android research paper topics

Advertisement

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

• Published: 14 September 2020
• Volume 26 , pages 285–303, ( 2021 )

Cite this article

• Iqbal H. Sarker   ORCID: orcid.org/0000-0003-1740-5517 1 , 2 ,
• Mohammed Moshiul Hoque 2 ,
• Md. Kafil Uddin 1 &
• Tawfeeq Alsanoosy 3  

23k Accesses

71 Citations

13 Altmetric

Explore all metrics

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on “ mobile data science and intelligent apps” in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

Similar content being viewed by others

Machine Learning: Algorithms, Real-World Applications and Research Directions

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

Machine learning and deep learning

Avoid common mistakes on your manuscript.

1 Introduction

Due to the recent development of science and technology in the world, the smartphone industry has made exponential growth in the mobile phone application market [ 1 ]. These devices are well known as one of the most important Internet-of-Things (IoT) devices as well, according to their diverse capabilities including data storage and processing [ 2 ]. Today’s smartphone is also considered as “a next-generation, multi-functional cell phone that facilitates data processing as well as enhanced wireless connectivity”, i.e., a combination of “a powerful cell phone” and a “wireless-enabled PDA” [ 3 ]. In our earlier paper [ 4 ], we have shown that users’ interest on “Mobile Phones” is more and more than other platforms like “Desktop Computer” , “Laptop Computer” or “Tablet Computer” for the last five years from 2014 to 2019 according to Google Trends data [ 5 ], shown in Fig.  1 .

Users’ interest trends over time where x-axis represents the timestamp information and y-axis represents the popularity score in a range of 0 (min) to 100 (max)

In the real world, people use smartphones not only for voice communication between individuals but also for various activities with different mobile apps like e-mailing, instant messaging, online shopping, Internet browsing, entertainment, social media like Facebook, Linkedin, Twitter, or various IoT services like smart cities, health or transport services, etc. [ 2 , 6 ]. Smartphone applications differ from desktop applications due to their execution environment [ 7 ]. A desktop computer application is typically designed for a static execution environment, either in-office or home, or other static locations. However, this static precondition is generally not applicable to mobile services or systems. The reason is that the world around an application is changing frequently and computing is moving toward pervasive and ubiquitous environments [ 7 ]. Thus, mobile applications should adapt to the changing environment according to the contexts and behave accordingly, which is known as context-awareness [ 8 ].

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. AI can be applied to various types of mobile data such as structured, semi-structured, and unstructured [ 9 ]. Popular AI techniques include machine learning (ML) and deep learning (DL) methods, natural language processing (NLP), as well as knowledge representation and expert systems (ES), can be used according to their data characteristics, in order to make the target mobile applications intelligent. AI-based models and their usage in practice can be seen in many intelligent mobile applications, such as personalized recommendation, virtual assistant, mobile business, healthcare services, and even the corona-virus COVID-19 pandemic management in recent days. A brief discussion of these apps and their relation with AI techniques within the area of mobile data science has been conducted in Section 6. This made a paradigm shift to context-aware intelligent computing , powered by the increasing availability of contextual smartphone data and the rapid progress of data analytics techniques. The intelligent smartphone applications and corresponding services are considered as “context-aware” because smartphones are able to know their users’ current contexts and situations, “adaptive” because of their dynamic changing capability depending on the users’ needs, and “intelligent” because of building the model based on data-driven artificial intelligence, which makes them able to assist the end-users intelligently according to their needs in their different day-to-day situations. Thus AI-based modeling for intelligent decision making, is the key to achieve our goal in this paper.

Based on the importance of AI in mobile apps, mentioned above, in this paper, we study on mobile data science and intelligent apps that covers how the artificial intelligence methods can be used to design and develop data-driven intelligent mobile applications for the betterment of human life in different application scenarios. Thus, the purpose of this paper is to provide a base reference for those academia and industry people who want to study and develop various AI-powered intelligent mobile apps considering these characteristics rather than traditional apps, in which we are interested.

The main contributions of this paper are listed as follows:

To provide a brief overview and concept of the mobile data science paradigm for the purpose of building data-driven intelligent apps. For this, we first briefly review the relevant methods and systems, to motivate our study in this area.

To present AI-based modeling for intelligent mobile apps where various machine learning and deep learning algorithms, the concept of natural language processing, as well as knowledge representation and rule-based expert systems, are used.

To discuss the usefulness of various AI-powered intelligent apps in several application domains, and the role of AI-based modeling in practice for the betterment of human life.

To highlight and summarize the potential research directions relevant to our study and analysis in the area of mobile data science and intelligent apps.

The rest of the paper is organized as follows. Section 2 motivates and defines the scope of our study. In Section 3, we provide a background of our study including traditional data science and context-aware mobile computing, and review the works related to data-driven mobile systems and services. We define and discuss briefly about mobile data science paradigm in Section 4. In Section 5, we present our AI-based modeling within the scope of our study. Various AI-powered intelligent apps are discussed and summarized in Section 6. In section 7, we highlight and summarize a number of research issues and potential future directions. In Section 8, we highlight some key points regarding our studies, and finally, Section 9 concludes this paper.

2 The motivation and scope of the study

In this section, our goal is to motivate the study of exploring mobile data analytics and artificial intelligence methods that work well together in data-driven intelligent modeling and mobile applications in the interconnected world, especially in the environment of today’s smartphones and Internet-of-Things (IoT), where these devices are well known as one of the most important IoT devices. Hence, we also present the scope of our study.

We are currently living in the era of Data Science (DS), Machine Learning (ML), Artificial Intelligence (AI), Internet-of-Things (IoT), and Cybersecurity, which are commonly known as the most popular latest technologies in the fourth industrial revolution (4IR) [ 10 , 11 ]. The computing devices like smartphones and corresponding applications are now used beyond the desktop, in diverse environments, and this trend toward ubiquitous and context-aware smart computing is accelerating. One key challenge that remains in this emerging research domain is the ability to effectively process mobile data and enhance the behavior of any application by informing it of the surrounding contextual information such as temporal context, spatial context, social context, environmental or device-related context, etc. Typically, by context, we refer to any information that characterizes a situation related to the interaction between humans, applications, and the surrounding environment [ 4 , 12 ].

For AI-based modeling, several machine learning and deep learning algorithms, the concept of natural language processing, as well as knowledge representation and rule-based expert systems, can be used according to their data characteristics, in order to make the target mobile applications intelligent. For instance, machine learning (ML) algorithms typically find the insights or natural patterns in mobile phone data to make better predictions and decisions in an intelligent systems [ 13 , 14 ]. Deep learning is a part of machine learning that allows us to solve complex problems even when using a diverse data set. Natural language processing (NLP) is also an important part of AI that derives intelligence from unstructured mobile content expressed in a natural language, such as English or Bengali [ 15 ]. Another important part of AI is knowledge representation and a rule-based expert system that is also considered in our analysis. Expert system (ES) typically emulates the decision-making ability of a human expert in an intelligent system that is designed to solve complex problems by reasoning through knowledge, represented mainly as IF-THEN rules rather than conventional procedural code.

Thus, the overall performance of the AI-based mobile applications depends on the nature of the contextual data, and artificial intelligence tasks that can play a significant role to build an effective model, in which we are interested in this paper. Overall, the reasons for AI-tasks in mobile applications and systems can be summarized as below -

to empower the evolution of the mobile industry by making smartphone apps as intelligent pieces of software that can predict future outcomes and make decisions according to users’ needs.

to learn from data including user-centric, and device-centric contexts, by analyzing the data patterns.

to deliver an enhanced personalized experience while adapting quickly to changing innovations and environments.

to better utilization of available resources with higher effectiveness and efficiency.

to understand the real-world problems and to provide intelligent and automated services accordingly as well as complex problems in this mobile domain.

to enable the smartphones more secured through predictive analytics by taking into account possible threats in real-time.

To achieve our goal, in this study, we mainly explore mobile data science and intelligent apps that aims at providing an overview of how AI-based modeling by taking into account various techniques’ that can be used to design and develop intelligent mobile apps for the betterment of human life in various application domains, briefly discussed in Section 5, and Section 6.

3 Background and related work

In this section, we give an overview of the related technologies of mobile data science that include the traditional data science, as well as the computing device and Internet, and context-aware mobile computing in the scope of our study.

3.1 Data science

We are living in the age of data [ 16 ]. Thus, relevant data-oriented technologies such as data science, machine learning, artificial intelligence, advanced analytics, etc. are related to data-driven intelligent decision making in the applications. Nowadays, many researchers use the term “data science” to describe the interdisciplinary field of data collection, pre-processing, inferring, or making decisions by analyzing the data. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. According to Cao et al. [ 16 ] “data science is a new interdisciplinary field that synthesizes and builds on statistics, informatics, computing, communication, management, and sociology to study data and its environments, to transform data to insights and decisions by following a data-to-knowledge-to-wisdom thinking and methodology”. As a high-level statement, it is the study of data to provide data-driven solutions for the given problems, as known as “the science of data”.

3.2 Computing devices and internet

The advancement of mobile computing and the Internet have played a central role in the development of the current digital age. The use of the Internet with mobile devices makes it the most popular computing device, for the people in the real world.

Mobile devices have become one of the primary ways, in which people around the globe communicate with each other for various purposes. While mobile phones may come in various forms in the real world, in this paper, they refer to smartphones or mobile devices with the capability of computing and Internet access. These devices have incorporated a variety of significant and interesting features to facilitate better information access through smart computing and the proper utilization of the devices for the benefit of the users. In recent times, the smartphones are becoming more and more powerful in both computing and the data storage capacity. As such, in addition to being used as a communication device, these smart mobile phones are capable of doing a variety of things relevant to users’ daily life such as instant messaging, Internet or web browsing, e-mail, social network systems, online shopping, or various IoT services like smart cities, health or transport services [ 2 , 6 ]. The future smartphones will be more powerful than current devices, communicate more quickly, store more data, and integrate new interaction technologies.

3.3 Context-aware Mobile computing

The notion of context has been used in numerous areas, including mobile and pervasive computing, human-centered computing, and ambient intelligence [ 17 ]. In the area of mobile and pervasive computing, several early works on context-aware computing, or context-awareness referred context as the location of people and objects [ 18 ]. Moreover, locational context, or user activities [ 17 , 18 ], temporal information [ 4 , 19 ], environmental information [ 20 ], user’s identity [ 21 ], or social context [ 22 , 23 ] are taken into account as contexts for different purposes. The state of the surrounding information of the applications are also considered as contexts in [ 24 , 25 ]. In [ 26 ], Schilit et al. claim that the important aspects of context are: (i) where you are, (ii) whom you are with, and (iii) what resources are nearby. Dey et al. [ 12 ] define context, which is perhaps now the most widely accepted. According to Dey et al. [ 12 ] “Context is any information that can be used to characterize the situation of an entity. An entity is person, place, or object that is considered relevant to the interaction between a user and an application, including the user and the application themselves”. We can also define context äs a specific type of knowledge to adapt application behavior.”

Based on the contextual information defined above, context-awareness can be the spirit of pervasive computing [ 27 ]. In general, context-awareness has adapting capability in the applications with the movement of mobile phone users, and thecontext-aware computing refers to sense the surrounding physical environment, and able to adapt application behavior. Therefore, context-awareness simply represents the dynamic nature of the applications. The use of contextual information in mobile applications is thus able to reduce the amount of human effort and attention that is needed for an application to provide the services according to user’s needs or preferences, in a pervasive computing environment [ 28 ]. Different types of contexts might have a different impact on the applications that are discussed briefly in our earlier paper, Sarker et al. [ 4 , 29 ].

3.4 Mobile systems and services

Research that relies on mobile data collected from diverse sources is mostly application-specific, which differs from application-to-application. A number of research has been done on mobile systems and services considering diverse sources of data. For instance, phone call logs [ 30 , 31 ] that contain context data related to a user’s phone call activities. In addition to call-related metadata, other types of contextual information such as user location, thesocial relationship between the caller a callee identified by the individual’s unique phone contact number are also recorded by the smart mobile phones [ 31 ]. Mobile SMS Log contains all the message including the spam and non-spam text messages [ 32 ] or good content and bad content [ 33 ] with their related contextual information such as user identifier, date, time, and other SMS related metadata, which can be used in the task of automatic filtering SMS spam for different individuals in different contexts [ 31 , 32 ], or predicting good time or bad time to deliver such messages [ 33 ]. App usages log contains various contextual information such as date, time-of-the-day, battery level, profile type such as general, silent, meeting, outdoor, offline, charging state such as charging, complete, or not connected, location such as home, workplace, on the way, etc. and other apps relatedmetadata with various kinds of mobile apps [ 34 , 35 , 36 , 37 , 38 ]. The notification log contains the contextual information such as notification type, user’s various physical activity (still, walking, running, biking and in-vehicle), user location such as home, work, or other, date, time-of-the-day, user’s response with such notifications (dismiss or accept) and other notification related metadata [ 39 ]. Weblog contains the information about user mobile web navigation, web searching, e-mail, entertainment, chat, misc., news, TV, netting, travel, sport, banking, and related contextual information such as date, time-of-the-day, weekdays, weekends [ 40 , 41 , 42 ]. Game log contains the information about playing various types such games of individual mobile phone users, and related contextual information such as date, time-of-the-day, weekdays, weekends etc. [ 43 ].

The ubiquity of smart mobile phones and their computing capabilities for vairous real life purposes provide an opportunity of using these devices as a life-logging device, i.g., personal e-memories [ 44 ]. In a more technical sense, life-logs sense and store individual’s contextual information from their surrounding environment through a variety of sensors available in their smart mobile phones, which are the core components of life-logs such as user phone calls, SMS headers (no content), App use (e.g., Skype, Whatsapp, Youtube etc.), physical activities form Google play API, and related contextual information such as WiFi and Bluetooth devices in user’s proximity, geographical location, temporal information [ 44 ]. Several applications such as smart context-aware mobile communication, intelligent mobile notification management, context-aware mobile recommendation etc. are popular in the area of mobile analytics and applications. Smart context-aware mobile communication (e.g., intelligent phone call interruption management) is one of the most compelling and widely studied applications [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ]. For mobile notification management, several research [ 39 , 56 , 57 , 58 , 59 ] has been done. Similarly, a number of research [ 34 , 60 , 61 , 62 , 65 ] has been done on recommendation system.

Various techniques are used in various applications, such as interruption management, activity recognition, recommendation system, mobile commerce, etc. in the area of mobile analytics. For instance, Seo et al. [ 66 ] design a context-aware configuration manager for smartphones PYP. An intelligent interruption management system is proposed in [ 48 ], use decision tree for making decisions. Bozanta et al. [ 67 ], Lee et al. [ 68 ] use classification technique to build a personalized hybrid recommender system. Turner et al. [ 59 , 69 ], Fogarty et al. [ 70 ] use classification technique in their interruptibility predictionand management system. In the area of transportation, Bedogni et al. [ 71 ] use classification techniques in their context-aware mobile applications. To adopt mobile learning, Tan et al. [ 72 ] investigates using a multi-layer perceptron model. In [ 43 ], Paireekreng et al. have proposed a personalization mobile game recommendation system. Moreover, regression techniques such as Linear regression [ 9 ], support vector regression [ 73 ], and ensemble learning techniques, such as Random Forest learning [ 74 ] are popular in the area of supervised learning.

Beside the above mentioned approaches, several researchers [ 34 , 35 , 39 ] use association rules that are used to build various context-aware mobile service according to users needs. A number of research [ 40 , 98 , 99 , 100 , 101 , 102 ] have been done based on clustering approach for different purposes in their study. Moreover, a significant amount of research [ 72 , 94 , 95 , 96 , 97 ] have been done on deep learning for various purposes in the area of mobile analytics. Moreover, context engineering including principal component analysis, or context correlation analysis [ 77 , 78 ] is another important issue to work in this area. In Table 1 , we have summarized this research based on the most popular approaches and data-driven tasks within the scope of our analysis.

Although various types of mobile phone data and techniques discussed above are used in the area of mobile analytics and systems for different purposes, a comprehensive AI-based modeling for building intelligent apps is being interested, according to the needs of the current in the community. Thus, in this paper, we focus on mobile data science and corresponding intelligent apps, where the most popular AI techniques including machine learning and deep learning methods, the concept of natural language processing, as well as knowledge representation and expert systems, can be used to build intelligent mobile apps in various application domains.

4 Mobile data science paradigm

In this section, we provide a brief overview of mobile data science and its related components within the scope of our study.

4.1 Understanding Mobile data

Mobile data science and data-driven intelligent apps are largely driven by the availability of data. Mobile datasets typically represent a collection of information records that consist of several attributes or contextual features and related facts. Thus,it’s important to understand the nature of mobile data containing various types of features and contexts. The reason is that raw data collected from relevant sources for a particular application can be used to analyze the various patterns or insight, to build a data-driven model to achieve our goal. Several datasets exist in the area of mobile analytics, such as phone call logs [ 30 ], apps logs [ 34 , 35 ], weblogs [ 40 ] etc. These context-rich historical mobile phone data are the collection of the past contextual information and users’ diverse activities [ 92 , 103 ]. Moreover, IoT data, smart cities data, business data, health data, mobile security data, or various sensors data associated with the mobile devices and target application can also be used as data sources. Intelligent apps are based on the extracted insight from such kinds of relevant datasets depending on apps characteristics. In the next, we summarize several characteristics of intelligent apps.

4.2 Intelligent apps characteristics

Intelligent apps offer personalized and adaptive user experiences, where artificial intelligence, the Internet-of-Things, and data analytics are the core components. Based on this, we have summarized the characteristics of intelligent apps to assist smartphone users in their daily life activities.

Action-Oriented: The foremost characteristic of intelligent apps is that these applications do not wait for users to make decisions in various situations. Rather, the apps can study user behavior and deliver personalized and actionable results using the power of predictive analytics.

Adaptive in Nature: The apps should be adaptive in nature. Every user is different in their use, the adaptability of the app plays a very crucial role. Meaning, they can easily upgrade their knowledge as per their surroundings to produce a highly-satisfying user experience.

Suggestive and Decision-Oriented: Generating suggestions and making decisions according to users’ needs and interests, could be an interesting characteristic of an intelligent app. Such suggestions may vary from user-to-user according to their interests and helps the users to decide what suits best for them.

Data-driven: Delivering a data-driven output is also one of the key features of intelligent apps. The intelligent apps gather data from a variety of sources, such as online, user interaction, sensors, etc. relevant to the target application and extracting data patterns, thus providing better user experience.

Context-awareness: Context awareness is the ability of an application to gather information about its surrounding environment at any given time and adapt behaviors accordingly. It makes the apps much smarter use by taking into account users contexts as well as the device’s contexts to proactively deliver highly relevant information and suggestions.

Cross-Platform Operation: The app also should have the ability to understand and process the desired output in a way that the users feel the same experience while working on cross platforms.

In this study, we take into account the above-discussed characteristics of mobile apps that could be able to intelligently assist the users in their diverse daily life activities. Based on these characteristics, in the next, we briefly discuss the concept of mobile data science and AI that can help to achieve the goal.

4.3 Mobile data science and AI

Data science is transforming the world’s industries. It is critically important for the future of intelligent mobile apps and services because of “apps intelligence is all about mobile data”. Traditionally, mobile application developers didn’t use data science techniques to make the apps intelligent considering the above characteristics. Although, a number of recent research [ 4 , 29 , 34 , 38 , 48 ] has been done based on machine learning techniques to model and build mobile applications, most of existing mobile applications are static or used custom-written rules like signatures, or manually defined heuristics for their different applications [ 47 , 66 ]. The main drawback of these custom-written rules-based approaches is that the knowledge or rules used by the applications are not automatically discovered; users need to define and maintain the rules manually. In general, users may not have the time, inclination, expertise, or interest to maintain rules manually. Although these rule-based approaches have their own merits in several cases, it needs too much manual work to keep up with the changing of userscontext landscape. On the contrary, data science can make a massive shift in technology and its operations, where AI techniques including machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to learn and making intelligent decisions. Thus, data science is considered as a practical application of machine learning, a major part of AI, with a complete focus on solving real-world problems. Overall, data science is a comprehensive process that involves data collection, pre-processing, data analysis, visualization, and decision making [ 16 ], whereas AI makes use of computer algorithms that can show human intelligence.

