research papers on data science ieee

Series Editorial: Artificial Intelligence and Data Science for Communications

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, recommendations.

Welcome to the first issue of the Artificial Intelligence and Data Science for Communications Series of IEEE Communications Magazine in 2023. The Series continues its growth in popularity among academicians, researchers, and practitioners as artificial ...

Advancing artificial intelligence research and dissemination through conference series: Benchmark, scientific impact and the MICAI experience

This article presents an overview, analysis and benchmark of the best-known artificial intelligence (AI) conferences, including the Mexican International Conference on Artificial Intelligence (MICAI) conference series, and describes how MICAI has ...

Artificial Intelligence: Theories, Models and Applications 7th Hellenic Conference on AI, SETN 2012, Lamia, Greece, May 28-31, 2012, Proceedings

Information, published in, publication history, contributors, other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

data science Recently Published Documents

Total documents.

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

Assessing the effects of fuel energy consumption, foreign direct investment and GDP on CO2 emission: New data science evidence from Europe & Central Asia

Documentation matters: human-centered ai system to assist data science code documentation in computational notebooks.

Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick iterations. Inspired by human documentation practices learned from 80 highly-voted Kaggle notebooks, we design and implement Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario. Themisto facilitates the creation of documentation via three approaches: a deep-learning-based approach to generate documentation for source code, a query-based approach to retrieve online API documentation for source code, and a user prompt approach to nudge users to write documentation. We evaluated Themisto in a within-subjects experiment with 24 data science practitioners, and found that automated documentation generation techniques reduced the time for writing documentation, reminded participants to document code they would have ignored, and improved participants’ satisfaction with their computational notebook.

Data science in the business environment: Insight management for an Executive MBA

Adventures in financial data science, gecoagent: a conversational agent for empowering genomic data extraction and analysis.

With the availability of reliable and low-cost DNA sequencing, human genomics is relevant to a growing number of end-users, including biologists and clinicians. Typical interactions require applying comparative data analysis to huge repositories of genomic information for building new knowledge, taking advantage of the latest findings in applied genomics for healthcare. Powerful technology for data extraction and analysis is available, but broad use of the technology is hampered by the complexity of accessing such methods and tools. This work presents GeCoAgent, a big-data service for clinicians and biologists. GeCoAgent uses a dialogic interface, animated by a chatbot, for supporting the end-users’ interaction with computational tools accompanied by multi-modal support. While the dialogue progresses, the user is accompanied in extracting the relevant data from repositories and then performing data analysis, which often requires the use of statistical methods or machine learning. Results are returned using simple representations (spreadsheets and graphics), while at the end of a session the dialogue is summarized in textual format. The innovation presented in this article is concerned with not only the delivery of a new tool but also our novel approach to conversational technologies, potentially extensible to other healthcare domains or to general data science.

Differentially Private Medical Texts Generation Using Generative Neural Networks

Technological advancements in data science have offered us affordable storage and efficient algorithms to query a large volume of data. Our health records are a significant part of this data, which is pivotal for healthcare providers and can be utilized in our well-being. The clinical note in electronic health records is one such category that collects a patient’s complete medical information during different timesteps of patient care available in the form of free-texts. Thus, these unstructured textual notes contain events from a patient’s admission to discharge, which can prove to be significant for future medical decisions. However, since these texts also contain sensitive information about the patient and the attending medical professionals, such notes cannot be shared publicly. This privacy issue has thwarted timely discoveries on this plethora of untapped information. Therefore, in this work, we intend to generate synthetic medical texts from a private or sanitized (de-identified) clinical text corpus and analyze their utility rigorously in different metrics and levels. Experimental results promote the applicability of our generated data as it achieves more than 80\% accuracy in different pragmatic classification problems and matches (or outperforms) the original text data.

Impact on Stock Market across Covid-19 Outbreak

Abstract: This paper analysis the impact of pandemic over the global stock exchange. The stock listing values are determined by variety of factors including the seasonal changes, catastrophic calamities, pandemic, fiscal year change and many more. This paper significantly provides analysis on the variation of listing price over the world-wide outbreak of novel corona virus. The key reason to imply upon this outbreak was to provide notion on underlying regulation of stock exchanges. Daily closing prices of the stock indices from January 2017 to January 2022 has been utilized for the analysis. The predominant feature of the research is to analyse the fact that does global economy downfall impacts the financial stock exchange. Keywords: Stock Exchange, Matplotlib, Streamlit, Data Science, Web scrapping.

Information Resilience: the nexus of responsible and agile approaches to information use

AbstractThe appetite for effective use of information assets has been steadily rising in both public and private sector organisations. However, whether the information is used for social good or commercial gain, there is a growing recognition of the complex socio-technical challenges associated with balancing the diverse demands of regulatory compliance and data privacy, social expectations and ethical use, business process agility and value creation, and scarcity of data science talent. In this vision paper, we present a series of case studies that highlight these interconnected challenges, across a range of application areas. We use the insights from the case studies to introduce Information Resilience, as a scaffold within which the competing requirements of responsible and agile approaches to information use can be positioned. The aim of this paper is to develop and present a manifesto for Information Resilience that can serve as a reference for future research and development in relevant areas of responsible data management.

qEEG Analysis in the Diagnosis of Alzheimers Disease; a Comparison of Functional Connectivity and Spectral Analysis

Alzheimers disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain, causing a decline in cognitive abilities and difficulties in engaging in day-to-day activities. This study compares an FFT-based spectral analysis against a functional connectivity analysis based on phase synchronization, for finding known differences between AD patients and Healthy Control (HC) subjects. Both of these quantitative analysis methods were applied on a dataset comprising bipolar EEG montages values from 20 diagnosed AD patients and 20 age-matched HC subjects. Additionally, an attempt was made to localize the identified AD-induced brain activity effects in AD patients. The obtained results showed the advantage of the functional connectivity analysis method compared to a simple spectral analysis. Specifically, while spectral analysis could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting that the AD patients brains are in a phase-locked state. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized in the centro-parietal and centro-temporal areas in the theta frequency band (4-8 Hz). The contribution of this study is that it applies a neural metric for Alzheimers detection from a data science perspective rather than from a neuroscience one. The study shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

Export Citation Format

Share document.

• IEEE Xplore Digital Library
• IEEE Standards
• IEEE Spectrum

Publications

IEEE Talks Big Data - Check out our new Q&A article series with big Data experts!

Call for Papers - Check out the many opportunities to submit your own paper. This is a great way to get published, and to share your research in a leading IEEE magazine!

Publications - See the list of various IEEE publications related to big data and analytics here.

Call for Blog Writers!

IEEE Cloud Computing Community is a key platform for researchers, academicians and industry practitioners to share and exchange ideas regarding cloud computing technologies and services, as well as identify the emerging trends and research topics that are defining the future direction of cloud computing. Come be part of this revolution as we invite blog posts in this regard and not limited to the list provided below:

• Cloud Deployment Frameworks
• Cloud Architecture
• Cloud Native Design Patterns
• Testing Services and Frameworks
• Storage Architectures
• Big Data and Analytics
• Internet of Things
• Virtualization techniques
• Legacy Modernization
• Security and Compliance
• Pricing Methodologies
• Service Oriented Architecture
• Microservices
• Container Technology
• Cloud Computing Impact and Trends shaping today’s business
• High availability and reliability

Call for Papers

No call for papers at this time.

IEEE Publications on Big Data

Read more at IEEE Computer Society.

IEEE Computer Magazine Special Issue on Big Data Management

• Big Data: Promises and Problems

Connecting the Dots With Big Data

• Better Health Care Through Data
• The Future of Crime Prevention
• Census and Sensibility
• Landing a Job in Big Data

Read more at The Institute.

Download full issue. (PDF, 5 MB)

IEEE Internet Computing July/August 2014

Web-Scale Datacenters

This issue of Internet Computing surveys issues surrounding Web-scale datacenters, particularly in the areas of cloud provisioning as well as networking optimization and configuration. They include workload isolation, recovery from transient server availability, network configuration, virtual networking, and content distribution.

Read more at IEEE Computer Society .

Networking for Big Data

The most current information for communications professionals involved with the interconnection of computing systems, this bimonthly magazine covers all aspects of data and computer communications.

Read more at IEEE Communications Society .

Special Issue on Big Data

Big data is transforming our lives, but it is also placing an unprecedented burden on our compute infrastructure. As data expansion rates outpace Moore's law and supply voltage scaling grinds to a halt, the IT industry is being challenged in its ability to effectively store, process, and serve the growing volumes of data. Delivering on the premise of big data in the post­Dennard era calls for specialization and tight integration across the system stack, with the aim of maximizing energy efficiency, performance scalability, resilience, and security.

Data Science

Featured article, related topics, top conferences on data science, top videos on data science.

Xplore Articles related to Data Science

Periodicals related to data science, e-books related to data science, courses related to data science, top organizations on data science, most published xplore authors for data science.

Subscribe to the PwC Newsletter

Join the community, search results for author: ieee, found 70 papers, 12 papers with code, variational neuron shifting for few-shot image classification across domains.

no code implementations • journal 2024 • Liyun Zuo , Baoyan Wang , Lei Zhang , Jun Xu , Member , IEEE , and Xiantong Zhen

Existing meta-learning models learn the ability of learning good representation or model parameters, in order to adapt to new tasks with a few training samples.

Instance Paradigm Contrastive Learning for Domain Generalization

no code implementations • IEEE Transactions on Circuits and Systems for Video Technology 2024 • Zining Chen , Weiqiu Wang , Zhicheng Zhao , Fei Su , Member , IEEE , Aidong Men , and Yuan Dong

In this paper, we propose an instance paradigm contrastive learning framework, introducing contrast between original features and novel paradigms to alleviate domain-specific distractions.

Event-Triggered Tracking Control for Nonlinear Systems With Prescribed Performance

no code implementations • IEEE TRANSACTIONS ON SYSTEMS 2024 • Ruihang Ji , Shuzhi Sam Ge , Kai Zhao , Member , and Haizhou Li , Fellow , IEEE

Abstract—This article addresses the entry capture problem (ECP) of uncertain nonlinear systems under asymmetric performance constraints.

An Ultralightweight Hybrid CNN Based on Redundancy Removal for Hyperspectral Image Classification

no code implementations • IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024 • Xiaohu Ma , Wuli Wang , Member , IEEE

Simultaneously, for PW-Conv, we design a spectral convolution with redundancy removal (R2Spectral-Conv).

Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing

no code implementations • TMC 2024 • Penglin Dai , Yaorong Huang , Kaiwen Hu , Xiao Wu , Huanlai Xing , and Zhaofei Yu , Member , IEEE

The objective is to design a unified solution to minimize task execution time under different MTO scenarios.

Ultra-Robust Real-Time Estimation of Gait Phase

no code implementations • IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023 • Mohammad Shushtari , Hannah Dinovitzer , Jiacheng Weng , and Arash Arami , Member , IEEE

The estimator is finally tested on a participant walking with an active exoskeleton, demonstrating the robustness of D67 in interaction with an exoskeleton without being trained on any data from the test subject with or without an exoskeleton.

Interaction-Aware Planning With Deep Inverse Reinforcement Learning for Human-Like Autonomous Driving in Merge Scenarios

1 code implementation • journal 2023 • Jiangfeng Nan , Weiwen Deng , Member , IEEE , Ruzheng Zhang , Ying Wang , Rui Zhao , Juan Ding

To consider the interaction factor, the reward function for planning is utilized to evaluate the joint trajectories of the autonomous driving vehicle (ADV) and traffic vehicles.

Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise Modeling

1 code implementation • IEEE Transactions on Geoscience and Remote Sensing 2023 • Chuangji Meng , Jinghuai Gao , Member , IEEE , Yajun Tian , Zhiqiang Wang

Thus, our proposed framework called VI-Non-IID inclines to have better noise characterization and generalization capabilities, which brings better performance on seismic field NA.

Spoof Trace Disentanglement for generic face antispoofing

no code implementations • journal 2023 • Yaojie Liu and Xiaoming Liu , Member , IEEE

Yet, it is a challenging task due to the diversity of spoof attacks and the lack of ground truth for spoof traces.

Bio-Inspired Feature Selection in Brain Disease Detection via an Improved Sparrow Search Algorithm

no code implementations • IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022 • Wenyu Yu , Hui Kang , Geng Sun , Member , Shuang Liang , and Jiahui Li , Student Member , IEEE

Finally, the proposed ISSA is utilized to solve the objective function.

VCI-LSTM: Vector Choquet Integral-based Long Short-Term Memory

no code implementations • IEEE 2022 • Mikel Ferrero-Jaurrieta , Zdenko Taka ́cˇ , Javier Ferna ́ndez , Member , IEEE , Lˇubom ́ıra Horanska ́ , Grac ̧aliz Pereira Dimuro , Susana Montes , Irene D ́ıaz and Humberto Bustince , Fellow , IEEE.

Choquet integral is a widely used aggregation oper- ator on one-dimensional and interval-valued information, since it is able to take into account the possible interaction among data.

