pattern recognition domains
• Research Proposal on Evolutionary optimization for deep learning hyperparameters
• Research Proposal on Deep Neural Networks for Computer Vision
• Research Proposal on Pre-training of entity embeddings
• Research Proposal on Graph Attention Networks
• Research Proposal on Deep Learning for Intelligent Wireless Networks
• Research Proposal on Multimedia representation learning
• Research Proposal on One-stage object detection using YOLO
• Research Proposal on Extreme Learning Machines
• Research Proposal on Generative adversarial networks for text generation
• Research Proposal on Transfer learning across multimedia domains
• Research Proposal on Dynamic Neural Networks
• Research Proposal on Transformer-based attention
• Research Proposal on Motion prediction and synthesis
• Research Proposal on Deep Learning for Autonomous Vehicles
• Research Proposal on Transfer Learning for Natural Language Processing
• Research Proposal on Deep reinforcement learning for image super-resolution
• Research Proposal on Long Short-Term Memory Networks
• Research Proposal on Multi-Task and Multi-Output Regression
• Research Proposal on Improved fingerprint recognition with deep learning
• Research Proposal on Radial Basis Function Networks
• Research Proposal on Domain Adaptation with Semi-Supervised Learning
• Research Proposal on Deep learning for protein structure prediction
• Research Proposal on Multi-Goal Reinforcement Learning
• Research Proposal on Reinforcement Learning with Convolutional Neural Networks
• Research Proposal on Deep learning for blood pressure prediction from wearable devices
• Research Proposal on Mutual Information Estimation
• Research Proposal on Hybrid Neural Architecture Search with combination of evolutionary and gradient-based methods
• Research Proposal on Deep Learning-based Image Restoration with Generative Adversarial Networks
• Research Proposal on Generalized Few-Shot Classification
• Research Proposal on Privacy-preserving feature engineering
• Research Proposal on Crop Yield Prediction using Deep Learning
• Research Proposal on Multimodal Deep Learning
• Research Proposal on Continuous Learning for Natural Language Processing
• Research Proposal on Structured Topic Modeling
• Research Proposal on Entity Embeddings
• Research Proposal on Attention for knowledge graph representation
• Research Proposal on Multi-agent Reinforcement Learning in Partially Observable Environments
• Research Proposal on Quantum Machine Learning
• Research Proposal on Fine-tuning of entity embeddings
• Research Proposal on Deep Reinforcement Learning for Multimodal Representation
• Research Proposal on One-Shot Learning
• Research Proposal on Object detection in 3D scenes
• Research Proposal on Multi-asset Portfolio Optimization with Deep Learning
• Research Proposal on Hierarchical Reinforcement Learning
• Research Proposal on Attention-based Neural Machine Translation
• Research Proposal on Video Deblurring with Deep Learning
• Research Proposal on Multiple Instance Learning
• Research Proposal on Clinical Event Prediction
• Research Proposal on Deep learning for improved identification of disease-causing genetic variations
• Research Proposal on Interpretable Machine Learning
• Research Proposal on Adversarial attacks and defenses in Convolutional Neural Networks
• Research Proposal on Named entity recognition in noisy and unstructured text
• Research Proposal on Density Estimation
• Research Proposal on Panoptic Segmentation
• Research Proposal on Improving the accuracy of treatment planning with deep learning
• Research Proposal on Imitation Learning
• Research Proposal on Deep Learning for Abnormal Event Detection in Surveillance Videos
• Research Proposal on Deep Reinforcement Learning for Microscopy Image Analysis
• Research Proposal on Active Learning
• Research Proposal on Predictive Analytics for Supply Chain Performance using Deep Learning
• Research Proposal on Face Recognition in the Wild
• Research Proposal on Object Detection using Deep Learning
• Research Proposal on Deep Learning for Compressed Sensing in Remote Sensing
• Research Proposal on Multi-task Neural Machine Translation
• Research Proposal on Image Segmentation using Deep Learning
• Research Proposal on Plant Leaf Shape and Texture Analysis with Deep Learning
• Research Proposal on Adversarial robustness in Belief Networks
• Research Proposal on Human Motion Recognition using Deep Learning
• Research Proposal on Interactive Topic Modeling with Human Feedback
• Research Proposal on Attention-based interpretation of neural networks
• Research Proposal on Dialogue Systems
• Research Proposal on Temporal Consistency Restoration in Videos using Deep Learning
• Research Proposal on Meta-Reinforcement Learning
• Research Proposal on Multimodal Representation Learning
• Research Proposal on Real-time Image Denoising with Deep Learning
• Research Proposal on Multi-Modal and Cross-Lingual Word Embeddings
• Research Proposal on Face Recognition using Deep Learning
• Research Proposal on Deep learning for blood pressure prediction in low-resource settings
• Research Proposal on Attention Mechanisms in Convolutional Neural Networks
• Research Proposal on Image Captioning using Deep Learning
• Research Proposal on Named entity recognition in a multilingual context
• Research Proposal on Graph-based pattern recognition
• Research Proposal on Named Entity Recognition
• Research Proposal on Deep learning for palmprint recognition
• Research Proposal on Action recognition in videos
• Research Proposal on Pharmacogenomics using Deep Learning
• Research Proposal on Deep learning for detecting abnormalities in medical images in radiology
• Research Proposal on Deep Learning for Cell Segmentation and Tracking
• Research Proposal on Action Recognition using Deep Learning
• Research Proposal on Transfer learning in Convolutional Neural Networks
• Research Proposal on Multi-Modal and Multi-Task Ensemble Learning
• Research Proposal on Microscopic Image Analysis using Deep Learning
• Research Proposal in Real-time analytics on big data streams
• Research Proposal on Augmentation for object detection and segmentation
• Research Proposal on Facial Expression Recognition using Deep Learning
• Research Proposal on Attention for visual data processing
• Research Proposal on Domain Adaptation for Health Record Analysis
• Research Proposal on Radiology using Deep Learning
• Research Proposal on Large-scale parallel hyperparameter optimization
• Research Proposal on Multi-modal Fusion for Facial Expression Recognition
• Research Proposal on Bioinformatics using Deep Learning
• Research Proposal on Neural Machine Translation with semantic representation
• Research Proposal on Cross-lingual Text Summarization
• Research Proposal on Text Summarization
• Research Proposal on Task-Oriented Dialogue Systems
• Research Proposal on Adversarial attacks and defenses in medical image analysis
• Research Proposal on Semantic Analysis
• Research Proposal on Image Captioning with Attention Mechanisms
• Research Proposal on Named entity recognition in low-resource languages using deep learning
• Research Proposal on Radiotherapy using Deep Learning
• Research Proposal on Deep Learning for Quantitative Image Analysis in Microscopy
• Research Proposal on Deep learning for improved classification of multi-view sequential data
• Research Proposal on Image Super Resolution Using Deep Learning
• Research Proposal on Deep Learning for Facial Emotion Recognition from Speech
• Research Proposal on Incorporating Background Knowledge in Topic Modeling
• Research Proposal on Neural Rendering
• Research Proposal on Deep learning in radiation therapy planning and optimization
• Research Proposal on Multi-modal Text Summarization
• Research Proposal on Biometric Recognition using Deep Learning
• Research Proposal on Neural rendering for improved video game graphics
• Research Proposal on Time-series Regression with Recurrent Neural Networks
• Research Proposal on Medical image analysis in resource-limited settings with deep learning
• Research Proposal on Meta-learning for few-shot multi-class classification
• Research Proposal on Automated evaluation of radiotherapy outcomes using deep learning
• Research Proposal on Domain-specific entity embeddings
• Research Proposal on Neural rendering for virtual and augmented reality applications
• Research Proposal on Semantic Segmentation using FCN
• Research Proposal on Improved gene expression analysis with deep learning
• Research Proposal on Multi-modal data analysis for disease prediction
• Research Proposal on Multi-level Deep Network for Image Denoising
• Research Proposal on Deep learning for image-based diagnosis in radiology
• Research Proposal on Video Inpainting with Deep Learning
• Research Proposal on Deep learning for predicting blood pressure response to treatment
• Research Proposal on Multi-agent Reinforcement Learning with Evolutionary Algorithms
• Research Proposal on Decentralized Multi-agent Reinforcement Learning
• Research Proposal on Deep Learning for Compressed Sensing Reconstruction
• Research Proposal on Deep Reinforcement Learning for Supply Chain Optimization
• Research Proposal on Multi-modal medical image analysis in radiology with deep learning
• Research Proposal on Deep Learning for Multimodal Fusion
• Research Proposal on Adversarial attacks and defenses in biometric recognition using deep learning
• Research Proposal on Deep Learning for Compressed Sensing in Wireless Communications
• Research Proposal on Human-in-the-loop Neural Architecture Search
• Research Proposal on Agricultural Resource Management with Deep Learning
• Research Proposal on Generative Models for Semi-Supervised Learning
• Research Proposal on Deep learning for predicting cancer treatment response
• Research Proposal on Graph Generative Models
• Research Proposal on Deep generative models for image super-resolution
• Research Proposal on Deep Learning for Drug Response Prediction
• Research Proposal on Transfer Learning for Face Recognition
• Research Proposal on Deep Reinforcement Learning for Facial Expression Recognition
• Research Proposal on Neural rendering for photorealistic image synthesis
• Research Proposal on Prediction of treatment response using deep learning
• Research Proposal on Deep Learning for Plant Species Identification
• Research Proposal on Deep transfer learning for medical image analysis
• Research Proposal on Improved drug discovery in neglected diseases using deep learning
• Research Proposal on Interpretability and Explainability of Convolutional Neural Networks
• Research Proposal on Cross-lingual semantic analysis
• Research Proposal on Deep learning for predicting genetic interactions
• Research Proposal on Deep Reinforcement Learning for Plant Disease Detection
• Research Proposal on Fine-grained named entity recognition
• Research Proposal on Transfer learning for sentiment analysis
• Research Proposal on Deep learning for predicting protein-protein interactions
• Research Proposal on Object detection with active learning
• Research Proposal on Deep learning for improving drug discovery through in silico experimentation
• Research Proposal on Cross-lingual Image Captioning
• Research Proposal on Deep Learning for Food Safety Prediction in Agriculture
• Research Proposal on Improved epigenetic analysis using deep learning
• Research Proposal on Deep Learning for Route Optimization in Logistics
• Research Proposal on Deep Learning for Predictive Maintenance in Supply Chain
• Research Proposal on Multi-modal Representation Learning for Sentiment Analysis
• Research Proposal on Plant Leaf Recognition with Computer Vision
• Research Proposal on Cross-lingual named entity recognition
• Research Proposal on Deep learning for semantic-aware image super-resolution
• Research Proposal on Generative Adversarial Networks with Convolutional Neural Networks
• Research Proposal on Attention in reinforcement learning
• Research Proposal on Multi-objective optimization for deep learning hyperparameters
• Research Proposal on Multi-modal entity embeddings
• Research Proposal on Dynamic Graph Neural Networks
• Research Proposal on Image Captioning with Visual and Language Context
• Research Proposal on Deep Learning for Portfolio Diversification and Optimization
• Research Proposal on Motion Compensation for Video Restoration using Deep Learning
• Research Proposal on Multi-modal deep learning for multi-view sequential data analysis
• Research Proposal on Deep learning for cancer diagnosis from medical images
• Research Proposal on Deep transfer learning for radiology image analysis
• Research Proposal on Deep learning for improved iris recognition
• Research Proposal on Processing high-velocity and high-volume data streams
• Research Proposal on Causal inference for multi-class classification
• Research Proposal on Deep Extreme Learning Machines
• Research Proposal on Meta-representation learning
• Research Proposal on Data augmentation in Neural Machine Translation
• Research Proposal on Fairness and Bias in Health Record Analysis
• Research Proposal on Multi-omics Integration for Personalized Medicine
• Research Proposal on Deep Learning for Micro-expression Recognition
• Research Proposal on Deep Learning for Compressed Sensing in Compressed Speech
• Research Proposal on Transfer learning for feature engineering
• Research Proposal in Sentiment analysis on multimodal data
• Research Proposal on Online Extreme Learning Machines
• Research Proposal on Deep Learning for Face Anti-spoofing
• Research Proposal on Domain adaptation and transfer learning for multi-class classification
• Research Proposal on Reinforcement Learning for Natural Language Processing
• Research Proposal on Transfer Learning for Word Embeddings
• Research Proposal on Multi-head attention
• Research Proposal on Model-agnostic interpretation methods
• Research Proposal on Deep Generative Models for Microscopy Image Synthesis
• Research Proposal on Deep learning for quality assurance in radiotherapy
• Research Proposal on Low-light image super-resolution with deep learning
• Research Proposal on Fine-tuning Pre-trained Transformer Models for Image Captioning
• Research Proposal on Deep Learning for Facial Expression Recognition in the Wild
• Research Proposal on Multi-modal semantic analysis
• Research Proposal on Deep learning for gait recognition
• Research Proposal on Graph Reinforcement Learning
• Research Proposal on Gradient-based optimization for deep learning hyperparameters
• Research Proposal on Object detection with transformers
• Research Proposal on Transfer learning for multimedia classification