The concept of mobile data science incorporates the methods and techniques of machine learning and AI and data science as well as the context-aware computing to build intelligent mobile apps. The combination of these technologies has given birth to the term “mobile data science”, which refers to collect a large amount of mobile data from different sources and analyze it using machine learning techniques through the discovery of useful insights or the data-driven patterns, which is primarily defined in our earlier paper [ 104 ]. It is, however, worth remembering that mobile data science is not just about a collection of AI techniques. Mobile data science is a process that can help mobile application developers or analysts to scale and automate the target apps in a smart way and in a timely manner. Thus in a broader sense, we can say that “Mobile data science is research or working area existing at the intersection of context-aware mobile computing, data science, and artificial intelligence, which is mainly data-focused associated with target mobile apps, applies AI techniques for modeling, and eventually making intelligent decisions in applications. Thus it aims to seek for optimizing solutions to build automated and intelligent mobile applications to intelligently assist the users in their various daily activities.”. Several key modules, such as data collection, data processing, context and usage analysis, and building models, are involved in mobile data science, which are discussed briefly in our earlier paper [ 104 ]. In this paper, we mainly explore on AI-based modeling and its role in mobile apps in various application domains ranging from personalized services to healthcare services, which includes machine learning (ML) and deep learning (DL) methods, the concept of natural language processing (NLP), as well as knowledge representation, and rule-based expert systems (ES).

Overall, the outputs of mobile data science are typically mobile data products, which can be a data-driven AI-based model, potential mobile service and recommendation, or the corresponding intelligent mobile apps. In Section 6, we have discussed about AI-powered intelligent mobile apps in several application domains within the area of mobile data science.

4.4 Mobile security and privacy

Although we focus on intelligent apps from the perspective of artificial intelligence within the scope of our study discussed above, mobile security and privacy could be another part related to mobile data science in terms of data-driven security solutions. In the real world, most of the people including business people use smartphones not only to communicate but also to plan and organize their various kinds of daily works and also in their private life with family and friends. In most cases, both the business or personal information are stored on smartphones and people use such information when needed [ 105 , 106 ]. Thus, in addition to intelligent apps, mobile security and privacy is also important. Smartphones collect and analyze the sensitive information to which access must be controlled to protect the privacy of the user and the intellectual property of the organization or the company. Besides, there are several threats to mobile devices, including mobile malware, botnet, denial-of-service (DoS), eavesdropping, phishing, data breaches, etc. [ 106 , 107 , 108 ]. In terms of security analytics, in our earlier paper, Sarker et al. [ 10 ], we have discussed various types of security data and the effectiveness of the data-driven cybersecurity modeling based on artificial intelligence, particularly using machine learning techniques. Thus data-driven intelligent solutions through finding security insight could be effective to detect and mitigate such kind of mobile security threats.

5 AI-based modeling for Mobile services

As discussed earlier, mobile data science is data-focused, applies various artificial intelligence methods that eventually seek for intelligent decision making in mobile applications or services. In our analysis, we divide the artificial intelligence methods into several categories, such as basic machine learning and deep learning algorithms, natural language processing, knowledge representation and expert systems, within the scope of our study. These AI-based methods potentially can be used to make intelligent decisions in apps, which are discussed briefly in the following.

5.1 Machine learning modeling with Mobile data

Machine Learning (ML) including deep neural network learning is an important part of Artificial Intelligence (AI) which can empower mobile devices to learn, explore, and envisage outcomes automatically without user interference. For instance, machine learning algorithms can do the analysis of targeted user behavior patterns utilizing phone log data to make personalized suggestions as well as recommendations for mobile phone users. Typically, a machine learning model for building intelligent apps is a collection of target app-related data from relevant diverse sources, such as phone logs, sensors, or external sources, etc. and the chosen algorithms that work on that data in order to deduce the output.

To build a model utilizing collected data, supervised learning is performed when specific target classes are defined to reach from a certain set of inputs [ 13 ]. For instance, to classify or predict the future outcome, several popular algorithms such as Navies Bayes [ 109 ], Decision Trees [ 93 , 110 , 111 ], K-nearest neighbors [ 112 ], Support vector machines [ 73 ], Adaptive boosting [ 113 ], Logistic regression [ 114 ] etc. can be used. Such classification techniques are capable to build a prediction model ranging from predicting next usage to smartphone security, e.g., predicting mobile malware attack. Several feature engineering tasks, such as feature selection, extraction, etc., or context pre-modeling [ 78 ] can make the resultant predictive model more effective. On the other hand, in unsupervised learning, data is not labeled or classified, and it investigates similarity among unlabeled data [ 9 ]. Several clustering algorithms such as K-means [ 115 ], K-medoids [ 116 ], Single linkage [ 117 ], Complete linkage [ 118 ], BOTS [ 75 ] can be used for such modeling by taking into account certain similarity measures depending on the data characteristics. For instance, considering certain similarity in users’ preferences or behavioral activities, and to generate suggestions and recommendations accordingly, these algorithms can play a role to achieve the goal. Moreover, association rule learning techniques such as AIS [ 119 ], Apriori [ 120 ], FP-Tree [ 121 ], RARM [ 122 ], Eclat [ 123 ], ABC-RuleMiner [ 29 ] can be used for building rule-based machine learning model for the mobile phone users. In addition to these basic machine learning techniques, several deep neural learning methods such as recurrent neural network, long-short term memory, convolutional neural network, multilayer perceptron, etc. that are originated from an Artificial Neural Network (ANN) can be used in the learning process [ 9 , 13 ]. In these deep learning models, several hidden layers can be included to complete the overall process.

To understand and analyze the actual phenomena with mobile data, the above-discussed machine learning and deep learning techniques are useful to build AI-based modeling, depending on the target application and corresponding data characteristics. Thus the machine learning models and corresponding mobile apps that are close to the reality, are able to make data-driven intelligent decisions in apps and can behave according to users’ needs. Overall, the machine learning models can change the future of mobile applications and industry because of its learning capability from data. Therefore, machine learning methods including deep neural networks, on a global scale, is able to make mobile platforms more user-friendly, improve users’ experiences, and aid in building intelligent applications.

5.2 Natural language processing for Mobile content

Natural Language Processing (NLP) is an important branch of artificial intelligence that typically deals with the interaction between computers and humans using the natural language. One of the ultimate goals of NLP is to derive intelligence from unstructured data or content expressed in a natural language, such as English or Bengali. As each language has a unique set of grammar and syntax, and convention, NLP techniques can make it possible for computers to read text, hear speech, interpret it, measure sentiment or to mine opinions, and eventually determine which parts are important in an intelligent system [ 124 ]. For instance, to extract sentiments associated with positive, neutral, or negative polarities for specific subjects from a text document, an NLP-based methodology can be used. Thus, NLP can play a significant role to build intelligent apps when unstructured mobile content is available, and to be an important part within the scope of our study.

In recent days, a large amount of content read on mobile devices is text-based, such as emails, web pages, comments, blogs, or documents [ 15 ]. NLP techniques particularly, text mining extracts patterns and structured information from textual content that could make the apps smarter and intelligent, in which we are interested. For instance, browsing through large amounts of textual content on a small-screen mobile device may be tedious or time-consuming. In some cases, the important information might be easily overlooked due to the small screen of the devices. Thus, document summarization based on NLP might be the potential solution to provide a summary with high quality and minimal time.

Information extraction from mobile content could be another example of NLP based modeling. It typically identifies instances of a particular class of events, entities, or relationships in a natural language text and creates a structured representation of the discovered information [ 15 ]. For instance, this can be used to automatically find all the occurrences of a specific type of entity, such as ‘business’, and gather complementary information in the form of metadata around them. In addition to information extraction, NLP techniques can also be used when needed to develop the new mobile content. For instance, response generation while replying to an email, question answering, e.g., a company might need a mobile app that can answer questions about various products or services. Similarly, medical information extraction, personalized recommendation system through comments or text mining, context-aware chatbot, etc. are also included within the area. Thus, NLP techniques can play a significant role to build AI-based modeling depending on the target application and corresponding data type and characteristics.

5.3 Domain knowledge representation and Mobile expert system modeling

Due to the diversity of mobile users, contexts, increasing information, and variations in mobile computing platforms, mobile applications today are facing the challenges to provide the expected services. In artificial intelligence (AI), knowledge representation and expert system modeling is considered as another important part to minimize this issue, and to build knowledge-base intelligent systems.

5.3.1 Knowledge representation

In the real world, knowledge is considered as the information about a particular domain. It is typically a study of how the beliefs, intentions, and judgments of an intelligent agent can be expressed suitably for automated reasoning to solve problems. Thus, the main purpose of knowledge representation is modeling the intelligent behavior of an agent. It allows a machine to learn from that knowledge and behave intelligently like a human being. Instead of trying to understand from the bottom-up learning, its main goal is to understand the problems from the top-down, and to focus on what an associated agent needs to know in order to behave intelligently. Knowledge can be of several types:

Declarative Knowledge: known as descriptive knowledge that represents to know about something, which includes concepts, facts, and objects, and expressed in a declarative sentence.

Structural Knowledge: represents the basic knowledge to solve problems which describes the relationship between concepts and objects.

Procedural Knowledge: known as imperative knowledge that is responsible for knowing how to do something which includes rules, strategies, procedures, etc.

Meta Knowledge: represents knowledge about other types of knowledge.

Heuristic Knowledge: represents knowledge of some experts in a field or subject that could be based on previous experiences.

To represent knowledge in Artificial Intelligence (AI), “Ontology” in general has become popular as a paradigm by providing a methodology for easier development of interoperable and reusable knowledge bases (KB). Ontologies can be used to capture, represent knowledge and describe concepts and the relationship that holds between those concepts. In general, ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. According to [ 125 ], formally, an ontology is represented as “{ O  =  C ,  R ,  I ,  H ,  A }, where { C  =  C 1 ,  C 2 , …,  C n } represents a set of concepts, and { R  =  R 1 ,  R 2 , …,  R m } represents a set of relations defined over the concepts. I represents a set of instances of concepts, and H represents a Directed AcyclicGraph (DAG) defined by the subsumption relation between concepts, and A represents a set of axioms bringing additional constraints on the ontology”. Let’s consider an inference rules in ontologies for deductive reasoning. A rule may exist which states “If a mobile user accepts phone calls from family at work and a phone call is from his mother, then the call has been answered.” Then a program could deduce from a social relationship ontology that the user answers her mother’s incoming call at work. Thus, particular domain ontologies can help for building an effective semantic mobile application. Moreover, ontologies capturing complex dependencies between concepts for a particular problem domain provide a flexible and expressive tool for modeling high-level concepts and relations among the given attributes, which allows both the system and the user to operate the same taxonomy and play an important role to build an expert system [ 125 ].

5.3.2 Mobile expert system modeling

A mobile expert system is an example of a knowledge-based system, which is broadly divided into two subsystems, such as the inference engine and the knowledge base, shown in Fig.  2 . The knowledge base typically represents facts and rules, while the inference engine applies the rules to the known facts to deduce new facts. The knowledge-base module is the core of this expert system as it consists of knowledge of the target mobile application domain as well as operational knowledge of apps’ decision rules. The user interface accepts the original facts and invokes the inference engine to activate the decision rules in the knowledge base. The system uses expert knowledge represented mainly as IF-THEN rules, to offer recommendations or making decisions in relevant application areas. For instance, by using the expert system model, the process of selecting the semantic outcome for mobile users becomes more appropriate according to expert recommendations. A rule consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called the consequent (conclusion or action).

A structure of a mobile expert system modeling

The basic syntax of a rule is:

IF < antecedent > THEN < consequent  > .

Such an IF-THEN rule-based expert system model can have the decision-making ability of a human expert in an intelligent system that is designed to solve complex problems as well through knowledge reasoning. To develop the knowledge base module, an ontology-based knowledge representation platform discussed earlier can play a major role to generate the conceptual rules. To provide a continuous supply of knowledge to a rule-based expert system, data mining, and machine learning techniques can be used. For instance, in our earlier approach “ABC-RuleMiner”, Sarker et al. [ 29 ], we have discovered a set of useful contextual rules for mobile phone users considering their behavioral patterns in the data. Domain experts having knowledge of business rules can then update and manage the rules according to the needs. Thus, the mobile expert systems can be used to make intelligent decisions in corresponding mobile applications.

6 AI-powered intelligent Mobile apps

An intelligent system typically tells what to do or what to conclude in different situations [ 126 ] and can act as an intelligent software agent. Thus, intelligent mobile apps are those applications that use AI-based modeling discussed above, in order to make intelligent decisions and to provide useful suggestions and recommendations. Based on this, the target mobile applications for various daily life services are outlined in the following subsections ranging from personalized to community services.

6.1 Personalized Mobile user experience

In the real world, people want their experience to be absolutely personalized these days. Thus, most of the mobile apps heavily rely on personalization to keep users engaged and interested. Users also now expect the applications to deliver unique experiences that may vary from user-to-user according to their own preferences. Thus understanding “user persona” is the key to creating personalized mobile applications that are based on users’ past experiences represented by users’ historical data. ML-based models can effectively discover useful insight from individuals’ phone data by taking into account users own behavioral activities, interactions, or preferences, and can be used to perform individual personalized services in various applications. For instance, an intelligent phone call interruption management system can be a real-life application based on the discovered rules, which handles the incoming phone calls automatically according to the behavior of an individual user [ 29 ]. Moreover, mobile notification management [ 58 , 59 ], apps usage prediction and management, etc. can be the real-life examples of personalized services for the end mobile phone users. Thus, the extracted insight from relevant contextual historical and real-time interaction data using ML-based models can be used to deliver rich and personalized experiences to the users in various day-to-day situations in their daily life activities. Similarly, a knowledge-based mobile expert system considering a set of context-aware IF-THEN rules, can also help to provide personalized services for individual users.

6.2 Mobile recommendation

Recommender systems are typically developed to overcome the problem of information overload by aiding users in the search for relevant information and helping them identify which items (e.g., media, product, or service) are worth viewing in detail. This task is also known as information filtering. According to [ 127 ], the most important feature of a recommender system is its ability to “guess” a user’s preferences and interests by analyzing the behavior of the user and/or the behavior of other users to generate personalized recommendations. In general, the traditional recommender systems mainly focus on recommending the most relevant items to users among a huge number of items [ 128 ]. However, mobile recommendation systems based on users’ contextual information such as temporal, spatial, or social etc. could be more interesting for the users [ 62 , 63 , 64 ]. The advanced mobile apps powered by predictive intelligent capabilities using ML-based models make recommend engines smart enough to analyze the user content preferences and cater to the appropriate content that the user is looking for. For instance, a mobile system generating shopping recommendations helps the user to find the most satisfying product by reducing search effort and information overload. Similarly, tourist guides [ 129 ], food or restaurant services [ 130 ], finding cheaper flights, accommodation, attractions, or leisure dissemination, etc. can be other real-life examples for the mobile phone users. Moreover, an NLP-based methodology can be a way to retrieve the best recommendation service based on public comments.

6.3 Mobile virtual assistance

An intelligent virtual assistant is also known as an intelligent personal assistant that is typically a software agent to perform tasks or services for an individual based on queries like commands or questions. The chatbot is sometimes used to refer to virtual assistants, which is a software application used to conduct an online chat conversation via text or text-to-speech. Several key advantages make the chatbots beneficial these days as they are able to provide 24*7 automated support, able to provide instant answers, good in handling customers or users, avoiding repetitive work, as well as save time and service cost. Intelligent mobile apps powered by AI are able to provide such services with higher accuracy. AI-based models including NLP and ML can be used to build such applications. Moreover, people are now typically spending more time on different messaging apps that are the platforms of communication and bots will be how their users access all sorts of services. Thus, chatbots can engage by answering basic questions in various services. For instance, online ordering, product suggestions, customer support, personal finance assistance, searching, and flight tracking, finding a restaurant, etc. A knowledge-based mobile expert system considering a set of IF-THEN rules, can also be applied to provide such service. Thus, different virtual assistant apps like voice assistants or chatbots offer interactive experiences to users, who are able to retrieve the necessary information effectively and efficiently according to their needs.

6.4 Internet of things (IoT) and smart cities

The Internet of Things (IoT) is typically a network of physical devices, and objects which utilize sensors, software, etc. for sending and receiving data. Smart cities use IoT devices as well to collect and analyze data, and become the most extensive application domain these days. In general, the smart city development is considered as a new way of thinking among cities, businesses, citizens, academia, industry people or others, who are the key stakeholders. As today’s smartphones are considered as one of the most important IoT devices [ 2 ], integrating mobile apps with IoT developments can dramatically improve the quality of human life. AI-based modeling in apps can provide relevant intelligent services in this domain, as well as can bring technology, government, and different layers of society together for the betterment of human life. For instance, machine learning-based modeling utilizing sensor data collected from parking places, or traffic signals, can be used for a better city planning for the governments. Similarly, a knowledge-based mobile expert system considering a set of IF-THEN rules, can help to make context-aware and timely decisions. Overall, AI-based modeling can assist the users in our most common daily life issues, such as questions, suggestions, general feedback, and reporting in various smart city services including smart governance, smart home, education, communication, transportation, retail, agriculture, health care, enterprise and many more.

6.5 Mobile business

Smart mobile apps have the potential to increase the operational excellence in the business-to-business as well as business-to-customer sectors. The new availability and advancement of AI and machine learning are causing a revolutionary shift in business and is considered as the new digital frontier for enterprises. Since, almost every organization deal with customer service, the businesses people think about intelligent interactions within mobile applications these days according to consumer demands. Businesses can leverage the data that are collected from various sources such as point-of-sale machines, online traffic, mobile devices, etc. to analyze and strategically improve the user experience. AI techniques can find trends from data and adjust the apps themselves to create more meaningful and context-rich opportunities to engage users. For instance, machine learning algorithms are capable to understand the customer behavior, interests, and provide them with more relevant product recommendations based on purchase history, fraud identification with credit cards, and visual search. By taking into account context-awareness, it can also empower businesses with prominent features, such as delivering precise location-based suggestions. Moreover, an NLP-based methodology of sentiment evaluation such as positive, neutral, or negetive sentiment (also known as opinion mining) on business data, e.g., review comments, can retrieve the best and perfect suggestions and product recommendations in terms of quality and quantity for the customers. AI positively impacts customer behavior by incorporating the chatbots as well in a mobile application, which may reduce the repetitive tasks and optimize manpower utilization. Similarly, knowledge-based mobile expert system considering a set of business IF-THEN rules, can make intelligent decisions. Overall, AI mobile applications in the business domain help in expanding businesses, introducing new products or services, identifying customer interests, and maintaining a prominent position in the global market.

6.6 Mobile healthcare and medicine

Intelligent mobile healthcare applications are bringing better opportunities for both the patients, medical practitioners, or related organizations through simplifying their physical interactions. These apps can provide opportunities to several health-related services such as medical diagnosis, medicine recommendation including e-prescription, suggesting primary precautions, remote health monitoring, or effectively patient management in the hospital. For assessing and strengthening health facilities, or building health management information systems (HMIS), various kinds of health data can be collecting from multiple sources on a wide variety of health topics to analyze [ 131 ]. With the help of AI methods including ML-based models, intelligent health services can be provided. Thus it may reduce the expense and time of the patients and clinics, as they offer customized medicines and drugs as well as give preventive measures through continuous information accumulation. Moreover, AI-powered mobile applications could also be applicable to find the best nearest doctor, to book a consultation, to keep reminders of medication, getting a basic knowledge of each medication, and more. Mobile healthcare app is also able to help doctors with remaining updates with real-time status of consultations, assigning duties to staff, ensuring the availability of equipment, maintaining a proper temperature for medicines, and more. In addition, the healthcare virtual assistant services like chatbots can be used to provide basic healthcare service as well, as these online programs can assist patients in many ways, such as scheduling appointments, answering common questions, aiding in the payment process, and even providing basic virtual diagnostics. Overall, AI-modeling based mobile healthcare services may create a new endeavor for all citizens in a country including the rural people of low-income countries.