Lightweight Deep Neural Network for Joint Learning of Underwater Object Detection and Color Conversion

no code implementations • journal 2022 • Chia-Hung Yeh , Chu-Han Lin , Li-Wei Kang , Member , Chih-Hsiang Huang , Min-Hui Lin , Chuan-Yu Chang , and Chua-Chin Wang , Senior Member , IEEE

Li-Wei Kang is with the Department of Electrical Engineering, National Taiwan Normal University, Taipei 106, Taiwan (e-mail: lwkang@ntnu. edu. tw).

Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process

no code implementations • IEEE Transactions on Smart Grid 2022 • Di Cao , Member , Junbo Zhao , Weihao Hu , Senior Member , Qishu Liao , Qi Huang , Zhe Chen , Fellow , IEEE

Abstract—This paper addresses the distribution system state estimation (DSSE) with unknown topology change.

STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network

no code implementations • IEEE Transactions on Industrial Informatics 2022 • Ryan Wen Liu , Maohan Liang , Jiangtian Nie , Yanli Yuan , Zehui Xiong , Member , IEEE , Han Yu

—The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT).

Coverage Control Algorithm for DSNs Based on Improved Gravitational Search

no code implementations • IEEE Sensors Journal 2022 • Yindi Yao , Huanmin Liao , Xiong Li , Student Member , IEEE , Feng Zhao , Xuan Yang , and Shanshan Hu

—In directional sensor networks (DSNs), coverage control is an important way to ensure efficient communication and reliable data transmission.

High-order Correlation Preserved Incomplete Multi-view Subspace Clustering

3 code implementations • IEEE Transactions on Image Processing 2022 • Zhenglai Li , Chang Tang , Xiao Zheng , Xinwang Liu , Senior Member , Wei zhang , Member , IEEE , and En Zhu

Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation.

A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework

no code implementations • IEEE Transactions on Intelligent Transportation Systems 2022 • Junchen Jin , Member , IEEE , Dingding Rong , Tong Zhang , Qingyuan Ji , Haifeng Guo , Yisheng Lv , Xiaoliang Ma , and Fei-Yue Wang

This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation.

Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification

no code implementations • 1 Dec 2021 • Hongmin Gao , Member , Zhonghao Chen , Student Member , IEEE , Chenming Li

Therefore, this letter proposes a shallow model for HSIC, which is called depthwise over-parameterized convolutional neural network (DOCNN).

Distributed Differential Evolution Based on Adaptive Mergence and Split for Large-Scale Optimization

1 code implementation • IEEE Transactions on Evolutionary Computation 2021 • Yinglan Feng , Liang Feng , Senior Member , Sam Kwong , and Kay Chen Tan , Fellow , IEEE

In this way, the number of subpopulations is adaptively adjusted and better performing subpopulations obtain more individuals.

Double Deep Q-learning Based Real-Time Optimization Strategy for Microgrids

no code implementations • 27 Jul 2021 • Hang Shuai , Xiaomeng Ai , Jiakun Fang , Wei Yao , Senior Member , Jinyu Wen , Member , IEEE

It is challenging to solve this kind of stochastic nonlinear optimization problem.

A Novel Deep Learning Method for Thermal to Annotated Thermal-Optical Fused Images

no code implementations • 13 Jul 2021 • Suranjan Goswami , IEEE Student Member , Satish Kumar Singh , Senior Member , Bidyut B. Chaudhuri , Life Fellow , IEEE

As a part of this work, we also present a new and unique database for obtaining the region of interest in thermal images based on an existing thermal visual paired database, containing the Region of Interest on 5 different classes of data.

Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving

no code implementations • IEEE Transactions on Intelligent Transportation Systems 2021 • Liangtian Wan , Yuchen Sun , Lu Sun , Member , Zhaolong Ning , Senior Member , and Joel J. P. C. Rodrigues , Fellow , IEEE

Abstract— In this paper, a novel system architecture including a massive multi-input multi-output (MIMO) or a reconfigurable intelligent surface (RIS) and multiple autonomous vehicles is considered in vehicle location systems.

Content-Preserving Image Stitching with Piecewise Rectangular Boundary Constraints

no code implementations • IEEE Transactions on Visualization and Computer Graphics 2021 • Yun Zhang , Yu-Kun Lai , and Fang-Lue Zhang , Member , IEEE

By analyzing the irregular boundary, we construct a piecewise rectangular boundary.

Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks

no code implementations • 17 Jun 2021 • Shiyu Jiao , Ximing Xie , Zhiguo Ding , Fellow , IEEE

This paper investigates the application of deep deterministic policy gradient (DDPG) to intelligent reflecting surface (IRS) based unmanned aerial vehicles (UAV) assisted non-orthogonal multiple access (NOMA) downlink networks.

Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data

no code implementations • 29 May 2021 • Karen Montano-Martinez , Sushrut Thakar , Shanshan Ma , Zahra Soltani , Student Member , Vijay Vittal , Life Fellow , Mojdeh Khorsand , Raja Ayyanar , Senior Member , Cynthia Rojas , Member , IEEE

Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs).

Context-aware taxi dispatching at city-scale using deep reinforcement learning

no code implementations • IEEE Transactions on Intelligent Transportation Systems 2021 • Zhidan Liu , Jiangzhou Li , and Kaishun Wu , Member , IEEE

Abstract— Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among different locations in a city.

Low-Complexity Symbol Detection and Interference Cancellation for OTFS System

no code implementations • 期刊 2021 • Huiyang Qu , Guanghui Liu , Lei Zhang , Shan Wen , Graduate Student Member , and Muhammad Ali Imran , Senior Member , IEEE

Orthogonal time frequency space (OTFS) is a two-dimensional modulation scheme realized in the delay- Doppler domain, which targets the robust wireless transmissions in high-mobility environments.

Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-V ein Identification

no code implementations • IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021 • Huafeng Qin , Mounim A. El-Y acoubi , Y a n t a o L i , Member , IEEE , and Chongwen Liu

Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples.

Joint Trajectory and Power Allocation Design for Secure Artificial Noise Aided UAV Communications

no code implementations • journals 2021 • Milad Tatar Mamaghani , Graduate Student Member , and Yi Hong , Senior Member , IEEE

This paper investigates an average secrecy rate (ASR) maximization problem for an unmanned aerial vehicle (UAV) enabled wireless communication system, wherein a UAV is employed to deliver confidential information to a ground destination in the presence of a terrestrial passive eavesdropper.

A 510-nW Wake-Up Keyword-Spotting Chip Using Serial-FFT-Based MFCC and Binarized Depthwise Separable CNN in 28-nm CMOS

no code implementations • journal 2021 • Weiwei Shan , Minhao Yang , Tao Wang , Yicheng Lu , Hao Cai , Lixuan Zhu , Jiaming Xu , Chengjun Wu , Longxing Shi , Senior Member , and Jun Yang , Member , IEEE

We propose a sub-µW always-ON keyword spotting (µKWS) chip for audio wake-up systems.

Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment

no code implementations • 2 Nov 2020 • Xingyu Lei , Student Member , Zhifang Yang , Member , Junbo Zhao , Juan Yu , Senior Member , IEEE

Case studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that the proposed method is capable of accurately accounting the influence of wind curtailment dispatch in CCO.

Systems and Control Systems and Control

CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images

no code implementations • 28 Oct 2020 • QiFeng Lin , Jianhui Zhao , Gang Fu , and Zhiyong Yuan , Member , IEEE

Extensive experiments on the public Dataset for Object deTection in Aerial images data set indicate that our CRPN can help our detector deal the larger image faster with the limited GPU memory; meanwhile, the SFNet is beneficial to achieve more accurate detection of geospatial objects with wide-scale range.

Frame-wise Cross-modal Matching for Video Moment Retrieval

1 code implementation • 22 Sep 2020 • Haoyu Tang , Jihua Zhu , Meng Liu , Member , IEEE , Zan Gao , Zhiyong Cheng

Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy.

Attention Transfer Network for Nature Image Matting

1 code implementation • IEEE Transactions on Circuits and Systems for Video Technology 2020 • Fenfen Zhou , Yingjie Tian , Member , IEEE , and Zhiquan Qi

Then, we introduce a scale transfer block to magnify the feature maps without adding extra information.

A New Multiple Source Domain Adaptation Fault Diagnosis Method between Different Rotating Machines

no code implementations • TRANSACTIONS ON INDUSTRIAL INFORMA TICS 2020 • un Zhu , Nan Chen , Member , IEEE , and Changqing Shen

To solve this issue, transfer learning is proposed by leveraging knowl- edge learned from source domain to target domain.

Learning Person Re-identification Models from Videos with Weak Supervision

no code implementations • 21 Jul 2020 • Xueping Wang , Sujoy Paul , Dripta S. Raychaudhuri , Min Liu , Yaonan Wang , Amit K. Roy-Chowdhury , Fellow , IEEE

In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision.

Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

no code implementations • IEEE Transactions on Industrial Informatics 2020 • Ameer Hamza Khan , Student Member , Shuai Li , and Xin Luo , Senior Member , IEEE

In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator.

Edge server deployment scheme of blockchain in IoVs

no code implementations • 16 Jun 2020 • Liya Xu , Mingzhu Ge , Weili Wu , Member , IEEE

In fact, the application of blockchain in IoVs can be implemented by employing edge computing.

Service Provisioning Framework for RAN Slicing: User Admissibility, Slice Association and Bandwidth Allocation

no code implementations • IEEE Transactions on Mobile Computing 2020 • Yao Sun , Shuang Qin , Member , Gang Feng , Lei Zhang , and Muhammad Ali Imran , SeniorMember , IEEE

Network slicing (NS) has been identified as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversified service requirements of users.

A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion

no code implementations • 5 Jun 2020 • Chunyan Yu , Rui Han , Meiping Song , Caiyu Liu , and Chein-I Chang , Life Fellow , IEEE

Abstract—Convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image classification (HSIC).

Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs

no code implementations • 16 May 2020 • I. Dey , H. Joshi , Member , N. Marchetti , Senior Member , IEEE

In this letter, we propose space-time spreading (STS) of local sensor decisions before reporting them over a wireless multiple access channel (MAC), in order to achieve flexible balance between diversity and multiplexing gain as well as eliminate any chance of intrinsic interference inherent in MAC scenarios.

Energy-Efficient Over-the-Air Computation Scheme for Densely Deployed IoT Networks

no code implementations • IEEE 2020 • Semiha Tedik Basaran , Student Member , Gunes Karabulut Kurt , and Periklis Chatzimisios , Senior Member , IEEE

The proposed MMSE estimator provides a signif- icant mean squared error improvement with reducing en- ergy consumption compared to the conventional estimator.

A Lightweight and Privacy-Preserving Authentication Protocol for Mobile Edge Computing

no code implementations • 27 Feb 2020 • Kuljeet Kaur∗ , Sahil Garg∗ , Georges Kaddoum∗ , Member , Mohsen Guizani† , Fellow , IEEE , and Dushantha Nalin K. Jayakody‡ , Senior Member , IEEE.

With the advent of the Internet-of-Things (IoT), vehicular networks and cyber-physical systems, the need for realtime data processing and analysis has emerged as an essential pre-requite for customers’ satisfaction.

Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

1 code implementation • 24 Feb 2020 • Chongwen Huang , Member , IEEE , Ronghong Mo , Chau Yuen , Senior Member

In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL).

Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model

no code implementations • TRANSACTION 2020 • Yazhou Hu , Wenxue Wang , Hao liu , and Lianqing Liu , Member , IEEE

In this algorithm, a reward function is defined according to the features of tracking control in order to speed up the learning process, and then an RL tracking controller with a kernel-based transition dynamic model is proposed.

Broad Learning System Based on Maximum Correntropy Criterion

no code implementations • 24 Dec 2019 • Yunfei Zheng , Badong Chen , Shiyuan Wang , Senior Member , Weiqun Wang , Member , IEEE

As an effective and efficient discriminative learning method, Broad Learning System (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems.

Localization and Clustering Based on Swarm Intelligence in UAV Networks for Emergency Communications

no code implementations • IEEE Internet of Things Journal 2019 • Muhammad Yeasir Arafat , Sangman Moh , Member , IEEE

Second, we propose an energy-efficient swarm-intelligence-based clustering (SIC) algorithm based on PSO, in which the particle fitness function is exploited for inter-cluster distance, intra-cluster distance, residual energy, and geographic location.

GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection

1 code implementation • 5 May 2019 • Qi. Wang , Senior Member , Zhenghang Yuan , Qian Du , Xuelong. Li , Fellow , IEEE

In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD).

VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection

no code implementations • 5 May 2019 • Yuan Yuan , Zhitong Xiong , Student Member , Qi. Wang , Senior Member , IEEE

Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance.

Discrete-Time Impulsive Adaptive Dynamic Programming

no code implementations • IEEE Transactions on Cybernetics 2019 • Qinglai Wei , Ruizhuo Song , Member , IEEE , Zehua Liao , Benkai Li , and Frank L. Lewis

Abstract—In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal impulsive control problems for infinite horizon discrete-time nonlinear systems.

Generalization of the Dark Channel Prior for Single Image Restoration

no code implementations • IEEE Transactions on Image Processing 2019 • Yan-Tsung Peng , Keming Cao , and Pamela C. Cosman , Fellow , IEEE

Abstract— Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media.

A MIP Model for Risk Constrained Switch Placement in Distribution Networks

no code implementations • IEEE 2019 • Milad Izadi , Student Member , IEEE and Amir Safdarian , Member , IEEE

The model is applied to the RBTS-Bus4 and a real distribution network.