• Research Proposal on Generative adversarial networks for representation learning
• Research Proposal on Representation Learning with Graphs for Word Embeddings
• Research Proposal on Deep Learning for Motion Anomaly Detection in Videos
• Research Proposal on Deep Reinforcement Learning for Face Recognition
• Research Proposal on Deep Learning for Microscopy Image Restoration and Denoising
• Research Proposal on Deep Reinforcement Learning for Text Summarization
• Research Proposal on Deep learning for medical image registration
• Research Proposal on Improved computer-aided diagnosis in radiology with deep learning
• Research Proposal on Multi-modal cancer diagnosis using deep learning
• Research Proposal on Improved single image super-resolution using deep learning
• Research Proposal on Image Captioning in the Wild
• Research Proposal on Graph Convolutional Networks
• Research Proposal on Deep Learning for Small-Molecule Property Prediction
• Research Proposal on Real-time image super-resolution using deep learning
• Research Proposal on Deep learning for improved image quality assessment in radiology
• Research Proposal on Quantum reinforcement learning
• Research Proposal on Adaptive attention
• Research Proposal on Transfer Ensemble Learning
• Research Proposal on Multi-Task and Multi-Modal Learning with Convolutional Neural Networks
• Research Proposal on Two-stage object detection using Faster R-CNN
• Research Proposal on Face Attribute Prediction and Analysis
• Research Proposal on Deep learning for medical image synthesis and augmentation
• Research Proposal on Weather Forecasting for Agriculture using Deep Learning
• Research Proposal on Deep Learning for Video Compression and Restoration
• Research Proposal on Non-Parametric Topic Modeling
• Research Proposal on Deep Learning for Demand Forecasting in Supply Chain Management
• Research Proposal on Soil Moisture Prediction using Deep Learning
• Research Proposal on Deep Learning for Predictive Portfolio Management
• Research Proposal on Plant Disease Image Analysis with Deep Learning
• Research Proposal on Inventory Optimization with Deep Learning
• Research Proposal on Attention-based Image Denoising
• Research Proposal on Deep Generative Models for Drug Repurposing
• Research Proposal on Deep Learning for Compressed Sensing in Compressed Video
• Research Proposal on Transfer Learning for Topic Modeling
• Research Proposal on Representation learning for graph-structured data
• Research Proposal on Federated Learning for Recommendation System
• Research Proposal on Adversarial Ensemble Learning
• Research Proposal on Graph-based Natural Language Processing
• Research Proposal on Cross-domain sentiment analysis
• Research Proposal on Unsupervised feature learning using Belief Networks
• Research Proposal on Quantum neural networks
• Research Proposal on Representation learning for speech data
• Research Proposal on Object detection with semantic segmentation
• Research Proposal on Zero-shot Neural Machine Translation
• Research Proposal on Dialogue State Tracking
• Research Proposal on Image Captioning with Semantic Segmentation
• Research Proposal on Deep Learning for Image Registration and Stitching
• Research Proposal on Text Summarization with Sentiment Analysis
• Research Proposal on Deep learning for radiation therapy-related toxicity prediction
• Research Proposal on Improved image quality assessment in medical imaging using deep learning
• Research Proposal on Scene synthesis and manipulation using neural rendering
• Research Proposal on Multi-modal biometric recognition using deep learning
• Research Proposal on Named entity recognition for multi-modal data
• Research Proposal on Improved lung cancer diagnosis using deep learning
• Research Proposal on Multi-view sequential data analysis in low-resource settings using deep learning
• Research Proposal on Deep Learning for Video Denoising
• Research Proposal on Multi-agent Reinforcement Learning for Dynamic Environments
• Research Proposal on Deep Learning for Quality Control in Supply Chain
• Research Proposal on Object detection with domain adaptation
• Research Proposal on Plant Disease Segmentation and Recognition with Deep Learning
• Research Proposal on Adversarial Attacks and Defences in Face Recognition
• Research Proposal on Anomaly Detection in Videos with Deep Reinforcement Learning
• Research Proposal on Integrating Electronic Health Records and Genomics for Personalized Medicine
• Research Proposal on Adversarial attacks and defenses in radiology using deep learning
• Research Proposal on Deep Generative Models for Synthetic Facial Expression Data
• Research Proposal on Transfer Learning for Text Summarization
• Research Proposal on Extractive Text Summarization
• Research Proposal on Multi-modal Face Recognition
• Research Proposal on Multi-frame super-resolution using deep learning
• Research Proposal on Spatial-Temporal Graph Convolutional Networks
• Research Proposal on Real-time neural rendering for interactive environments
• Research Proposal on Convolutional Neural Networks for Object Detection and Segmentation
• Research Proposal on Transfer learning for named entity recognition
• Research Proposal on Transfer Learning for Semi-Supervised Learning
• Research Proposal on Deep learning for early cancer detection
• Research Proposal on Imitation Learning and Inverse Reinforcement Learning
• Research Proposal on Deep reinforcement learning for multi-view sequential data analysis
• Research Proposal on Attention for sequential reasoning
• Research Proposal on Deep learning for drug repurposing and de-novo drug discovery
• Research Proposal on Generative adversarial networks for domain adaptation
• Research Proposal on Crop Growth Monitoring using Deep Learning
• Research Proposal in Opinion mining on social media
• Research Proposal on Deep Learning for Video Frame Interpolation
• Research Proposal on Multi-lingual entity embeddings
• Research Proposal on Multi-agent Reinforcement Learning with Communication
• Research Proposal on Semantic augmentation
• Research Proposal on Deep Learning for Supplier Selection in Supply Chain
• Research Proposal on Domain adaptation in Neural Machine Translation
• Research Proposal on Deep Learning for Plant Leaf Disease Diagnosis
• Research Proposal on Multi-Turn conversational Dialogue Systems
• Research Proposal on Attention-based Multimodal Representation Learning
• Research Proposal on Deep Generative Models for Face Synthesis
• Research Proposal on Fine-grained Plant Disease Recognition with Deep Learning
• Research Proposal on Deep Reinforcement Learning for Drug Discovery
• Research Proposal on Deep Learning for Compressed Sensing in Medical Imaging
• Research Proposal on Transfer Learning for Microscopy Image Analysis
• Research Proposal on Multi-object Anomaly Detection in Videos with Deep Learning
• Research Proposal on Adversarial Training for Text Summarization
• Research Proposal on Human-in-the-loop Active Learning
• Research Proposal on Contextual word embeddings for semantic analysis
• Research Proposal on Scalable Neural Architecture Search for large-scale datasets and hardware accelerators
• Research Proposal on Deep learning for super-resolution of microscopy images
• Research Proposal on Causal inference and causal feature engineering
• Research Proposal on Improved 3D object rendering using deep neural networks
• Research Proposal on Convolutional Neural Networks (CNN) for Computer Vision tasks
• Research Proposal on Deep transfer learning for bioinformatics analysis
• Research Proposal on Self-training and Co-training for Semi-Supervised Learning
• Research Proposal on Video Super-resolution using Deep Learning
• Research Proposal on Meta-Learning for Few-shot Semi-Supervised Learning
• Research Proposal on Fertilizer Recommendation System using Deep Learning
• Research Proposal on Non-Linear Regression with Gaussian Processes
• Research Proposal on Adversarial Training for Image Denoising
• Research Proposal on Active and Semi-Supervised Ensemble Learning
• Research Proposal on Improved blood pressure prediction in cardiovascular disease patients using deep learning
• Research Proposal on Privacy-preserving Natural Language Processing
• Research Proposal on Named entity disambiguation using deep learning
• Research Proposal on Continuous Learning and Adaptation for Word Embeddings
• Research Proposal on Deep reinforcement learning in medical imaging and radiology
• Research Proposal on Incremental and online machine learning for data streams
• Research Proposal on Adversarial training for image super-resolution
• Research Proposal on Attention in federated learning
• Research Proposal on Multi-modal Microscopy Image Analysis
• Research Proposal Topic on Attention Mechanism for Natural Language Processing with Deep Learning
• Research Proposal on Mode collapse and stability in generative adversarial networks
• Research Proposal on Image Captioning with Scene Graphs
• Research Proposal Topics on Convolutional Neural Networks Research Challenges and Future Impacts
• Research Proposal on Adversarial attacks and defenses in sentiment analysis
• Research Proposal on Multi-person Motion Analysis
• Research Proposal on Graph Neural Network for Graph Analytics
• Research Proposal on Sentiment polarity detection
• Research Proposal on Transformer-based Neural Machine Translation
• Research Proposal on Deep Reinforcement Learning Methods for Active Decision Making
• Research Proposal on Cross-modal correspondence learning
• Research Proposal on Graph Transformer Networks
• Research Proposal on Deep Learning based Medical Imaging
• Research Proposal on Representation learning for pattern recognition
• Research Proposal on Mixup and cutmix data augmentation
• Research Proposal On Pre-trained Word embedding for Language models
• Research Proposal on Multi-modal data analysis using Belief Networks
• Research Proposal on Object detection with instance segmentation
• Research Proposal on Medical Machine Learning for Healthcare Analysis
• Research Proposal on Multi-modal data analysis using Extreme Learning Machines
• Research Proposal on Graph-based entity embeddings
• Research Proposal on Generative Adversarial Network
• Research Proposal on Quantum clustering algorithms
• Research Proposal on Sentiment analysis for low-resource languages
• Research Proposal on Hyperparameter Optimization and Fine-Tuning in Deep Neural Network
• Research Proposal on Transfer learning for hyperparameter optimization
• Research Proposal on Self-attention for sequential data
• Research Proposal on Deep Learning Models for Epilepsy Detection
• Research Proposal on Geometrical transformations for data augmentation
• Research Proposal on Meta-Learning for Word Embeddings
• Research Proposal Topics on Deep Learning Models for Epilepsy Detection
• Research Proposal on Anchor-free object detection
• Research Proposal on Multi-Task and Multi-lingual Natural Language Processing
• Research Proposal on Machine Learning in Alzheimer-s Disease Detection
• Research Proposal on Graph Autoencoders
• Research Proposal on Graph-based Semi-Supervised Learning
• Research Proposal on Machine Learning in Cancer Diagnosis
• Research Proposal on Human Pose Estimation
• Research Proposal on Adversarial Reinforcement Learning
• Research Proposal on Machine Learning in Covid-19 Diagnosis
• Research Proposal on Medication Recommendation
• Research Proposal in Light-weight and Efficient Convolutional Neural Networks for deployment on edge devices
• Research Proposal on Machine Learning in Diagnosis of Diabetes
• Research Proposal on Cross-age Face Recognition
• Research Proposal on Adversarial robustness in pattern recognition
• Research Proposal on Machine Learning in Heart Disease Diagnosis
• Research Proposal on Image Captioning with Transfer Learning
• Research Proposal on Representation learning for time series data
• Research Proposal on Machine Learning in Parkinson-s Diagnosis
• Research Proposal on Deep Learning for Toxicology and Safety Assessment
• Research Proposal on Instance Segmentation using Mask R-CNN
• Research Proposal on Deep Learning Models for Epileptic Focus Localization
• Research Proposal on Adversarial Training for Robust Microscopy Image Analysis
• Research Proposal on Dialogue Generation using Generative Models
• Research Proposal on Preprocessing Methods for Epilepsy Detection
• Research Proposal on Neural Text Summarization
• Research Proposal on Convolutional Deep Belief Networks
• Research Proposal on Human-in-the-loop Deep Reinforcement Learning
• Research Proposal on Multi-modal data analysis for multimedia classification
• Research Proposal on Interactive Machine Learning with Human Feedback
• Research Proposal on Online and Stream-based Regression
• Research Proposal on Deep Learning for Compressed Sensing in Image and Video Processing
• Research Proposal on Multi-view and multi-modal fusion for multi-class classification
• Research Proposal on Deep Learning for Risk Management in Portfolio Optimization
• Research Proposal on Sparse and Low-Rank Regression
• Research Proposal on Epilepsy Prediction
• Research Proposal on Deep Learning for Early Disease Detection in Plants
• Research Proposal on Compressed Sensing with Deep Autoencoders
• Research Proposal on Deep Learning for Multi-modal Representation in Healthcare
• Research Proposal on Deep Learning for Algorithmic Trading and Portfolio Optimization
• Research Proposal on Deep Learning for Predictive Sourcing in Supply Chain Management
• Research Proposal on Cross-modal Representation Learning with Deep Learning
• Research Proposal on Multi-agent Reinforcement Learning for Resource Allocation
• Research Proposal on Cooperative Multi-agent Reinforcement Learning
• Research Proposal on Plant Leaf Segmentation and Recognition with Deep Learning
• Research Proposal on Multi-topic Modeling with Deep Learning
• Research Proposal on Deep Learning for Topic Modeling
• Research Proposal on Supply Chain Risk Management with Deep Learning
• Research Proposal on Video Color Correction with Deep Learning
• Research Proposal on Fine-grained Plant Leaf Recognition with Deep Learning
• Research Proposal on Self-Supervised Image Denoising
• Research Proposal on Multi-class Plant Disease Recognition with Deep Learning
• Research Proposal on Deep Learning for Pest and Disease Detection in Crops
• Research Proposal on Topic Modeling with Graph-based Approaches
• Research Proposal on Video Restoration with Generative Adversarial Networks
• Research Proposal on Precision Irrigation Scheduling with Deep Learning
• Research Proposal on Deep learning for improved representation of multi-view sequential data