6.7 The novel coronavirus COVID-19

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus [ 131 ]. COVID-19 apps typically are known as the mobile software applications that use digital contact tracing in response to the COVID-19 pandemic, i.e. the process of identifying persons (“contacts”) who may have been in contact with an infected person. According to the World Health Organization (WHO) [ 131 ], most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Thus, in order to keep this infectious disease in control, “contact tracing” is an important factor. Smartphone apps are playing a big role in the response to the COVID-19 pandemic. These apps are being used to track infected people, social distancing, detecting COVID-19 symptoms, self-quarantine guidelines, the latest communication to the citizens, and ease the burden on healthcare staff. Thus, mobile apps are considered as an effective control strategy against the spread of COVID-19 or similar future pandemics, considering the patient and social sensing data. An intelligent framework and mobile application design will not only strengthen the fight against ongoing COVID-19 challenges based on the collected data by mobile phones, but also against similar disasters in a post-COVID world.

In addition to these application areas, AI-based models in mobile applications can also be applicable to several other domains, such as financial, manufacturing, smart robotics, security and privacy, and many more. Thus, the impact of AI-models in mobile app development and user experience is significant in these days and can be considered as next-generation mobile learning.

7 Research issues and future directions

With the rapid development of smartphones, Internet-of-Things (IoT), and AI technologies, the most fundamental challenge is to explore the relevant data collected from diverse sources and to extract useful insights for future actions. Thus, in this section, we highlight and analyze the main challenges and research issues in the scope of our study. In the following, the issues that we identified and corresponding future directions are discussed briefly.

According to our study in this paper, source datasets are the primary component to work in the area of mobile data science. Thus, collecting real-world data such as categorical, numerical, or textual relevant to a particular application is the first step for building an intelligent smartphone apps, which may vary from service to service. For instance, to manage mobile interruptions, the relevant contextual information and an individual’s behavioral data is needed to be analyzed [ 4 ]. Similarly, for smart healthcare services, patient data and corresponding contextual information might be useful. Thus, to facilitate the extraction of reliable insight from the data using AI techniques and to use the knowledge in context-aware applications, integrating and effective management of mobile data is important. The reason is that AI methods particularly machine learning techniques highly impact on data [ 9 ]. Therefore, establishing a large number of recent datasets from diverse sources and to integrate and manage such information for effective data analysis is needed, which could be one of the major challenges to work in the area of mobile data science and data-driven intelligent applications.

The next challenge is an effective modeling of mobile users and their activities from the relevant data. The main goal of mobile user modeling is the customization and adaptation of systems to the user’s specific needs. The system needs to output the ‘right’ outcome at the ‘right’ time or contexts in the ‘right’ way [ 4 ]. Thus, several aspects such as context-dependency, individual user behavior, and their preferences in different contexts are needed to take into account for an effective user modeling and to build corresponding intelligent apps. The reason is that usage patterns of mobile phones vary greatly between individuals behaving differently in different contexts. Thus considering various contexts, such as temporal, spatial, social, etc. and their effective modeling based on these contexts are important to build an intelligent app [ 93 ]. For this purpose, data preparation, discretization of contexts, and discovery of useful insights are the key issues [ 4 ]. Moreover, the concept of RecencyMiner [ 76 ] can be more effective because of considering the recent pattern-based insights. Therefore, effectively modeling mobile users considering these aspects, could be another research issue in the area of mobile data science and intelligent applications.

The context-sensitive features in mobile data and their patterns are of high interest to be discovered and analyzed to make context-aware intelligent decisions for a particular application in a pervasive computing environment. The traditional analytical techniques including data science and machine learning may not be applicable to make real-time decisions for analyzing smartphone data, because of a large number of data processing that may reduce the performance of mobile phones. For instance, the association rule mining technique [ 120 ] may discover a large number of redundant rules that become useless and make the decision-making process complex and ineffective [ 29 ]. Such traditional techniques may not be applicable for analyzing smartphone data. Thus, a deeper understanding is necessary on the strengths and weaknesses of state-of-the-art big data processing and analytics systems to realize large-scale context-awareness and to build a smart context-aware model. Therefore effectively building a data-driven context-aware model for intelligent decision-making on smartphones, could be another research issue in the area of mobile data science and intelligent applications.

Real-life mobile phone datasets may contain many features or high-dimensions of contexts, which may cause several issues such as increases model complexity, may arise over-fitting problem, and consequently decreases the outcome of the resultant AI-based model [ 77 ]. The reason is that the performance of AI methods particularly machine learning algorithms heavily depends on the choice of features or data representation. Having irrelevant features or contextual information in the data makes the model learn based on irrelevant features that consequently decrease the accuracy of the models [ 132 ]. Thus the challenge is to effectively select the relevant and important features or extracting new features that are known as feature optimization. In the area of AI, particularly data science and machine learning, feature optimization problem is considered as an important pre-processing step that helps to build an effective and simplified model and consequently improves the performance of the learning algorithms by removing the redundant and irrelevant features [ 111 ]. Therefore, feature optimization could be a significant research issue in the area of mobile data science and intelligent applications.

The next challenge is the extraction of the relevant and accurate information from the unstructured or semi-structured data on mobile phones. A large amount of content such as emails, web pages, or documents is read on these devices frequently that is text-based [ 15 ]. Thus the problem of information overload arises due to the small screen of the devices rather than the desktop computer. Therefore effectively mining the contents or texts considering these aspects, could be another research issue in the area of mobile data science and intelligent applications. Natural language processing (NLP) techniques can help to make such text-based apps smarter, by automatically analyzing the meaning of content and taking appropriate actions on behalf of their users. Due to the devices’ limited input and processing capabilities rather than desktop computers, it is then needed to develop novel approaches that can bring NLP power to smartphones. Several NLP tasks such as automatic summarization, information extraction, or new content development, etc. could be useful to minimize the issue.

Mobile expert system uses expert knowledge represented mainly as IF-THEN rules, to offer recommendations or making decisions in relevant application areas. However, the development of large-scale rule-based systems may face numerous challenges. For instance, the reasoning process can be very complex, and designing of such systems becomes hard to manage [ 133 ]. There is still a lack of lightweight rule-based inference engines that will allow for reasoning on mobile devices [ 133 ]. Thus a set of concise and effective rules will be beneficial in terms of outcome and simplicity for such a rule-based expert system for mobile devices. Moreover, ontologies [ 125 ] capturing complex dependencies between concepts for a particular problem domain provides a flexible and expressive tool for modeling high-level concepts and relations among the given attributes, which allows both the system and the user to operate the same taxonomy and play an important role to build an expert system. This is where the ontological modeling and reasoning is useful. Thus, an effective design of ontology, or knowledge representation model for the respective problem domain could be another research issue.

The mobility of computing devices, e.g., smartphones, applications, and users leads to highly dynamic computing environments. Unlike desktop applications, which rely on a carefully configured, and largely static set of resources, pervasive computing applications are subjected to changes in available resources such as network connectivity, user contexts, etc. Moreover, they are frequently required to cooperate spontaneously and opportunistically with previous unknown software services to accomplish tasks on behalf of users. Thus, pervasive computing software must be highly adaptive and flexible. As an example, an application may need to modify it’s style of output following a transition from an office environment to a moving vehicle, to be less intrusive [ 4 ]. Thus to effectively adapt to the changing environment according to users’ needs is important, which is important in the area of mobile data science and intelligent applications. Context-awareness represents the ability of mobile devices to sense their physical environment and adapt their behavior accordingly, incorporating this property in the applications could be a potential solution to overcome this issue.

8 Discussion

Although several research efforts have been directed towards intelligent mobile apps, discussed throughout the paper, this paper presents a comprehensive view of mobile data science and intelligent apps in terms of concepts and AI-based modeling. For this, we have conducted a literature review to understand the contexts, mobile data, context-aware computing, data science, intelligent apps characteristics, and different types of mobile systems and services, as well as the used techniques, related to mobile applications. Based on our discussion on existing work, several research issues related to mobile datasets, user modeling, intelligent decision making, feature optimization, mobile text mining based on NLP, mobile expert system, and context-aware adaptation, etc. are identified that require further research attention in the domain of mobile data science and intelligent apps.

The scope of mobile data science is broad. Several data-driven tasks, such as personalized user experience, mobile recommendations, virtual assistant, mobile business, and even mobile healthcare system including the COVID-19 smartphone app, etc. can be considered as the scope of mobile data science. Traditionally mobile app development mostly focused on knowledge that is not automatically discovered [ 47 , 66 ]. Taking the advantage of large amounts of data with rich information, AI is expected to help with studying much more complicated yet much closer to real-life applications, which then leads to better decision making in relevant applications. Considering the volume of collected data and the features, one can decide whether the standalone or cloud-based application is more suitable to provide the target service. Thus, the output of AI-based modeling can be used in many application areas such as mobile analytics, context-aware computing, pervasive computing, health analytics, smart cities, as well as the Internet of things (IoT). Moreover, intelligent data-driven solutions could also be effective in AI-based mobile security and privacy, where AI works with huge volumes of security event data to extract the useful insights using machine learning techniques [ 10 ].

Although the intelligent apps discussed in this paper can play a significant role in the betterment of human life in different directions, several dependencies may pose additional challenges, such as the availability of network and the data transfer speeds or the battery life of mobile devices. Moreover, privacy and security issues may become another challenge while considering the data collection and processing over the cloud or within the device. Taking the advantages of these issues considering the application type and target goal, we believe this analysis and guidelines will be helpful for both the researchers and application developers to work in the area of mobile data science and intelligent apps.

9 Conclusion

In this paper, we have studied on mobile data science and reviewed the motivation of using AI in mobile apps to make it intelligent. We aimed to provide an overview of how artificial intelligence can be used to design and develop data-driven intelligent mobile applications for the betterment of human life. For this, we have presented an AI-based modeling that includes machine learning and deep learning methods, the concept of natural language processing, as well as knowledge representation and expert systems. Such AI-based modeling can be used to build intelligent mobile applications ranging from personalized recommendations to healthcare services including COVID-19 pandemic management, that are discussed briefly in this paper. A successful intelligent mobile system must possess the relevant AI-based modeling depending on the data characteristics. The sophisticated algorithms then need to be trained through collected data and knowledge related to the target application before the system can assist the users with suggestions and decision making. We have concluded with a discussion about various research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps, that can help the researchers to do future research in the identified directions.

Peng M, Zeng G, Sun Z, Huang J, Wang H, Tian G (2018) Personalized app recommendation based on app permissions. World Wide Web 21(1):89–104

Google Scholar  

El Khaddar MA, Boulmalf M (2017) Smartphone: the ultimate iot and ioe device. Smartphones from an applied research perspective , page 137

Zheng P, Ni LM (2006) Spotlight: the rise of the smart phone. IEEE Distributed Systems Online 7(3):3–3

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

MathSciNet   Google Scholar  

Google trends. In https://trends.google.com/trends/ , 2019

Pejovic V, Musolesi M (2014) Interruptme: designing intelligent prompting mechanisms for pervasive applications. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA, 13–17 September, pp.∼897–908. ACM, New York, USA

Finin T, Joshi A, Kagal L, Ratsimore O, Korolev V, Chen H (2001) Information agents for mobile and embedded devices. Cooperative Information Agents V, pages 264–286

Damiao Ribeiro de Almeida, Cláudio de Souza Baptista, Elvis Rodrigues da Silva, Cláudio EC Campelo, Hugo Feitosa de Figueirêdo, and Yuri Almeida Lacerda (2006) A context-aware system based on service-oriented architecture. In Advanced Information Networking and Applications, 2006 . AINA 2006 . 20th International Conference on , volume 1, pages 6–pp. IEEE

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

MATH   Google Scholar  

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

Ślusarczyk B (2018) Industry 4.0: Are we ready? Polish Journal of Management Studies , 17

Dey AK (2001) Understanding and using context. Pers Ubiquit Comput 5(1):4–7

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

Witten IH, Frank E, Trigg LE, Hall MA, Holmes G, Cunningham SJ (1999) Weka: Practical machine learning tools and techniques with java implementations

Sateli B, Cook G, Witte R (2013) Smarter mobile apps through integrated natural language processing services. In International Conference on Mobile Web and Information Systems , pages 187–202. Springer

Cao L (2017) Data science: a comprehensive overview. ACM Computing Surveys (CSUR) 50(3):43

Dourish P (2004) What we talk about when we talk about context. Pers Ubiquit Comput 8(1):19–30

Schilit BN, Theimer MM (1994) Disseminating active map information to mobile hosts. IEEE Netw 8(5):22–32

Brown PJ, Bovey JD, Chen X (1997) Context-aware applications: from the laboratory to the marketplace. IEEE Pers Commun 4(5):58–64

Brown PJ (1995) The stick-e document: a framework for creating context-aware applications. Electronic Publishing-Chichester 8:259–272

Ryan N, Pascoe J, Morse D (1999) Enhanced reality fieldwork: the context aware archaeological assistant. Bar International Series 750:269–274

Franklin D, Flaschbart J (1998) All gadget and no representation makes jack a dull environment. In Proceedings of the AAAI 1998 Spring Symposium on Intelligent Environments , pages 155–160

Hull R, Neaves P, Bedford-Roberts J (1997) Towards situated computing. In Wearable Computers, 1997 . Digest of Papers., First International Symposium on , pages 146–153. IEEE

Ward A, Jones A, Hopper A (1997) A new location technique for the active office. IEEE Pers Commun 4(5):42–47

Rodden T, Cheverst K, Davies K, Dix A (1998) Exploiting context in hci design for mobile systems. In Workshop on human computer interaction with mobile devices , pages 21–22. Glasgow

Schilit B, Adams N, Want R (1994) Context-aware computing applications. In Mobile Computing Systems and Applications, 1994 . WMCSA 1994 . First Workshop on , pages 85–90. IEEE

Shi Y (2006) Context awareness, the spirit of pervasive computing. In Pervasive Computing and Applications, 2006 1st International Symposium on , pages 6–6. IEEE

Christos Anagnostopoulos, Athanasios Tsounis, and Stathes Hadjiefthymiades (2005) Context management in pervasive computing environments. In Pervasive Services, 2005 . ICPS’05 . Proceedings. International Conference on , pages 421–424. IEEE

Sarker IH, Kayes ASM (2020) Abc-ruleminer: User behavioral rule-based machine learning method for context-aware intelligent services. Journal of Network and Computer Applications , page 102762

Phithakkitnukoon S, Dantu R, Claxton R, Eagle N (2011) Behavior-based adaptive call predictor. ACM Transactions on Autonomous and Adaptive Systems 6(3):21:1–21:28

Eagle N, Pentland AS (2006) Reality mining: sensing complex social systems. Personal and ubiquitous computing 10(4):255–268

Almeida TA, Hidalgo JMG, Yamakami A (2011) Contributions to the study of sms spam filtering: new collection and results. In Proceedings of the 11th ACM symposium on Document engineering , pages 259–262. ACM

Fischer JE, Yee N, Bellotti V, Good N, Benford S, Greenhalgh C (2010) Effects of content and time of delivery on receptivity to mobile interruptions. In Proceedings of the 12th international conference on Human computer interaction with mobile devices and services , pages 103–112. ACM

Zhu H, Chen E, Xiong H, Yu K, Cao H, Tian J (2014) Mining mobile user preferences for personalized context-aware recommendation. ACM Transactions on Intelligent Systems and Technology (TIST) 5(4):58

Srinivasan V, Moghaddam S, Mukherji A (2014) Mobileminer: Mining your frequent patterns on your phone. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA, 13–17 September, pp.∼389–400. ACM, New York, USA

Kim J, Mielikäinen T (2014) Conditional log-linear models for mobile application usage prediction. In Machine Learning and Knowledge Discovery in Databases , pages 672–687. Springer

Liao Z-X, Pan Y-C, Peng W-C, Lei P-R (2013) On mining mobile apps usage behavior for predicting apps usage in smartphones. In Proceedings of the 22nd International Conference on Information & Knowledge Management , pages 609–618. ACM

Zhu H, Chen E, Xiong H, Cao H, Tian J (2014) Mobile app classification with enriched contextual information. IEEE Trans Mob Comput 13(7):1550–1563

Mehrotra A, Hendley R, Musolesi M (2016) Prefminer: mining user’s preferences for intelligent mobile notification management. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 12–16 September, pp. ∼ 1223–1234. ACM, New York, USA.

Halvey M, Keane MT, Smyth B (2005) Time based segmentation of log data for user navigation prediction in personalization. In Proceedings of the International Conference on Web Intelligence, Compiegne, France, 19–22 September, pp. ∼ 636–640. IEEE Computer Society, Washington, DC, USA.

Halvey M, Keane MT, Smyth B (2006) Time based patterns in mobile-internet surfing. In Proceedings of the SIGCHI Conference on Human Factors in computing systems, Montreal, Quebec, Canada, 22–27 April, pp.∼31–34. ACM, New York, USA

Bordino I, Donato D (2012) Extracting interesting association rules from toolbar data. In International Conference on Information and Knowledge Management . ACM

Paireekreng W, Rapeepisarn K, Wong KW (2009) Time-based personalised mobile game downloading. In Transactions on Edutainment II, pp. ∼ 59–69

Rawassizadeh R, Tomitsch M, Wac K, Tjoa AM (2013) Ubiqlog: a generic mobile phone-based life-log framework. Personal and ubiquitous computing 17(4):621–637

Danninger M, Kluge T, Stiefelhagen R (2006) Myconnector: analysis of context cues to predict human availability for communication. In Proceedings of the 8th International Conference on Multimodal Interfaces , pages 12–19. ACM

Khalil A, Connelly K (2005) Improving cell phone awareness by using calendar information. In Human-Computer Interaction , pages 588–600. Springer

Dekel A, Nacht D, Kirkpatrick S (2009) Minimizing mobile phone disruption via smart profile management. In Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services , page 43. ACM

Zulkernain S, Madiraju P, Ahamed SI, Stamm K (2010) A mobile intelligent interruption management system. J. UCS 16(15):2060–2080

Pielot M (2014) Large-scale evaluation of call-availability prediction. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing , pages 933–937. ACM

Knittel J, Shirazi AS, Henze N, Schmidt A (2013) Utilizing contextual information for mobile communication. In Extended Abstracts on Human Factors in Computing Systems , pages 1371–1376. ACM

Smith J, Dulay N (2014) Ringlearn: Long-term mitigation of disruptive smartphone interruptions. In International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) , pages 27–35. IEEE

Stern H, Pammer V, Lindstaedt SN (2011) A preliminary study on interruptibility detection based on location and calendar information. Proceedings of Context-Systems Design, Evaluation and Optimisation(CoSDEO)

Vilwock W, Madiraju P, Ahamed SI (2013) A system implementation of interruption management for mobile devices. In Proceedings of the 16th International Conference on Computational Science and Engineering , pages 181–187. IEEE

Bohmer M, Lander C, Gehring S, Brumby DP, Kruger A (2014) Interrupted by a phone call: exploring designs for lowering the impact of call notifications for smartphone users. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , pages 3045–3054. ACM

Grandhi SA, Jones Q (2015) Knock knock whos there? putting the user in control of managing interruptions. International Journal of Human-Computer Studies 79:35–50

Shirazi AS, Henze N, Dingler T, Pielot M, Weber D, Schmidt A (2014) Large-scale assessment of mobile notifications. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , pages 3055–3064. ACM

Iqbal ST, Horvitz E (2010) Notifications and awareness: a field study of alert usage and preferences. In Proceedings of the 2010 ACM conference on Computer supported cooperative work , pages 27–30. ACM

Kanjo E, Kuss DJ, Ang CS (2017) Notimind: Utilizing responses to smart phone notifications as affective sensors. IEEE Access 5:22023–22035

Turner LD, Allen SM, Whitaker RM (2015) Push or delay? decomposing smartphone notification response behaviour. In Human Behavior Understanding , pages 69–83. Springer

Park M-H, Hong J-H, Cho S-B (2007) Location-based recommendation system using bayesian user’s preference model in mobile devices. In International Conference on Ubiquitous Intelligence and Computing , pages 1130–1139. Springer