PEA265: Perceptual Assessment of Video Compression Artifacts

no code implementations • 1 Mar 2019 • Liqun Lin , Shiqi Yu , Tiesong Zhao , Member , Zhou Wang , Fellow , IEEE

To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs.

A Provably Secure and Efficient Identity-Based Anonymous Authentication Scheme for Mobile Edge Computing

no code implementations • 22 Feb 2019 • Xiaoying Jia，Debiao He , Neeraj Kumar , and Kim-Kwang Raymond Choo , Senior Member , IEEE

Mobile edge computing (MEC) allows one to overcome a number of limitations inherent in cloud computing, although achieving the broad range of security requirements in MEC settings remains challenging.

Location-Centered House Price Prediction: A Multi-Task Learning Approach

no code implementations • 7 Jan 2019 • Guangliang Gao , Zhifeng Bao , Jie Cao , A. K. Qin , Timos Sellis , Fellow , IEEE , Zhiang Wu

Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently.

DATS: Dispersive Stable Task Scheduling in Heterogeneous Fog Networks

no code implementations • Conference 2018 • Zening Liu , Xiumei Yang , Yang Yang , Kunlun Wang , and Guoqiang Mao , Fellow , IEEE

Abstract—Fog computing has risen as a promising architecture for future Internet of Things (IoT), 5G and embedded artificial intelligence (AI) applications with stringent service delay requirements along the cloud to things continuum.

Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks

no code implementations • IEEE INTERNET OF THINGS JOURNAL, VOL. 6, NO. 3 2018 • Jiawen Kang , Rong Y u , Xumin Huang , Maoqiang Wu , Sabita Maharjan , Member , Shengli Xie , and Y an Zhang , Senior Member , IEEE

Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i. e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources.

Optimal Training for Residual Self-Interference for Full-Duplex One-Way Relays

no code implementations • 13 Aug 2018 • Xiaofeng Li , Cihan Tepedelenlio˘glu , and Habib ¸Senol , Member , IEEE

For the former, we propose a training scheme to estimate the overall channel, and for the latter the CRB and the optimal number of relays are derived when the distance between the source and the destination is fixed.

Medical Image Synthesis with Deep Convolutional Adversarial Networks

1 code implementation • IEEE Transactions on Biomedical Engineering 2018 • Dong Nie , Roger Trullo , Jun Lian , Li Wang , Caroline Petitjean , Su Ruan , Qian Wang , and Dinggang Shen , Fellow , IEEE

To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN.

Single Image Dehazing Using Color Ellipsoid Prior

1 code implementation • IEEE Transactions on Image Processing 2018 • Trung Minh Bui , Student Member , and Wonha Kim , Senior Member , IEEE

The proposed method constructs color ellipsoids that are statistically fitted to haze pixel clusters in RGB space and then calculates the transmission values through color ellipsoid geometry.

Significantly Fast and Robust Fuzzy C-MeansClustering Algorithm Based on MorphologicalReconstruction and Membership Filtering

no code implementations • IEEE 2018 • Tao Lei , Xiaohong Jia , Yanning Zhang , Lifeng He , Hongy-ing Meng , Senior Member , and Asoke K. Nandi , Fellow , IEEE

However, the introduction oflocal spatial information often leads to a high computationalcomplexity, arising out of an iterative calculation of the distancebetween pixels within local spatial neighbors and clusteringcenters.

An Integrated Platform for Live 3D Human Reconstruction and Motion Capturing

no code implementations • 8 Dec 2017 • Dimitrios S. Alexiadis , Anargyros Chatzitofis , Nikolaos Zioulis , Olga Zoidi , Georgios Louizis , Dimitrios Zarpalas , Petros Daras , Senior Member , IEEE

The latest developments in 3D capturing, processing, and rendering provide means to unlock novel 3D application pathways.

Robust Single Image Super-Resolution via Deep Networks With Sparse Prior

1 code implementation • journals 2016 • Ding Liu , Zhaowen Wang , Bihan Wen , Student Member , Jianchao Yang , Member , Wei Han , and Thomas S. Huang , Fellow , IEEE

We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data.

A Decentralized Cooperative Control Scheme With Obstacle Avoidance for a Team of Mobile Robots

no code implementations • journal 2013 • Hamed Rezaee , Student Member , and Farzaneh Abdollahi , Member , IEEE

The problem of formation control of a team of mobile robots based on the virtual and behavioral structures is considered in this paper.

A Grid-Based Evolutionary Algorithm for Many-Objective Optimization

1 code implementation • IEEE Transactions on Evolutionary Computation 2013 • Shengxiang Yang , Member , IEEE , Miqing Li , Xiaohui Liu , and Jinhua Zheng

Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO).

Physiological Parameter Monitoring from Optical Recordings with a Mobile Phone

no code implementations • 29 Jul 2011 • Christopher G. Scully , Student Member , Jinseok Lee , Joseph Meyer , Alexander M. Gorbach , Domhnull Granquist-Fraser , Yitzhak Mendelson , Member , and Ki H. Chon , Senior Member , IEEE

We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor.

Performance Analysis of Two Hop Amplify-and-Forward Systems with Interference at the Relay

no code implementations • journal 2010 • Himal A. Suraweera , Member , HariK.Garg , and A. Nallanathan , Senior Member , IEEE

Abstract—We analyze the performance of a two hop channel state information (CSI)-assisted amplify-and-forward system, with co-channel interference at the relay.

Efficiently Indexing Large Sparse Graphs for Similarity Search

no code implementations • 18 Feb 2010 • Guoren Wang , Bin Wang , Xiaochun Yang , IEEE Computer Society , and Ge Yu , Member , IEEE

Abstract—The graph structure is a very important means to model schemaless data with complicated structures, such as protein- protein interaction networks, chemical compounds, knowledge query inferring systems, and road networks.

ANALYSIS OF CALIBRATED SEA CLUTTER AND BOAT REFLECTIVITY DATA AT C- AND X-BAND IN SOUTH AFRICAN COASTAL WATERS

no code implementations • IEEE 2007 • Ron Rubinstein , Member , Tomer Peleg , Student Member , and Michael Elad , Fellow , IEEE

Abstract—The synthesis-based sparse representation model for signals has drawn considerable interest in the past decade.

Parameter-free Geometric Document Layout Analysis

no code implementations • IEEE Transactions on Pattern Analysis and Machine Intelligence 2001 • Seong-Whan Lee , Senior Member , IEEE , and Dae-Seok Ryu

Based on the proposed periodicity measure, multiscale analysis, and confirmation procedure, we could develop a robust method for geometric document layout analysis independent of character font sizes, text line spacing, and document layout structures.

For IEEE Members

Ieee spectrum, follow ieee spectrum, support ieee spectrum, enjoy more free content and benefits by creating an account, saving articles to read later requires an ieee spectrum account, the institute content is only available for members, downloading full pdf issues is exclusive for ieee members, downloading this e-book is exclusive for ieee members, access to spectrum 's digital edition is exclusive for ieee members, following topics is a feature exclusive for ieee members, adding your response to an article requires an ieee spectrum account, create an account to access more content and features on ieee spectrum , including the ability to save articles to read later, download spectrum collections, and participate in conversations with readers and editors. for more exclusive content and features, consider joining ieee ., join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of spectrum’s articles, archives, pdf downloads, and other benefits. learn more about ieee →, join the world’s largest professional organization devoted to engineering and applied sciences and get access to this e-book plus all of ieee spectrum’s articles, archives, pdf downloads, and other benefits. learn more about ieee →, access thousands of articles — completely free, create an account and get exclusive content and features: save articles, download collections, and talk to tech insiders — all free for full access and benefits, join ieee as a paying member., data science news & articles.

Showing 14 posts that have the tag “data-science”

This Startup Is Building the Internet of Underwater Things

Wsense’s innovative networking systems are transforming how we explore ocean environments, deploying data science and ai to fight wildlife trafficking, nyu tandon’s juliana freire is leading a team aimed at using data science to bring down criminals trafficking humans and exotic animals, pfizer’s edge in the covid-19 vaccine race: data science, a year in the life of the data scientists who helped bring pfizer's covid-19 vaccine to the public in record time, engineering bias out of ai, machines that learn the worst human impulses can still relearn, robotics news in your inbox, weekly.

IEEE.org | IEEE Xplore Digital Library | IEEE Standards | IEEE Spectrum | More Sites

January 2023

Regular Papers

Volume 111, Issue 1

Scanning the Issue

Machine learning for emergency management: a survey and future outlook.

By C. Kyrkou, P. Kolios, T. Theocharides, and M. Polycarpou

This article surveys machine learning for all phases of emergency management, focusing on key characteristics and challenges, and its application across the different phases and operations.

Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review

By M. M. Hossain Shuvo, S. K. Islam, J. Cheng, and B. I. Morshed

This article provides a comprehensive review of the state-of-the-art tools and techniques for efficient edge inference, a vital element of artificial intelligence on edge.

Technology Prospects for Data-Intensive Computing

By K. Akarvardar and H.-S. P. Wong

This article advances the idea that data-intensive computing will further cement semiconductor technology as a foundational technology with multidimensional pathways for growth.

Point of View

A perspective vision of micro/nano systems and technologies as enablers of 6g, super-iot, and tactile internet.

By J. Iannacci

Scanning Our Past

The information age and naval command & control.

By D. Boslaugh, P. Marland, and J. Vardalas

• Pingback: กระเบื้องยาง SPC ราคา

Comments are closed.

International Journal of Data Science and Analytics

• Focuses on fundamental and applied research outcomes in data and analytics theories, technologies and applications.
• Promotes new scientific and technological approaches for strategic value creation in data-rich applications.
• Encourages transdisciplinary and cross-domain collaborations.
• Strives to bring together researchers, industry practitioners, and potential users of data science and analytics.
• Addresses challenges ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization.

Latest issue

Volume 18, Issue 3

Latest articles

On the utility of probabilistic closed-form proxy models for describing supercomputer network traffic data.

• Obadare O. Awoleke
• Kapil Sachdev
• Kevin A. Brown

Enhancing aspect-based sentiment analysis using data augmentation based on back-translation

• Alireza Taheri
• Azadeh Zamanifar
• Amirfarhad Farhadi

Automated recommendation model using ordinal probit regression factorization machines

• Nilufar Zaman
• Angshuman Jana

Proposal for optimizing number of servers in closed BCMP queueing network

• Shinya Mizuno
• Yuki Komiyama
• Haruka Ohba

Incremental mining algorithms for generating and updating frequent patterns for dynamic databases against insert, update, and support changes

• Sivaiah Borra
• R. Rajeswara Rao

Journal updates

Cfp: theoretical and practical data science and analytics .

Submission Deadline: 15 April 2024

Guest Editor: Fragkiskos Malliaros

CfP: Innovative Hardware and Architectures for Ubiquitous Data Science

Submission Deadline: 10 September 2023

Guest Editors: Dr. Faheem Khan, Dr. Umme Laila, Dr. Muhammad Adnan Khan.

CfP: CCF BigData conference Journal Track on ‘Data Science in China’

Cfp: learning from temporal data.

Submission Deadline: 17 November 2023

Guest Editors: João Mendes-Moreira, Joydeep Chandra, Albert Bifet

Journal information

• EI Compendex
• Emerging Sources Citation Index
• Google Scholar
• Japanese Science and Technology Agency (JST)
• OCLC WorldCat Discovery Service
• TD Net Discovery Service
• UGC-CARE List (India)

Rights and permissions

Editorial policies

© Springer Nature Switzerland AG

• Find a journal
• Publish with us
• Track your research

Fully Open Access Topical Journals

A Growing Collection of Gold Fully Open Access (OA) Options

IEEE offers more options than ever to authors with the launch of new gold fully open access journals spanning a wide range of technologies. These journals are significant additions to IEEE’s well-known and respected portfolio of fully open access journals. In addition, many of the journals featured here target an accelerated publication time frame of 10 weeks for most accepted papers to help get your research exposed faster. Visit the publication home page of each title for details.

The fully open access journals are accepting submissions. Please see each journal’s description below for more details. All of the titles are fully compliant with funder mandates including Plan S. All IEEE Open Access titles, current and new, will be hosted on the IEEE Xplore ® platform.

Call for Papers

Submit a paper to an ieee fully open access journal.

IEEE Open Journal of Antennas and Propagation

High-quality, peer reviewed research covering antennas, including analysis, design, development, measurement, standards, and testing; radiation, propagation, and the interaction of electromagnetic waves with discrete and continuous media.

This fully open access journal publishes high-quality, peer reviewed papers covering antennas, including analysis, design, development, measurement, standards, and testing; radiation, propagation, and the interaction of electromagnetic waves with discrete and continuous media; and applications and systems pertinent to antennas, propagation, and sensing, such as applied optics, millimeter-and sub-millimeter-wave techniques, antenna signal processing and control, radio astronomy, and propagation and radiation aspects of terrestrial and space-based communication, including wireless, mobile, satellite, and telecommunications at all frequencies. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Konstantina (Nantia) Nikita Professor National Technical University of Athens, Greece

Learn More and Submit a Paper

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Addresses the growing field of applications in Earth observations and remote sensing and provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society.