• Research Proposal on Deep learning for predicting drug efficacy and toxicity
• Research Proposal on Improved analysis of large-scale genomics data with deep learning
• Research Proposal on Deep learning for summarization and visualization of multi-view sequential data
• Research Proposal on Personalized cancer diagnosis using deep learning
• Research Proposal on Deep transfer learning for cancer diagnosis
• Research Proposal on Improved biometric recognition in low-resource settings using deep learning
• Research Proposal on Deep learning for improved facial recognition
• Research Proposal on Improved voice recognition with deep learning
• Research Proposal on Deep learning for patient-specific dose modeling
• Research Proposal on Neural rendering for product visualization in e-commerce
• Research Proposal on Deep learning for computer-aided diagnosis
• Research Proposal on Deep learning for improved medical image interpretation in radiology
• Research Proposal on Summarization with Pre-trained Language Models
• Research Proposal on Image super-resolution with attention mechanisms in deep learning
• Research Proposal on Deep Learning for Cross-cultural Facial Expression Recognition
• Research Proposal on Deep learning for dose prediction in radiotherapy
• Research Proposal on Deep Learning for Drug-Drug Interaction Prediction
• Research Proposal on Multi-modality medical image analysis with deep learning
• Research Proposal on Adversarial Training for Fair and Robust Drug Response Prediction
• Research Proposal on Improved segmentation of anatomical structures in radiotherapy using deep learning
• Research Proposal on Transfer Learning for Facial Expression Recognition
• Research Proposal on Abstractive Text Summarization
• Research Proposal on Deep Learning for Livestock Health Monitoring
• Research Proposal on Adversarial Training for Robust Facial Expression Recognition
• Research Proposal on Multi-class Plant Leaf Recognition with Deep Learning
• Research Proposal on Deep Learning for Object Detection and Classification in Microscopy Images
• Research Proposal on Multi-modal Representation Learning for Image and Text
• Research Proposal on Transfer Learning for Drug Response Prediction
• Research Proposal on Deep Learning for Event Detection in Video Surveillance
• Research Proposal on Time Series Data Analysis
• Research Proposal on Face Detection and Landmark Localization
• Research Proposal on Human-in-the-loop Anomaly Detection
• Research Proposal on Machine Learning for Pattern Recognition
• Research Proposal on Cross-Lingual Dialogue Systems
• Research Proposal on Deep Learning for Facial Action Unit Detection
• Research Proposal on Regression Model for Machine Learning
• Research Proposal on Medical Concept Embedding
• Research Proposal on Semantic parsing and question answering
• Research Proposal on Deep learning Algorithms and Recent advancements
• Research proposal on Natural Language Processing using Deep Learning
• Research Proposal on Predictive Analytics to forecast future outcomes
• Research proposal on Deep Learning-based Contextual Word Embedding for Text Generation
• Research Proposal on Discourse Representation-Aware Text Generation using Deep Learning Model
• Research Proposal on Deep Autoencoder based Text Generation for Natural Language
• Research Proposal on Reinforcement Learning
• Research Proposal Topics on Conversational Recommendation Systems
• Research Proposal on Pre-trained Deep Learning Model based Text Generation
• Research Proposal on Text Sequence Generation with Deep Transfer Learning
• Research Proposal in Modeling Deep Semi-Supervised Learning for Non-Redundant Text Generation
• Research Proposal in Utterances and Emoticons based Multi-Class Emotion Recognition
• Research Proposal in Negation Handling with Contextual Representation for Sentiment Classification
• Research Proposal on Deep Learning-based Emotion Classification
• Research Proposal in Sentiment Classification in Social Media with Deep Contextual Embedding
• Research Proposal in Deep Learning-based Emotion Classification in Conversational Text
• Research Proposal on Attention Mechanism-based Argument Mining using Deep Neural Network
• Research Proposal in Adaptive Deep Learning with Topic Extraction for Argument Mining
• Research Proposal on Context-aware Argument Mining with Deep Semi-supervised Learning
• Research Proposal in Deep Transfer Learning-based Sequential Keyphrase Generation
• Research Proposal on Deep Bi-directional Text Analysis for Sarcasm Detection
• Research Proposal in Emotion Transition Recognition with Contextual Embedding in Sarcasm Detection
• Research Topic on Attention-based Sarcasm Detection with Psycholinguistic Sources
• Research Proposal in Deep Attentive Model based Irony Text and Sarcasm Detection
• Research Proposal Topic on Discourse Structure and Opinion based Argumentation Mining
• Research Proposal in Sarcasm Detection using Syntactic and Semantic Feature Representation
• Research Proposal in Multi-Class Behavior Modeling in Deep Learning-based Sarcasm Detection
• Research Proposal on Deep Transfer Learning for Irony Detection
• Research Proposal in Deep Neural Network-based Sarcasm Detection with Multi-Task Learning
• Research Proposal in Deep Learning-Guided Credible User Identification using Social Network Structure and User-Generated Content
• Research Proposal in Semi-supervised Misinformation Detection in Social Network
• Research Proposal in Deep Contextualized Word Representation for Fake News Classification
• Research Proposal on Self-Attentive Network-based Rumour Classification in Social Media
• Research Proposal in Multi-Modal Rumour Classification with Deep Ensemble Learning
• Research Proposal in Hybrid Deep Learning Model based Fake News Detection in Social Network
• Research Proposal on Anomaly Detection by Applying the Machine Learning Technique
• Research Proposal on Transformer based Opinion Mining Approach for Fake News Detection
• Research Proposal in Data Augmentation for Deep Learning-based Plant Disease Detection
• Research Proposal in Multi-Class Imbalance Handling with Deep Learning in Plant Disease Detection
• Research Proposal on Incremental Learning-based Concept Drift Detection in Stream Classification
• Research Proposal in Class-Incremental Learning for Large-Scale IoT Prediction
• Research Proposal in Time-series Forecasting using Weighted Incremental Learning
• Research Proposal on Deep Reinforcement Learning based Time Series Prediction
• Research Proposal in Federated Learning for Intelligent IoT Healthcare System
• Research Proposal on Deep Learning based Stream Data Imputation for IoT Applications
• Research Proposal on Deep Incremental Learning-based Cyber Security Threats Prediction
• Research Proposal in Aspect based Opinion Mining for Personalized Recommendation
• Research Proposal in Personalized Recommendation with Contextual Pre-Filtering
• Research Proposal in Temporal and Spatial Context-based Group Recommendation
• Research Proposal on Session based Recommender System with Representation learning
• Research Proposal in Serendipity-aware Product Recommendation
• Research Proposal in Deep Preference Prediction for Novelty and Diversity-Aware Top-N Recommendation
• Research Proposal on Personalized Recommendation with Neural Attention Model
• Research Proposal in Cross-domain Depression Detection in Social Media
• Research Proposal in Emotional Feature Extraction in Depression Detection
• Research Proposal in Contextual Recommendation with Deep Reinforcement Learning
• Research Proposal on Deep Neural Network-based Cross-Domain Recommendation
• Research Proposal in Multimodal Extraction for Depression Detection
• Research Proposal on Early Depression Detection with Deep Attention Network
• Research Proposal in Proactive Intrusion Detection in Cloud using Deep Reinforcement Learning
• Research Proposal in Context Vector Representation of Text Sequence for Depression Detection
• Research Proposal in Sparsity Handling in Recommender System with Transfer Learning
• Research Proposal on Artificial Intelligence and Lexicon based Suicide Attempt Prevention
• Research Proposal in Modeling Deep Neural Network for Mental Illness Detection from Healthcare Data
• Research Proposal in Deep Learning-based Domain Adaptation for Recommendation
• Research proposal on Emotion Classification using Deep Learning Models
• Research Proposal in Topic Modeling for Personalized Product Recommendation
• Research Proposal in Deep Reinforcement Learning based Resource Provisioning for Container-based Cloud Environment
• Research Proposal in Energy and Delay-Aware Scheduling with Deep Learning in Fog Computing
• Research Proposal in Spammer Detection in Social Network from the Advertiser Behavior Modeling
• Research Proposal in Social Information based People Recommendation
• Research Proposal in Deep Learning-based Advertiser Reliability Computation in Social Network
• Research Proposal in Artificial Neural Network-based Missing Value Imputation in Disease Detection
• Research Proposal in Deep Transfer Learning-based Disease Detection
• Research Proposal in Aspect based Depressive Feature Extraction for Multi-Class Depression Classification
• Research Proposal in Environmental Data-Aware Behavior Modeling and Depression Detection
• Research Proposal on User Credibility Detection in Social Networks using Deep Learning Models
• Research Proposal in Incremental Clustering Model for Malware Classification
• Research Proposal in Deep Feature Extraction with One-class Classification for Anomaly Detection
• Research Proposal in Fake News Dissemination Detection with Deep Neural Network
• Research Proposal in Deep Learning-based Prediction for Mobile Cloud Offloading Decision-Making
• Research Proposal in Hybrid Deep Learning-based Traffic Congestion Prediction in VANET
• Research Proposal in Deep Ensemble Learning-based Android Malware Detection
• Research Proposal in Proactive Prediction of Insider Threats using Deep Neural Network
• Research Proposal in Delay-constrained Computation Offloading with Deep Learning in Fog Environment
• Research Proposal in Latency-aware IoT Service Placement in Fog through Deep Learning Prediction
• Research Proposal in Proactive Management of Edge Resources with Federated Learning
• Research Proposal in Reinforcement Learning with Incentive Mechanism for Offloading Decision in Edge Computing
• Research Proposal on Multi-Agent Offloading Decision in Fog Computing using Deep Reinforcement Learning
• Research Proposal in Energy-efficient Edge Computation with Light-weight Deep Learning Model
• Research Proposal Topic on Integrating Deep Learning with Rule-based Model for Sarcasm Detection
• Research Proposal in Deep Incremental Learning based Intrusion Detection in IoT Environment
• Research Proposal for Deep Learning based Explainable Recommendation Systems
• Research Proposal in Federated Learning-based Cooperative Management of Fog Computation Resources
• Research Proposal in Mobility-aware Collaborative Resource Management in Edge Intelligence System
• Research Proposal in Deep learning-driven Computation Offloading in Fog-Cloud Environment
• Research Proposal on Zero-day Attack Detection using Deep Neural Network
• Research Proposal in Deep Learning-based Dynamic Workload Prediction in Cloud
• Research Proposal in Federated Learning-based Dynamic Resource Scheduling in Edge-Cloud Environment
• Research Proposal in Anomaly-aware Cloud Security Model with Deep Transfer Learning
• Research Proposal in Federated Transfer Learning for Smart Decision-Making in Cloud Environment
• Research Proposal on Explicit and Implicit Feedback-Aware Top-N Recommendation
• Research Proposal in Disease Diagnosis using Unsupervised Deep Learning
• Research Proposal for Hybrid Deep Learning based Recommendation
• Research Proposal in Privacy Preservation using Deep Neural Network in Cloud Computing
• Research Proposal in Machine learning Techniques for Social Media Analytics
• Adversarial-aware Modeling of Federated Learning with Personalization
• Research Proposal in Adversarial-aware Modeling of Federated Learning with Personalization
• Research Proposal in Energy-efficient Federated Learning for Resource-Constrained Edge Environment
• Research Proposal in Deep Learning-based Task Offloading for Dynamic Edge Computing
• Research Proposal in Dynamic Joint Resource Allocation for Federated Edge Learning
• Research Proposal in Deep Ensemble Learning-based Housing Price Prediction with Self-Attention Mechanism
• Research Proposal in Pre-trained One-Class Anomaly Detection with Unsupervised Learning
• Research Proposal in Unsupervised Deep Learning-based Anomaly Detection in IoT Environment
• Research Proposal in Deep Federated Learning-based IoT Malware Detection
• Research Proposal in Deep Learning-based Contextual Text Generation for Conversational Text
• Research Proposal in Designing Intelligent Internet of Things with Deep Reinforcement Learning for Mobile Edge Computing
• Research Proposal on Adaptive IoT Traffic Prediction with Deep Neural Network
• Research Proposal in Deep Learning-based Congestion-aware Dynamic Routing in IoT Network
• Machine Learning Based intelligent Trust Computational Model for IoT Applications
• Research proposal in Ensemble Machine Learning for Big Data Stream Processing
• One-Shot Learning
• PhD Guidance and Support Enquiry
• Masters and PhD Project Enquiry
• Research Topics in Machine Learning
• PhD Research Guidance in Machine Learning
• Latest Research Papers in Machine Learning
• Literature Survey in Machine Learning
• Python Projects In Machine Learning
• Python Sample Source Code
• PhD Thesis in Machine Learning
• PhD Projects in Machine Learning
• Python Project Titles in Machine Learning
• Leading Journals in Machine Learning
• Leading Research Books in Machine Learning
• Research Proposal Topics in Natural Language Processing (NLP)
• Research Topics in Medical Machine Learning
• Research Proposal On Pre-trained Word Embedding for Language Models
• Research Proposal Topics on Deep learning Algorithms and Recent advancements
• Research Topics in Federated Learning for Smart City Application
• Research Topics in Computer Science
• PhD Thesis Writing Services in Computer Science
• PhD Paper Writing Services in Computer Science
• How to Write a PhD Research Proposal in Computer Science
• Ph.D Support Enquiry
• Project Enquiry
• Research Guidance in Machine Learning
• Research Proposal in Machine Learning
• Research Papers in Machine Learning
• Ph.D Thesis in Machine Learning
• Research Projects in Machine Learning
• Project Titles in Machine Learning
• Project Source Code in Machine Learning