Zheng VW, Cao B, Zheng Y, Xie X, Yang Q (2010) Collaborative filtering meets mobile recommendation: A user-centered approach. In AAAI , volume 10, pages 236–241

Kim K-j, Ahn H, Jeong S (2010) Context-aware recommender systems using data mining techniques. In Proceedings of world academy of science, engineering and technology , volume 64, pages 357–362

Liu Q, Ge Y, Li Z, Chen E, Xiong H (2011) Personalized travel package recommendation. In Data Mining (ICDM), 2011 IEEE 11th International Conference on , pages 407–416. IEEE

Shin D, Lee J-w, Yeon J (2009) Context-aware recommendation by aggregating user context. In IEEE Conference on Commerce and Enterprise Computing, Vienna, Austria, Austria, 20–23 July, pp.∼423–430. IEEE Computer Society, Washington, DC, USA

Liu B, Kong D, Cen L, Gong NZ, Jin H, Xiong H (2015) Personalized mobile app recommendation: Reconciling app functionality and user privacy preference. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining , pages 315–324. ACM

Seo S-s, Kwon A, Kang J-M, Strassner J (2011) Pyp: design and implementation of a context-aware configuration manager for smartphones. In International Workshop on Smart Mobile Applications

Bozanta A, Kutlu B (2018) Developing a contextually personalized hybrid recommender system. Mob Inf Syst 2018:1–13

Lee W-P (2007) Deploying personalized mobile services in an agent-based environment. Expert Syst Appl 32(4):1194–1207

Turner LD, Allen SM, Whitaker RM (2015) Interruptibility prediction for ubiquitous systems: conventions and new directions from a growing field. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing , pages 801–812. ACM

Fogarty J, Hudson SE, Atkeson CG, Avrahami D, Forlizzi J, Kiesler S, Lee JC, Yang J (2005) Predicting human interruptibility with sensors. ACM Transactions on Computer-Human Interaction (TOCHI) 12(1):119–146

Bedogni L, Di Felice M, Bononi L (2016) Context-aware android applications through transportation mode detection techniques. Wirel Commun Mob Comput 16(16):2523–2541

Tan GW-H, Ooi K-B, Leong L-Y, Lin B (2014) Predicting the drivers of behavioral intention to use mobile learning: A hybrid sem-neural networks approach. Computers in Human Behavior 36:198–213

Sathiya Keerthi S, Shevade SK, Bhattacharyya C, Murthy KRK (2001) Improvements to platt’s smo algorithm for svm classifier design. Neural computation 13(3):637–649

Breiman L (2001) Random forests. Mach Learn 45(1):5–32

Sarker IH, Colman A, Kabir MA, Han J (2018) Individualized time-series segmentation for mining mobile phone user behavior. The Computer Journal, Oxford University, UK 61(3):349–368

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

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

Sarker IH, Alqahtani H, Alsolami F, Khan AI, Abushark YB, Siddiqui MK (2020) Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling. Journal of Big Data 7(1):1–23

Pielot M, De Oliveira R, Kwak H, Oliver N (2014) Didn’t you see my message?: predicting attentiveness to mobile instant messages. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , pages 3319–3328. ACM

Bayat A, Pomplun M, Tran DA (2014) A study on human activity recognition using accelerometer data from smartphones. Procedia Computer Science 34:450–457

Ayu MA, Ismail SA, Matin AFA, Mantoro T (2012) A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition. Procedia Engineering 41:224–229

Fetter M, Seifert J, Gross T (2011) Predicting selective availability for instant messaging. In IFIP Conference on Human-Computer Interaction , pages 503–520. Springer

Fisher R, Simmons R (2011) Smartphone interruptibility using density-weighted uncertainty sampling with reinforcement learning. In 2011 10th International Conference on Machine Learning and Applications and Workshops , volume 1, pages 436–441. IEEE

Swati K, Patankar AJ (2014) Effective personalized mobile search using knn. In 2014 International Conference on Data Science & Engineering (ICDSE) , pages 157–160. IEEE

Middleton SE, Shadbolt NR, De Roure DC (2004) Ontological user profiling in recommender systems. ACM Transactions on Information Systems (TOIS) 22(1):54–88

Anagnostopoulos T, Anagnostopoulos C, Hadjiefthymiades S, Kyriakakos M, Kalousis A (2009) Predicting the location of mobile users: a machine learning approach. In Proceedings of the 2009 international conference on Pervasive services , pages 65–72. ACM

Riboni D, Bettini C (2011) Cosar: hybrid reasoning for context-aware activity recognition. Pers Ubiquit Comput 15(3):271–289

Zhong E, Tan B, Mo K, Yang Q (2013) User demographics prediction based on mobile data. Pervasive and mobile computing 9(6):823–837

Wang Y, Feng D, Li D, Chen X, Zhao Y, Niu X (2016) A mobile recommendation system based on logistic regression and gradient boosting decision trees. In 2016 International Joint Conference on Neural Networks (IJCNN) , pages 1896–1902. IEEE

Ernsting C, Dombrowski SU, Oedekoven M, Julie LO, Kanzler M, Kuhlmey A, Gellert P et al (2017) Using smartphones and health apps to change and manage health behaviors: a population-based survey. Journal of medical Internet research 19(4):e101

Sarker IH (2019) A machine learning based robust prediction model for real-life mobile phone data. Internet of Things 5:180–193

Hong J, Suh E-H, Kim J, Kim SY (2009) Context-aware system for proactive personalized service based on context history. Expert Syst Appl 36(4):7448–7457

Sarker IH, Colman A, Han J, Khan AI, Abushark YB, Salah K (2019) Behavdt: A behavioral decision tree learning to build user-centric context-aware predictive model. Mobile Networks and Applications , pages 1–11

Alawnah S, Sagahyroon A (2017) Modeling of smartphones’ power using neural networks. EURASIP Journal on Embedded Systems 2017(1):22

Leong L-Y, Hew T-S, Tan GW-H, Ooi K-B (2013) Predicting the determinants of the nfc-enabled mobile credit card acceptance: a neural networks approach. Expert Syst Appl 40(14):5604–5620

Chong AY-L (2013) Predicting m-commerce adoption determinants: a neural network approach. Expert Syst Appl 40(2):523–530

Rajashekar D, Nur Zincir-Heywood A, Heywood MI (2016) Smart phone user behaviour characterization based on autoencoders and self organizing maps. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) , pages 319–326. IEEE

Kandasamy K, Kumar CS (2015) Modified pso based optimal time interval identification for predicting mobile user behaviour in location based services. Indian Journal of Science and Technology 8(S7):185–193

Hartono RN, Pears R, Kasabov N, Worner SP (2014) Extracting temporal knowledge from time series: A case study in ecological data. In Proceedings of the International Joint Conference on Neural Networks, Beijing, China, 6–11 July, pp. ∼ 4237–4243. IEEE Computer Society, Washington, DC, USA

Keogh E, Chu S, Hart D, Pazzani M (2004) Segmenting time series: a survey and novel approach. Data mining in time series databases 57:1–22

Shokoohi-Yekta M, Chen Y, Campana B, Hu B, Zakaria J, Keogh E (2015) Discovery of meaningful rules in time series. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 10–13 August, pp. ∼ 1085–1094. ACM, New York, USA

Zhang G, Liu X, Yang Y (2015) Time-series pattern based effective noise generation for privacy protection on cloud. IEEE Trans Comput 64(5):1456–1469

Cao H, Bao T, Yang Q, Chen E, Tian J (2010) An effective approach for mining mobile user habits. In Proceedings of the International Conference on Information and knowledge management, Toronto, ON, Canada, 26–30 October, pp. ∼ 1677–1680. ACM, New York, USA

Iqbal H Sarker (2018) Mobile data science: Towards understanding data-driven intelligent mobile applications. EAI Endorsed Transactions on Scalable Information Systems , 5(19)

La Polla M, Martinelli F, Sgandurra D (2012) A survey on security for mobile devices. IEEE communications surveys & tutorials 15(1):446–471

Otrok H, Mizouni R, Bentahar J et al. (2014) Mobile phishing attack for android platform pages 18–23

Dunham K (2008) Mobile malware attacks and defense

Lin J, Yu W, Zhang N, Yang X, Zhang H, Zhao W (2017) A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J 4(5):1125–1142

John GH, Langley P (1995) Estimating continuous distributions in bayesian classifiers. In Proceedings of the Eleventh conference on Uncertainty in artificial intelligence , pages 338–345. Morgan Kaufmann Publishers Inc.

Quinlan JR (1993) C4.5: Programs for machine learning. Machine Learning

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

Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Machine learning 6(1):37–66

Freund Y, Schapire RE et al. (1996) Experiments with a new boosting algorithm. In Icml , volume 96, pages 148–156. Citeseer

Le Cessie S, Van Houwelingen JC (1992) Ridge estimators in logistic regression. Journal of the Royal Statistical Society: Series C (Applied Statistics) 41(1):191–201

MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In Fifth Berkeley symposium on mathematical statistics and probability , volume 1

Rokach L (2010) A survey of clustering algorithms. In Data Mining and Knowledge Discovery Handbook , pages 269–298. Springer

Sneath PHA (1957) The application of computers to taxonomy. Journal of General Microbiology , 17(1)

Sorensen T (1948) Method of establishing groups of equal amplitude in plant sociology based on similarity of species. Biol. Skr. , 5

Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In ACM SIGMOD Record , volume 22, pages 207–216. ACM

Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In Proceedings of the International Joint Conference on Very Large Data Bases, Santiago Chile, pp. ∼ 487–499. , volume 1215

Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In ACM Sigmod Record , volume 29, pages 1–12. ACM

Das A, Ng W-K, Woon Y-K (2001) Rapid association rule mining. In Proceedings of the tenth international conference on Information and knowledge management , pages 474–481. ACM

Zaki MJ (2000) Scalable algorithms for association mining. IEEE transactions on knowledge and data engineering 12(3):372–390

Sun S, Luo C, Chen J (2017) A review of natural language processing techniques for opinion mining systems. Information fusion 36:10–25

Maedche A, Staab S (2001) Ontology learning for the semantic web. IEEE Intell Syst 16(2):72–79

Grosan C, Abraham A (2011) Rule-based expert systems. Int Underw Syst Des, pages 149–185

Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decision Support Systems 74:12–32

Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132

Marine-Roig E, Martin-Fuentes E, Daries-Ramon N (2017) User-generated social media events in tourism. Sustainability 9(12):2250

Belanche D, Flavián M, Pérez-Rueda A (2020) Mobile apps use and wom in the food delivery sector: The role of planned behavior, perceived security and customer lifestyle compatibility. Sustainability 12(10):4275

World health organization: Who. http://www.who.int/

Yi B-J, Lee D-G, Rim H-C (2015) The effects of feature optimization on high-dimensional essay data. Math Probl Eng 2015:1–12

Bobek S, Nalepa GJ, Ślażyński M (2019) Heartdroid†rule engine for mobile and context-aware expert systems. Expert Systems 36(1):e12328

Download references

Author information

Authors and affiliations.

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

Iqbal H. Sarker & Md. Kafil Uddin

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

Iqbal H. Sarker & Mohammed Moshiul Hoque

RMIT University, Melbourne, VIC, 3000, Australia

Tawfeeq Alsanoosy

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Iqbal H. Sarker .

Additional information

Publisher’s note.

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

Rights and permissions

Reprints and permissions

About this article

Sarker, I.H., Hoque, M.M., Uddin, M.K. et al. Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions. Mobile Netw Appl 26 , 285–303 (2021). https://doi.org/10.1007/s11036-020-01650-z

Download citation

Published : 14 September 2020

Issue Date : February 2021

DOI : https://doi.org/10.1007/s11036-020-01650-z

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Mobile data science
• Artificial intelligence
• Machine learning
• Natural language processing
• Expert system
• Data-driven decision making
• Context-awareness
• Intelligent mobile apps
• Find a journal
• Publish with us
• Track your research

• Enabling Opportunity

Dig deeper with in‑depth resources, white papers, case studies, and surveys.

The android ecosystem, you say you want a mobile revolution….

Google CEO Sundar Pichai shares how Android is changing the ways people use mobile devices.

Android fuels innovation and choice

Discover how Android offers more choice and freedom to manufacturers, developers, and consumers.

Four myths about Android

ZDNet looks into common myths about Android and sets the record straight.

Economic impact

Google is a growth engine for europe.

Discover all the ways Google is helping dozens of European businesses grow online

Mobile tech drives the EU economy

See how the development of mobile devices and software contributes £73bn to the EU economy.

For the next five billion: Android One

The Android One initiative is helping people across the globe access affordable mobile devices.

Breaking the $100 barrier

See how Google teamed up with device maker Infinix to sell Android phones in Africa for less than $100.

Launching Android One in Africa

By partnering with regional players, Google has helped get low-cost phones to developing countries.

The growing mobile Internet economy

Explore the impact of mobile technology in the 13 countries that make up about 70 percent of global GDP.

Embracing innovation

How emerging markets fuel disruption.

Discover why markets like China, Vietnam and India are primed to disrupt the global mobile industry.

Keep Android harmonized

The president of the App Developers Alliance argues against further fragmentation of the Android ecosystem.

Let’s talk about Android

Discover the impact of Android’s open-source model on competition in the field of mobile operating systems.

Explore these carousel items. Use the previous and next buttons, as well as the keyboard arrows, to change the displayed item.

Help | Advanced Search

Computer Science > Operating Systems

Title: android os case study.

Abstract: Android is a mobile operating system based on a modified version of the Linux kernel and other open source software, designed primarily for touchscreen mobile devices such as smartphones and tablets. It is an operating system for low powered devices that run on battery and are full of hardware like Global Positioning System (GPS) receivers, cameras, light and orientation sensors, Wi-Fi and LTE (4G telephony) connectivity and a touch screen. Like all operating systems, Android enables applications to make use of the hardware features through abstraction and provide a defined environment for applications. The study includes following topic: Background And History Android Architecture Kernel And StartUp Process Process Management Deadlock CPU Scheduling Memory Management Storage Management I/O Battery Optimization

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

DBLP - CS Bibliography

Bibtex formatted citation.

Bibliographic and Citation Tools

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

• Institution

arXivLabs: experimental projects with community collaborators

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

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

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

Mobile Systems

Mobile devices are the prevalent computing device in many parts of the world, and over the coming years it is expected that mobile Internet usage will outpace desktop usage worldwide. Google is committed to realizing the potential of the mobile web to transform how people interact with computing technology. Google engineers and researchers work on a wide range of problems in mobile computing and networking, including new operating systems and programming platforms (such as Android and ChromeOS); new interaction paradigms between people and devices; advanced wireless communications; and optimizing the web for mobile settings. In addition, many of Google’s core product teams, such as Search, Gmail, and Maps, have groups focused on optimizing the mobile experience, making it faster and more seamless. We take a cross-layer approach to research in mobile systems and networking, cutting across applications, networks, operating systems, and hardware. The tremendous scale of Google’s products and the Android and Chrome platforms make this a very exciting place to work on these problems.

Some representative projects include mobile web performance optimization, new features in Android to greatly reduce network data usage and energy consumption; new platforms for developing high performance web applications on mobile devices; wireless communication protocols that will yield vastly greater performance over today’s standards; and multi-device interaction based on Android, which is now available on a wide variety of consumer electronics.

Recent Publications

Some of our teams.

We're always looking for more talented, passionate people.

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

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Users of the main smartphone operating systems (iOS, Android) differ only little in personality

* E-mail: [email protected]

Affiliations Department of Psychology, University of Konstanz, Konstanz, Germany, Department of Psychology, University of Cambridge, Cambridge, United Kingdom

Affiliation Department of Psychology, University of Konstanz, Konstanz, Germany

• Friedrich M. Götz, 
• Stefan Stieger, 
• Ulf-Dietrich Reips

• Published: May 3, 2017
• https://doi.org/10.1371/journal.pone.0176921
• Reader Comments

The increasingly widespread use of mobile phone applications (apps) as research tools and cost-effective means of vast data collection raises new methodological challenges. In recent years, it has become a common practice for scientists to design apps that run only on a single operating system, thereby excluding large numbers of users who use a different operating system. However, empirical evidence investigating any selection biases that might result thereof is scarce. Henceforth, we conducted two studies drawing from a large multi-national (Study 1; N = 1,081) and a German-speaking sample (Study 2; N = 2,438). As such Study 1 compared iOS and Android users across an array of key personality traits (i.e., well-being, self-esteem, willingness to take risks, optimism, pessimism, Dark Triad, and the Big Five). Focusing on Big Five personality traits in a broader scope, in addition to smartphone users, Study 2 also examined users of the main computer operating systems (i.e., Mac OS, Windows). In both studies, very few significant differences were found, all of which were of small or even tiny effect size mostly disappearing after sociodemographics had been controlled for. Taken together, minor differences in personality seem to exist, but they are of small to negligible effect size (ranging from OR = 0.919 to 1.344 (Study 1), η p 2 = .005 to .036 (Study 2), respectively) and may reflect differences in sociodemographic composition, rather than operating system of smartphone users.

Citation: Götz FM, Stieger S, Reips U-D (2017) Users of the main smartphone operating systems (iOS, Android) differ only little in personality. PLoS ONE 12(5): e0176921. https://doi.org/10.1371/journal.pone.0176921

Editor: Susana Jiménez-Murcia, Hospital Universitari de Bellvitge, SPAIN

Received: October 13, 2016; Accepted: April 19, 2017; Published: May 3, 2017

Copyright: © 2017 Götz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data files are available from the Open Science Framework database (URL: https://osf.io/2nmhu/ ).

Funding: The publication fee will be covered by the Open Access publication fund of the University of Konstanz ( https://www.kim.uni-konstanz.de/en/services/scholarly-publishing-and-open-access/open-access-publication-funds/ ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Introduction

Following the advent and proliferation of smartphones, app-based research has spread across the scientific landscape, ranging from fields as diverse as physics [ 1 ], tourism [ 2 , 3 ] and geology [ 4 , 5 ] to medicine [ 6 , 7 ]. Of note, it falls on especially fertile grounds in psychology, booming throughout the discipline, from biopsychology [ 8 ] to neuroscience [ 9 , 10 ] and personality research [ 11 , 12 , 13 ]. The reasons for this momentum are manifold:

Smartphone technology enables researchers to collect an abundance of data (high volume), that arrives as a continuous stream in real time (i.e., high velocity) from various, multifaceted sources i.e., high variety [ 14 , 15 ]. On top of experience sampling, i.e., the repeated, context-sensitive assessment of cognitive, affective, and behavioral measures across a certain period [ 16 ], modern smartphones may grant researchers access to global positioning system (GPS) based location data, communication logs, video and audio capture, motion sensing, and biosensors [ 14 , 15 , 17 ]. Linking these data to geographic information system data (e.g., climate and neighborhood characteristics) further allows scientists to paint an unprecedentedly fine-grained picture of people’s dynamic physical and social surroundings [ 18 , 19 ]. Leveraging this mechanism would facilitate to gain an in-depth understanding of the complex person × environment interactions that shape human behavior and might spur breakthroughs in personality and social psychology [ 19 ].

From a users’ perspective, science apps (i.e., mobile phone applications that serve scientific purposes as a research tool) are highly convenient as they do no longer require participants to be physically present in a lab [ 20 ] or, as in early Internet-based research, at a desktop or laptop computer. Increasingly often, both data collection and transmission happen automatically in the background, effectively reducing participant burden to a minimum [ 8 , 15 ].

Similarly, unlike their predecessors, personal digital assistants (PDAs), smartphones are already established as an integrated part of our lives [ 11 , 21 ]. Spending considerable amounts of time with smartphones has become a standout lifestyle feature [ 22 ], reflecting the contemporary philosophy of life in modern societies.

Given the remarkable growth rates of the smartphone market, the number of people carrying a smartphone is expected to skyrocket from 1 billion at the beginning of the decade [ 21 , 23 , 24 ], and 2 billion in 2016 [ 25 ] to over 5 billion by 2025 [ 17 ]. Against this backdrop, it is especially noteworthy, that smartphones have already begun to penetrate the emerging markets of developing countries and might soon become more common than computers [ 19 ]. In the absence of any available alternatives, in some regions smartphones may even constitute a monopoly-like structure, providing the only means to connect people to the Internet. In view of these tendencies, it appears encouraging, that data that has been submitted through mobile devices has been shown to be no less valid or reliable than data that was obtained from desktop users [ 26 ] or during laboratory experiments [ 23 ]. Marking the next leap towards a valid test of the universality of psychological theories [ 23 ], this trend thus has the capacity to pave the way for the inclusion of previously under-studied populations and ultimately, a wider coverage of cross-cultural research [ 20 ].