The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. Papers should address current issues and techniques in applied remote and in situ sensing, their integration, and applied modeling and information creation for understanding the Earth. Applications are for the Earth, oceans and atmosphere. Topics can include observations, derived information such as forecast data, simulated information, data assimilation and Earth information techniques to address science and engineering issues of the Earth system. The technical content of papers must be both new and significant.

IEEE Open Journal of Circuits and Systems

Featuring high-quality peer reviewed research covering the theory, analysis, design, tools, and implementation of circuits and systems.

This fully open access journal publishes high-quality, peer-reviewed papers covering the theory, analysis, design, tools, and implementation of circuits and systems. This includes their theoretical foundations, applications, and architectures, as well as circuits and systems implementation of algorithms for signal and information processing. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Gabriele Manganaro, Ph.D., FIEEE Technology Director Analog Devices, Inc., USA

IEEE Open Journal of the Communications Society

Featuring high-quality peer reviewed research covering science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks.

As a fully open access journal publishing high-quality peer reviewed papers,  IEEE Open Journal of the Communications Society  covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks, including but not limited to: Systems and network architecture, control and management; Protocols, software and middleware; Quality of service, reliability and security; Modulation, detection, coding, and signaling; Switching and routing; Mobile and portable communications; Terminals and other end-user devices; Networks for content distribution and distributed computing; and Communications-based distributed resources control. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Octavia A. Dobre, Dipl.-Ing., Ph.D. Professor and Research Chair Memorial University, Canada

IEEE Open Journal of the Computer Society

Forum for rapid publication of open access articles describing high-impact results in all aspects of theory, design, practice, and application relating to computer and information processing science and technology.

The IEEE Open Journal of the Computer Society (OJ-CS) is a rigorously peer-reviewed forum for rapid publication of open access articles describing high-impact results in all areas of interest to the IEEE Computer Society. This new fully open access journal complements existing IEEE Computer Society publications by providing a rapid review cycle and a thorough review of technical articles. It is dedicated to publishing articles on the latest emerging topics and trends in all aspects of computing with a scope that encompasses all aspects of theory, design, practice, and application relating to computer and information processing science and technology. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Dr. Song Guo Department of Computing The Hong Kong Polytechnic University

IEEE Open Journal of Control Systems

Publication of the IEEE Control Systems Society, this journal aims to publish high-quality papers on the theory, design, optimization, and applications of dynamic systems and control.

The IEEE Open Journal of Control Systems covers the theory, design, optimization, and applications of dynamic systems and control. The field integrates elements of sensing, communication, decision and actuation components, as relevant for the analysis, design and operation of dynamic systems and control. The systems considered include: technological, physical, biological, economic, organizational and other entities, and combinations thereof. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Sonia Martínez University of California, San Diego United States

IEEE Data Descriptions

Now Accepting Submissions! This new publication is a peer-reviewed journal that publishes short articles on all aspects of data: data descriptors, data collections, and metadata.

IEEE Data Descriptions is a peer-reviewed journal that publishes short articles on all aspects of data: data descriptors, data collections, and metadata. Its overarching purpose is to promote publicly available datasets (open access or subscription-based access) in support of reproducible science while at the same time bringing insights into the associated dataset, data collection methods, and data quality. The metadata collected provides enhanced dataset discoverability and creates a foundation for future data science tools such as auto-discovery and mashups.

Datasets described in IEEE Data Descriptions must be findable, accessible, interoperable, and reusable. The dataset needs to be of a quality high enough that other researchers can use it for their research experimentation and have some permanence. Articles describing datasets must be comprehensive and follow the outlined sections listed in Author Information. The preference is for data to be stored within IEEE DataPort, however, IEEE Data Descriptions accepts submissions where data is stored at other persistent/permanent locations.

Editor-in-Chief: Stephen Makonin Simon Fraser University Vancouver, Canada

IEEE Journal of the Electron Devices Society

Featuring high quality research in the field of electron and ion devices ranging from fundamentals to applied research.

Featuring high-quality research in the field of electron and ion devices ranging from fundamentals to applied research, this journal provides authors an affordable outlet for rapid publishing and universal access, coupled with superior technical quality.

IEEE Open Journal of Engineering in Medicine and Biology

High-quality research covering the development and application of engineering concepts and methods to biology, medicine and health sciences.

As a fully open access journal publishing high-quality peer reviewed papers, IEEE Open Journal of Engineering in Medicine and Biology covers the development and application of engineering concepts and methods to biology, medicine and health sciences to provide effective solutions to biological, medical and healthcare problems. It encompasses the development of mathematical theories, physical, biological and chemical principles, computational models and algorithms, devices and systems for clinical, industrial and educational applications. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Paolo Bonato Associate Professor Harvard University, USA

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits

Multi-disciplinary research in solid-state circuits using exploratory materials and devices for novel energy efficient computation beyond standard CMOS (Complementary Metal Oxide Semiconductor) technology.

Multi-disciplinary research in solid-state circuits using exploratory materials and devices for novel energy efficient computation beyond standard CMOS (Complementary Metal Oxide Semiconductor) technology. Focus is on the exploration of materials, devices and computation circuits to enable Moore’s Law to continue for computation beyond a 10 to 15 year horizon (beyond end of the roadmap for CMOS technologies) with the associated density scaling and improvement in energy efficiency.

IEEE Open Journal on Immersive Displays

Now Accepting Submissions! New publication will be home to publications in display science and applications.

The IEEE Open Journal on Immersive Displays (OJID) will be home to publications in display science and applications. The field of displays is diverse, ranging from the science and engineering of materials and devices to their application in high definition, form-factor-independent displays featuring interactivity, virtual and augmented reality, and 3D content. Submissions on advanced fabrication processing, thin film active and passive devices, and lifetime and reliability evaluation are welcome when display is the focus or where there is a direct relationship to the nature of the display system. Tutorial and review papers extending the frontiers of immersive display technologies and novel applications are also published.

Editor-in-Chief: Arokia Nathan University of Cambridge Hertfordshire, U.K.

IEEE Journal of Indoor and Seamless Positioning and Navigation

Publishes original research in the fields of localization and tracking of people, robots, and objects.

IEEE Journal of Indoor and Seamless Positioning and Navigation (J-ISPIN) publishes original research in the fields of localization and tracking of people, robots, and objects. It covers all aspects of localization systems, including sensing, communications, location-based services, mapping, protocols, human interfaces and standards. The scope includes methods and systems addressing indoor environments as well as those enabling seamless transition between heterogeneous indoor contexts or between indoor and outdoor environments, for example where Global Navigation Satellites Systems are underperforming or unavailable.

Editor-in-Chief: Valérie Renaudin Senior Researcher University Gustave Eiffel, France

IEEE Open Journal of the Industrial Electronics Society

Featuring high quality research covering the theory and applications of electronics, controls, communications, instrumentation and computational intelligence to industrial and manufacturing systems and processes.

This fully open access journal publishes high-quality, peer-reviewed papers covering the theory and applications of electronics, controls, communications, instrumentation and computational intelligence to industrial and manufacturing systems and processes. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Dr. Leopoldo Garcia Franquelo Professor, Electronics Engineering Universidad de Sevilla, Spain

IEEE Open Journal of Industry Applications

Covering the advancement of the theory and practice of electrical and electronic engineering in the development, design, manufacture and application of electrical systems, apparatus, devices, and controls to the processes and equipment of industry and commerce.

As a fully open access journal publishing high-quality peer reviewed papers, IEEE Open Journal of Industry Applications covers the advancement of the theory and practice of electrical and electronic engineering in the development, design, manufacture and application of electrical systems, apparatus, devices, and controls to the processes and equipment of industry and commerce; the promotion of safe, reliable, and economic installations; industry leadership in energy conservation and environmental, health, and safety issues; the creation of voluntary engineering standards and recommended practices; and the professional development of its readers. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Professor Pericle Zanchetta Fellow IEEE Faculty of Engineering University of Nottingham, UK

IEEE Open Journal of Instrumentation and Measurement

Publication of the Instrumentation and Measurement Society, this journal publishes papers on the science, technology, and application of instrumentation and measurement.

The IEEE Open Journal of Instrumentation and Measurement publishes papers on the science, technology, and application of instrumentation and measurement. Instrumentation and measurement, in the current context of the IEEE IMS community, consists of methods, instruments, systems, and applications for measurement, detection, tracking, monitoring, characterization, identification, sensing, estimation, recognition, or diagnosis of a physical phenomenon; or metrology and measurement theory including measurement uncertainty, instrument precision, calibration, etc.

Editor-in-Chief: Shervin Shirmohammadi University of Ottawa Canada

IEEE Open Journal of Intelligent Transportation Systems

Featuring high-quality research covering the theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS).

As a fully open access journal publishing high-quality peer reviewed papers, IEEE Open Journal of Intelligent Transportation Systems covers theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS), defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Dr. Bart van Arem Full Professor of Transport Modelling Delft University of Technology, The Netherlands

IEEE Transactions on Machine Learning in Communications and Networking

Featuring high-quality manuscripts on advances in machine learning methods for and applications to communications and networking.

The IEEE Transactions on Machine Learning in Communications and Networking publishes high-quality manuscripts on advances in machine learning methods for and applications to communications and networking. Furthermore, articles developing novel communication and networking techniques for distributed machine learning algorithms are of interest. Both theoretical contributions (including new theories, techniques, concepts, algorithms, and analyses) and practical contributions (including system experiments, prototypes, and new applications) are encouraged.

Editor-in-Chief: Walid Saad Professor Virginia Tech Research Center – Arlington, USA

IEEE Journal of Microwaves

Covering articles on the theory, techniques and applications of guided wave and wireless technologies and spanning the electromagnetic spectrum from RF/microwave through millimeter-waves and terahertz.

The IEEE Journal of Microwaves is a fully open access publication covering the complete scope of the Microwave Theory and Techniques Society which includes articles on the theory, techniques and applications of guided wave and wireless technologies and spanning the electromagnetic spectrum from RF/microwave through millimeter-waves and terahertz, covering the aspects of materials, components, devices, circuits, modules, and systems which involve the generation, modulation, demodulation, control, transmission, sensing and effects of electromagnetic signals.

Editor-in-Chief: Peter H. Siege THz Global, NASA Jet Propulsion Laboratory, California Institute of Technology Pasadena, California

IEEE Open Journal of Nanotechnology

Featuring high-quality, peer reviewed research covering the theory, design, and development of nanotechnology and its scientific, engineering, and industrial applications.

As a fully open access journal publishing high-quality peer reviewed papers, IEEE Open Journal of Nanotechnology covers the theory, design, and development of nanotechnology and its scientific, engineering, and industrial applications. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Co-Editors-in-Chief: Professor Wen J. Li Chair Professor of Biomedical Engineering Associate Provost City University of Hong Kong, Hong Kong

Professor Jin-Woo Kim Professor of Biological Engineering and Nanoscience & Engineering University of Arkansas, USA

Professor Seiji Samukawa Director of Innovative Energy Research Center, Institute of Fluid Science (IFS) Principal Investigator of Advance Institute for Materials Research (AIMR) Tohoku University, Japan

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Covering the rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement, and more.

Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.

IEEE Photonics Journal

Dedicated to the rapid disclosure of research at the forefront of all areas of photonics and addressing issues ranging from fundamental understanding to emerging technologies.

Breakthroughs in the generation of light and its control and utilization have given rise to the field of Photonics: a rapidly expanding area of science and technology with major technological and economic impact. IEEE Photonics Journal is an online-only journal dedicated to the rapid disclosure of top-quality peer-reviewed research at the forefront of all areas of photonics. Contributions addressing issues ranging from fundamental understanding to emerging technologies and applications are within the scope of the Journal.

IEEE Open Access Journal of Power and Energy

High-quality, peer reviewed research covering the development, planning, design, construction, maintenance, installation, and operation of equipment, structures, power systems and usage of electric energy, including its measurement and control.

As a fully open access journal publishing high-quality peer reviewed papers, the IEEE Open Access Journal of Power and Energy publishes articles focused on the development, planning, design, construction, maintenance, installation, and operation of equipment, structures, and power systems for the safe, sustainable, economic, and reliable conversion, generation, transmission, distribution, storage, and usage of electric energy, including its measurement and control. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Fangxing “Fran” Li The University of Tennessee Knoxville, TN 37996 USA [email protected]

IEEE Open Journal of Power Electronics

Covering the development and application of power electronic systems and technologies, which encompass the effective use of electronic components, the application of circuit theory and design techniques and the development of analytical methods and tools.

The IEEE Open Journal of Power Electronics covers the development and application of power electronic systems and technologies, which encompass the effective use of electronic components, the application of circuit theory and design techniques and the development of analytical methods and tools toward efficient electronic conversion, control and conditioning of electric power to enable the sustainable use of energy. As a fully open access journal publishing high-quality peer reviewed papers, the Society’s aim is to publish novel developments as well as tutorial and survey articles including those of value to both the R&D and practicing professionals in the field. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Alan Mantooth, Ph.D., P.E., FIEEE Distinguished Professor of Electrical Engineering University of Arkansas, USA

IEEE Transactions on Privacy

Now Accepting Submissions! New publication will provide a multidisciplinary forum for theoretical, methodological, engineering, and applications aspects of privacy and data protection, including specification, design, implementation, testing, and validation.