deep learning Recently Published Documents

Total documents.

• Latest Documents
• Most Cited Documents
• Contributed Authors
• Related Sources
• Related Keywords

Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods.

A deep learning approach for remote heart rate estimation

Weakly supervised spatial deep learning for earth image segmentation based on imperfect polyline labels.

In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems in earth science and remote sensing (e.g., mapping the nationwide river streams for water resource management). Although extensive efforts have been made to reduce the reliance on labeled data (e.g., semi-supervised or unsupervised learning, few-shot learning), the complex nature of geographic data such as spatial heterogeneity still requires sufficient training labels when transferring a pre-trained model from one region to another. On the other hand, it is often much easier to collect lower-quality training labels with imperfect alignment with earth imagery pixels (e.g., through interpreting coarse imagery by non-expert volunteers). However, directly training a deep neural network on imperfect labels with geometric annotation errors could significantly impact model performance. Existing research that overcomes imperfect training labels either focuses on errors in label class semantics or characterizes label location errors at the pixel level. These methods do not fully incorporate the geometric properties of label location errors in the vector representation. To fill the gap, this article proposes a weakly supervised learning framework to simultaneously update deep learning model parameters and infer hidden true vector label locations. Specifically, we model label location errors in the vector representation to partially reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy.

Prediction of Failure Categories in Plastic Extrusion Process with Deep Learning

Hyperparameters tuning of faster r-cnn deep learning transfer for persistent object detection in radar images, a comparative study of automated legal text classification using random forests and deep learning, a semi-supervised deep learning approach for vessel trajectory classification based on ais data, an improved approach towards more robust deep learning models for chemical kinetics, power system transient security assessment based on deep learning considering partial observability, a multi-attention collaborative deep learning approach for blood pressure prediction.

We develop a deep learning model based on Long Short-term Memory (LSTM) to predict blood pressure based on a unique data set collected from physical examination centers capturing comprehensive multi-year physical examination and lab results. In the Multi-attention Collaborative Deep Learning model (MAC-LSTM) we developed for this type of data, we incorporate three types of attention to generate more explainable and accurate results. In addition, we leverage information from similar users to enhance the predictive power of the model due to the challenges with short examination history. Our model significantly reduces predictive errors compared to several state-of-the-art baseline models. Experimental results not only demonstrate our model’s superiority but also provide us with new insights about factors influencing blood pressure. Our data is collected in a natural setting instead of a setting designed specifically to study blood pressure, and the physical examination items used to predict blood pressure are common items included in regular physical examinations for all the users. Therefore, our blood pressure prediction results can be easily used in an alert system for patients and doctors to plan prevention or intervention. The same approach can be used to predict other health-related indexes such as BMI.

Export Citation Format

Share document.

Deep Learning-based Image Text Processing Research *

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

• [email protected]
• +91-97 91 62 64 69

Research Proposal Deep Learning

In general, deep learning is the latest and growing technology that used for supporting the prediction, classification, and analysis of any real-time tasks . Deep learning technology makes use of neural networks to enhance the systems in the forms of automated thinking and adjusting according to the tasks . This is actually a branch of machine learning. As it is retrieving the data from the multilayers of the neural networks in that sense, it is known as deep learning. Are you looking for an article regarding research proposal deep learning ? Undoubtedly this is for you!!!

The arrival of deep learning comes from machine learning and the difference is that machine learning needs manual feature engineering whereas, deep learning doesn’t need manual feature engineering but they can perform automatically in learning / training the data . In the subsequent passage, we discuss the overview of deep learning.  

What is Deep Learning?

• Deep learning is the technology that imitates human behaviors without being programmed
• They are capable of handling the feature classification automatically
• On the other hand, machine learning needs human intervention in the feature engineering

This is a small overview of deep learning. This will help you understand the further aspects of deep learning.   

How to do Research in Deep Learning?

• Read the journal papers, special issues, and magazines
• Different types of literature research
• Discovery of the deep learning issues
• Finding the new optimum solutions
• Modeling and executing the system
• Analyze the performance and outcomes

The above listed are the important criteria that are influenced in deep learning researches . For better research, you can approach us because we are having filtered candidates who are overwhelmed to assist the students and scholars in the fields of research.

In the following passage, our experts have mentioned to you the taxonomy of the deep learning models for your better understanding. Research proposal deep learning is having much weight in the recent days. So let’s start your research today itself with our assistance. Let us jump into the taxonomy of deep learning.

Taxonomy of Deep Learning 

• Adversarial Auto Encoder
• Convolutional Restricted Boltzmann Machine
• Stacked Deep Gaussian Model
• Deep AutoEncoder
• Space Autoencoder
• Denoising Auto encoder
• Sparse Coding
• Restricted Boltzmann Machine
• Deep Belief Network
• Deep Boltzmann Machine
• Convolutional Neural Networks
• Multilayer Perceptron
• Recurrent Neural Networks
• Gated Feed Forward Neural Networks
• Long Short Term Memory
• Gated Recurrent Unit

This is how the deep learning concept is classified according to the models called generative, discriminative, and hybrid models . This deep learning technology is subject to some kind of drawbacks, we would like to explain them briefly in the following passage. Are you interested in moving on? Then we go!

Pitfalls in Deep Learning Models

• Contradiction in the performance of the phase in the training and evaluation by decreasing and increasing respectively
• Estimation of the performance is quite difficult in deep learning models
• The models learn the unwanted data which is actually unnecessary of the training set
• Highly featured models are highly compatible in some cases and vice versa
• Analysis of the deep learning performance states about the hidden and fresh data

These are some of the deep learning drawbacks indulged in it. However, its merits are phenomenal. Deep learning technology has taken a place in the industry by features. They are widely used in every new generation of technology. In this regard, our experts have mentioned to you the deep learning algorithms for your better understanding .

Recent Deep Learning Algorithms

• Improved the disappearing issues in the gradient systems
• Reduced the fault rate in the deep neural networks
• Enduring learning
• Deep neural architecture evaluation cost is decreased by the inception V3 algorithm by bottleneck and asymmetric filters
• Multilevel feature presentation and hierarchical features
• This is the combination of inception blocks & enduring learning
• This algorithm makes use of the direct interconnections and auxiliary connections
• Deep neural networks’ training procedure is followed in this algorithm

The above listed are the important deep learning algorithms that are used widely. We hope you would have understood the above-mentioned aspects. For your better understanding furthermore, we have listed you about the process of deep learning .

In addition to that, we would like to intervene in accordance with our remarks in the fields of researches and projects execution and assistance . Especially, we are good at research proposal deep learning. If you are in need of this service and others approach us. Let’s get into the following phase.

Process of Deep Learning

• Step 1 : Discovery of the problem
• Step 2 : Defining the datasets  
• Step 3 : Identification of the features
• Step 4 : Preprocessing the data
• Step 5 : Choosing the algorithms
• Step 6 : Training by deep learning model
• Step 7 : Estimation of the dataset
• Step 8 : Tuning the parameters
• Step 9 : Classifier to perform classification

The listed above are the steps involved in the deep learning process . In this regard, we discuss the current research areas in deep learning for ease of understanding and implementation. Select the appropriate and suitable research areas according to your interest and capability. Let’s get into that.

Current Research Ideas in Deep Learning

• Wireless Technology
• Computer Vision
• Audio Enrichment
• Signal Enrichment
• Control Panel
• Unmanned Driving Technology

The aforementioned are some of the important research areas in which one can frame novel ideas for   deep learning projects . Doing research in these areas will give you fruitful results in your academic victories. Because they are vital these days without deep learning we cannot imagine the modern world. Things are becoming complex, according to that we need to equip ourselves.

In fact, our researchers are eminently offering the research guidance with visual demonstrations for a better understanding of the students and scholars. If you are interested in stepping out to implement  deep learning based projects then approach us for a great experience . Let’s discuss the latest topics in deep learning.