In a nutshell, smartphones are ubiquitous, fairly unobtrusive, remotely accessible, sensor-rich, and computationally powerful [ 17 , 22 ], thus setting the stage for the emerging field of Psychoinformatics at the crossroads of psychology and computer science [ 15 , 24 , 25 ]. Accordingly, smartphones may empower researchers to conduct large-scale longitudinal studies in real-world settings at low cost, featuring heterogeneous, global samples. Thanks to that, scientists may base future discoveries on an abundance of precise, and ecologically valid behavioral data above and beyond traditional self-reports [ 17 , 19 , 27 , 28 ]. Likewise, due to the remarkable computational powers, there are no apparent boundaries restricting the content of smartphone-based research and even highly complex cognitive tasks can be administered with ease [ 23 ]. Making use of these innovative possibilities would enable researchers to cross-validate, or challenge existing findings from lab settings [ 16 , 25 ] and extend the scientific body of knowledge beyond traditional Internet-based research that set out to achieve the same goals [ 29 – 31 ].

Thus, harnessing this untapped potential seems imperative. However, caution is warranted as some caveats (e.g., ethical and technical considerations with respect to data privacy and confidentiality, data transmission and storage solutions, security issues, app quality, and safety) prevail that need to be addressed for the sake of sound and adequate research practices [ 14 , 15 , 17 , 25 , 27 , 32 ]. In view of the constantly growing plethora of apps, guidance is needed to identify trustworthy, proper science apps. On a more methodological note, concerns have been voiced regarding potential technology-induced selection biases e.g., [ 33 ]. By definition, smartphone research is limited to the population of smartphone owners, creating coverage issue [ 34 ]. Yet, this might be less problematic, given the afore-mentioned rapidly expanding distribution of smartphones worldwide that will soon rise to a level of almost complete coverage.

Moreover, even though Lane and Manner [ 35 ] did find that smartphone ownership was predicted by extraversion, the authors have argued themselves that personality is, overall, a rather weak predictor of smartphone ownership. Hence, we tentatively conclude that it appears appropriate to assume that coverage issues, i.e. differences between smartphone owners and non-smartphone owners do not undermine the generalizability of app-based research to a worrying extent. Nevertheless, this does not rule out other systematic biases within the population of smartphone users.

Conducting a thorough analysis of the major smartphone operating systems (OS) in terms of their suitability as a research tool (i.e., Android, iOS, BlackBerry, Symbian, and Windows Mobile), Oliver [ 36 ] concluded that while every platform has its pros and cons, none of them is ideal or even generally superior to its competitors. As a solution, researchers could develop several native science apps in the respective programming languages or come up with web-based hybrid apps by means of cross-platform development tools (CPDTs). Alternatively, if social scientists do not see themselves fit for programming, interdisciplinary collaborations with trained computer scientists may prove effective [ 27 ].

However, while studies that accommodate both major systems (i.e., Android and iOS) do exist [ 37 , 38 ], they are much more of the exception rather than the general rule. In contrast, it is fairly common for psychological app-based studies to be run solely on either Android [ 39 – 42 ], iOS [ 43 – 46 ] or a different OS [ 13 , 47 ].

Perhaps puzzling at first, this pattern can be explained as follows: Programming multiple apps, one for each system is a tedious and time-consuming process that requires, above all, sufficient knowledge and skills in at least two programming languages, which makes it less desirable. In the meantime, CPDTs are still developing, often failing to live up to the performance of their native counterparts [ 48 , 49 ]. To make things worse, Miller [ 17 ] has pointed out that there is currently only a small minority of psychologists, sufficiently tech-savvy and advanced in computer science to program apps efficiently by themselves. While it is hence understandable, that researchers shy away from coming up with science apps that accommodate both systems, it might jeopardize the data’s generalizability if user personality is related to smartphone operating systems as much as it is related to computer operating systems [ 33 ].

In other words, if iOS and Android users were to differ systematically regarding fundamental psychological characteristics, results of smartphone app studies would be inherently biased and per se compromised in their external validity. This would be a particularly harsh setback for the burgeoning field of personality research in Psychoinformatics [ 11 , 12 , 13 ], whose results would become questionable at best. As, to our knowledge, no study has examined this possibility so far, we aim to compare iOS and Android users along an array of personality traits.

To that end, we come up with two studies that complement each other.

More precisely, Study 1 employs a holistic personality assessment to screen for potential differences in various diverse traits across a large multi-national sample. Beyond the Big Five personality traits [ 50 ] at the core, it seeks to capture other facets of user personality that tap into different aspects and may therefore add incremental value and explanatory power. As such it draws from positive psychology by collecting data on well-being [ 42 ], global self-esteem [ 51 ] and optimism [ 52 ]. In juxtaposition, it also turns to more sinister traits, namely risk proneness [ 53 ], pessimism [ 52 ] and the Dark Triad (i.e., narcissism, psychopathy, and Machiavellianism; [ 54 ]).

Building on that, Study 2 aims to consolidate those findings and further extend the scope of our research to the computer realm, drawing from an even bigger, German-speaking sample and takes not only iOS and Android, but also Windows and Mac OS users into account (throughout the remainder of this article, Mac OS refers to the computer division of Apple, i.e. Mac operating systems that run on iMacs and MacBooks.). In recognition of the pre-eminent position of the Big Five taxonomy, as the predominant personality framework in mobile phone and Internet studies [ 12 , 55 ], and in the absence of notable effects for the other personality traits in Study 1, Study 2 is deliberately restricted to the Big Five [ 50 ]. This approach limits error due to multiple testing issues [ 12 ] and together with the enhanced statistical power, arising from the large sample, allows for even more rigorous testing. Study 2 improves further on Study 1 in assessing participants’ OS non-reactively, i.e., automatically upon accessing the questionnaire, thereby avoiding self-reports, which are prone to evoke biases. Given their overlapping, yet complementary design, we believe that if the results of Study 1 and Study 2 converge, one could claim with some confidence that indeed, personality differences between users of different operating systems do–or do not exist.

Research questions and hypotheses

In marketing research and consumer psychology, brands are believed to have a personality, featuring a unique set of characteristics usually attributed to humans [ 56 ]. Henceforth, attitudes towards specific brands can be formed on the basis of these personality traits. Accordingly, these attitudes may serve the purpose of allowing consumers to express their self-concepts through the purchase, use and ownership of particular brands [ 57 ].

Reflecting its rather unique firm philosophy and marketing strategy, the Apple brand personality was built to convey qualities such as nonconformity, innovation, and creativity [ 58 ]. Unlike PC in the computer domain, or Samsung, SONY, and Nokia in the smartphone sector, Apple has successfully managed to become a lifestyle brand, echoing a modern, youthful philosophy of life that rests on the pillars of freedom, imagination, and simplicity at the heart of a seemingly truly humanistic, caring company. Lending empirical support to these observations, research has shown that whereas consumers describe Apple as exciting, SONY is rather seen as competent and sincere [ 59 ].

Moreover, the iPhone has become a status symbol for some people, inducing a feeling of belonging to a societal avant-garde in those who carry it [ 60 ]. Contrarily, consistent with its strategy to target the mass market, Samsung has cultivated a fairly different brand personality, emphasizing values such as ruggedness and functionality [ 60 ]. Summed up, on the one hand Apple stands for an outgoing, adventurous and lively brand personality, on the other hand it gives rise to an elitist self-definition of its customers, who may seek social approval and boost their self-esteem by being identified with Apple products. Meanwhile, Android brands (e.g., Samsung, SONY) appear to promote a more down-to-earth approach, grounded in a reliable, but significantly less fancy and glamorous product assortment. While we do not want to give in to mere speculations, drawing from the presented findings, we formulated the following hypotheses:

Hypothesis 1 (Study 1).

On average , iOS users will score higher on global self-esteem than Android users , reflecting the widespread belief that whereas the iPhone is a status symbol that carries prestige, fashionability, and exclusivity, all of which are suitable to make one feel valued and special, thus promoting enhanced self-esteem, Android smartphones fail to exert this same power.

Hypothesis 2 (Study 1, Study 2).

On average , iOS users will show higher Extraversion than Android users . Owing to Apple’s brand image as young, daring, outgoing and creative–an array of personality characteristics that seems to be rather closely linked to an extraverted personality, enhanced extraversion can be expected in accordance with the notion, that brand personality is supposed to mirror one’s own personality.

As neither the existing literature, nor common sense would allow similarly specific predictions, we refrained from formulating additional hypotheses for the other variables. Nonetheless, we believe that the inclusion of these constructs is conducive to the overall aim of the present research which is to detect any noteworthy personality differences as a function of users’ OS. Henceforth we tried to accomplish the most extensive coverage of user personality given existing constraints (e.g., questionnaire length) and adopted an exploratory approach in the search of potential differences. Likewise we investigated whether participants’ language (i.e., English or German) moderated the observed links, without holding any directional expectations.

Because there is very litte research on the topic of personality differences regarding used operating systems, we assumed a low effect size ( d = 0.2 and η p 2 = 0.01 according to [ 61 ]). A power analysis (α = 5%, power = 80%, two-tailed) recommends a minimal sample size of N = 788 for Study 1 and N = 1,096 for Study 2.

Materials and methods–study 1

Participants.

The sample was comprised of 1,081 participants, 624 (58%) of whom reported to be female, while 449 (41%) reported to be male, and 8 (1%) who did not disclose their sex. Reported age ranged from 18 to 94 years ( M = 24.5, SD = 8.1). Recruitment ensued online on various national and international platforms (e.g., Facebook, reddit), as well as on campus at the University of Konstanz, Germany, by word-of-mouth and custom-tailored advertisement of the study in introductory psychology lectures. Following this twofold strategy, the obtained sample comprised 507 participants (46.9%) from German-speaking countries (Germany: 44.8%, Switzerland: 1.2%, Austria: 0.9%) and 574 participants (53.1%) who were either from English-speaking countries or mastered English fluently (USA: 25.5%, Australia: 3.9%, UK: 2.5%, Canada: 2.3%).

Reported monthly budget ranged from less than 250€ to 5,001€ or more, with 76% of the sample disposing of 2,000€ or less per month, while 11% chose not to reveal their monthly budget. Regarding OS usage, 573 participants (53.0%) identified themselves as Android users, 444 participants (41.1%) indicated they use an iPhone. Meanwhile a small proportion indicated that they use either a Windows Phone (3.3%), a completely different operating system (1.1%), or no smartphone at all (1.3%). For parsimony’s sake only users of the two main OS (i.e., Android and iOS) were considered for further analysis resulting in a final sample size of 1,017.

Furthermore, the participant pool was mostly made up of college students (65.6%), active members of the workforce (31.7%), and high school students (8.8%), while others were unemployed (2.7%) or did not disclose their occupation (2.0%). (Please note that the accumulated percentages may exceed 100 percent, as participants could indicate multiple occupations, e.g. being a college student while working full-time.) The majority of the sample reported to be single (48.3%) or currently engaged in a romantic relationship (39.5%), while small fractions were married (7.3%), divorced (0.6%), or widowed (0.1%), or did not report their present marital status (2.1%).

Questionnaire length in electronically distributed online surveys deserves special attention, as the same content may appear longer on Web sites, stretching across several pages, as opposed to traditional paper-and-pencil questionnaires [ 62 ], also see [ 63 ], for the one-item-one-screen design. Furthermore, dropout decisions are based on study attributes, such as survey length [ 64 ] or incompatibilities of technology used [ 65 ]. Similarly, previous research has shown that dropout risk rose by 40% from a 10-minute questionnaire to a 30-minute questionnaire [ 66 ]. Motivated by those findings, we deliberately decided to limit the online questionnaire to a restricted number of items that would take no longer than 15 minutes to complete, in order to decrease participant burden and, in turn, foster participation. Henceforth, aside from a small battery of demographic questions, we aimed to employ short, yet effective measures that are well-suited for group-level analysis [ 67 , 68 ] and possess satisfying psychometric properties [ 67 ].

In line with this rationale, we assessed Big Five personality traits with the Mini-IPIP [ 50 ], which contains 20 items and has repeatedly been shown to have acceptable reliability estimates [ 69 , 70 ].

Moreover, we chose to gauge global self-esteem by means of the Single-Item Self-Esteem Scale (SISE) that has been successfully translated into other languages before [ 71 ] and demonstrated to be of satisfactory validity [ 51 ]. Similarly, we employed a single-item measure of well-being, which has been a robust indicator in previous research with German-speaking samples [ 42 ].

Furthermore, we chose the Dirty Dozen [ 54 ] as a representative of negative personality attributes, consisting of 12 items, which have been shown to be an efficient, psychometrically acceptable measure of the Dark Triad [ 72 ].

Apart from that, we employed some short scales, which originate from the Leibniz Institute for the Social Sciences (GESIS) and have been validated on large, stratified samples, to measure the following constructs: risk proneness (1 item), [ 53 ], optimism and pessimism (2 items), [ 52 ]. In addition, we also assessed social desirability (6 items; 2 subscales), [ 73 ].

However, both subscales yielded unacceptably low reliability estimates (NQminus α = .564, PQplus α = .495) and were henceforth not considered for any further statistical analysis. Aside from the said GESIS measures (i.e., optimism, pessimism, risk proneness), which have been validated extensively on large, stratified German samples, it was ensured that the German versions of our instruments had been translated by professionals and repeatedly used in previous studies so that their appropriateness and precision could be taken for granted (see [ 74 ] for Mini-IPIP, see [ 42 ] for well-being). The only exceptions were the Dirty Dozen and the SISE. In the absence of established German versions, the scales were translated from the original English using the parallel-blind technique [ 75 ].

The survey was designed for optimization on regular computers and smartphones, using the SoSci Survey online tool ( https://www.soscisurvey.de/ ). The questionnaire was administered online and available for a period of 3 months in English and German. As a general rule, participation was unpaid and voluntary, without further incentives, such as personalized feedback from the questionnaire.

However, psychology students, enrolled at the University of Konstanz, were offered course credit for participation. Beyond that, upon inclusion in the sample, participants automatically entered a lottery, raffling off Amazon gift card vouchers of a value of 100€ in total, unless they specifically requested otherwise.

The present study was conducted in accordance with the Ethical Guidelines of the German Psychological Society (DGPs) and the Ethical Guidelines of the Department of Psychology, University of Konstanz. Formal ethics approvals for this type of research (i.e., noninvasive, not affecting the physical or psychological integrity, the right for privacy or other personal rights of interest) are required neither by these guidelines nor by German laws.

All participants consented to the terms of the study, which were outlined in detail, preceding the actual questionnaire. As such, providing informed consent was made a prerequisite to proceed to the main part of the survey. Participants were explicitly told that they could revoke their consent and withdraw from the study at any time without any personal disadvantages arising from it. Furthermore, anonymity was ensured and no harmful procedures were applied. The same precautions and ethical standards were also upheld throughout Study 2.

Statistical analysis

Following a twofold analysis procedure, we initially checked for potential differences in demographic variables, between self-reported iOS and Android users. Hereafter, we employed inferential statistics to account for possible distinctions with respect to the available personality measurements, beyond the influences of sociodemographic variables. Thereby we conducted both, confirmatory and exploratory analyses.

Results–study 1

Demographics.

At first, we ran a series of χ 2 -tests to investigate the sample’s demographic composition. In this context, we did neither detect any significant differences for marital status (χ 2 = 4.18, df = 4, p = .38), nor for participant’s sex (χ 2 = 1.03, df = 1, p = .31). Similarly, a t -test failed to unveil any significant differences in reported age between iOS users ( M = 24.23, SD = 8.10) and Android users ( M = 24.40, SD = 7.63), t = -0.358, p = .72. However, significant differences although of very low effect size emerged in terms of the distribution of participants’ monthly budget, with iOS users tending to have access to somewhat larger financial resources (χ 2 = 22.75, df = 9, p = .007; r sp = .07).

Personality traits

Given the multitude of variables and the risk of type I error that would have resulted from multiple testing, when conducting individual ANOVAs for every trait, we decided to run a binary logistic regression model instead, whereby smartphone OS (i.e., iOS vs. Android) was predicted by well-being, SISE, risk proneness, optimism, pessimism, the Dark Triad, the Big Five as well as sex, age and monthly budget. To that end, we employed a hierarchical analysis approach, featuring three stages and thus a step-wise increase in our model’s complexity.

First, we entered the sociodemographic variables (i.e., sex, age, and monthly budget) alone to predict users’ OS. Second, we entered both the sociodemographic variables and the personality traits (i.e., well-being, SISE, risk proneness, optimism, pessimism, Dark Triad, Big Five) to see whether this would lead to a significant improvement of the model’s fit to the data above and beyond the predictive power of sociodemographic factors. Third, in order to consider moderating effects that might arise from differences grounded in language or culture, we decided to rerun the full model (step 2) independently for the English-speaking and German-speaking subsamples. Please note, that separate ANOVAs (respectively ANCOVAs when controlling for sociodemographic variables) provide a more fine-grained picture and allow to tease out personality traits’ individual contributions. However, the results remain largely unchanged and the little effects that emerge mostly disappear when controlling for age, sex, and monthly budget. Additional ANOVA-based analyses are available in an online supplement (see S1 Table ).

Overall, the data demonstrated that differences between iOS and Android users were largely absent. While Model 1 was significant and accounted for 1.6% of variance (Nagelkerke R 2 = .016) entering the personality constructs in a second step significantly improved the predictive power of the model (step: χ 2 = 23.700, df = 13, p = .034), with the proportion of explained variance rising to 5% (Nagelkerke R 2 = .050) (see Table 1 , column 1 and 2). Moreover, of all variables only two emerged as statistically significant predictors of user OS. Higher monthly budget predicted a higher likelihood of using iOS ( OR = 0.922), whereas Openness to Experience was related to an increased probability of using Android ( OR = 1.343). However, in both cases Odds Ratios gravitated towards 1.0, indicating a weak relationship and were far off common thresholds of a strong effect (e.g., 3.0 for positive associations, [ 75 ]).

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

https://doi.org/10.1371/journal.pone.0176921.t001

After the language-based split of the sample was performed to unpack potential cultural differences, almost identical patterns were observed in the English subsample (see Model 2a, Table 1 , column 3). In contrast, the model for the German sample could not reliably predict smartphone OS above chance level and dropped below the threshold of statistical significance. Accordingly, no single predictor reached statistical significance. Of note, however, both Neuroticism ( b = -0.28, p = .066, OR = 0.750, 95% CI: 0.551, 1.019) and Openness to Experience ( b = 0.305, p = .069, OR = 1.357, 95% CI: 0.976, 1.885) approached statistical significance, with the latter mirroring the effect that was observed in the other models (see Model 2b, Table 1 , column 4).

In the absence of any significant differences between iOS- and Android users in self-esteem (H1) or Extraversion (H2), none of our hypotheses received empirical support, although Extraversion did approach statistical significance ( b = -0.236, p = .081, OR = 0.790, 95% CI: 0.612, 1.019) in the English subsample, showing a trend in the hypothesized direction.

In sum, our data suggest that iOS- and Android users show only minimal differences regarding psychological concepts. If anything, Android users tend to be a little more open, while iOS users may be slightly wealthier. Yet, all effect sizes were small to tiny. While Table 2 provides a summary of the measures’ descriptive statistics, detailed results of the logistic regression model are displayed in Table 1 .

https://doi.org/10.1371/journal.pone.0176921.t002

Materials and methods–study 2

Our second sample differed from the first sample insofar, as it was larger and more homogeneous with respect to its cultural composition. Several research assistants sent the link to the online questionnaire to their friends, relatives, and acquaintances by using various online channels (e.g., Facebook, Email). This snowball sampling procedure led to a community-based convenience sample of German-speaking participants, which was effectively reduced to 2,036 German-speaking participants due to the following reasons:

First, we excluded data sets from 26 participants who had exhibited suspicious responses that raised doubts about their seriousness in answering the questionnaire (always giving the same highly implausible answer to the delinquency questions, e.g., ‘99’). Second, as the study did specifically aim to compare Android-, iOS-, Windows, and Mac OS users, 145 participants relying on other operating systems were not considered for further analysis. In the final sample, 1,345 participants (66.1%) reported to be female and 685 participants (33.6%) reported to be male, 6 participants chose not to disclose their sex (0.3%). Age ranged from 18 to 78 years ( M = 25.5, SD = 11.64). Furthermore, the vast majority (79.3%) had at least graduated from high school.