The IEEE Transactions on Privacy provides a multidisciplinary forum for theoretical, methodological, engineering, and applications aspects of privacy and data protection, including specification, design, implementation, testing, and validation. Privacy, in this context, is defined as the freedom from unauthorized intrusion in its broadest sense, arising from any activity in information collection, information processing, information dissemination or invasion. The transactions publishes articles reporting significant advances in theoretical models and formalization as well as engineering tools supporting the above activities, design frameworks and languages, architectures, infrastructures, model-based approaches, study cases, and standards.

IEEE Transactions on Quantum Engineering

Publishing regular, review, and tutorial articles based on the engineering applications of quantum phenomena, including quantum computation, information, communication, software, hardware, devices, and metrology.

Publishes regular, review, and tutorial articles based on the engineering applications of quantum phenomena, including quantum computation, information, communication, software, hardware, devices, and metrology. Articles also address quantum-engineering aspects of superconductivity, magnetics, microwave techniques, photonics, and signal processing.

IEEE Journal of Selected Areas in Sensors

Now Accepting Submissions! New publication of the IEEE Sensors Council, this journal publishes papers in all areas of the field of interest of the IEEE Sensors Council.

The IEEE Journal of Selected Areas in Sensors publishes papers in all areas of the field of interest of the IEEE Sensors Council, i.e., the theory, design, simulation, fabrication, manufacturing and application of devices for sensing and transducing physical, chemical, and biological phenomena, with emphasis on the electronics, physics and reliability aspects of sensors and integrated sensor-actuators. The Journal is built exclusively from papers on selected topics of current interest to the Sensors community.

Editor-in-Chief: Chonggang Wang InterDigital, Inc. USA

IEEE Open Journal of Signal Processing

High-quality, peer reviewed research covering the enabling technology for the generation, transformation, extraction, and interpretation of information.

This fully open access journal publishes high-quality, peer-reviewed papers covering the enabling technology for the generation, transformation, extraction, and interpretation of information. It comprises the theory, algorithms with associated architectures and implementations, and applications related to processing information contained in many different formats broadly designated as signals. Signal processing uses mathematical, statistical, computational, heuristic, and/or linguistic representations, formalisms, modeling techniques and algorithms for generating, transforming, transmitting, and learning from signals. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Brendt Wohlberg Los Alamos National Laboratory, USA

IEEE Open Journal of the Solid-State Circuits Society

High-quality, peer reviewed research covering the design, implementation, and application of solid-state integrated circuits.

As a fully open access journal publishing high-quality peer reviewed papers, IEEE Open Journal of the Solid-State Circuits Society covers design, implementation and application of solid-state integrated circuits. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Jan Craninckx Distinguished Member of Technical Staff IMEC, Belgium

IEEE Open Journal of Systems Engineering

Provides a forum for practitioners, scientists, academics, and researchers engaged in the discipline of Systems Engineering.

The IEEE Open Journal of Systems Engineering (OJSE) is an open access journal that is sponsored by the consortium of IEEE Aerospace and Electronic Systems Society, IEEE Systems, Man, and Cybernetics Society, and the IEEE Systems Council. OJSE provides a forum for practitioners, scientists, academics, and researchers engaged in the discipline of Systems Engineering. Multidisciplinary aspects of systems engineering is a focus of this journal. Methodologies, tools, principles, and applied engineering aspects of the process of systems engineering for complex systems are of interest. The methodologies, tools, and principles include such elements as model-based systems engineering; digital thread; requirements generation, flowdown, tracking, needs analysis, validation/verification; integration and test; and full life cycle of the target system. OJSE deals primarily with the science, methodology, and tools of systems engineering, rather than the results of the application of systems engineering that is the focus of other IEEE journals.

Editor-in-Chief: W. Dale Blair Principal Research Engineer Georgia Tech Research Institute, USA

IEEE Systems, Man, and Cybernetics Letters

Now Accepting Submissions! New publication of the IEEE Systems, Man, and Cybernetics Society (SMC), this journal covers the ultimate aims and key strategies of the SMC society towards the next generation of symbiotic human and machine intelligence systems.

IEEE Systems, Man, and Cybernetics Letter (SMC-L) will cover the ultimate aims and key strategies of the SMC society towards the next generation of symbiotic human and machine intelligence systems. The feature of SMC-L is highlighted by its rapid publication of peer-reviewed short articles within 5 pages, which provide a timely and concise account of innovative research ideas, novel application results, and significant theoretical findings, as well as analyses of emerging trends and groundbreakingly work in SMC fields. SMC-L will provide a new means for members and readers to complement established SMC transactions.

Editor-in-Chief: Prof. Yingxu Wang Editor-In-Chief Univ. of Calgary, Canada

IEEE Journal of Translational Engineering in Health and Medicine

Bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance.

This journal bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. Its aim is to provide a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. The journal provides an active forum for clinical research and relevant state-of-the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies.

IEEE Open Journal of Ultrasonics, Ferroelectrics, and Frequency Control

Covering high-quality, peer reviewed research theory, technology, materials, and applications relating to the generation, transmission, and detection of ultrasonic waves and related phenomena.

OJ-UFFC covers theory, technology, materials, and applications relating to: the generation, transmission, and detection of ultrasonic waves and related phenomena; medical ultrasound, and associated technologies; ferroelectric, piezoelectric, and piezomagnetic materials; frequency generation and control, timing, and time coordination and distribution. This interest ranges from fundamental studies to the design and/or applications of devices, sensors, systems and manufacturing technologies within the general scope defined above. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Steven Freear University of Leeds, School of Electronic and Electrical Engineering Leeds, United Kingdom

IEEE Open Journal of Vehicular Technology

Featuring high-quality, peer reviewed research on the theoretical, experimental and operational aspects of electrical and electronics engineering in mobile radio, motor vehicles and land transportation.

This fully open access journal publishes high-quality, peer-reviewed papers covering the theoretical, experimental and operational aspects of electrical and electronics engineering in mobile radio, motor vehicles and land transportation. (a) Mobile radio shall include all terrestrial mobile services. (b) Motor vehicles shall include the components and systems and motive power for propulsion and auxiliary functions. (c) Land transportation shall include the components and systems used in both automated and non-automated facets of ground transport technology. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Dr. Sumei Sun Fellow of the IEEE Principal Scientist, Institute for Infocomm Research Adjunct Professor, National University of Singapore, Singapore

Sign up for IEEE open access news

Sign Up Now

Institutions

Learn about open access options

Answers to common questions on open access

Society Members

APC discounts now available

• Latest News

• Cryptocurrencies
• White Papers

Top 10 Must-Read Data Science Research Papers in 2022

Data Science plays a vital role in many sectors such as small businesses, software companies, and the list goes on. Data Science understands customer preferences, demographics, automation, risk management, and many other valuable insights. Data Science can analyze and aggregate industry data. It has a frequency and real-time nature of data collection.

There are many data science enthusiasts out there who are totally into Data Science. The sad part is that they couldn't follow up with the latest research papers of Data Science. Here, Analytics Insight brings you the latest Data Science Research Papers. These research papers consist of different data science topics including the present fast passed technologies such as AI, ML, Coding, and many others. Data Science plays a very major role in applying AI, ML, and Coding. With the help of data science, we can improve our applications in various sectors. Here are the Data Science Research Papers in 2024

10DATA SCIENTISTS THAT TECH ENTHUSIASTS CAN FOLLOW ON LINKEDIN

ARE YOU A JOB SEEKER? KNOW THE IMPACT OF AI AND DATA SCIENCE

TOP 10 PYTHON + DATA SCIENCE COURSES YOU SHOULD TAKE UP IN 2022  

The Research Papers Includes

Documentation matters: human-centered ai system to assist data science code documentation in computational notebooks.

The research paper is written by April Yi Wang, Dakuo Wang, Jaimie Drozda, Michael Muller, Soya Park, Justin D. Weisz, Xuye Lui, Lingfei Wu, Casey Dugan.

This research paper is all about AMC transactions on Computer-Human Interaction. This is a combination of code and documentation. In this research paper, the researchers have Themisto an automated documentation generation system. This explores how human-centered AI systems can support data scientists in Machine Learning code documentation.

Assessing the effects of fuel energy consumption, foreign direct investment and GDP on CO2 emission: New data science evidence from Europe & Central Asia

The research paper is written by- Muhammad Mohsin, SobiaNaseem, Muddassar Sarfraz Tamoor, Azam

This research paper deals with how bad the effects of fuel consumption are and how data science is playing a vital role in extracting such huge information.

Impact on Stock Market across Covid-19 Outbreak

The research paper is written by-CharmiGotecha

This paper analyses the impacts of a pandemic from 2019-2022 and how it has affected the world with the help of data science tools. It also talks about how data science played a major role in recovering the world from covid losses.

Exploring the political pulse of a country using data science tools

The research paper is written by Miguel G. Folgado, Veronica Sanz

This paper deals with how data science tools/techniques are used to analyses complex human communication. This study paper is an example of how Twitter data and different types of data science tools for political analysis.

Situating Data Science

The research paper is written by-Michelle HodaWilkerson, Joseph L. Polman

This research paper gives detailed information about regulating procurement understanding the ends and means of public procurement regulation.

VeridicalFlow: a Python package for building trustworthy data science pipelines with PCS

The research paper is written by- James Duncan, RushKapoor, Abhineet Agarwal, Chandan Singh, Bin Yu

This research paper is more of a journal of open-source software than a study paper. It deals with the open-source software that is the programs available in the systems that are related to data science.

From AI ethics principles to data science practice: a reflection and a gap analysis based on recent frameworks and practical experience

The research paper is written by-IlinaGeorgieva, ClaudioLazo, Tjerk Timan, Anne Fleur van Veenstra

This study paper deals with the field of AI ethics, its frameworks, evaluation, and much more. This paper contributes ethical AI by mapping AI ethical principles onto the lifestyle of artificial intelligence -based digital services or products to investigate their applicability for the practice of data science.

Building an Effective Data Science Practice

The research paper is written by Vineet Raina, Srinath Krishnamurthy

This paper is a complete guide for an effective data science practice. It gives an idea about how the data science team can be helpful and how productive they can be.

Detection of Road Traffic Anomalies Based on Computational Data Science

The research paper is written by Jamal Raiyn

This research paper gives an idea about autonomous vehicles will have control over every function and how data science will be part of taking full control over all the functions. Also, to manage large amounts of data collected from traffic in various formats, a Computational Data Science approach is proposed by the researchers.

Data Science Data Governance [AI Ethics]

The research paper is written by Joshua A. Kroll

This paper analyses and gives brief yet complete information about the best practices opted by organizations to manage their data which encompass the full range of responsibilities borne by the use of data in automated decision making, including data security, privacy, avoidance of undue discrimination, accountability, and transparency.

Disclaimer: Analytics Insight does not provide financial advice or guidance. Also note that the cryptocurrencies mentioned/listed on the website could potentially be scams, i.e. designed to induce you to invest financial resources that may be lost forever and not be recoverable once investments are made. You are responsible for conducting your own research (DYOR) before making any investments. Read more here.

Related Stories

50+ IEEE Projects For CSE [Updated 2024]

In the dynamic realm of Computer Science Engineering (CSE), staying updated about the latest developments is essential for students to thrive in their academic and professional journeys. One key avenue for this exploration is engaging in IEEE (Institute of Electrical and Electronics Engineers) projects tailored for CSE students. This blog aims to provide a comprehensive guide on IEEE projects for CSE, helping students understand the importance of choosing the right projects and navigating the complexities of implementation.

What are IEEE Standards?

Table of Contents

IEEE, as a globally recognized authority in technology standards, plays a pivotal role in shaping the landscape of CSE projects. Its standards not only ensure the quality of projects but also contribute to the seamless integration of technological advancements.

By adhering to IEEE standards, CSE students can enhance the credibility and reliability of their projects, making them valuable assets in the academic and professional spheres.

How to Select the Right IEEE Project?

Selecting the right IEEE project is a critical step in a student’s academic and professional journey. Here’s a step-by-step guide to help you navigate this process:

• Identify Your Interests and Strengths:
• Consider your passions within the vast field of Computer Science.
• Assess your skills and strengths to determine areas where you excel.
• Stay Updated on Industry Trends:
• Keep abreast of current trends and emerging technologies in Computer Science Engineering.
• Choose a project that aligns with the latest advancements in the industry.
• Evaluate Project Relevance:
• Assess the practicality and relevance of potential projects in real-world scenarios.
• Opt for projects that address current challenges or contribute to industry needs.
• Understand Project Scope and Complexity:
• Gauge the complexity of projects and ensure it aligns with your skill level.
• Consider the time and resources required to complete the project successfully.
• Explore IEEE Project Databases:

Utilize IEEE databases and resources to explore a variety of project options.

Narrow down projects that match your interests and align with your academic goals.