Project Topics in Deep Learning

• This research will reveal the underwater classes which hidden in nature
• By researching the underwater aspects we can equip a tool to identify the impossible sorts indulged in the underwater
• As it is impossible hence researching in this aspect will lead to the creation of the new automated system
• The title itself signifies the nature of the research, this is actually based on the model of the language
• Researching in this area will provide us with the better and accurate handwriting identification system
• Computer vision research is mainly benefitted the disaster management with effective image processing methods
• Landslides are pictured by the satellite’s sensors in time intervals and recorded for the future comparisons
• This is highly beneficial in forecasting future landslides by picturing the hills and valleys
• Tools consistency is in need of 3D geometry to yield the precise outcomes in the subdividing processes
• Semantic maps need to be updated for the intelligent according to the real-time happenings
• Enduring learning method will ensure the enrichment in the medical imaging analytics
• Choosing the appropriated source and target province will result in the transfer learning model which is used in the huge scale of data acquirements
• The tracker’s outcome may vary when the head position is changed
• For eliminating this issue 3D model centered gaze evaluation & head tracking, exact iris segmentation method will be implemented in the upcoming frameworks
• Synthetic frameworks will help to identify the complex handwritten data by training the frameworks
• Bag of visual words model is in need of the best combination of the parameters to perform properly
• For this, an automated framework should be improved for better spatial recognition and for eliminating the errors
• This detection is based on the geometric presence of the lumen
• Tracheal ring incoherence are done previously for the enhanced accuracy of the lumen by utilizing the center point which is absorbed from the subsequent works

These are the latest topics indulged with deep learning . So far, we have almost discussed all the aspects involved in crafting research proposal deep learning. We are hoping that you are grasping the stated information. In the following passage, our experts additionally revealed to you the tools and toolboxes that are used in deep learning. Are you interested? Because this is the important part of the research doing. Selecting the appropriate tool will result in the best research outcomes. Let’s get into the next phase.  

Tools and Toolboxes for Deep Learning

• By using the Matlab code we can permit the systems to learn and gain experience from it
• It is a Matlab allied toolbox used in the convolutional neural networks and they are capable of reading the raw logs and learning from it
• It is also a Matlab allied toolbox for the deep learning concepts
• This is written in javascript and is trained by the convolutional neural networks
• This is a python and GPU allied library which is inclusive of restricted Boltzmann machines and neural networks
• This is strengthened the significance of the sampling code along with RBM execution and estimates the function panels exactly
• As these is python and C++ based frameworks they are very simple in the utilization of the NVidia Cuda operations
• This is Mshadow based framework that is meant to speed and reliable deep learning concepts
• This is the interface of Python & Matlab with Cuda and C++ neural networks as a toolbox

These are the most commonly used tools and toolboxes. In the meantime research proposal of deep learning and other concepts are involved with several writing steps. This is very important while preparing a proposal of research for this you need mentor’s advice and assistance in the relevant fields. As we are offering many proposals, our researchers are very confident in the emerging edges in the proposed areas . The next phase is all about how to write the research proposal deep learning. Let’s get into that.

Research Proposal Writing Service 

• Overall summary of the proposal/thesis with maximum coverage
• This covers the overall idea of the research and the frameworks of the research
• Comparison with the literature in the relevant fields of research
• Explication of the methods and techniques used in the research areas
• State the limitations of the research as much as possible
• Conclude the proposal with your results and their impacts on the technology
• APA style quotes should be presented and they don’t have the Bibliography sections

This is how the proposal writing is done to impress and exhibit the research in a proper way. Framing Research proposal Deep Learning in this format will be effective. Till now, we had discussed deep learning and their overall view in this article. Doing research is the primary thing but drafting the research proposal is very important because they are the perfect representation of the research which will showcase the overall view of your research. For this, you can have an opinion with our experts for a better proposal formation. We are always there to help you in the fields of research and thesis writing.

Opening Hours

• Mon-Sat 09.00 am – 6.30 pm
• Lunch Time 12.30 pm – 01.30 pm
• Break Time 04.00 pm – 04.30 pm
• 18 years service excellence
• 40+ country reach
• 36+ university mou
• 194+ college mou
• 6000+ happy customers
• 100+ employees
• 240+ writers
• 60+ developers
• 45+ researchers
• 540+ Journal tieup

Payment Options

Our Clients

Social Links

• Terms of Use

Opening Time

Closing Time

• We follow Indian time zone

June 15, 2018

How to write a PhD research proposal on ‘deep learning’

Due to the complexity of the concepts, it is essential to know how to write a PhD research proposal on ‘deep learning’ early enough before the actual writing.

Deep learning is a collection of algorithms in use for machine learning to model the high-level concepts in data using model architectures that are a composition of various nonlinear transformations. It is part of the methods to learn representations of data.  An algorithm is ‘deep’ if the input of data passes through a series of nonlinear transformations before it has become an output.

Deep learning allows computational models with processing layers to learn data representations with various levels of abstraction without the need for manual identification of features. It relies on the available Write my literature review training process to discover the critical patterns in input examples. An example of how to implement deep learning is when online service provider such as Netflix uses it to predict what a customer is going to order.

Organizing a Ph.D. proposal on deep learning

 Begin by familiarizing with deep learning algorithms before writing your organize your ideas in these sections.

Literature review 

A literature review should provide a survey of relating works to clarify the sphere of your work.   Find contemporary publications that relate to your research.  Determine the reason why there is no modern data on the problem if all that you find is old.   Deep learning has many uses, and it could be that some of the relating work might appear in another field. Many people have more interest in using the concepts than addressing it, and it is a challenge for many researchers in this area.

Problem statement

The purpose of this section is to explain the problem to solve in the study. It requires much learning on the current state of deep learning to determine the problems on which the people are focusing. Identify the specific problem to address.

The significance of study

The purpose of this part is to explain the problem and importance of tackling it.  This section should present arguments supporting the relevance of the problem and reasons why anyone should care. It puts the project into context.

Explains the difficulties that you will tackle for you to solve the problem.   These challenges can be show stoppers for the project or be irrelevant because they focus on a different aspect. Explain the reason for acknowledging the difficulty but decided to avoid addressing it explicitly.   Describe the ways of resolving or working around all other challenges.

A background of a Ph.D. research proposal provides a summary that helps readers to understand the approach.

Methodology

 A methodology is the method you intend to use for solving, provides a sound validation of a verification method for the approach.  You should be concise on the steps to follow, what you want to achieve, the scope and timeline.

Limitations and delimitations

Explains limitations, delimitations, and assumptions about the way you used when narrowing down the problem. Gauge all the efforts of past researchers in solving the questions using the methods you propose.

Preliminary experiments and results

 The purpose of this section is to explain all the tests, observations and conclusions. It should also analyze the results.

 Bibliography

 A bibliography is the last section that lists the related works and the cited references. You include all of them on this section.

A research proposal is provisional and might change as your study continues. It is still essential to ensure that it is error-free and in a consistent structure before sending it to evaluators.

ABOUT AUTHOR

AugustusWilliams

• Bathroom (2)
• Bathroom Improvement (3)
• Boiler Maintenance (1)
• Business (17)
• Carpet Cleaning (1)
• Construction (11)
• Cooking (5)
• Dining Chairs (1)
• Electrical Repair (1)
• Electrician (19)
• Furniture (3)
• Garbage Removal (1)
• Garden (35)
• Gardening (1)
• Home Improvement (139)
• Home Remodel (2)
• Interior Design (50)
• Interior Designer (1)
• Kids Games (1)
• Lifestyle (27)
• Office Furniture (2)
• Office Rental (1)
• Paver Installation (1)
• Plumbing (55)
• Plumbing Services (1)
• Real Estate (23)
• Roof Repair (1)
• Travel (10)
• Tree Removal (1)
• Tree Trimming (2)
• Uncategorized (7)
• Window Coverings (1)
• Wood Plank Walls (1)

• Our Promise
• Our Achievements
• Our Mission
• Proposal Writing
• System Development
• Paper Writing
• Paper Publish
• Synopsis Writing
• Thesis Writing
• Assignments
• Survey Paper
• Conference Paper
• Journal Paper
• Empirical Paper
• Journal Support
• Research Topics in Deep Learning 2024

The below points mention how to boost up deep learning projects and its possible ways by using correct tools, techniques and algorithms’ you are looking forward for project topics in deep learning have a look at our page and get yourself engaged with a team of spectaculars professionals for your research work.

• Developing new deep learning algorithms for specific tasks:

We enhance the new types of neural networks, new training algorithm, and open the door ways to improve the performance of deep learning models to perform specific tasks. For example, medical image analysis, financial forecasting, and autonomous driving.

• Improving the interpretability of deep learning models:

Deep learning models are more difficult and complex to understand and this area we mainly focus on developing new techniques and to elaborate the working process of deep learning models to make user-friendly. This is more important to build trust in deep learning and understood the advancements in this field.

• Addressing the challenges of deep learning:

Deep learning models are crucial to use and train so that, the research area in this sector aims to improve new methods to make deep learning models more convenient, scalable, robust to noise and handle adversarial attack’s.

• Applying deep learning to new domains:

Deep learning has been successfully approached in all fields and performs variety of tasks. But still some domains are frequently used the deep learning methods. This area mainly develops new applications in deep learning in domains such as healthcare, finance, and security. We use this technique for expanding the reach and to have a great effect on deep learning.

Some specific research topic based on deep learning which are well handled by us are  descripted below,

• To perform natural language processing tasks, we have to improve the new deep learning algorithms to E.g.) machine translation, text summarization, and question answering.
• We can generate tasks and enhance deep learning algorithms for speech recognition.
• Computer vision tasks are performed by us, such as image classification, object detection and video segmentation through new deep learning algorithms.
• Developing new deep learning algorithms by us to do robotics tasks such as navigation, planning and control.
• We utilize new deep learning algorithms for medical image analysis tasks such as diagnosis and cancer detection.
• Through new deep learning algorithms, we can perform financial forecasting tasks such as stock market prediction and fraud detection.
• We enhance the new process for drug discovery, predicting climate conditions and social media analysis.
• In federated learning, we allow the trained models distributed across several devices without sharing the data itself. Federated learning is also a type of distributed deep learning.
• The Explainable AI (XAI) is the field to improve AI models which is more transparent and make understood to humans through new methods.
• New techniques are developed for robust AI, which is the field of advanced AI models to handle adversarial attacks and other forms of noise.

Make yourself involved in our research topic assistance, as we have vast of opportunities to make the significant improvement in the field of deep learning.

  Where is Deep Learning research going?

The following areas are the place where we work meticulously for your deep learning project. Have your journal manuscript done without any flaws from phdservices.org team and we assure you get a high rank.

• Self -Supervised Learning: We train the model by using data in its self-supervisory signal and decrease the need of extensive labelled datasets. Contrastive learning is the technique involved in part of this trend.
• Few-shot and Zeroshot Learning: In this learning, the labour-intensive nature of data labelling is provided by us. The area of interest in these models can learn productively from a small number of labelled examples or even from the descriptions without the need of labelled samples.
• Transformers Everywhere: These are primarily designed transformers to perform Natural Language Processing (NLP) tasks and we use architectures like BERT, GPT, etc. It spread through other fields which consists computer vision and even bioinformatics.
• Neural Architecture Search (NAS): This network used by us to detect the best neural architecture for a given problem.
• Capsule Networks: These networks have the capability to replace traditional neural layers. We can able to understand the spatial hierarchy’s in-between its characteristics.
• Energy-Efficient Deep learning: Deep learning models are becoming larger so, we boost them to make more energy-efficient during both training and inference. This process involves knowledge distillation, pruning and quantization.
• Robustness and Generalization: There is a raising concern to tackle the adversarial attacks and that is out of the distribution generalization in deep learning. The research aims to create robust and trustworthy models.
• Interpretability and Explainability: Deep learning has to be adapted in critical areas such as healthcare and finance. We use models that should be explain their decisions and to be interpretable.
• Hybrid Models: We integrate the symbolic AI with neural networks to bring together the best of both that is the potential of learning about neural networks and explaining the symbolic AI.
• Neuroscience -inspired deep learning: It descripts the brain functions that learn the paradigms and motivate the new neural network architectures.
• Ethical AI and Fairness: We ensure that our trained AI models must not be in involved in societal inequalities and should be fair and unbiased. This process consist the methods to find and diminishes the biases in datasets and models .
• Federated Learning: In decentralized data sources, the models are trained by us and we must confirm the data privacy and minimizes the chances for data transfer.
• Reinforcement Learning Enhancements: The reinforcement learning are being progressed to make the model more efficient, standard and scalable.
• Cross-modal and Multimodal Learning: These models are used by us to learn and predict the upcoming beyond various data, such as audio, images and text in a hybrid style.
• Personalized and Continual Learning: The model in this sector can able to learn and adapt themselves to new data without forgetting the previous task, more efficiently in a user-specific context.