In line with the approach adopted in Study 1 we chose to design the online questionnaire in a fashion that would allow participants to complete it in no more than 15 minutes for the sake of enhanced retention rates and increased data quality. Paralleling the procedure of Study 1, we relied on the Mini-IPIP [ 50 ] to measure Big Five personality traits. Due to a technical failure in the online questionnaire, one item of the Conscientiousness subscale was asked twice and one item was not asked. Therefore, the mean score of the Conscientiousness subscale is only based on three instead of four items. On top of this we also used a short battery of questions revolving around delinquency, which were part of a different research project and are henceforth not touched upon in the scope of this article.

The questionnaire was developed in accordance with standards for optimal depiction on both, regular computers and smartphones, using the SoSci Survey online tool ( https://www.soscisurvey.de ). As such, it was exclusively accessible online for a period of two months, and was in German only. In the absence of any financial or otherwise incentives, participation was per se unpaid and voluntary. The same ethical precautions and procedures were applied as in Study 1.

Results–study 2

We followed a similar analysis procedure as outlined in Study 1. However, unlike Study 1, Study 2 included smartphone and computer users alike, resulting in four groups that were compared with each other (i.e., Mac OS, Windows, iOS, Android). As this design would have required running a multinomial regression analysis, with three different models (changing the reference group to determine pairwise group differences) per column, we chose to compute ANOVAs and ANCOVAs instead, which were deemed more parsimonious and easily comprehensible in the given context. Moreover, compared to Study 1, adopting this method bore a considerably smaller risk of suffering from multiple testing issues due to the reduced set of variables.

First, we carried out χ 2 -tests to account for the distribution of primary demographic attributes across the OS groups. Results yielded significant results for sex (χ 2 = 27.44, df = 3, p < .001, φ = .116) and educational level (χ 2 = 63.54, df = 18, p < .001, φ = .177), reflecting a deviation from a balanced distribution between the respective OS groups. Distribution of participants’ sex was rather balanced among Android users (standardized residuals -0.2 for women and 0.3 for men), slightly more male-dominated among Windows users (standardized residuals -0.7 and 1.0, respectively), clearly male-dominated among Mac OS users (standardized residuals -2.0 and 2.9, respectively), and clearly female-dominated among iOS users (standardized residuals 2.1 and -3.0, respectively). With respect to educational level, computer users were more likely to have graduated from university than users using smartphones (standardized residuals: Mac OS, college degree: 2.1, Windows, college degree: 4.1, iOS, college degree: -1.8, Android, college degree: -3.1).

In line with this finding, an ANOVA yielded significant age differences ( F = 51.88, df = 3, p < .001, η p 2 = .071), with Scheffé post-hoc tests indicating that Windows users ( M = 30.1, SD = 15.4) were on average significantly older than Mac OS users ( M = 27.2, SD = 12.2) who were, in turn, on average older than Android users ( M = 23.3, SD = 8.8), and iOS users ( M = 23.0, SD = 8.5). Given this pattern we would like to suggest that the differences in educational level might actually stem from age differences, suggesting that smartphone users in our sample may not yet have graduated from college despite proactively pursuing a higher education.

In order to give a general overview, Table 3 exhibits descriptive parameters. The inferential analysis was carried out in two stages. At first, we ran ANOVAs on Big Five personality traits ( Table 4 , 1st and 2nd column). Thereafter, we conducted an ANCOVA to control for potential moderating effects of age, participant sex, and educational level ( Table 4 , 3rd and 4th column). Finally, we computed pairwise mean-differences (Bonferroni corrected) to pinpoint the concrete nature and direction of effects between the existing subgroups ( Table 4 , 5th column).

https://doi.org/10.1371/journal.pone.0176921.t003

https://doi.org/10.1371/journal.pone.0176921.t004

In resemblance with the patterns from Study 1, no significant differences between OS groups were detected, except for the Big Five personality traits Extraversion (η p 2 = .036) and Openness to Experience (η p 2 = .010), both of which were of rather small effect size (small: η p 2 = .01, medium: η p 2 = .06, large: η p 2 = .14, 76). Of note, the difference for Neuroticism also reached statistical significance, although with a tiny effect size of η p 2 = .005. However, the observed differences for Neuroticism and Openness to Experience vanished once age, sex, and educational level were controlled for ( Table 4 ). Solely the reported differences for Extraversion (η p 2 = .007) prevailed, even after accounting for the afore-mentioned moderators—although with a sharp drop in effect size.

Windows users displayed the lowest values on Extraversion, differing significantly from iOS- and Android users, both of which exhibited more extraversion. With respect to Hypothesis 2, this outcome fails to elicit significant differences between iOS- and Android users, and, like the Study 1 does not lend support to the hypothesis. However, it should be noted that the hypothesized differences were found before controlling for sociodemographic variables. All analyses are summarized in Table 4 .

In a nutshell, very much alike Study 1, we demonstrated that in spite of a few significant differences between the users of the most prominent operating systems in key psychological concepts, namely Big Five personality traits, those differences are of small to tiny effect size. In our present studies, controlling statistically for age, sex, and educational level led to an almost complete disappearance of said effects.

General discussion

Today, the rise of smartphones is already transforming our lives and will most likely continue to do so in the next years to come, as mobile technology becomes more and more ubiquitous all around the world. The new technology now impacts on various domains of our lives, yielding manifold consequences that echo throughout society. In the future this trend may be further amplified as everyday objects, e.g., fridges and cars will harbor remarkable computational powers and constant Internet connectivity, giving rise to the Internet of Things (IoT) [ 15 , 25 ].

While it is widely anticipated that social scientific research will benefit from leveraging the enormous potential of these technologies, a number of methodological, technical, ethical, and practical hurdles peculiar to smartphone-based research prevail, which need to be dealt with, first (e.g., data privacy and confidentiality, data transmission and storage solutions, security issues, app quality, and safety; [ 14 , 15 , 17 , 27 , 32 ]).

Raising and addressing another issue linked to science apps, the present studies aimed to provide a clue as to whether researchers would need to accommodate both predominant smartphone operating systems (i.e., iOS and Android), in order not to jeopardize the generalizability of their findings.

For Study 1 a step-wise analysis procedure did yield a significant impact of Openness to Experience besides differences in sociodemographic variables. At first glance, this might pose a threat to the generalizability mentioned above. However, it is important to note, that all observed effects were of small or even tiny effect size in accordance with common classifications (e.g., [ 76 ]). Likewise, both our hypotheses, assuming differences in self-esteem and Extraversion, respectively, could not be confirmed and were henceforth rejected.

Bearing this in mind, it appears legitimate to assume that in spite of minor differences between iOS and Android users, none of the found differences are sufficiently strong to be of actual practical relevance. However, this impression may be misleading. On the contrary, we would like to stress that whereas it is relatively easy to statistically eliminate the influence of sociodemographic variables, it is by far less so when it comes to gathering actual samples via certain technologies. Replicating the classic study by Buchanan and Reips [ 33 ] the present results hint that in the given context, sociodemographic factors are a force to be reckoned with that exerts a sizable impact on the studied effects. This is reflected in the fact, that the only other significant predictor of smartphone OS, aside from Openness to Experience in Study 1 was monthly budget. In a similar vein, the observation, that most effects in Study 2 vanished after sociodemographic variables were controlled for, attests to the same possibility. Unless being accounted for by matched samples, by nature, the distribution of such sociodemographic variables may vary profoundly between operating systems. In conclusion, to avoid undue biases threatening the data’s validity, great care should be taken in terms of sample composition in science app studies, especially when recruiting ad-hoc samples.

Strengths and limitations

In spite of our efforts to conduct the present research in the most beneficial and effective way, some drawbacks persisted nonetheless, which we intend to address in the following section. To start with, both studies used ad-hoc samples with very little recruitment restrictions. Although these community-based samples are more diverse in sample characteristics than common student samples and do henceforth generate a higher usability of the resulting data [ 77 ], some disadvantages need to be considered.

Notably, as a direct consequence, arising from our recruitment strategy, we faced a skewed sex distribution in the sample of Study 2, with roughly two thirds of the sample being women. This might sound worrying, because Big Five personality traits have been shown to vary as a function of sex, especially in well-developed wealthy and egalitarian societies just like Germany [ 78 ]. Both samples featured a rather wide range in terms of age, which is of interest as Big Five personality traits have also been reported to change dynamically across the lifespan [ 79 ]. This being said, one might turn this heterogeneity into an asset, as it reflects the actual age composition of the target population better than traditional psychological studies that are notoriously prone to draw from college student samples only [ 30 ]. As age was not even close to being a significant predictor of smartphone OS in Study 1, we are confident that the age distribution was fairly comparable between iOS and Android users and did not impair the results’ validity. Nonetheless, in keeping with the findings above on the link between sociodemographic variables and Big Five personality traits, we controlled statistically for sex, age, and educational level in Study 2. Of note, this had a strong influence on the obtained results that merits further attention.

From a methodological point of view, Study 2 may receive the critique that most people tend to own and use both, a smartphone and a computer system. Consequently their placement in the respective compared groups could be perceived as reflecting an arbitrary snapshot rather than a clear-cut, permanent membership in one particular user group (i.e., continuously favoring the usage of one electronic device over the other). Taking on this potential caveat, we analyzed switching patterns between smartphones and computers, drawing from a sizable longitudinal sample ( n = 204) with an average of 48 data points per person, accumulating to a total 9,745 data points that has been collected in the frame of a different research project [ 80 ]. Consistent with our claim, results indicated that 92% of all participants kept using the same device in at least 80% of all data collection waves. Against this backdrop, it appears reasonable that a pronounced preference for a single electronic device exists in most people which allows to sort participants into the user groups that we employed throughout Study 2.

Furthermore, although we have mounted our best efforts to ascertain a holistic and balanced assessment of user personality, with a strong emphasis on the Big Five taxonomy, acknowledging its role as a key concept in smartphone-based personality research [ 11 , 12 ], we cannot rule out the possibility that we have failed to detect significant differences across users of different OS along unmeasured personality dimensions. While we tried to minimize this danger by assessing a host of vastly different characteristics to cover as much of users’ personality as possible, some traits may have fallen through the cracks, such as Gray’s reward sensitivity or, similarly, social desirability, which we did measure but could not analyze due to a lack of reliability. Faced with a length-breadth tradeoff, when designing our questionnaire, we chose to pursue a holistic, yet parsimonious approach to maintain participant motivation, reduce fatigue, boredom and dropout and yield high-quality data [ 64 , 66 , 67 ]. However, future research should expand on our findings and consider other personality traits.

While it is very clear, that our research leaves some room for improvement, it benefits from an array of assets that deserve to be mentioned. To start with, we would like to stress that thanks to its design, Study 2 can be interpreted as an in-built replication of Study 1, although with a somewhat narrower focus, concentrating on Big Five personality traits in a German-speaking sample. Beyond that, it makes two valuable contributions in extension of Study 1. Notably, we assessed OS, the grouping variable in question automatically, unlike Study 1 where we relied on self-reports. Moreover, it widens the horizon of the study, by taking desktop computer OS into account as well.

Due to the novelty of smartphones in general and science apps in particular, a refined research philosophy as well as best practices to accommodate their use as data collection tools are currently still lacking. In recognition of the arising challenges, the present investigation represents an attempt to mark another step towards a robust, unified methodology for smartphone- and computer-based social science studies. Such studies provide an easy, yet cost-effective way of collecting vast amounts of ecologically valid data from diverse, geographically widely scattered samples. Events can be recorded in real time, as they occur.

Still, special care has to be taken, when employing smartphones and science apps, as an inadequate manner of using them for research purposes, may both, undermine data quality and compromise ethical standards. Against this backdrop, we aimed to shed new light on a potentially harmful selection bias that emerges following the widespread use of science apps that are compatible with one OS only. We argued, that if iOS and Android users were to differ significantly in personality, as marketing research and consumer psychology hint, the scientific community would need to introduce hybrid apps, or independently designed identical native apps for both systems, as a gold standard for app-research, for external validity’s sake. Thankfully for less tech-savvy scholars, according to our findings, this effort is not to be considered a necessity, in spite of potentially distorting differences in sociodemographic composition that researchers should be aware of. More to the point, minor differences in personality do exist, but they are of negligible effect size.

Supporting information

S1 table. pairwise comparisons of personality traits between ios and android users (study 1)..

Note . Bold values indicate significance ( p < .05). small: η p 2 = .010, medium: η p 2 = .060, large: η p 2 = .140; Cohen (1988). I…iOS, A…Android.

https://doi.org/10.1371/journal.pone.0176921.s001

Author Contributions

• Conceptualization: FMG SS.
• Data curation: FMG SS.
• Formal analysis: FMG SS.
• Funding acquisition: SS UDR.
• Investigation: FMG SS.
• Methodology: FMG SS.
• Project administration: FMG SS UDR.
• Resources: UDR.
• Software: FMG SS.
• Supervision: SS UDR.
• Validation: FMG SS UDR.
• Visualization: FMG SS.
• Writing – original draft: FMG.
• Writing – review & editing: FMG SS UDR.
• View Article
• Google Scholar
• PubMed/NCBI
• 18. Reips U-D. Schöne neue Forschungswelt: Zukunftstrends [Beautiful new world of research: Future trends]. In: König C, Stahl M, Wiegand E, editors. Nicht-reaktive Erhebungsverfahren. Bonn: GESIS Schriftenreihe, Band 1; 2009. pp. 129–138.
• 29. Birnbaum M H. Psychological experiments on the Internet. San Diego, CA: Academic Press; 2000.
• 30. Reips U-D. Das psychologische Experimentieren im Internet [Psychological experimenting on the Internet]. In: Batinic B, editor. Internet für Psychologen. Göttingen: Hogrefe; 1997. pp. 245–265.
• 31. Reips U-D. The web experiment method: Advantages, disadvantages, and solutions. In: Birnbaum M H, editor. Psychological experiments on the Internet. San Diego, CA: Academic Press; 2000. pp. 89–118.
• 33. Buchanan T, Reips, U-D. Platform-dependent biases in Online Research: Do Mac users really think different? In: Jonas KJ, Breuer P, Schauenburg B, Boos M, editors. Perspectives on Internet Research: Concepts and Methods. 2001. Available from: http://www.uni-konstanz.de/iscience/reips/pubs/papers/Buchanan_Reips2001.pdf
• 41. Staiano J, Lepri B, Aharony N, Pianesi F, Sebe N, Pentland A. Friends don’t lie: Inferring personality traits from social network structure. Proceedings of the 2012 ACM Conference on Ubiquitous Computing. 2012;321–330.
• 47. Rachuri KK, Musolesi M, Mascolo C, Rentfrow PJ, Longworth C, Aucinas A. EmotionSense: A mobile phones based adaptive platform for experimental social psychology research. Proceedings of the 12th ACM international conference on Ubiquitous computing. 2010; 281–290.
• 48. Dalmasso I, Datta SK, Bonnet C, Nikaein N. Survey comparison and evaluation of cross platform mobile application development tools. 9th International Wireless Communications and Mobile Computing Conference (IWCMC). 2013; 323–328.
• 53. Beierlein C, Kovaleva A, Kemper CJ, Rammstedt B. Eine Single-Item-Skala zur Erfassung von Risikobereitschaft: Die Kurzskala Risikobereitschaft-1 (R-1) [A single item scale for the measurement of risk proneness: The short scale Risk Proneness-1 (R-1)] (GESIS Working Papers 2014|34). Köln: GESIS; 2014. Available from: http://www.gesis.org/fileadmin/kurzskalen/working_papers/R1_WorkingPapers_2014-34.pdf
• 61. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Erlbaum; 1988.
• 63. Reips U-D. Design and formatting in Internet-based research. In: Gosling S, Johnson J, editors. Advanced methods for conducting online behavioral research. Washington, DC: American Psychological Association; 2011. pp. 29–43.
• 65. Schwarz S, Reips U-D. CGI versus JavaScript: A Web experiment on the reversed hindsight bias. In: Reips U-D, Bosnjak M, editors. Dimensions of Internet Science. Lengerich: Pabst; 2001. pp. 75–90.
• 73. Kemper CJ, Beierlein C, Bensch D, Kovaleva A, Rammstedt B. Eine Kurzskala zur Erfassung des Gamma-Faktors sozial erwünschten Antwortverhaltens: Die Kurzskala Soziale Erwünschtheit-Gamma (KSE-G) [A short scale for assessing the gamma-factor of social desirable response behavior: The short scale Social Desirability-Gamma (KSE-G)]. (GESIS Working Papers 2012|25). Köln: GESIS; 2012. Available: http://www.gesis.org/fileadmin/kurzskalen/working_papers/KSE_G_Workingpaper.pdf
• 75. Behling O, Law KS. Translating questionnaires and other research instruments: Problems and solutions. 1st ed. Thousand Oaks, CA: Sage; 2000.
• 80. Stieger S, Götz FM, Reips, UD. Well-being during the UEFA European soccer championship. Unpublished raw data.

For enquiries call:

+1-469-442-0620

• Web Development

Top 40 Android Project Ideas & Topics of 2024 [Source Code]

Home Blog Web Development Top 40 Android Project Ideas & Topics of 2024 [Source Code]

Android is the most popular mobile operating system in the world, with over two billion active users. Android OS has been dominating the market for a while now and there are around 2+ million apps available for Android as of 2022.  Some well-known examples include Facebook Messenger, WhatsApp, Instagram, Snapchat, Tumblr, Netflix, and Uber.

If you're a developer like me, you'll find plenty of Android project ideas to choose from. Whether you want to create a basic app or something more complex, there's an Android project for everyone.

In this blog post, I will outline some of the best Android app project ideas and concepts for you to consider when starting your next project. We will also provide helpful tips on how to select the right idea or topic for your specific needs. And with a Full Stack Developer certification course , you'll be well on your way to becoming a professional Android developer.

What is an Android Project?

An Android project is a project that is developed for the Android platform. Android projects can be developed in any programming language using Kotlin, Java, and C++ languages. But the most popular language for Android development is Java.

Android projects are typically developed using the Android SDK, which includes a set of tools and libraries that allow developers to create Android mini project topics applications. Furthermore, Android projects can be deployed to devices running the Android operating system, which includes smartphones, tablets, and other devices.

Why Do You Need to Work on Android Projects?

I've noticed that Android projects are gaining a lot of popularity these days. It seems like being able to work on them is becoming increasingly important if you want to stay up-to-date with the latest trends in the tech world. As someone who enjoys staying current in the field, I find it crucial to delve into Android projects to keep my skills sharp and relevant. Here are some reasons why:

• Firstly, they allow you to build apps for a range of devices, including phones, tablets, and even wearables.
• Secondly, they are based on open-source software which means that anyone can contribute to the development of the platform.
• Finally, Android projects give you the opportunity to work with a team of developers from all over the world. This can be a useful way to learn new skills and gain experience working on a variety of different projects.

Coming to students, Android project ideas can be a great way for them to get hands-on experience with the latest mobile technology. Not only will they learn about the newest features and capabilities of Android devices, but they will also have the opportunity to create their own unique applications. What's more, by taking up Android projects for final year, students can demonstrate their abilities to potential employers and showcase their skillset to the world. So if you are a student looking for a way to make your mark in the world of mobile technology, don't hesitate to give Android project ideas a try. You never know where it might lead.

Top Android Project Ideas for College Students

1. location based garbage management system for smart city.