• Consult with Mentors and Peers:
• Seek guidance from professors, mentors, or peers who can provide valuable insights.
• Discuss your interests and goals to receive recommendations tailored to your profile.
• Consider Personal and Academic Goals:
• Align the chosen project with your academic objectives and career aspirations.
• Ensure the project contributes to your skill development and overall growth.
• Evaluate Resource Availability:
• Assess the availability of resources, including hardware, software, and expertise.
• Choose a project that can be feasibly implemented with the resources at your disposal.
• Assess Project Impact:
• Consider the potential impact of the project on your academic and professional portfolio.
• Choose projects that showcase your abilities and contribute meaningfully to your field.
• Plan for Continuous Learning:
• Opt for projects that offer opportunities for continuous learning and skill enhancement.
• Embrace challenges that push you to expand your knowledge and capabilities.

Remember, the right IEEE project for CSE students should align with their interests, match their skill level, contribute to their academic and career goals, and be feasible within the available resources. By following these steps, you can make an informed decision and embark on a rewarding project journey.

50+ IEEE Projects for CSE [Category Wise]

Machine learning and ai projects.

• Image Recognition using Convolutional Neural Networks (CNN)
• Natural Language Processing (NLP) for Sentiment Analysis
• Predictive Analytics for Stock Market Trends
• Autonomous Vehicle Navigation using Reinforcement Learning

Data Science and Big Data Projects

• Predictive Analytics for Disease Outbreaks
• Fraud Detection in Financial Transactions
• Social Media Analytics for User Behavior Prediction
• Large-scale Data Processing with Hadoop and Spark

Cyber Security Projects

• Intrusion Detection System using Machine Learning
• Blockchain-Based Secure Healthcare Records
• Biometric Authentication Systems
• Network Security Monitoring and Analysis

Internet of Things (IoT) Projects

• Smart Home Automation System
• Industrial IoT for Predictive Maintenance
• Healthcare Monitoring using IoT Devices
• Energy Management in Smart Cities

Cloud Computing Projects

• Cloud-Based E-Learning System
• Resource Allocation in Cloud Computing
• Cloud Security and Encryption
• IoT Integration with Cloud Services

Blockchain Projects

• Supply Chain Transparency using Blockchain
• Decentralized Voting System
• Blockchain-Based Identity Management
• Smart Contracts for Legal Processes

Mobile App Development Projects

• Health and Fitness Tracking App
• Augmented Reality (AR) Gaming Application
• Location-Based Services for Tourism
• Secure Messaging App with End-to-End Encryption

Computer Vision Projects

• Human Activity Recognition using Computer Vision
• Object Detection and Tracking in Video Streams
• Facial Recognition for Access Control
• Augmented Reality Applications

Web Development Projects

• Content Recommendation System for Websites
• E-Commerce Platform with Personalized Shopping
• Online Learning Management System
• Social Networking Platform with Advanced Features

Networking Projects

• Software-Defined Networking (SDN) for Improved Network Management
• Quality of Service (QoS) in Wireless Networks
• IoT Communication Protocols
• Network Function Virtualization (NFV) Implementation

Edge Computing Projects

• Real-time Video Analytics at the Edge
• Edge-based Health Monitoring for Remote Areas
• Intelligent Traffic Management using Edge Devices
• Edge Computing for IoT Security

Biomedical Engineering Projects

• Wearable Devices for Continuous Health Monitoring
• Computer-Aided Diagnosis System for Medical Images
• Brain-Computer Interface for Assistive Technology
• Predictive Modeling for Disease Outbreaks in Healthcare

Human-Computer Interaction (HCI) Projects

• Gesture Recognition System for Human-Computer Interaction
• Voice User Interface (VUI) for Smart Assistants
• Augmented Reality (AR) for Enhancing User Experience
• Accessibility Features for Software Applications

Methodology for Implementing IEEE Projects

Implementing IEEE projects in Computer Science Engineering involves a systematic methodology to ensure successful execution. Below is a step-by-step guide that outlines the key phases and considerations in the implementation process:

• Project Selection and Definition:
• Define Clear Objectives: Clearly outline the goals and objectives of the project.
• Choose a Methodology: Select a development methodology (e.g., Waterfall, Agile) based on the project’s nature.
• Literature Review and Research:
• Review Existing Work: Explore relevant literature and existing projects in the chosen domain.
• Identify Gaps and Challenges: Determine gaps in current research and challenges that the project aims to address.
• Requirement Analysis:
• Define User Requirements: Gather and document user requirements comprehensively.
• Create a Functional Specification: Develop a detailed specification document outlining the system’s functionalities.
• Design Phase:
• Architectural Design: Create a high-level architecture and design the system’s structure.
• Detailed Design: Develop detailed designs for each module or component of the project.
• Development:
• Coding: Write code according to the design specifications.
• Use Version Control: Implement version control systems (e.g., Git) to manage code changes.
• Unit Testing: Test individual components to ensure they function as intended.
• Integration Testing: Verify that components work seamlessly together.
• System Testing: Evaluate the system as a whole against defined requirements.
• Documentation:
• Technical Documentation: Create detailed documentation for code, algorithms, and system architecture.
• User Documentation: Develop user manuals and guides for easy system understanding.
• Deployment:
• Prepare for Deployment: Ensure all dependencies are met and system requirements are fulfilled.
• Deploy in Staging Environment: Test the project in a controlled environment before deployment to production.
• Evaluation and Validation:
• User Acceptance Testing (UAT): Have end-users validate the system against their requirements.
• Performance Testing: Evaluate the system’s performance under various conditions.
• Feedback and Iteration:
• Gather Feedback: Collect feedback from users, stakeholders, and testing teams.
• Iterate and Improve: Implement necessary changes based on feedback to enhance the project.
• Final Documentation and Presentation:
• Compile Final Documentation: Update documentation to reflect the final state of the project.
• Prepare for Presentation: Create presentations summarizing the project’s objectives, methodology, and outcomes.
• Knowledge Transfer and Maintenance:
• Knowledge Sharing: Conduct knowledge transfer sessions to share insights with team members or successors.
• Maintenance Plan: Develop a plan for ongoing maintenance and updates, if necessary.
• Publication and Dissemination (Optional):
• Prepare Research Papers: If applicable, document the research findings for publication.
• Present at Conferences: Share project outcomes at relevant conferences or forums.
• Reflect and Learn:
• Post-Implementation Review: Conduct a post-implementation review to identify lessons learned.
• Reflect on Challenges: Assess challenges faced during implementation for future improvement.

By following this comprehensive methodology, you can streamline the implementation process of IEEE projects, ensuring a structured and successful outcome.

Each phase is crucial, and attention to detail in planning, development, testing, and documentation is key to the project’s overall success.

Challenges and Solutions

Embarking on an IEEE project journey is not without its challenges. This section identifies common obstacles that students may encounter during the execution of their projects and offers strategies to overcome them.

Real-life examples of successful project execution serve as inspirations, demonstrating that challenges can be surmounted with perseverance, creativity, and strategic problem-solving.

In conclusion, navigating the world of IEEE projects for CSE offers students a pathway to not only enhance their academic knowledge but also to contribute meaningfully to the ever-evolving field of technology.

By understanding IEEE standards, choosing the right projects, overcoming challenges, and embracing the benefits, students can position themselves as leaders in the dynamic and exciting realm of Computer Science Engineering. 

The future holds limitless possibilities, and IEEE projects serve as a gateway to unlocking the potential of aspiring CSE professionals.

Related Posts

Step by Step Guide on The Best Way to Finance Car

The Best Way on How to Get Fund For Business to Grow it Efficiently

DATA SCIENCE IEEE PAPERS AND PROJECTS-2020

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is related to data mining and big data.

FREE IEEE PAPER AND PROJECTS

Ieee projects 2022, seminar reports, free ieee projects ieee papers.

IEEE 2024-2025 : Data Science Projects

For Outstation Students, we are having online project classes both technical and coding using net-meeting software

For details, call: 9886692401/9845166723.

DHS Informatics  providing  latest 2024-2025 IEEE projects  on Data science for the final year engineering students. DHS Informatics trains all students to develop their project with good idea what they need to submit in college to get good marks. DHS Informatics offers placement training in Bangalore and the program name is  OJT  –  On Job Training , job seekers as well as final year college students can join in this placement training program and job opportunities in their dream IT companies. We are providing IEEE projects for B.E / B.TECH, M.TECH, MCA, BCA, DIPLOMA students from more than two decades.

Python  Final year CSE projects in Bangalore

• Python 2024 – 2025 IEEE PYTHON PROJECTS CSE | ECE | ISE
• Python 2024 – 2025 IEEE PYTHON MACHINE LEARNING PROJECTS
• Python 2024 – 2025 IEEE PYTHON IMAGE PROCESSING PROJECTS
• Python 2024 – 2025 IEEE IOT PYTHON RASPBERRY PI PROJECTS

DATA SCIENCE PROJECTS

A data mining based model for detection of fraudulent behaviour in water consumption.

Abstract:  Fraudulent behavior in drinking water consumption is a significant problem facing water supplying companies and agencies. This behavior results in a massive loss of income and forms the highest percentage of non-technical loss. Finding efficient measurements for detecting fraudulent activities has been an active research area in recent years. Intelligent data mining techniques can help water supplying companies to detect these fraudulent activities to reduce such losses. This research explores the use of two classification techniques (SVM and KNN) to detect suspicious fraud water customers. The main motivation of this research is to assist Yarmouk Water Company (YWC) in Irbid city of Jordan to overcome its profit loss. The SVM based approach uses customer load profile attributes to expose abnormal behavior that is known to be correlated with non-technical loss activities. The data has been collected from the historical data of the company billing system. The accuracy of the generated model hit a rate of over 74% which is better than the current manual prediction procedures taken by the YWC. To deploy the model, a decision tool has been built using the generated model. The system will help the company to predict suspicious water customers to be inspected on site.                                                                                                                                                                                                                                   

Correlated Matrix Factorization for Recommendation with Implicit Feedback

Abstract:  As a typical latent factor model, Matrix Factorization (MF) has demonstrated its great effectiveness in recommender systems. Users and items are represented in a shared low-dimensional space so that the user preference can be modeled by linearly combining the item factor vector V using the user-specific coefficients U. From a generative model perspective, U and V are drawn from two independent Gaussian distributions, which is not so faithful to the reality. Items are produced to maximally meet users’ requirements, which makes U and V strongly correlated. Meanwhile, the linear combination between U and V forces a bisection (one-to-one mapping), which thereby neglects the mutual correlation between the latent factors. In this paper, we address the upper drawbacks, and propose a new model, named Correlated Matrix Factorization (CMF). Technically, we apply Canonical Correlation Analysis (CCA) to map U and V into a new semantic space. Besides achieving the optimal fitting on the rating matrix, one component in each vector (U or V) is also tightly correlated with every single component in the other. We derive efficient inference and learning algorithms based on variational EM methods. The effectiveness of our proposed model is comprehensively verified on four public data sets. Experimental results show that our approach achieves competitive performance on both prediction accuracy and efficiency compared with the current state of the art.                                                                                                                                                                                         

Heterogeneous Information Network Embedding for Recommendation

Abstract:  Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommended systems, called HIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.                                                         

NetSpam: A Network-Based Spam Detection Framework for Reviews in Online Social Media

Abstract:  Nowadays, a big part of people rely on available content in social media in their decisions (e.g., reviews and feedback on a topic or product). The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research, and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this paper, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review data sets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features helps us to obtain better results in terms of different metrics experimented on real-world review data sets from Yelp and Amazon Web sites. The results show that NetSpam outperforms the existing methods and among four categories of features, including review-behavioral, user-behavioral, review-linguistic, and user-linguistic, the first type of features performs better than the other categories.                                                                                                                                                                                         

Comparative Study to Identify the Heart Disease Using Machine Learning Algorithms

Abstract: Nowadays, heart disease is a common and frequently present disease in the human body and it’s also hunted lots of humans from this world. Especially in the USA, every year mass people are affected by this disease after that in India also. Doctor and clinical research said that heart disease is not a suddenly happen disease it’s the cause of continuing irregular lifestyle and different body’s activity for a long period after then it’s appeared in sudden with symptoms. After appearing those symptoms people seek for a treat in hospital for taken different test and therapy but these are a little expensive. So awareness before getting appeared in this disease people can get an idea about the patient condition from this research result. This research collected data from different sources and split that data into two parts like 80% for the training dataset and the rest 20% for the test dataset. Using different classifier algorithms tried to get better accuracy and then summarize that accuracy. These algorithms are namely Random Forest Classifier, Decision Tree Classifier, Support Vector Machine, k-nearest neighbor, Logistic Regression, and Naive Bayes. SVM, Logistic Regression, and KNN gave the same and better accuracy as other algorithms. This paper proposes a development that which factor is vulnerable to heart disease given basic prefix like sex, glucose, Blood pressure, Heart rate, etc. The future direction of this paper is using different devices and clinical trials for the real-life experiment.

A machine learning approach for opinion mining online customer reviews

Abstract :This study was conducted to apply supervised machine learning methods in opinion mining online customer reviews. First, the study automatically collected 39,976 traveler reviews on hotels in Vietnam on Agoda.com website, then conducted the training with machine learning models to find out which model is most compatible with the training dataset and apply this model to forecast opinions for the collected dataset. The results showed that Logistic Regression (LR), Support Vector Machines (SVM) and Neural Network (NN) methods have the best performance in opinion mining in Vietnamese language. This study is valuable as a reference for applications of opinion mining in the field of business.