What is a good deep learning project?

                Get your projects build by phdservices.org researchers and code viewed perfectly by our experts’ programmers and deep learning problems solved along with multiple revising and formatting for a good dep learning project. If you are seeking out for a dissertation proposal, contact us, we give you 24/7 worldwide research support.

Have a look at the topics we what we have worked at.

• Survey on Sentiment Analysis using Deep Learning
• A multi-scale sentiment recognition network based on deep learning
• On Designing Interfaces to Access Deep Learning Inference Services
• A Deep Learning-Based Posture Estimation Approach for Poultry Behavior Recognition
• Deep Learning-based Incomplete Regions Estimation and Restoration of 3D Human Face Point Cloud
• StreamDL: Deep Learning Serving Platform for AMI Stream Forecasting
• A Deep Learning Aided Intelligent Framework for Condition Monitoring of Electrical Machinery
• Graph Convolutional Network Augmented Deep Reinforcement Learning for Dependent Task Offloading in Mobile Edge Computing
• Multi-Layer Ensemble Deep Reinforcement Learning based DDoS Attack Detection and Mitigation in Cloud-SDN Environment
• Network Abnormal Traffic Detection Model Based on Semi-Supervised Deep Reinforcement Learning
• Deep Learning based Compressive Sensing – Radial Basis Functional Neural Network for Image Fusion
• Deep Learning Edge Detection in Image Inpainting
• Dynamic Computing Resources Allocation for Multiple Deep Learning Tasks
• Ultra Low-Power Deep Learning Applications at the Edge with Jetson Orin AGX Hardware
• Deep learning model based on inceptionResnet-v2 for Finger vein recognition
• Deep Learning in Image Classification: An Overview
• Cognitive foundations of knowledge science and deep knowledge learning by cognitive robots
• Depressive and Non-depressive Tweets Classification using a Sequential Deep Learning Model
• Architecture of Intelligent Decision Embedding Knowledge for Power Grid Generation-Load Look-ahead Dispatch Based on Deep Reinforcement Learning
• Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.

Develop Proposal Idea

We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.

Comparison/Experiments

We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

Scrutinizing Paper Quality

We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

Paper Status Tracking

We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

3. Confidential Info

We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.

• Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
• Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.

CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.

4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

5. No Duplication

After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.

Client Reviews

I ordered a research proposal in the research area of Wireless Communications and it was as very good as I can catch it.

I had wishes to complete implementation using latest software/tools and I had no idea of where to order it. My friend suggested this place and it delivers what I expect.

It really good platform to get all PhD services and I have used it many times because of reasonable price, best customer services, and high quality.

My colleague recommended this service to me and I’m delighted their services. They guide me a lot and given worthy contents for my research paper.

I’m never disappointed at any kind of service. Till I’m work with professional writers and getting lot of opportunities.

- Christopher

Once I am entered this organization I was just felt relax because lots of my colleagues and family relations were suggested to use this service and I received best thesis writing.

I recommend phdservices.org. They have professional writers for all type of writing (proposal, paper, thesis, assignment) support at affordable price.

You guys did a great job saved more money and time. I will keep working with you and I recommend to others also.

These experts are fast, knowledgeable, and dedicated to work under a short deadline. I had get good conference paper in short span.

Guys! You are the great and real experts for paper writing since it exactly matches with my demand. I will approach again.

I am fully satisfied with thesis writing. Thank you for your faultless service and soon I come back again.

Trusted customer service that you offer for me. I don’t have any cons to say.

I was at the edge of my doctorate graduation since my thesis is totally unconnected chapters. You people did a magic and I get my complete thesis!!!

- Abdul Mohammed

Good family environment with collaboration, and lot of hardworking team who actually share their knowledge by offering PhD Services.

I enjoyed huge when working with PhD services. I was asked several questions about my system development and I had wondered of smooth, dedication and caring.

I had not provided any specific requirements for my proposal work, but you guys are very awesome because I’m received proper proposal. Thank you!

- Bhanuprasad

I was read my entire research proposal and I liked concept suits for my research issues. Thank you so much for your efforts.

- Ghulam Nabi

I am extremely happy with your project development support and source codes are easily understanding and executed.

Hi!!! You guys supported me a lot. Thank you and I am 100% satisfied with publication service.

- Abhimanyu

I had found this as a wonderful platform for scholars so I highly recommend this service to all. I ordered thesis proposal and they covered everything. Thank you so much!!!

Related Pages

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Roles Project administration

* E-mail: [email protected]

Affiliation College of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, China

Roles Writing – original draft

Roles Writing – review & editing

• Jianbin Yao, 
• Yushu Wu, 
• Jianhua Liu, 
• Hansheng Wang

• Published: May 9, 2024
• https://doi.org/10.1371/journal.pone.0300746
• Reader Comments

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

Citation: Yao J, Wu Y, Liu J, Wang H (2024) Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages. PLoS ONE 19(5): e0300746. https://doi.org/10.1371/journal.pone.0300746

Editor: Andrea Mastinu, University of Brescia: Universita degli Studi di Brescia, ITALY

Received: November 26, 2023; Accepted: March 4, 2024; Published: May 9, 2024

Copyright: © 2024 Yao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data supporting the findings of this study are available within the article and its supplementary information files. The minimal dataset for multimodal deep learning is available in the Kaggle repository, accessible at https://www.kaggle.com/datasets/jianbinyao/minimum-dataset/data .

Funding: This work was supported in part by Major Science and Technology Projects of the Ministry of Water Resources (Grant No.SKS-2022029), Projects of Open Cooperation of Henan Academy of Sciences (Grant No.220901008) and the Key Scientific Research Projects of Henan Higher Education Institutions (No.24A520022). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Located in the arid and semi-arid regions of China, the North China Plain is the main wheat-producing area in the country and also one of the regions frequently hit by drought. China is among the countries most severely affected by meteorological disasters worldwide, with diverse types of disasters, high intensities, and frequent occurrences. Agricultural meteorological disasters lead to significant reductions in grain production each year. The annual average grain loss nationwide is 20.628 million tons, of which drought accounts for about 60% of the total loss, resulting in an average grain reduction percentage of 4.7% [ 1 ]. Statistics show that China experiences an average of 7.5 droughts annually, affecting an average crop area of 20–30 million hm2 and leading to a grain reduction of 250–300 billion hm2. This poses a significant challenge to grain production and security [ 2 ]. The impact of drought on wheat yield and quality depends on factors such as the severity, duration, timing, and location of the drought. Research indicates that the reduction in wheat yield is not only associated with the extent of drought stress but also with the growth stage at which the stress occurs [ 3 ]. In particular, during the wheat jointing, earing, and grain-filling stages, drought stress severely affects wheat growth and yield levels, decreasing both its yield and quality [ 4 ]. Hence, obtaining real-time drought monitoring information during critical wheat growth stages, accurately identifying wheat drought stress, and promptly adopting efficient irrigation measures to prevent the intensification of drought, are fundamental for ensuring wheat drought early warning and disaster mitigation, playing a vital role in enhancing grain production.

Traditional drought monitoring methods include agricultural meteorological drought monitoring, soil moisture monitoring, thermal infrared imaging technology, hyperspectral imaging, chlorophyll fluorescence technology, and manual diagnosis. Although these methods can determine crop drought, they all have certain lag or limitations [ 5 , 6 ]. For example, issues such as uneven distribution of ground monitoring stations, long update cycles, limited coverage range, and excessive reliance on meteorological data. For irrigated agricultural areas, agricultural meteorological drought monitoring information has its limitations. While irrigation can alter soil moisture conditions, it cannot quickly change the air humidity and temperature in meteorological monitoring systems [ 7 ]. In comparison, soil moisture monitoring is a common indirect method. Still, due to its limited coverage and accuracy, its application faces some constraints [ 8 ]. To directly monitor crop drought stress based on the affected entity, researchers use thermal infrared imaging, hyperspectral imaging, and chlorophyll fluorescence technologies to diagnose and monitor the water status of the canopy and leaves [ 9 ]. For example, Romano et al. successfully analyzed corn’s drought resistance using thermal infrared images, selecting drought-resistant corn varieties [ 10 ]. Mangus et al., with the aid of high-resolution thermal infrared images, delved into the relationship between canopy temperature and soil moisture [ 11 ]. Although thermal infrared technology provides crop drought stress information by monitoring the temperature difference in the canopy, its spatial coverage is limited, and it’s affected by environmental conditions and crop varieties [ 12 ]. Hyperspectral technology reflects crop stress status through spectral features [ 13 ], and is widely used in crop drought stress monitoring, with the drought-sensitive band typically located between 1200nm-2500nm [ 14 ]. Chlorophyll fluorescence is sensitive to the early stages of crop drought stress, but monitoring severe drought stress using chlorophyll fluorescence parameters is challenging. Currently, chlorophyll fluorescence technology is limited to studies on small plants or crops during the seedling stage [ 15 ]. To address these issues, modern approaches utilize advanced technologies such as remote sensing, meteorological models, groundwater level monitoring, and machine learning. These technologies improve the spatiotemporal resolution of monitoring, reduce latency, and enhance the accuracy and timeliness of monitoring through the analysis of multisource data.

Currently, monitoring large crops or in-field crop phenotypes remains a challenging task. However, with the continuous advancement of computer vision and image processing technologies, deep learning methods based on two-dimensional digital images have been widely used for the identification and classification of biotic and abiotic stresses in crops [ 16 ]. Deep learning is an image recognition method that combines image feature extraction and classification. Compared to traditional machine learning, it can automatically extract image features, achieving higher recognition accuracy, and more accurately and objectively identify and grade stresses. At the same time, deep learning models have been proven to be superior to previous image recognition techniques [ 17 ], with numerous studies showing their high recognition accuracy and broad application range advantages [ 18 , 19 ].

In precision agriculture tasks, especially in plant monitoring, a myriad of monitoring methods have generated a significant amount of data [ 20 ]. To handle these data, there are two choices: one is to build models on each modality and evaluate their performance; the other is to combine plant growth data collected from various sources [ 21 ]. Currently, many studies have been conducted aiming to achieve multimodal data fusion. One fusion approach is to establish an integrated convolutional neural network by enhancing the contextual data of plant disease diagnosis. ContextNet is used to extract contextual data, Convolutional Neural Networks (CNN) is used for visual feature extraction, and both are integrated with the fused Mutual Correction Framework (MCF) network. This algorithm has an accuracy of 97.5% on a dataset containing 50,000 crop disease samples [ 22 ]. Another method is to develop a rice disease diagnosis model using multimodal fusion. The proposed diagnostic model can extract numerical features from data collected by sensors, visual features from images, and further combine these features with a connection layer. Results indicate that the accuracy of the multimodal fusion model exceeds that of the single modality model [ 23 ]. Despite some progress in current research on drought stress phenotypes, diagnosing crop drought stress using a single phenotype feature still has its limitations. Using multi-source sensors to obtain crop phenotype information, integrating crop color, texture, morphology, and physiological feature parameters, and employing pattern recognition algorithms to non-destructively, accurately, and quickly diagnose and monitor crop drought stress, is an important future development direction.

Therefore, this paper chooses the DenseNet-121 model to extract the phenotypic features of winter wheat during key growth stages under drought stress. It integrates agricultural meteorological data obtained through Wireless Sensor Networks (WSN) with deep learning image data, constructing the winter wheat drought stress recognition S-DNet model based on multimodal deep learning.

Materials and methods

Data preparation.