There are many Android project ideas for college students that focus on improving the efficiency of city services. One such idea is a location-based garbage management system. This system would use GPS to track the location of garbage trucks and ensure that they are picking up trash from all parts of the city. The system could also provide real-time updates on the level of garbage in each truck, so the city can adjust its collection schedule as needed. In addition, the system could send alerts to residents when their trash has been picked up, so that they can put out their garbage on the correct day.

Source Code: Garbage Management System

2. OnRoad Vehicle Breakdown Help Assistance

Imagine you are driving on the highway, and your car suddenly breaks down. You're miles away from the nearest town and you have no cell service. What do you do? OnRoad Vehicle Breakdown Help Assistance is an Android project that students can create to help you in just such a situation. The app uses GPS to pinpoint your location and then sends out an SOS signal to a preselected list of contacts. The contacts can then track your location and provide assistance, whether that's coming to pick you up or calling a tow truck. The app can also include a database of nearby service stations, hotels, and restaurants, so you can find help even if you're in an unfamiliar area. 

Source Code: OnRoad Vehicle Breakdown Help Assistance

3. Agri Shop: Farmers Online Selling Application  

The Agri Shop application would allow farmers to sell their products directly to consumers through an online platform. This would be one of the best Android project topics for college students as it would provide a real-world opportunity to develop an e-commerce application. In addition to selling agricultural products, an Agri shop can also offer information and resources on topics like sustainable farming practices, organic gardening, and more. There are many challenging aspects to this project, from designing the user interface to integrating payment methods. However, the rewards could be significant, both for the farmers who use the application and for the students who develop it.  

Source Code: Agri Shop

4. Women's Security with SMS Alert based android app

Android project ideas for college students are many but women's security should be given prime importance. Women security with SMS alert based android app would allow women to send an SMS alert to a designated contact in an emergency. The app would also have a GPS tracker that would help to track the location of the user in real-time. This would ensure that help is always available in case of an emergency. Moreover, the app would also provide a list of safe places and helplines that could be useful in an emergency.

Source Code: Women's Safety App

5. COVID-19 Online Test Results & availability booking of Covid Hospital

While there are a limited number of testing centers available, many people are turning to online resources to book appointments. However, there is a lot of confusion about how these online tests work and what the results mean. One of the best Android project ideas for final year students could be creating an app that provides information about online COVID-19 tests, including how to book an appointment and interpret the results. This app could also provide information about nearby testing centers and hospitals with available beds for COVID-19 patients.

Source Code: COVID-19 Bed Management System

6. A Food Wastage Reduction Android Application

Food wastage is a global problem, and it’s one that college students can help solve with a food wastage reduction Android application. The app can track what food is purchased and what gets eaten, and it can give users tips on how to use up leftovers or components that would otherwise be thrown away. These applications aim to help users reduce the amount of food they waste by providing tips and tricks on how to store food properly, cook in bulk, and portion out meals. Thus, food wastage and awareness apps can be one of the best Android app ideas for final-year projects and research. 

Source Code: Food Waste Management System

7. eVoting : SMS OTP Verification System-Based Mobile Application

By developing an SMS OTP verification system-based mobile application, college students can not only stay current but also develop a valuable skill set. The security of electronic voting is a major concern, and by implementing an SMS OTP verification system, students can help to ensure that votes are cast safely and securely. In addition to providing a crucial service, such Android projects for students can also help them gain experience in coding and project development.

Source Code: eVoting App

8. e-Vaccination management System Android app

E-Vaccination Management System can be among the excellent Android app ideas for students. It is an app that would be used to manage the vaccination records of individuals and store them electronically. This app would have features like adding new vaccines, editing existing vaccines, deleting vaccines and viewing the vaccination schedule. It would also have a feature to send reminders to users about their upcoming vaccinations.

Source Code: e-Vaccination App

9. Toll Gate App For Android-Based Payment

Such an app would allow users to input their payment information and then use their smartphone to pay for tolls. Moreover, the app could be used to track payments and provide data on usage patterns. This information could be used to improve traffic flow and reduce congestion. This would be a perfect project for college students who are interested in developing Android apps.

Source Code: Tollgate App

10. Grievance App: College Campus for Hostel, Food, Admin, and Certificate

This Android project would allow students to quickly and easily lodge a complaint or request information on anything from hostel facilities to food options on campus. The app would also provide contact information for relevant staff members, as well as give users updates on the status of their grievances. Additionally, the Grievance App would allow students to rate the response they received from administrators, providing valuable feedback that could help improve the system. 

Source Code: Grievance App

11. Bus Pass Management System

Bus Pass Management System can be one of the crucial Android project ideas for students, especially college-going. By developing this system, they would be able to help the passengers to keep track of their bus passes. The main aim of this project is to develop an Android application that can be used by students, women, and senior citizens to get details about their bus pass. The features of this system would include passenger management, bus pass management, and fare management.

Source Code: Bus Pass System

12. Agro App: Manage Famers Govt Aided Scheme And Crop Information

Android application ideas for projects related to farmers are the need of the hour. The Agro App would provide farmers with up-to-date information about government schemes, market prices, and weather conditions. It would also allow farmers to manage their crops and track their progress. The Agro App would be a valuable tool for both farmers and policy-makers, as it would help to improve agricultural productivity and ensure that government schemes are being efficiently implemented.

Source Code: Agro App

13. Online Book Store: Ecommerce Application

For college students looking for challenging and engaging Android application project ideas, an online book store could be the perfect solution. Online bookstores offer customers the convenience of being able to shop from home and have the added bonus of being open 24/7. For college students, an online bookstore can be an effective way to save time and money. With so many course reading lists to get through, being able to order books online and have them delivered to your door can be a huge time-saver.

Source Code: Android Bookstore

Android Development Project for Beginners

1. photo management application.

A photo management application is one of the perfect Android project ideas for beginners which is not only straightforward to develop, but it also provides an opportunity to learn about important aspects of Android development such as UI design and user experience. Furthermore, a photo management app can be developed relatively quickly, meaning that you can get a finished product out to the market in a short period of time. 

Source Code: Photo Management App

2. Tic Tac Toe Game

Tic Tac Toe is a classic game that has been enjoyed by people of all ages for centuries. While there are many complex games available for Android, Tic Tac Toe is an ideal project for beginners. The game is relatively simple to code, and there are many tutorials available online. The finished product can be easily customized with different graphics and sound effects.

Source Code: Tic Tac Toe Game  

3. News Application

News apps are considered perfect Android app project ideas for beginners, as they require relatively little coding and can be completed quickly. Furthermore, there is a large demand for news apps, as people are always looking for new ways to keep up with the latest information. And with a little creativity, you can even create a unique and successful news app that stands out from the crowd.

Source Code: News App

4. Music Application

Creating different music applications can be excellent Android app development project ideas for beginners as it is a simple project that can be easily completed within a short span of time. The project requires minimalistic coding and can be implemented using readily available tools and libraries. Moreover, the project is flexible and can be easily customized as per the need of the user.

Source Code: Music App

5. Tuition Notes Application

The tuition Notes Application can help to improve one's Android development skills by providing an opportunity to build a working Android application from scratch. The Tuition Notes Application project guide provides step-by-step instructions on how to build the tuition notes app, and includes all the source code needed. Such best Android projects for beginners can help them build a strong foundation for their portfolio and be well-prepared to take on more challenging projects.

Source Code: Tuition Notes App

6. College Alert Application

This application allows users to receive notifications about important events happening on their college campus. Beginners can learn how to use various Android features to create an engaging and user-friendly app.

The College Alert app would be particularly useful for students who have a busy schedule or who are commuting to campus. By receiving alerts about upcoming events, they can plan their day accordingly and avoid missing out on important information.

Source Code: College Alert App

Android Development Project for Intermediates

1. online exam application.

It is an application that can be used to administer exams online. The main advantage of this application is that it can be used to administer exams to a large number of people at the same time. This is perfect for intermediaries who want to administer exams to their students. The Online Exam Application has a user-friendly interface and is very easy to use. It is also possible to create exams with different types of questions, such as multiple choice, essay and short answer.

Source Code: Online Exam App

2. Online Voting System  

Online Voting System can allow users to cast their ballot from anywhere in the world. The app would work by allowing users to create an account and then log in to their account on the day of the election. Once they have logged in, they would be able to see all of the candidates and issues on the ballot. They would then be able to select their choices and submit their ballot. The app would also allow users to view results and get updates on election night.

Source Code: Online Voting System

3. Online Food Delivery Application  

As the demand for online food delivery services continues to grow, there is a good opportunity for developers to create innovative and user-friendly apps that can help make the ordering and delivery process even more efficient and enjoyable. In addition, if you are interested in mastering HTML, CSS, JavaScript and building advanced web applications using frameworks like React and Angular, you can go for a Web Application Development course . 

Source Code: Online Food Delivery App

4. Women Saftey Application

Intermediaries can use this type of app to help provide information and support to women who may be facing danger or who are in an unsafe situation. The app can be used to send alerts to contacts in the event of an emergency, as well as to provide information on safe places to go and how to stay safe in general. Additionally, the app can be used to provide support and advice to women who have been the victims of violence or who are at risk of being victimized.

Source Code: Women Safety App

5. Online Vaccination System

The Online Vaccination System project can allow users to schedule and track their vaccinations, as well as receive reminders when it is time to get vaccinated. The project also provides a searchable database of vaccine information. This can be extremely useful for parents who are looking for up-to-date information on vaccines.

Source Code: Online Vaccination Syatem

6. Shopping Cart App

Shopping Cart App is a great way to learn how to use Android's various features and to practice your Java programming skills. The project can be completed in a few hours and can help you get started with Android development. You can make the app relatively simple to understand and follow. This type of app can be extremely useful for businesses, allowing customers to easily browse and purchase products while on the go.

Source Code: Shop App

7. Vehicle Financing App

Vehicle Financing App is one of the unique Android project ideas for intermediaries who want to help people with bad credit get financed for a vehicle. The app will work like this: users will enter their credit score and the amount they are willing to put down, and the app will show them what kind of financing they qualify for. The app would also allow customers to fill out a form with their basic information (e.g. name, address, income, etc.) and receive customized offers from various lenders. The app will also provide tips on how to improve their credit score so that they can get better terms in the future. 

Source Code: Finance App

Advanced Android Project Ideas

1. women jobs application.

In this project, android developers can use the latest technologies to create an app that helps women find jobs. The app can be used to search for jobs by keywords, location, company, or other criteria. Users can also set up alerts so they are notified when new jobs matching their criteria are posted. The app can also provide information about salary ranges, job descriptions, and company culture. This project can help Android developers to learn how to use the latest technologies to create a powerful job search tool.

Source Code: Women Job App

2. E-Banking Application

This application can be used to transfer money, pay bills, check account balances and even locate the nearest ATM machine. This project can be developed using Android Studio and the Android SDK. The E-Banking Application can be developed as a standalone application or it can be integrated with the existing bank's systems. This project will require a good understanding of the Android platform and Java programming language. Students who are interested in this project should have a good understanding of the Android platform and its APIs.

Source Code: E-banking App

3. Panchayat Services Application

Panchayat services application can help people to know about the services provided by the panchayat and also provide them an easy way to get in touch with the panchayat. The application can also help to keep track of the work done by the panchayat and provide transparency to the people. The application can also provide information about the panchayat election and help people to participate in it. The application can also help to create awareness about the various schemes of the government and how they can be utilized by the people.

Source Code: Panchayat Service App

4. Hostel Management Application

The Hostel Management Application is an advanced Android project idea that can be used by students who are looking for a challenging and innovative project topic. This application can be used to manage hostels, guest houses, and other similar accommodation facilities. It can be used to track guest information, manage bookings, and generate reports. The application can also be used to communicate with guests via SMS and email. This project would be ideal for students who are interested in developing Android applications with advanced features.

Source Code: Hostel Management App

5. School Management System

Nowadays schools are opting for School management systems and Android project ideas that will help them to keep a track of all the activities going on in the school. One can create this advanced app and it could include all the information about the students, faculty, and staff. This system can be used to update information and monitor different events. For instance, if there is a change in the timetable, it can be reflected in the school management system so that everyone is aware of it. 

Source Code: School Management App

6. Health Care Management System

The main aim of this project is to develop an android application that can be used by any hospital or clinic for managing their patients’ data. This application can be very helpful for the doctors as well as for patients. It can provide a better way to manage and store the patient’s data. This project can also help to reduce the paperwork in hospitals. The main features of this project are: it can provide a more efficient way to store and share patient data; it can help to reduce the paperwork in the hospitals; and it can efficiently manage and store the patient’s data.

Source Code: Health care Management App

7. E-Commerce Application for Mobile

This project idea of developing an e-commerce application that can be operated on mobiles only is an advanced android project idea. This application should allow registered users to login and buy/sell their products. The major challenge lies in maintaining the security of user data as it has become a prime concern these days. Along with that, the mode of payment also needs to be secure so that the user feels confident enough to make transactions. If these aspects are taken care of, this project idea can really be a game-changer in the e-commerce industry.

Source Code: 

8. Bus tracker Android Application

This application would be very useful for people who use public transport on a regular basis. The bus tracker Android application would allow the user to track the location of the bus in real-time. The user would also be able to see the estimated time of arrival of the bus at their stop. This application would also send notifications to the user when the bus is about to reach their stop. Thus, the bus tracker Android application would be a very useful and advanced Android project idea. 

Source Code: Bus Tracking App

Unlock the Power of Python: Master the Art of Programming with our Python Online Course . Join Now and Discover the Endless Possibilities of Python!

Trending Android Project Topics

1. android sales crm app.

The Android Sales CRM (Customer relationship management) App would be beneficial for sales people. With this app, they can keep track of their customer's contact information, previous purchases, and upcoming sales appointments. This app can also send automated follow-up emails and notifications to customers after a purchase is made or an appointment is missed. 

Source Code: Android CRM App

2. Android Geofencing App

The Android Geofencing App would be ideal for businesses that want to track their employees' whereabouts. With this app, businesses can set up virtual perimeter fences around specific locations. When an employee enters or leaves the geofenced area, the app will send a notification to the business.

Source Code: Geofrncing App

3. Android Spy Camera App

The Android Spy Camera App would be perfect for people who want to secretly take pictures or videos. With this app, they can activate the camera remotely and take pictures or videos without anyone knowing.

Source Code: Android Hidden Camera App

4. Android Tour Recommendation App

The Android Tour Recommendation App would be useful for travelers who want to find the best tours in their destination city. With this app, travelers can input their travel dates and preferences, and the app will recommend tours based on their budget and interests.

Source Code: Tour Guide App

5. Android Step counter App

The Android Step Counter App can assist people who want to stay fit and active. With this app, they can track their daily steps, distance traveled, and calories burned. The app can also provide detailed reports of their progress over time.

Source Code: Step Counter App

6. Retail Store Inventory App

The Retail Store Inventory App can work best for businesses that want to track their inventory in real-time. With this app, businesses can scan barcodes or QR codes to track products as they come in and out of stock. The app can also generate reports of inventory levels and product turnover rates.

Source Code: Inventory App

How to Start a Career in Android Development?

If you are interested in Android development, there are a few things that I’d like to share with you before you get started.

• First, you will need to learn a programming language. Java is the most popular language for Android development, but there are other options as well.
• Once you have chosen a language, it's time to learn the basics of Android development. This includes understanding the Android application lifecycle and the different parts of an Android app.
• You should also familiarize yourself with the Android SDK and Android Studio, and other main tools for Android development.
• Once you have a basic understanding of how Android development works, it's time to start writing your apps. Start with small Android app development project topics that you can complete relatively quickly. This will help you get a feel for the development process and give you a chance to experiment with different features.
• Once you have completed a few projects, you can start distributing them to friends and family or even bring them on the Google Play Store. As you continue your journey as an Android developer, don't forget to share your experiences with others.
• You can even share different Android development project ideas to help others. There are many online forums and groups where beginners can ask questions and learn from each other. By sharing your knowledge, you can help make the world of Android development more accessible to everyone.

Unlock the Power of Data with our Data Analytics Bootcamp . Gain the skills you need to excel in the world of data science . Join us today!

So, there you have it - the top Android project ideas and topics to help get your creativity flowing. I hope that this list has given you some inspiration for your next app development project. With mobile devices becoming more and more popular, don’t miss out on the opportunity to create something truly innovative and useful for Android users around the world.  

Whether you want Android final-year project ideas or advanced app ideas, you can find everything covered here. And if you need help getting started or want to discuss a specific project idea, you can go for KnowledgeHut Full Stack Developer certification course to learn about building rich and functional apps. Not only this, you can even land a software Dev job by going through plenty of hands-on exercises and assignments.

Frequently Asked Questions (FAQs)

It is important to consider your interests and expertise when choosing Android app development topics. If you have a specific passion or area of expertise, try to find a project that will allow you to utilize those skills. You can consult with experienced developers and do your research to ensure that your chosen topic is achievable and realistic. 

An API is an acronym for Application Programming Interface, which enables two applications to communicate with each other through software. APIs are used by apps like Facebook, instant messaging apps, and weather apps. 

With Android Studio, you can develop apps for Android phones, Android Wear, tablets, Android Auto, and Android TV. Structured code modules simplify the process of building, testing, and debugging your project. 

The main concepts in Android are Activities, Views, User Interactions, Layouts, Screen Size, fragments, Intents, Broadcast Receivers, Content Providers and Services. These concepts are the building blocks that make up an Android app.  

In your project, modules are groups of source files and build settings used to divide functionality into discrete units. There can be one or many modules in your project, and each module may depend on another. Each module can be built, tested, and debugged independently.

Prateek Singh

Prateek Singh is a highly-skilled at building front-end interfaces. He loves JavaScript ecosystem and have designed & developed multiple products in his career. He has worked in Fintech, E-Commerce, Healthcare & Semi-conductor industries. Prateek enjoys conversation on Programming, Sports, Art, and Space Science. He is fascinated about origin of the universe, the existential reality & the design around us. 

Avail your free 1:1 mentorship session.

Something went wrong

Upcoming Web Development Batches & Dates

operating systems Recently Published Documents

Total documents.

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

Models for Efficient Utilization of Resources for Upgrading Android Mobile Technology

The need of the customers to be connected to the network at all times has led to the evolution of mobile technology. Operating systems play a vitol role when we talk of technology. Nowadays, Android is one of the popularly used operating system in mobile phones. Authors have analysed three stable versions of Android, 6.0, 7.0 and 8.0. Incorporating a change in the version after it is released requires a lot of rework and thus huge amount of costs are incurred. In this paper, the aim is to reduce this rework by identifying certain parts of a version during early phase of development which need careful attention. Machine learning prediction models are developed to identify the parts which are more prone to changes. The accuracy of such models should be high as the developers heavily rely on them. The high dimensionality of the dataset may hamper the accuracy of the models. Thus, the authors explore four dimensionality reduction techniques, which are unexplored in the field of network and communication. The results concluded that the accuracy improves after reducing the features.

Integrated maintenance planning approach to optimize budget allocation for subway operating systems

Performance analysis of real-time and general-purpose operating systems for path planning of the multi-robot systems.

In general, modern operating systems can be divided into two essential parts, real-time operating systems (RTOS) and general-purpose operating systems (GPOS). The main difference between GPOS and RTOS is the system istime-critical or not. It means that; in GPOS, a high-priority thread cannot preempt a kernel call. But, in RTOS, a low-priority task is preempted by a high-priority task if necessary, even if it’s executing a kernel call. Most Linux distributions can be used as both GPOS and RTOS with kernel modifications. In this study, two Linux distributions, Ubuntu and Pardus, were analyzed and their performances were compared both as GPOS and RTOS for path planning of the multi-robot systems. Robot groups with different numbers of members were used to perform the path tracking tasks using both Ubuntu and Pardus as GPOS and RTOS. In this way, both the performance of two different Linux distributions in robotic applications were observed and compared in two forms, GPOS, and RTOS.