Hybrid Machine Learning Classification Technique for Improve Accuracy of Heart Disease

Abstract: The area of medical science has attracted great attention from researchers. Several causes for human early mortality have been identified by a decent number of investigators. The related literature has confirmed that diseases are caused by different reasons and one such cause is heart-based sicknesses. Many researchers proposed idiosyncratic methods to preserve human life and help health care experts to recognize, prevent and manage heart disease. Some of the convenient methodologies facilitate the expert’s decision but every successful scheme has its own restrictions. The proposed approach robustly analyze an act of Hidden Markov Model (HMM), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree J48 along with the two different feature selection methods such as Correlation Based Feature Selection (CFS) and Gain Ratio. The Gain Ratio accompanies the Ranker method over a different group of statistics. After analyzing the procedure the intended method smartly builds Naive Bayes processing that utilizes the operation of two most appropriate processes with suitable layered design. Initially, the intention is to select the most appropriate method and analyzing the act of available schemes executed with different features for examining the statistics.

Novel Supervised Machine Learning Classification Technique for Improve Accuracy of Multi-Valued Datasets in Agriculture

Abstract: In the modern era, many reasons for agricultural plant disease due to unfavorable weather conditions. Many reasons that influence disease in agricultural plants include variety/hybrid genetics, the lifetime of plants at the time of infection, environment(soil, climate), weather (temperature, wind, rain, hail, etc), single versus mixed infections, and genetics of the pathogen populations. Due to these factors, diagnosis of plant diseases at the early stages can be a difficult task. Machine Learning (ML) classification techniques such as Naïve Bayes (NB) and Neural Network (NN) techniques were compared to develop a novel technique to improve the level of accuracy

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Abstract: Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure them. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer’s disease (AD), brain tumor, epilepsy, and Parkinson’s disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment.

Prediction of Chronic Kidney Disease - A Machine Learning Perspective

Abstract: Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is required as soon as possible. Machine learning technique has become reliable for medical treatment. With the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For this perspective, Chronic Kidney Disease prediction has been discussed in this article. Chronic Kidney Disease dataset has been taken from the UCI repository. Seven classifier algorithms have been applied in this research such as artificial neural network, C5.0, Chi-square Automatic interaction detector, logistic regression, linear support vector machine with penalty L1 & with penalty L2 and random tree. The important feature selection technique was also applied to the dataset. For each classifier, the results have been computed based on (i) full features, (ii) correlation-based feature selection, (iii) Wrapper method feature selection, (iv) Least absolute shrinkage and selection operator regression, (v) synthetic minority over-sampling technique with least absolute shrinkage and selection operator regression selected features, (vi) synthetic minority over-sampling technique with full features. From the results, it is marked that LSVM with penalty L2 is giving the highest accuracy of 98.86% in synthetic minority over-sampling technique with full features. Along with accuracy, precision, recall, F-measure, area under the curve and GINI coefficient have been computed and compared results of various algorithms have been shown in the graph. Least absolute shrinkage and selection operator regression selected features with synthetic minority over-sampling technique gave the best after synthetic minority over-sampling technique with full features. In the synthetic minority over-sampling technique with least absolute shrinkage and selection operator selected features, again linear support vector machine gave the highest accuracy of 98.46%. Along with machine learning models one deep neural network has been applied on the same dataset and it has been noted that deep neural network achieved the highest accuracy of 99.6%

Potato Disease Detection Using Machine Learning

Abstract: In Bangladesh potato is one of the major crops. Potato cultivation has been very popular in Bangladesh for the last few decades. But potato production is being hampered due to some diseases which are increasing the cost of farmers in potato production. However, some potato diseases are hampering potato production that is increasing the cost of farmers. Which is disrupting the life of the farmer. An automated and rapid disease detection process to increase potato production and digitize the system. Our main goal is to diagnose potato disease using leaf pictures that we are going to do through advanced machine learning technology. This paper offers a picture that is processing and machine learning based automated systems potato leaf diseases will be identified and classified. Image processing is the best solution for detecting and analyzing these diseases. In this analysis, picture division is done more than 2034 pictures of unhealthy potato and potato’s leaf, which is taken from openly accessible plant town information base and a few pre-prepared models are utilized for acknowledgment and characterization of sick and sound leaves. Among them, the program predicts with an accuracy of 99.23% in testing with 25% test data and 75% train data. Our output has shown that machine learning exceeds all existing tasks in potato disease detection.

A Comparative Evaluation of Traditional Machine Learning and Deep Learning Classification Techniques for Sentiment Analysis

Abstract :With the technological advancement in the field of digital transformation, the use of the internet and social media has increased immensely. Many people use these platforms to share their views, opinions and experiences. Analyzing such information is significant for any organization as it apprises the organization to understand the need of their customers. Sentiment analysis is an intelligible way to interpret the emotions from the textual information and it helps to determine whether that emotion is positive or negative. This paper outlines the data cleaning and data preparation process for sentiment analysis and presents experimental findings that demonstrates the comparative performance analysis of various classification algorithms. In this context, we have analyzed various machine learning techniques (Support Vector Machine, and Multinomial Naive Bayes) and deep learning techniques (Bidirectional Encoder Representations from Transformers, and Long Short-Term Memory) for sentiment analysis

A Comprehensive Review on Email Spam Classification using Machine Learning Algorithms

Abstract: Email is the most used source of official communication method for business purposes. The usage of the email continuously increases despite of other methods of communications. Automated management of emails is important in the today’s context as the volume of emails grows day by day. Out of the total emails, more than 55 percent is identified as spam. This shows that these spams consume email user time and resources generating no useful output. The spammers use developed and creative methods in order to fulfil their criminal activities using spam emails, Therefore, it is vital to understand different spam email classification techniques and their mechanism. This paper mainly focuses on the spam classification approached using machine learning algorithms. Furthermore, this study provides a comprehensive analysis and review of research done on different machine learning techniques and email features used in different Machine Learning approaches. Also provides future research directions and the challenges in the spam classification field that can be useful for future researchers.

Heart Disease Prediction using Hybrid machine Learning Model

Abstract: Heart disease causes a significant mortality rate around the world, and it has become a health threat for many people. Early prediction of heart disease may save many lives; detecting cardiovascular diseases like heart attacks, coronary artery diseases etc., is a critical challenge by the regular clinical data analysis. Machine learning (ML) can bring an effective solution for decision making and accurate predictions. The medical industry is showing enormous development in using machine learning techniques. In the proposed work, a novel machine learning approach is proposed to predict heart disease. The proposed study used the Cleveland heart disease dataset, and data mining techniques such as regression and classification are used. Machine learning techniques Random Forest and Decision Tree are applied. The novel technique of the machine learning model is designed. In implementation, 3 machine learning algorithms are used, they are 1. Random Forest, 2. Decision Tree and 3. Hybrid model (Hybrid of random forest and decision tree). Experimental results show an accuracy level of 88.7% through the heart disease prediction model with the hybrid model. The interface is designed to get the user’s input parameter to predict the heart disease, for which we used a hybrid model of Decision Tree and Random Forest

Heart Failure Prediction by Feature Ranking Analysis in Machine Learning

Abstract: Heart disease is one of the major cause of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the field of clinical data analysis. With the advanced development in machine learning (ML), artificial intelligence (AI) and data science has been shown to be effective in assisting in decision making and predictions from the large quantity of data produced by the healthcare industry. ML approaches has brought lot of improvements and broadens the study in medical field which recognizes patterns in the human body by using various algorithms and correlation techniques. One such reality is coronary heart disease, various studies gives impression into predicting heart disease with ML techniques. Initially ML was used to find degree of heart failure, but also used to identify significant features that affects the heart disease by using correlation techniques. There are many features/factors that lead to heart disease like age, blood pressure, sodium creatinine, ejection fraction etc. In this paper we propose a method to finding important features by applying machine learning techniques. The work is to design and develop prediction of heart disease by feature ranking machine learning. Hence ML has huge impact in saving lives and helping the doctors, widening the scope of research in actionable insights, drive complex decisions and to create innovative products for businesses to achieve key goals.

Design of face detection and recognition system to monitor students during online examinations using Machine Learning algorithms

Abstract: Today’s pandemic situation has transformed the way of educating a student. Education is undertaken remotely through online platforms. In addition to the way the online course contents and online teaching, it has also changed the way of assessments. In online education, monitoring the attendance of the students is very important as the presence of students is part of a good assessment for teaching and learning. Educational institutions have adopting online examination portals for the assessments of the students. These portals make use of face recognition techniques to monitor the activities of the students and identify the malpractice done by them. This is done by capturing the students’ activities through a web camera and analyzing their gestures and postures. Image processing algorithms are widely used in the literature to perform face recognition. Despite the progress made to improve the performance of face detection systems, there are issues such as variations in human facial appearance like varying lighting condition, noise in face images, scale, pose etc., that blocks the progress to reach human level accuracy. The aim of this study is to increase the accuracy of the existing face recognition systems by making use of SVM and Eigenface algorithms. In this project, an approach similar to Eigenface is used for extracting facial features through facial vectors and the datasets are trained using Support Vector Machine (SVM) algorithm to perform face classification and detection. This ensures that the face recognition can be faster and be used for online exam monitoring.

IEEE DATA SCIENCE PROJECTS (2024-2025)

DHS Informatics believes in students’ stratification, we first brief the students about the technologies and type of Data Science projects and other domain projects. After complete concept explanation of the IEEE Data Science projects, students are allowed to choose more than one IEEE Data Science projects for functionality details. Even students can pick one project topic from Data Science and another two from other domains like Data Science,Data mining, image process, information forensic, big data, Data Mining, block chain etc. DHS Informatics is a pioneer institute in Bangalore / Bengaluru; we are supporting project works for other institute all over India. We are the leading final year project centre in Bangalore / Bengaluru and having office in five different main locations Jayanagar, Yelahanka, Vijayanagar, RT Nagar & Indiranagar.

We allow the ECE, CSE, ISE final year students to use the lab and assist them in project development work; even we encourage students to get their own idea to develop their final year projects for their college submission.

DHS Informatics first train students on project related topics then students are entering into practical sessions. We have well equipped lab set-up, experienced faculties those who are working in our client projects and friendly student coordinator to assist the students in their college project works.

We appreciated by students for our Latest IEEE projects & concepts on final year Data Mining projects for ECE, CSE, and ISE departments.

Latest IEEE 2024-2025 projects on Data Mining with real time concepts which are implemented using Java, MATLAB, and NS2 with innovative ideas. Final year students of computer Data Mining, computer science, information science, electronics and communication can contact our corporate office located at Jayanagar, Bangalore for Data Science project details.

DATA SCIENCE

Data Science is mining knowledge from data, Involving methods at the intersection of machine learning, statistics, and database systems. Its the powerful new technology with great potential to help companies focus on the most important information in their data warehouses. We have the best in class infrastructure, lab set up , Training facilities, And experienced research and development team for both educational and corporate sectors.

Data Science is the process of searching huge amount of data from different aspects and summarize it to useful information. Data Science is logical than physical subset. Our concerns usually implicate mining and text based classification on Data Science projects for Students.

The usages of variety of tools associated to data analysis for identifying relationships in data are the process for Data Science. Our concern support data mining projects for IT and CSE students to carry out their academic research projects.

Data Science is the process of searching huge amount of data from different aspects and summarize it to useful information. Data Science is logical than physical subset. Our concerns usually implicate mining and text based classification on data Science projects for Students. The usages of variety of tools associated to data analysis for identifying relationships in data are the process for data Science. Our concern support data Science projects for IT and CSE students to carry out their academic research projects.

Relational Statics

The popularity of the term “data science” has exploded in business environments and academia, as indicated by a jump in job openings. However, many critical academics and journalists see no distinction between data science and statistics. Writing in Forbes, Gil Press argues that data science is a buzzword without a clear definition and has simply replaced “business analytics” in contexts such as graduate degree programs.In the question-and-answer section of his keynote address at the Joint Statistical Meetings of American Statistical Association, noted applied statistician Nate Silver said, “I think data-scientist is a sexed up term for a statistician….Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician.”Similarly, in business sector, multiple researchers and analysts state that data scientists alone are far from being sufficient in granting companies a real competitive advantage and consider data scientists as only one of the four greater job families companies require to leverage big data effectively, namely: data analysts, data scientists, big data developers and big data engineers.

On the other hand, responses to criticism are as numerous. In a 2014 Wall Street Journal article, Irving Wladawsky-Berger compares the data science enthusiasm with the dawn of computer science. He argues data science, like any other interdisciplinary field, employs methodologies and practices from across the academia and industry, but then it will morph them into a new discipline. He brings to attention the sharp criticisms computer science, now a well respected academic discipline, had to once face.Likewise, NYU Stern’s Vasant Dhar, as do many other academic proponents of data science,argues more specifically in December 2013 that data science is different from the existing practice of data analysis across all disciplines, which focuses only on explaining data sets. Data science seeks actionable and consistent pattern for predictive uses.This practical engineering goal takes data science beyond traditional analytics. Now the data in those disciplines and applied fields that lacked solid theories, like health science and social science, could be sought and utilized to generate powerful predictive models.