In the experiment, the setting of the drought level during the three key growth stages of wheat refers to the requirements of the "Field Investigation and Grading Technical Specifications of Winter Wheat Disaster" Part One: Winter Wheat Drought Disaster (NY/T 2283–2012) from the Agricultural Industry Standards of the People’s Republic of China [ 24 ]. The drought levels are divided into five categories: Optimum moisture (OM), Light drought (LD), Moderate drought (MD), Severe drought (SD), and Extreme drought (ED), as shown in Table 1 . Due to uneven soil moisture distribution in the field and the difficulty of accurate water replenishment, soil moisture sensors were deployed using a node deployment strategy based on the greedy ant colony algorithm, with a calibrated accuracy of ±1%. Soil moisture data was obtained by setting up soil moisture monitoring equipment in the field. Through the monitoring equipment deployed, images of wheat at different drought levels (Optimum, Light, Moderate, Severe, and Extreme) were captured, establishing a drought stress image dataset corresponding to wheat and soil moisture monitoring data.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0300746.t001

Dataset description.

The experiment was conducted from April 2021 to June 2022 in the Efficient Agricultural Water Use Laboratory of North China University of Water Resources and Electric Power. The experiment selected three stages of winter wheat that are significantly affected by drought stress: rise-jointing (RJ), heading-flowering (HF) and flowering-maturity (FM). By monitoring soil moisture sensors in real-time, sample images of wheat at different drought levels during the three key growth stages were collected. After annotation and screening, a total of 12,500 images (see Table 2 ) were used for model training. The time of wheat image collection is shown in Table 3 , and some samples of winter wheat images are shown in Fig 1 .

https://doi.org/10.1371/journal.pone.0300746.g001

https://doi.org/10.1371/journal.pone.0300746.t002

https://doi.org/10.1371/journal.pone.0300746.t003

Sensors and mini weather stations were deployed in the field research area to collect agricultural meteorological data (as shown in Table 4 ). Monitoring equipment was used to obtain wheat drought stress image data. Meteorological data was collected through temperature sensors, air humidity sensors, soil moisture sensors, light sensors, pH sensors, rainfall sensors, wind speed and direction sensors, ground net radiometers, etc.; soil information was gathered through soil pH values, soil moisture, and soil heat flux, etc.

https://doi.org/10.1371/journal.pone.0300746.t004

Proposed framework

For precision agriculture tasks, fusing multiple data sources can enhance the understanding of real-world scenarios [ 25 ]. Thus, this section introduces an end-to-end multi-modal framework for winter wheat phenotypic analysis. This framework employs meteorological drought data to describe drought characteristics, combined with a deep learning model to identify winter wheat phenotypic drought traits. The overall workflow of the model is depicted in Fig 2 . Compared to traditional CNN architectures, an added digital agriculture meteorological data module extracts meteorological drought traits, further enhancing perception in real data scenarios when fused with image drought traits. The next section will discuss the architecture of these baseline models and the proposed multimodal fusion technology.

https://doi.org/10.1371/journal.pone.0300746.g002

Baseline 1: SPEI.

The most widely used in the monitoring and analysis of meteorological drought are the Standardized Precipitation Index (SPI) [ 26 ] and the Palmer Drought Severity Index (PDSI) [ 27 ]. However, in the monitoring of meteorological drought, one index cannot comprehensively and objectively reflect the real situation of dry and wet surface [ 28 ].

To fully leverage the advantages of both PDSI and SPI indices, the Standardized Precipitation Evapotranspiration Index(SPEI) was developed. The SPEI was proposed by Vincente-Serrano et al [ 29 ], and is built on the SPI by introducing the potential evapotranspiration term, integrating the effects of precipitation and temperature on evapotranspiration. In some regions of China, the SPEI index has been applied to meteorological drought studies. For example: Safwan Mohammed et al. examined the intensity, duration, and severity of agricultural drought using the SPI and SPEI for Hungary from 1961 to 2010. They revealed the impact of drought on maize and wheat yields by analyzing standardized yield residuals and crop-drought elasticity factors [ 30 ]; Cheng Junqi et al. took Xinjiang as an example and analyzed the increase in drought frequency in China due to global warming based on the SPEI index from 1961 to 2020. They studied the impact on cotton, wheat, and maize yields [ 31 ]; Shengli Liu et al. focused on summer maize in the Huang-Huai-Hai agricultural region of China. They quantitatively analyzed the impact of drought on crop yields using annual phenological data and the SPEI from 1981 to 2010 [ 32 ]; Liu Ying et al. utilized various data sources, including CRU precipitation data, to study drought propagation and the impact of water resources on vegetation in the karst region of southwestern China. They employed the SPI and the random forest method for their research [ 33 ].

SPEI is built on the SPI by introducing the potential evapotranspiration term, and like the SPI, SPEI is also a drought index based on a probability model. The calculation steps of SPEI are as follows:

Based on SPEI, the drought level classification is shown in Table 5 .

https://doi.org/10.1371/journal.pone.0300746.t005

Baseline 2: DenseNet-121.

DenseNet (Densely Connected Convolutional Network) is a deep convolutional neural network structure proposed by Gao et al. in 2019 [ 36 ]. The network structure of the DenseNet series is shown in Fig 3 . Unlike traditional convolutional neural networks, the output of each layer in DenseNet is connected with the outputs of all previous layers, forming a densely connected structure. This kind of connection ensures more thorough feature propagation, effectively reducing the vanishing gradient problem, and enhancing both the training efficiency and generalization capacity of the model. DenseNet-121 consists of 121 layers. This network adopts a new architecture that is both concise and efficient, demonstrating superior performance over the Residual Network (ResNet) on the CIFAR metric.

https://doi.org/10.1371/journal.pone.0300746.g003

Multimodal fusion.

When employing deep learning models for image classification, the prediction results are typically given as a probability distribution, with each category receiving a confidence score. However, relying solely on image classification results may not meet the needs of practical applications, especially when other relevant information is combined with image classification, the current digital agrometeorological data includes meteorological and soil-related information, such as temperature, air humidity, light intensity, wind speed, soil moisture, precipitation, trace elements, soil pH value, etc. After fusing data from different sources, the network is more elastic, fault tolerance and accuracy than when using only one data source. By merging winter wheat phenotypic image traits with SPEI text traits, we enhance model performance, resulting in a drought monitoring model called S-DNet, which integrates SPEI with DenseNet-121. The model’s framework is shown in Fig 4 .

https://doi.org/10.1371/journal.pone.0300746.g004

Among them, the basic idea of decision layer fusion is to use an adaptive weighted fusion method, merging the probability vector of different meteorological drought levels derived from SPEI with the probability vector of wheat drought levels identified by the DenseNet-121 model. This method allows for the organic combination of the prediction results of both approaches, fully utilizing each of their feature information, and thus yielding a more comprehensive drought probability vector. The framework of decision layer fusion is shown in Fig 5 . Before the data fusion at the decision layer, it’s imperative to ensure that the drought probability vectors from SPEI and DenseNet-121 model are consistent, ensuring both modalities output the same drought categories, laying the groundwork for consistent fusion. At the same time, depending on the real-time meteorological conditions, weights are allocated to the probability vector of each method, ensuring a balance among various factors.

https://doi.org/10.1371/journal.pone.0300746.g005

Evaluation metrics.

This research uses several metrics to evaluate the winter wheat drought stress identification and grading model, including the accuracy of drought stress identification ( A 1), the precision of drought stress classification ( F 1), and the comprehensive evaluation metric F 1 score. The accuracy A 1 evaluates the precise degree of drought identification, the precision P 1 assesses classification results, and the F 1 score is the harmonic mean of precision and recall, evaluating the model’s identification accuracy for winter wheat drought images, integrating the strengths and weaknesses of both.

In Table 6 : TP represents the number of true positive samples predicted as positive by the model; TN denotes the number of true negative samples predicted as negative by the model; FP stands for the number of actual positive samples predicted as negative; FN signifies the number of actual negative samples predicted as positive.

https://doi.org/10.1371/journal.pone.0300746.t006

Results and discussion

Multimodal fusion results.

https://doi.org/10.1371/journal.pone.0300746.g006

https://doi.org/10.1371/journal.pone.0300746.t007

Fig 6 illustrates the comparison results of drought probabilities under multimodal conditions. As evident from the bar charts, by using the adaptive weighted fusion method, the S-DNet model can harness the advantages of both modalities, yielding a slight increase in the final drought prediction probability compared to single-modal image recognition, thereby enhancing the precision and reliability of drought level prediction through image data.

Comparative analysis

In order to further improve the accuracy of the winter wheat drought identification model, SPEI is calculated based on the agricultural meteorological data obtained from WSN and fused with the convolutional neural network (CNN) model, DenseNet-121, which was pre-trained using image data. A multi-modal fusion network framework, SPEI-DenseNet-121 (S-DNet), is proposed. By learning the features of both image and non-image data, combined with deep learning classification techniques, a study on the drought conditions during the three key growth stages of winter wheat under drought stress is carried out. A performance evaluation comparison test is conducted between the unimodal DenseNet-121 model and the multimodal S-DNet model. In the experiment, randomly initialized weights were used; both unimodal and multimodal used the same SGD optimizer for training, with a batch size of 64 and a fixed learning rate of 0.001, employing a gradual learning rate strategy. Performance evaluation indicators including classification accuracy, loss values, and F1 scores were calculated to assess the performance of the various models. The results are shown in Table 8 and Fig 7 .

https://doi.org/10.1371/journal.pone.0300746.g007

https://doi.org/10.1371/journal.pone.0300746.t008

The confusion matrix [ 38 ] is one of the tools used to evaluate model factors. It is a matrix-style heatmap where rows represent true category labels and columns represent predicted outcomes. The confusion matrices for the unimodal DenseNet-121 model and the multimodal S-DNet model results are shown in Fig 7 .

To enhance the accuracy of drought degree monitoring during the key growth stages of wheat, a deep learning fusion strategy based on SPEI was explored. This involved integrating crop image data with non-image agricultural meteorological data collected by sensors, leading to the proposal of a multimodal fusion S-DNet network model. The model recognizes and classifies the drought degree of wheat during its key growth stages based on WSN data features and image learning features. Results show that: ① Compared to the unimodal DenseNet-121 network model, the S-DNet has superior accuracy, robustness, and practicality. It displayed significantly better performance when identifying and grading the drought degree during wheat’s key growth stages, with an average identification accuracy of 96.4%. ② The multimodal fusion S-DNet model’s drought identification accuracy surpassed that of the unimodal DenseNet-121 model, improving the average model identification accuracy by 2.8 percentage points across the three key stages. ③ Compared to the confusion matrix of the unimodal DenseNet-121, the multimodal fusion confusion matrix better captures the interrelationships between different modes, thereby enhancing classification accuracy and reliability. ④ By fusing deep learning’s DenseNet-121 model with SPEI meteorological data, the model is better equipped to understand and grasp the inherent patterns in the data. This multimodal fusion method offers a more comprehensive and enriched information, enhancing the model’s robustness and generalizability.

In conclusion, this study proposed a novel multimodal deep learning approach for monitoring drought stress in winter wheat, aiming to improve the accuracy and efficiency of drought stress assessment during critical growth stages. By collecting and analyzing drought stress images of winter wheat at the Rise-Jointing, Heading-Flowering, and Flowering-Maturity stages, a dataset corresponding to soil moisture monitoring data was established. The DenseNet-121 model was employed as the base network to extract drought features, and a multimodal deep learning-based S-DNet model was developed by integrating meteorological factors, IoT technology, and the meteorological drought index SPEI obtained through WSN sensors.

The results demonstrate that the multimodal S-DNet model significantly outperforms the single-modal DenseNet-121 model, achieving an average drought recognition accuracy of 96.4%. This indicates the model’s high robustness and generalization capability, enabling non-destructive, accurate, and rapid monitoring of drought stress in winter wheat. The study’s findings suggest that the multimodal fusion network provides a reliable and effective approach for evaluating drought stress in winter wheat, with broad applications in agricultural production and resource management.