Java Final year CSE projects in Bangalore

• Java Information Forensic / Block Chain B.E Projects
• Java  Cloud Computing B.E Projects
• Java  Big Data with Hadoop B.E Projects
• Java  Networking & Network Security B.E Pr ojects
• Java  Data Mining / Web Mining / Cyber Secu rity B.E Projects
• Java DataScience / Machine Learning  B.E Projects
•  Java Artificaial Inteligence B.E Projects
• Java  Wireless Sensor Network B.E Projects
• Java  Distributed & Parallel Networking B.E Projects
• Java Mobile Computing B.E Projects

Android Final year CSE projects in Bangalore

• Android  GPS, GSM, Bluetooth & GPRS B.E Projects
• Android  Embedded System Application Projetcs for B.E
• Android  Database Applications Projects for B.E Students
• Android  Cloud Computing Projects for Final Year B.E Students
• Android  Surveillance Applications B.E Projects
• Android  Medical Applications Projects for B.E

Embedded  Final year CSE projects in Bangalore

• Embedded  Robotics Projects for M.tech Final Year Students
• Embedded  IEEE Internet of Things Projects for B.E Students
• Embedded   Raspberry PI Projects for B.E Final Year Students
• Embedded  Automotive Projects for Final Year B.E Students
• Embedded  Biomedical Projects for B.E Final Year Students
• Embedded  Biometric Projects for B.E Final Year Students
• Embedded  Security Projects for B.E Final Year

MatLab  Final year CSE projects in Bangalore

• Matlab  Image Processing Projects for B.E Students
• MatLab  Wireless Communication B.E Projects
• MatLab  Communication Systems B.E Projects
• MatLab  Power Electronics Projects for B.E Students
• MatLab  Signal Processing Projects for B.E
• MatLab  Geo Science & Remote Sensors B.E Projects
• MatLab  Biomedical Projects for B.E Students

Reliant’s paper-scouring AI takes on science’s data drudgery

AI models have proven capable of many things, but what tasks do we actually want them doing? Preferably drudgery — and there’s plenty of that in research and academia. Reliant hopes to specialize in the kind of time-consuming data extraction work that’s currently a specialty of tired grad students and interns.

“The best thing you can do with AI is improve the human experience: reduce menial labor and let people do the things that are important to them,” said CEO Karl Moritz Hermann. In the research world, where he and co-founders Marc Bellemare and Richard Schlegel have worked for years, literature review is one of the most common examples of this “menial labor.”

Every paper cites previous and related work, but finding these sources in the sea of science is not easy. And some, like systematic reviews, cite or use data from thousands.

For one study , Hermann recalled, “The authors had to look at 3,500 scientific publications, and a lot of them ended up not being relevant. It’s a ton of time spent extracting a tiny amount of useful information — this felt like something that really ought to be automated by AI.”

They knew that modern language models could do it: One experiment put ChatGPT on the task and found that it was able to extract data with an 11% error rate. Like many things LLMs can do, it’s impressive but nothing like what people actually need.

“That’s just not good enough,” said Hermann. “For these knowledge tasks, menial as they may be, it’s very important that you don’t make mistakes.”

Reliant’s core product, Tabular, is based on an LLM in part (Llama 3.1), but augmented with other proprietary techniques, is considerably more effective. On the multi-thousand-study extraction above, they said it did the same task with zero errors.

What that means is you dump a thousand documents in, say you want this, that, and the other data out of them, and Reliant pores through them and finds that information — whether it’s perfectly labeled and structured or (far more likely) it isn’t. Then it pops all that data and any analyses you wanted done into a nice UI so you can dive down into individual cases.

“Our users need to be able to work with all the data all at once, and we’re building features to allow them to edit the data that’s there, or go from the data to the literature; we see our role as helping the users find where to spend their attention,” Hermann said.

This tailored and effective application of AI — not as splashy as a digital friend but almost certainly much more viable — could accelerate science across a number of highly technical domains. Investors have taken note, funding an $11.3 million seed round; Tola Capital and Inovia Capital led the round, with angel Mike Volpi participating.

Like any application of AI, Reliant’s tech is very compute-intensive, which is why the company has bought its own hardware rather than renting it a la carte from one of the big providers. Going in-house with hardware offers both risk and reward: You have to make these expensive machines pay for themselves, but you get the chance to crack open the problem space with dedicated compute.

“One thing that we’ve found is it’s very challenging to give a good answer if you have limited time to give that answer,” Hermann explained — for instance, if a scientist asks the system to perform a novel extraction or analysis task on a hundred papers. It can be done quickly, or well, but not both — unless they predict what users might ask and figure out the answer, or something like it, ahead of time.

“The thing is, a lot of people have the same questions, so we can find the answers before they ask, as a starting point,” said Bellemare, the startup’s chief science officer. “We can distill 100 pages of text into something else, that may not be exactly what you want, but it’s easier for us to work with.”

Think about it this way: If you were going to extract the meaning from a thousand novels, would you wait until someone asked for the characters’ names to go through and grab them? Or would you just do that work ahead of time (along with things like locations, dates, relationships, etc.) knowing the data would likely be wanted? Certainly the latter — if you had the compute to spare.

This pre-extraction also gives the models time to resolve the inevitable ambiguities and assumptions found in different scientific domains. When one metric “indicates” another, it may not mean the same thing in pharmaceuticals as it does in pathology or clinical trials. Not only that, but language models tend to give different outputs depending on how they’re asked certain questions. So Reliant’s job has been to turn ambiguity into certainty — “and this is something you can only do if you’re willing to invest in a particular science or domain,” Hermann noted.

As a company, Reliant’s first focus is on establishing that the tech can pay for itself before attempting anything more ambitious. “In order to make interesting progress, you have to have a big vision but you also need to start with something concrete,” said Hermann. “From a startup survival point of view, we focus on for-profit companies, because they give us money to pay for our GPUs. We’re not selling this at a loss to customers.”

One might expect the firm to feel the heat from companies like OpenAI and Anthropic, which are pouring money into handling more structured tasks like database management and coding, or from implementation partners like Cohere and Scale. But Bellemare was optimistic: “We’re building this on a groundswell — any improvement in our tech stack is great for us. The LLM is one of maybe eight large machine learning models in there — the others are fully proprietary to us, made from scratch on data propriety to us.”

The transformation of the biotech and research industry into an AI-driven one is certainly only beginning and may be fairly patchwork for years to come. But Reliant seems to have found a strong footing to start from.

“If you want the 95% solution, and you just apologize profusely to one of your customers once in a while, great,” said Hermann. “We’re for where precision and recall really matter, and where mistakes really matter. And frankly, that’s enough; we’re happy to leave the rest to others.”

(This story originally had Hermann’s name incorrect — my own error, I have changed it throughout.)

More TechCrunch

Get the industry’s biggest tech news, techcrunch daily news.

Every weekday and Sunday, you can get the best of TechCrunch’s coverage.

Startups Weekly

Startups are the core of TechCrunch, so get our best coverage delivered weekly.

TechCrunch Fintech

The latest Fintech news and analysis, delivered every Tuesday.

TechCrunch Mobility

TechCrunch Mobility is your destination for transportation news and insight.

After changing its license, Redis drops its biggest release yet

Redis, the company behind the popular in-memory data store, which is often used as a cache, vector database or streaming engine, today announced the launch of Redis 8. With this…

360 One lifts its valuation of India’s National Stock Exchange to $29.9B

360 One Asset, an investor in National Stock Exchange (NSE), has increased its valuation for India’s top stock exchange to $29.9 billion.

Get in on the ground floor with Tesla’s humanoid by pretending to be one for pay

Tesla is hiring individuals standing between 5’7 and 5’11 to carry up to 30 pounds for seven hours a day to train its Optimus robot.

DeepMind workers sign letter in protest of Google’s defense contracts

At least 200 workers at DeepMind, Google’s AI R&D division, are displeased with Google’s reported defense contracts — and according to Time, they circulated a letter internally back in May…

NASA to decide Saturday whether astronauts will ride Boeing’s Starliner home — or use SpaceX’s Dragon instead 

NASA officials will announce their final decision on Saturday as to whether two NASA astronauts — Butch Wilmore and Sunita Williams — will return to Earth on Boeing’s Starliner spacecraft…

Gimbal Space takes on legacy suppliers with fast-paced component supply chain

America’s space industry seems mature, but the supply chain that provides all the parts and components for rockets, satellites, and other spacecraft is considerably less so. Gimbal Space is aiming to…

Waymo wants to chauffeur your kids

Soon, parents in range of Waymo robotaxis might not have to worry about picking up their kids from after-school activities — or any time, really. The San Francisco Standard reports…

Synapse’s collapse has frozen nearly $160M from fintech users — here’s how it happened

Here is a timeline of Synapse’s troubles and the ongoing impact it is having on banking consumers. 

Founder of failed fintech Synapse says he’s raised $11M for new robotics startup

Tens of millions of customer dollars remain unaccounted for at his previous startup, fintech Synapse. But that’s not deterring Sankaet Pathak from forging full steam ahead with his new robotics…

Cruise’s robotaxis are coming to the Uber app in 2025

Cruise, General Motors’ self-driving subsidiary, said it has signed a multi-year partnership with ride-hailing giant Uber to bring its robotaxis to the ride-hailing platform in 2025.  Cruise didn’t say when…

Boston Dynamics’ new electric Atlas can do push-ups

Until today, we’ve seen exactly 40 seconds of Boston Dynamics’ new electric Atlas in action. The Hyundai-owned robotics stalwart is very much still in the early stages of commercializing the…

AI sales rep startups are booming. So why are VCs wary?

When you really probe venture capitalists about investing in AI startups, they’ll tell you that businesses are experimenting wildly but are very slow to add AI solutions into their ongoing…

Former Alphabet X spinout Mineral sells technology to John Deere

Citing a crowded market and profit concerns, Mineral ceased operation and pivoted to technology licensing.

Relive the Myspace days by adding a favorite song to your Instagram profile

A nod to the Myspace era, Instagram just launched a new feature that allows you to add music to your profile. The feature is in collaboration with singer Sabrina Carpenter,…

Ford pivots on EVs, Waymo doubles its robotaxi ridership and Canoo leaves California

Welcome back to TechCrunch Mobility — your central hub for news and insights on the future of transportation. Sign up here for free — just click TechCrunch Mobility! Monterey Car…

Ecovacs says it will fix bugs that can be abused to spy on robot owners

After saying users “do not need to worry excessively” about a series of security flaws, Ecovacs said it will — in fact — roll out fixes.

Alphabet X’s latest spinout brings computer vision and AI to salmon farms

Tidal, which quietly spun out of the department in mid-July, has its own grand ambitions to “feed humanity sustainably.”

India’s open commerce network expands into digital lending

India’s ONDC has launched digital lending on its network as it expands into financial services after powering e-commerce, mobility and logistics with its standardized framework.

Under DMA probe, Apple tweaks design of EU browser choice screens, expands app default settings

Apple continues to adjust its approach to compliance with the European Union’s Digital Markets Act (DMA): Announcing another batch of changes Thursday, the iPhone maker showed off redesigned browser choice…

Threads spotted exploring ads, but says ‘no immediate timeline’ toward monetization

These findings indicate that Threads engineers are exploring ad technology, but that doesn’t mean Threads will debut ads anytime soon, as some suspect.

Cruise recalls robotaxi fleet to resolve federal safety probe

The recall and the conclusion of the probe takes one worry off of Cruise’s plate at a time when the company is under great scrutiny.

Cache Energy’s mysterious white pellets could help kill coal and natural gas

The pellets can be stored in piles or silos, moved around using conveyor belts, and transported via rail cars.

Creatopy, which automates ad creation using AI, raises a $10M Series A

The startup — which was developed in Romania but has a U.S. TopCo — now considers more than 5,000 brands and agencies as its clients.

Tilt raises $18M Series A to build on its real-time shopping app’s success

Live-shopping has grown into a massive sector in Asia, but the phenomenon is yet to see similar uptake in Western markets. But two tech founders are hoping the growth-hacking skills…

Former Expedia exec’s startup uses AI to help smaller companies book travel

Small businesses and startups often lack a dedicated travel desk, forcing executives and founders to rely on human assistants or consuming and cumbersome travel apps. Expedia’s former SVP of consumer…

Final 48 hours to secure your discounted tickets for TechCrunch Disrupt 2024

We’re down to the last 2 days to save up to $600 on TechCrunch Disrupt 2024 tickets! Prices will rise after August 23 at 11:59 p.m. PT. Don’t miss your…

China autonomous vehicle startup WeRide delays US IPO

WeRide is delaying plans to go public, according to CNBC. The self-driving tech company aimed to hit the Nasdaq this week, but now says it needs more time to complete…

Trace Machina is building a simulation testing platform to update safety-critical applications

When a faulty CrowdStrike update brought down airports, 911 call centers and hospitals last month, it showed how a defective update could impact critical infrastructure. Now imagine that this update…

Meta lets you cross-post from Instagram and Facebook to Threads. Here’s how to do it.

Connecting Threads more closely to Meta’s larger app ecosystem and its billions of users could also help boost Threads’ app — which recently surpassed 200 million active users — even…

Harmonyze wants to build AI agents to help franchisors make sense of unstructured data

For some businesses, there is a clear path to growth that doesn’t involve acquiring other companies or expanding organically: franchising. The U.S. has more than 800,000 franchise businesses, according to…

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.