Acknowledgments

The work was supported by Major Science and Technology Projects of the Ministry of Water Resources(Grant No.SKS-2022029), Projects of Open Cooperation of Henan Academy of Sciences(Grant No.220901008), the Key Scientific Research Projects of Henan Higher Education Institutions(No.24A520022) and the North China University of Water Conservancy and Electric Power High-level experts Scientific Research foundation(202401014).

• View Article
• PubMed/NCBI
• Google Scholar
• 3. Wang J, Xiong Y, Li F-M, Siddique K, Turner N. Effects of Drought Stress on Morphophysiological Traits, Biochemical Characteristics, Yield, and Yield Components in Different Ploidy Wheat: A Meta-Analysis. 2017.
• 24. NY/T2283-2012. Technical specification for field survey and grading of winter wheat disasters. Beijing: China Agricultural Press. 2012.
• 26. Khandelwal R, Goyal H, Shekhawat RS, editors. Spatio-temporal Standardized Precipitation Index Selection using Geospatial Technique. 2023 IEEE International Conference on Contemporary Computing and Communications (InC4); 2023 21–22 April 2023.

Our approach

• Responsibility
• Infrastructure
• Try Meta AI

CORE MACHINE LEARNING

A newcomer's guide to deep learning for inverse design in nano-photonics.

January 18, 2024

Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as light concentration, routing, and filtering. Designing these devices is a challenging task. Traditionally, solving this problem has relied on computationally expensive, iterative methods. In recent years, deep learning techniques have emerged as promising tools for tackling the inverse design of nanophotonic devices. While several review articles have provided an overview of the progress in this rapidly evolving field, there is a need for a comprehensive tutorial that specifically targets newcomers without prior experience in deep learning. Our goal is to address this gap and provide practical guidance for applying deep learning to individual scientific problems. We introduce the fundamental concepts of deep learning and critically discuss the potential benefits it offers for various inverse design problems in nanophotonics. We present a suggested workflow and detailed, practical design guidelines to help newcomers navigate the challenges they may encounter. By following our guide, newcomers can avoid frustrating roadblocks commonly experienced when venturing into deep learning for the first time. In a second part, we explore different iterative and direct deep learning-based techniques for inverse design, and evaluate their respective advantages and limitations. To enhance understanding and facilitate implementation, we supplement the manuscript with detailed Python notebook examples, illustrating each step of the discussed processes. While our tutorial primarily focuses on researchers in (nano-)photonics, it is also relevant for those working with deep learning in other research domains. We aim at providing a solid starting point to empower researchers to leverage the potential of deep learning in their scientific pursuits.

Abdourahman Khaireh Walieh

Pauline Bennet

Olivier Teytaud

Antoine Moreau

Peter Wiecha

Research Topics

Core Machine Learning

Related Publications

May 07, 2024

ReTaSA: A Nonparametric Functional Estimation Approach for Addressing Continuous Target Shift

Hwanwoo Kim , Xin Zhang , Jiwei Zhao , Qinglong Tian

April 04, 2024

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

Jonathan Lebensold , Maziar Sanjabi , Pietro Astolfi , Adriana Romero Soriano , Kamalika Chaudhuri , Mike Rabbat , Chuan Guo

March 28, 2024

On the Identifiability of Quantized Factors

Vitoria Barin Pacela , Kartik Ahuja , Simon Lacoste-Julien , Pascal Vincent

March 13, 2024

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Jiawei Zhao , Zhenyu Zhang , Beidi Chen , Zhangyang Wang , Anima Anandkumar , Yuandong Tian

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment..

Product experiences

Foundational models

Latest news

Meta © 2024

• Biosimilars
• Cataract Therapeutics
• Gene Therapy
• Optic Relief
• Geographic Atrophy
• Ocular Surface Disease
• Practice Management
• Therapeutics
• Understanding Antibiotic Resistance
• Ocular Allergy
• Clinical Diagnosis

ARVO 2024: How a deep learning model can benefit femtosecond laser-assisted cataract surgery

Dustin Morley, PhD, principal research scientist at LENSAR, discusses research on applying deep learning to benefit FLACS procedures.

At the 2024 ARVO meeting in Seattle, Washington, the Eye Care Network took time to speak with Dustin Morley, PhD. Morley, a principal research scientist at LENSAR, spoke about his recent work investigating the benefit of deep learning models in femtosecond laser-assisted cataract surgery (FLACS) procedures.

Video transcript

Please note: The below transcript has been lightly edited for clarity.

Dustin Morley, PhD:

Hello, I'm Dustin Morley, principal research scientist at LENSAR. And I'm here today at ARVO 2024 to present our research in applying modern artificial intelligence in the form of deep learning to the benefit of FLACS procedures. As we know, correctly identifying the anterior and posterior surfaces of the cataractous lens as well as the cornea is critical for a safe and effective FLACS procedure. So our study goal was to determine if deep learning was a suitable method to completely and fully solve this problem toward the benefit of FLACS procedures.

So to study this, we obtained de-identified Scheimpflug scans for a total of 973 eyes, the vast majority of which contained cataract, and the dataset contained a wide variety of different cataract morphologies within it. On that data set, we also performed aggressive data augmentation to simulate things like illumination changes or geometric differences, such as warping the images, or rotating the images, things of that nature. And on that full composite dataset, we designed and trained a deep convolutional neural network based on the U-Net architecture to identify and classify all pixels belonging to the anterior and posterior surfaces of the lens and cornea. And from those pixels, we then apply a RANSAC algorithm to take those pixels and obtain best-fitting geometric curves, which we could then project into 3D space for a composite 3D reconstruction, which was ultimately needed to correctly position all of the laser pattern for the treatment of the eye. And to assess how well the model performed, we did twofold cross validation, specifically on the 692 images that were both cataractous eyes that were imaged by our newer ALLY system. And we use the ability to obtain that final 3D reconstruction of the surface as our final endpoint. And what we found when we did that was that there were zero failures to reconstruct the anterior and posterior cornea surface and zero failures to reconstruct the anterior lens surface. And there were five failures to reconstruct the posterior lens surface. But for three of those, the task was legitimately impossible because the cataracts were so advanced that the surface itself was just completely invisible. So that leaves, really, only two failures in terms of what was actually doable, for a success rate of 99.7%. And so therefore, from that, we conclude that deep learning is in fact very well suited to fully solve the problem of locating the anterior and posterior surfaces of the cataractous lens, even in...the presence of very advanced and challenging cataract artifacts, as well as the cornea.

And based on that, Lensar has incorporated this deep learning model into our latest next generation femtosecond laser, the ALLY system, and thereby eliminating the need for manual surface placement as part of the FLACS procedure workflow. I found, as the developer doing it, that [there was] the streamlined process, whenever you want to make it better, it's the same. You do the same thing every time: you get more images, you label more images, and then you train it on your new expanded dataset, and then it gets better. So it's just the same thing, it's much better than the old days of trying to cram a set of handcrafted rules together to make it all work. And then finding one case that suddenly doesn't fit into the rules and then having to redesign the whole thing. Again, this is just nice and perfectly streamlined. You just get more data, label it and train it on the extra data. Well, the next research part is, I mean, we're always continuing to collect images and seeing if we eventually run into types of cataracts that are maybe so rare that we haven't seen before, and maybe ones that we might still struggle on. Haven't seen very many of those yet, but there's a lot of people in the world, lots of different ways that cataracts can look. You know, you never know when you're gonna finally hit the one in 100,000 or the one in a million type thing that's completely different from everything else. And so we'll just always be on the lookout for those, and incorporating them, as well as seeing what other avenues we could explore with the same deep learning technology on similar imaging modalities.

EyePod: Transforming the MIGS pipeline into clinical reality

Research suggests tear fluid testing as promising method for Huntington’s Disease monitoring

Highlighting results from first FDA-reported study for treatment of presbyopia

ARVO 2024: AI may open new frontiers in vision research

ARVO 2024: Researchers detail novel glaucoma shunt

2 Commerce Drive Cranbury, NJ 08512

609-716-7777

The latest news in Healthcare IT – straight to your inbox.

• Global Edition

Cedars-Sinai research shows deep learning model could improve AFib detection

Photo: Cedars Sinai

A new artificial intelligence approach developed by investigators in Cedars-Sinai's Los Angeles-based Smidt Heart Institute has been shown to detect abnormal heart rhythms associated with atrial fibrillation that might otherwise be unnoticed by physicians.

WHY IT MATTERS Researchers at Smidt Heart Institute say the findings point to the potential for artificial intelligence to be used more widely in cardiac care.

In a recent study, published in npj Digital Medicine , Cedars-Sinai clinicians show how the deep learning model was developed to analyze images from echocardiogram imaging, in which sound waves show the heart's rhythm.

Researchers trained a program to study more than 100,000 echocardiogram videos from patients with atrial fibrillation, they explain. The model distinguished between echocardiograms showing a heart in sinus rhythm – normal heartbeats – and those showing a heart in an irregular heart rhythm.

The program was able to predict which patients in sinus rhythm had experienced – or would develop – atrial fibrillation within 90 days, they said, noting that the AI model evaluating the images performed better than estimating risk based on known risk factors.

"We were able to show that a deep learning algorithm we developed could be applied to echocardiograms to identify patients with a hidden abnormal heart rhythm disorder called atrial fibrillation," explained Dr. Neal Yuan, a staff scientist with the Smidt Heart Institute.

"Atrial fibrillation can come and go," he added, "so it might not be present at a doctor's appointment. This AI algorithm identifies patients who might have atrial fibrillation even when it is not present during their echocardiogram study."

THE LARGER TREND The Smidt Heart Institute is the biggest cardiothoracic transplant center in California and the third-largest in the United States.

An estimated 12.1 million people in the United States will have atrial fibrillation in 2030, according to the CDC. During AFib, the heart's upper chambers sometimes beat in sync with the lower chamber and sometimes they do not – making the arrhythmia often difficult for clinicians to detect. In some patients, the condition causes no symptoms at all.

Researchers say a machine learning model trained to analyze echo imaging could help clinicians detect early and subtle changes in the hearts of patients with undiagnosed arrhythmias.

Indeed, AI has long shown big promise for early detection of AFib, as evidenced by similar studies at health systems such as Geisinger and Mayo Clinic .

ON THE RECORD "We're encouraged that this technology might pick up a dangerous condition that the human eye would not while looking at echocardiograms," said Dr. David Ouyang, a cardiologist and AI researcher in the Smidt Heart Institute. "It might be used for patients at risk for atrial fibrillation or who are experiencing symptoms associated with the condition."

"The fact that this program predicted which patients had active or hidden atrial fibrillation could have immense clinical applications," added Dr. Christine M. Albert, chair of the Department of Cardiology at the Smidt Heart Institute. "Being able to identify patients with hidden atrial fibrillation could allow us to treat them before they experience a serious cardiovascular event."

Mike Miliard is executive editor of Healthcare IT News Email the writer: [email protected] Healthcare IT News is a HIMSS publication.

More regional news

Ascension hit by cyberattack

Not all physicians dislike EHR data entry

Cloud-based AI services could help fight health misinformation

White Papers

More Whitepapers

More Webinars

More Stories

• Artificial Intelligence
• Cloud Computing
• Government & Policy
• Interoperability
• Patient Engagement
• Population Health
• Precision Medicine
• Privacy & Security
• Women In Health IT
• Learning Center
• Research Papers
• Special Projects
• In-Person Events

The Daily Brief Newsletter

Search form, top stories.

Help | Advanced Search

Computer Science > Machine Learning

Title: kan: kolmogorov-arnold networks.

Abstract: Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Study: Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN

A new research paper was published in Oncotarget , titled "Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN."

Radiation dosage limits the sequential PET/CT studies oncology patients can undergo during their treatment follow-up course.

In this new study, researchers from the National Institutes of Health's National Cancer Institute proposed an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans.

"AI-generated PET images have clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality in prostate cancer patients," write the researchers.

A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA (prostate-specific membrane antigen) PET-CT studies from 302 prostate cancer patients split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based.

Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling.

Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05).

"The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality," state the authors.

More information: Kevin C. Ma et al, Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN, Oncotarget (2024). DOI: 10.18632/oncotarget.28583

Provided by Impact Journals LLC