A Comparative Study on Webpage Browsing Performance between Proprietary and Open Source Operating Systems on Wireless Networks

This experimental research study determined and compared the webpage browsing performance of proprietary and open source operating systems on wireless networks. It was intended to reveal the significant differences in the webpage browsing performance between proprietary and open source operating systems on wireless networks when classified as to hardware specifications and type’s web content. The researchers used the JavaScript Console of the Google Chrome web browser application to determine the time of the webpage to fully load. Operating system was the independent variable. Hardware specifications which were classified as old system and new system and types of web content which was also classified as static and dynamic webpages were the intervening variables. Webpages browsing performance was the dependent variable. The statistical tools used were arithmetic mean, and t-test. It also revealed that there were significant differences in the webpage browsing performance between proprietary and open source operating system on wireless networks when classified as to hardware specification and web content. The proprietary and open source operating systems were statistically different when classified as to hardware specifications and type of web content.

Understanding Users’ Behavior towards Applications Privacy Policies

Recently, smartphone usage has increased tremendously, and smartphones are being used as a requirement of daily life, equally by all age groups. Smartphone operating systems such as Android and iOS have made it possible for anyone with development skills to create apps for smartphones. This has enabled smartphone users to download and install applications from stores such as Google Play, App Store, and several other third-party sites. During installation, these applications request resource access permissions from users. The resources include hardware and software like contact, memory, location, managing phone calls, device state, messages, camera, etc. As per Google’s permission policy, it is the responsibility of the user to allow or deny any permissions requested by an app. This leads to serious privacy violation issues when an app gets illegal permission granted by a user (e.g., an app might request for granted map permission and there is no need for map permission in the app, and someone can thereby access your location by this app). This study investigates the behavior of the user when it comes to safeguarding their privacy while installing apps from Google Play. In this research, first, seven different applications with irrelevant permission requests were developed and uploaded to two different Play Store accounts. The apps were live for more than 12 months and data were collected through Play Store analytics as well as the apps’ policy page. The preliminary data analysis shows that only 20% of users showed concern regarding their privacy and security either through interaction with the development team through email exchange or through commenting on the platform and other means accordingly.

INFLUENCE OF LOG LENGTH ON THE PRODUCTIVITY OF WOOD HARVESTING AND TRANSPORTATION

The selection of machines and the development of operating systems are the major challenge for reducing costs in harvesting and forest transportation. This work aimed to carry out a technical analysis of harvesting and forest transport activities in two different log lengths (6 and 7m). The operational cycles of the Harvester, Forwarder and combined road train vehicle in mechanized harvest areas were evaluated. The technical analysis was performed through studies of times and movements, determining the operational efficiency and productivity of the machines. According to the results, processing consumed most of the harvester's operational cycle, while in the forwarder, the most time was consumed  35,2 and 45,2 m³·he-1 and 42,84 and 75,42 m³.he-¹. The larger log size led to an increase in the productivity of the harvester by 28% and the forwarder by 48%. Among the studied models of road train vehicles, the one that showed the best results both in the analysis made with a length of 6 m and 7 m, was the dimensions with 2.35 m in width and 2.85 in height. These vehicles had a total gross weight of 63.52 tonnes for logs with a length of 6m and 69.17 tons for logs of 7m, with an 8.17% higher performance compared to 6m logs. With the obtained results it can be concluded that the increase in the length of the logs increased the productivity and the performance of the harvest and the forest transport.

Benchmarking of mobile apps on heart failure

ABSTRACT Objective: to analyze the mobile apps on heart failure available in the main operating systems and their usability. Methods: benchmarking of mobile applications, systematic research, comprising 38 mobile applications for analysis of general information, functionalities and usability. Usability was assessed using System Usability Scale and Smartphone Usability Questionnaire, followed by the calculation of the agreement index and the exact binomial distribution test, with a significance level of p> 0.05 and a proportion of 0.90. Results: mobile applications had English as the predominant language (73.7%), were directed to patients (71.1%) and the predominant theme was disease knowledge (34.2%). Functionalities ranged from general features to the need for an internet connection. In assessing usability, heart failure was shown to be 92.1% -94.7% and p <0.05. Final considerations: the mobile apps on heart failure have varied content and adequate usability. However, there is a need to develop more comprehensive mobile applications.

The Influence of Pop Up Notification on Visual Attention and Learning

The tutorial videos contain an explanation of a learning material taught to students. The use of tutorial videos is common during the COVID-19 pandemic. This situation makes the teachers change the learning model into a video conferences or tutorial videos. However, the use of tutorial videos is often accompanied by opening other applications in parallel causing pop-up notifications to appear. The pop-up notification makes students not focus on the material explained in the tutorial videos. This raises the question of whether it will affect the learning process in understanding the learning material. Therefore, this study aimed to explore the influence of pop-up notifications on tutorial videos. Eye movements of all participants (N = 50) were recorded when viewing tutorial videos on various operating systems with or without the pop-up notification. Based on the results, after being shown a tutorial videos with a pop-up notification, participants paid attention to the pop-up notification. However, there were no significant differences in learning outcomes of students after viewing tutorial videos with or without pop-up notification.

HARDWARE AND SOFTWARE OF AUTOMATIC CONTROL SYSTEM OF FUEL COMBUSTION PROCESS IN LOW AND MEDIUM POWER BOILERS. PART 2. ALGORITHMIC SOFTWARE

The efficiency of the functioning of boiler units depends on the availability of reliable information on the progress of technological processes. The lack of control and measuring systems for the composition of the exhaust gases leads to low efficiency of the boiler unit, in particular, due to poor-quality fuel combustion. Therefore, in modern operating conditions of boiler units, it is relevant to develop technological solutions focused on finding and minimizing the causes and mechanisms of the formation of harmful substances in exhaust gases. Due to the fact that replacement of outdated boiler units with new ones requires significant capital investments, a promising direction is the modernization of existing boiler units. It is a low-cost and efficient way of rational use of fuel while simultaneously reducing the level of harmful substances in exhaust gases. It remains relevant to ensure the functioning of the control systems for the composition of the air-fuel mixture (AFM) with a given speed and high reliability of maintaining the excess air ratio (EAR) at the stoichiometric level. In the article the high-quality algorithm is proposed for the operation of an automatic control system for the combustion of fuel in boilers of medium and low power by regulating the ratio of the components of the AFM for the burner with feedback according to the signals of the oxygen sensor. The algorithms for the operation of the frequency regulator of the ratio of the components of the AFM in various operating modes are considered. The developed algorithms allowed maintaining the stoichiometric air-fuel ratio in the boiler furnace, reducing the level of toxic emissions into the atmosphere and increasing the boiler efficiency by optimizing the fuel combustion process. The AFM ratio programmer is made in the LM Programmer technical programming environment and works with Windows operating systems (XP, Vista, 7, 8, 10) and oxygen sensors manufactured by Bosch. The visualization of the control process of the fuel combustion process is made in the technical programming environment LogWorks 3 and operates in the environment of Windows operating systems.

Export Citation Format

Share document.

Research on Development of Android Applications

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.

R Discovery: Academic Research

About this app

Data safety.

Ratings and reviews

• Flag inappropriate

• Show review history

What's new

App support, similar apps.

Choose Your Test

Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

What's Next?

Are you also learning about dynamic equilibrium in your science class? We break this sometimes tricky concept down so it's easy to understand in our complete guide to dynamic equilibrium .

Thinking about becoming a nurse practitioner? Nurse practitioners have one of the fastest growing careers in the country, and we have all the information you need to know about what to expect from nurse practitioner school .

Want to know the fastest and easiest ways to convert between Fahrenheit and Celsius? We've got you covered! Check out our guide to the best ways to convert Celsius to Fahrenheit (or vice versa).

These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

Student and Parent Forum

Our new student and parent forum, at ExpertHub.PrepScholar.com , allow you to interact with your peers and the PrepScholar staff. See how other students and parents are navigating high school, college, and the college admissions process. Ask questions; get answers.

Ask a Question Below

Have any questions about this article or other topics? Ask below and we'll reply!

Improve With Our Famous Guides

• For All Students

The 5 Strategies You Must Be Using to Improve 160+ SAT Points

How to Get a Perfect 1600, by a Perfect Scorer

Series: How to Get 800 on Each SAT Section:

Score 800 on SAT Math

Score 800 on SAT Reading

Score 800 on SAT Writing

Series: How to Get to 600 on Each SAT Section:

Score 600 on SAT Math

Score 600 on SAT Reading

Score 600 on SAT Writing

Free Complete Official SAT Practice Tests

What SAT Target Score Should You Be Aiming For?

15 Strategies to Improve Your SAT Essay

The 5 Strategies You Must Be Using to Improve 4+ ACT Points

How to Get a Perfect 36 ACT, by a Perfect Scorer

Series: How to Get 36 on Each ACT Section:

36 on ACT English

36 on ACT Math

36 on ACT Reading

36 on ACT Science

Series: How to Get to 24 on Each ACT Section:

24 on ACT English

24 on ACT Math

24 on ACT Reading

24 on ACT Science

What ACT target score should you be aiming for?

ACT Vocabulary You Must Know

ACT Writing: 15 Tips to Raise Your Essay Score

How to Get Into Harvard and the Ivy League

How to Get a Perfect 4.0 GPA

How to Write an Amazing College Essay

What Exactly Are Colleges Looking For?

Is the ACT easier than the SAT? A Comprehensive Guide

Should you retake your SAT or ACT?

When should you take the SAT or ACT?

Stay Informed

Get the latest articles and test prep tips!

Looking for Graduate School Test Prep?

Check out our top-rated graduate blogs here:

GRE Online Prep Blog

GMAT Online Prep Blog

TOEFL Online Prep Blog

Holly R. "I am absolutely overjoyed and cannot thank you enough for helping me!”

Advertisement

Research Paper Topics for Android

• V  21.0

Security Status

Softonic review

Research Paper Topics: A Comprehensive Guide

Research Paper Topics is an Android application developed by Creative Writing Apps . It offers a complete guide on how to write a research paper and provides research paper ideas. The app is a full version with no additional fees or hidden costs.

The app's menu contains three main options. The first option provides a step-by-step guide on how to write a research paper, covering topics such as research paper format, introduction, cover page, ideas, title page, and abstract. The second option offers research paper topics for various categories, including college, psychology, criminal justice, sociology, history, and American literature. The third option provides research paper examples, and users can request samples or ask for help by filling out a contact form.

User reviews about Research Paper Topics

Have you tried Research Paper Topics? Be the first to leave your opinion!

Explore More

نسب الرسول صلى الله عليه وسلم

نسب الرسول صلى الله عليه وسلم: A Detailed Explanation of the Prophet's Lineage

농수산물 실시간 경매 가격 정보

Real-time Agricultural Auction Price Information

Dusun Bible

A free app for Android, by Global Bible Apps FCBH.

Slash: High-Quality LGBT+ Stories for Young Readers

Urdu Motivational Quotes

A free app for Android, by Adab Apps Collection.

Kamus Madura

Kamus Madura - A Bilingual Dictionary for Preserving Local Language

Malayalam Calendar 2017

A free program for Android, by RoseSoft Systems.

Kamus Bahasa Sunda Indonesia L

Kamus Bahasa Sunda Indonesia L: The Perfect Offline Tool for Learning Sundanese Language

موسوعة نداءات الرحمن

A free program for Android, by Rehab youssef said.

孕婦小知識: A Comprehensive Guide for Expecting Mothers

Quranic verse Ethiopian

Quranic verse Ethiopian: An App for Immersing in Islamic Life

مائة فائدة من سورة يوسف

Related Articles

Star Wars Battlefront Classic Collection receives its second major update.

Goodbye to making money thanks to TikTok

Catwoman makes her directorial debut with a movie starring Channing Tatum: this is its trailer.

This mythical figure of television finally has its own documentary: discover the trailer

Breaking news.

Fallout Shelter releases an update based on the Amazon series

The official 3D wireless headphones for PlayStation plummet to their all-time low

We will watch the FIFA Club World Cup 2025 on Apple TV+, it’s not a joke.

The new Dragon Ball anime seems to be delayed and it’s causing Goku fans to explode.

AI-generated password: Google Gemini could have that feature soon.

What has happened to the price of Bitcoin after its fourth “halving”?

Laws concerning the use of this software vary from country to country. We do not encourage or condone the use of this program if it is in violation of these laws.

In Softonic we scan all the files hosted on our platform to assess and avoid any potential harm for your device. Our team performs checks each time a new file is uploaded and periodically reviews files to confirm or update their status. This comprehensive process allows us to set a status for any downloadable file as follows:

It’s extremely likely that this software program is clean.

What does this mean?

We have scanned the file and URLs associated with this software program in more than 50 of the world's leading antivirus services; no possible threat has been detected.

This software program is potentially malicious or may contain unwanted bundled software.

Why is the software program still available?

Based on our scan system, we have determined that these flags are possibly false positives .

What is a false positive?

It means a benign program is wrongfully flagged as malicious due to an overly broad detection signature or algorithm used in an antivirus program.

It’s highly probable this software program is malicious or contains unwanted bundled software.

Why is this software program no longer available in our Catalog?

Based on our scan system, we have determined that these flags are likely to be real positives.

Your review for Research Paper Topics

Thank you for rating!

What do you think about Research Paper Topics? Do you recommend it? Why?

OOPS! This is embarrassing...

Something’s gone terribly wrong. Try this instead

• Skip to primary navigation
• Skip to main content
• Skip to primary sidebar

Android Apps for Research Paper Organisation

Joseph Osbourne | 01 Oct 2023 | Featured

A digital research paper organiser is a super helpful tool made just for students, researchers, and writers to keep their research papers organised and on point.  Initially, it was created to make things easier and more efficient. In fact, it takes all the complicated stuff and makes it way less scary.

With a digital research paper organiser, you can put research papers in a very systematic way. It makes sure that every part, including the intro, thesis statement, methodology, findings, and conclusion, is all clear and connected in a logical way. Such tools have a super easy interface that lets you outline your paper, organise sections, and add content without any hassle.

No doubt, it can make your study experience better. But what else can help you when you are working on a research project? Actually, turning to research paper writing services and an online research paper writer in a digital organiser could be great helpers. These services can assist you a lot, ensuring that your content meets high academic standards. By the way, have you heard about apps for research organisation? If not, keep reading! Here is the list of them. 

Zotero is a super handy companion. This research paper organizer helps you gather and organise all your research stuff, store PDFs, and make citations in different styles.

• Collect and organize materials.
• Store and sync PDFs.
• Generate citations in various styles.
• Collaborate with groups.
• Create bibliographies.
• Web browser integration for easy reference capture.

EndNote makes faster to organise and manage your references. It’s got features for formatting citations, searching online databases, and working together with your buddies.

• Organize and manage references.
• Format citations and bibliographies.
• Search online databases.
• Collaborate with others.
• Import PDFs and annotate.
• Sync with the desktop version.

ReadCube is one of the best best research apps. It is all about making your reading experience better by organising and annotating academic papers. It also has cool features for students to find more articles on the same topic.

• Import and organize academic papers.
• Annotate and highlight text.
• Access supplementary materials.
• Discover related articles.
• Sync across devices.
• Built-in reference manager.

Papership is a research app for students that integrates with other cool Android apps for students, including Mendeley, Zotero, and CiteULike. It offers advanced features for organizing, reading, and annotating research papers.

• Integration with Mendeley, Zotero, and CiteULike.
• Organize and read research papers.
• Annotate PDFs.
• Synchronize with reference libraries.
• Collaboration features.
• Advanced search capabilities.

RefME makes citing sources super easy with instant citation generation. It also has cool features for scanning book barcodes and working together on citations.

• Generate citations instantly.
• Scan book barcodes for references.
• Collaborative citation creation.
• Export citations to various formats.
• Cross-platform synchronization.
• Access reference materials on the web.

The Best Ways to Use Research Applications

Just having research organization tools is not enough. You gotta use them effectively if you wanna get all the sweet benefits. We’re gonna show you how to get the most out of your research organising app. 

Step 1: Pick the app that’s right for you

Before you can start using research apps for your education, you gotta pick the one that suits you best. There are a bunch of great options like Zotero, Mendeley, EndNote, RefWorks, and ReadCube, and more. Make sure the app you pick:

• matches your goals
• supports your citation style
• has the tools you need for taking notes, managing PDFs, and organising references.

Step 2: Get your stuff together

Once you’ve picked your app, it’s time to start getting your research stuff in order. Start off by importing or adding all your stuff like documents, articles, PDFs, and references into the app’s library. Most apps let you drag and drop files or have browser extensions to save web articles. Make sure you use the same naming style for your files so you can find them easily later on.

Step 3: Make a nice folder structure

Put all your research stuff into folders or categories that make sense to you. You could make folders for different research projects, topics, or chapters, depending on what you need. Having a good folder structure is gonna make it way easier for you to find the docs you need for writing.

Step 4: Add some tags and keywords

Most research paper writing apps have tagging and keyword stuff. Use these tools to slap on some tags or keywords that actually make sense for your documents. This makes finding specific info must faster. Make sure you tag stuff consistently so our database stays organised.

Step 5: Annotate and highlight

Lots of research apps let you annotate and highlight text right in the documents. Just use these features to:

• mark important parts
• highlight the main points. 

Step 6: Sync on all your devices

To make things super easy, just sync your research app on all your devices – your computer, tablet, or even your phone. This way, you can get to your research materials wherever you go, so you can work on your projects without any interruptions.

Step 7: Keep up with the latest info

Keep updating your research paper writing app to the latest version so you can get all the cool bug fixes, security upgrades, and new features. Note that using old software can cause problems like stuff not working together or losing your data, so it’s super important to keep everything up to date.

Step 8: Work together with other people

If you’re working on a project with others, there are apps that can help you stay organised and collaborate with each other. Why not invite your colleagues to check out and add to your research stuff? 

Step 9: Make sure you back up all your data, just in case

Apps usually have backup features, but it’s always a good idea to make extra backups , especially for important information. You might wanna think about using cloud storage or external hard drives for some extra security.

Wrapping Up

If you’re a student, academic, or just someone doing research, apps can change how you do things in a root . They’ll save you time and make you way more productive.

There are so many cool apps out there for researchers. You’ve got Mendeley and Zotero for managing references, and Paperpile and EndNote for those who like things simple and easy to use. As technology keeps getting better, these research paper organisation apps will also keep improving. So, check out all the options, pick the one that works best for you, and start your research journey with more organisation and efficiency.

Reader Interactions

Join the discussion: cancel reply.

We never share our visitor/user details. For more info, Please read our privacy policy before submitting your comment.

Save my name, email, and website in this browser for the next time I comment.

This site uses Akismet to reduce spam. Learn how your comment data is processed .

• Android Apps

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

• Machine learning
• Social justice
• Black holes
• Classes and programs

Departments

• Aeronautics and Astronautics
• Brain and Cognitive Sciences
• Architecture
• Political Science
• Mechanical Engineering

Centers, Labs, & Programs

• Abdul Latif Jameel Poverty Action Lab (J-PAL)
• Picower Institute for Learning and Memory
• Lincoln Laboratory
• School of Architecture + Planning
• School of Engineering
• School of Humanities, Arts, and Social Sciences
• Sloan School of Management
• School of Science
• MIT Schwarzman College of Computing

Researchers detect a new molecule in space

Press contact :.

Previous image Next image

New research from the group of MIT Professor Brett McGuire has revealed the presence of a previously unknown molecule in space. The team's open-access paper, “ Rotational Spectrum and First Interstellar Detection of 2-Methoxyethanol Using ALMA Observations of NGC 6334I ,” appears in April 12 issue of The Astrophysical Journal Letters .

Zachary T.P. Fried , a graduate student in the McGuire group and the lead author of the publication, worked to assemble a puzzle comprised of pieces collected from across the globe, extending beyond MIT to France, Florida, Virginia, and Copenhagen, to achieve this exciting discovery. 

“Our group tries to understand what molecules are present in regions of space where stars and solar systems will eventually take shape,” explains Fried. “This allows us to piece together how chemistry evolves alongside the process of star and planet formation. We do this by looking at the rotational spectra of molecules, the unique patterns of light they give off as they tumble end-over-end in space. These patterns are fingerprints (barcodes) for molecules. To detect new molecules in space, we first must have an idea of what molecule we want to look for, then we can record its spectrum in the lab here on Earth, and then finally we look for that spectrum in space using telescopes.”

Searching for molecules in space

The McGuire Group has recently begun to utilize machine learning to suggest good target molecules to search for. In 2023, one of these machine learning models suggested the researchers target a molecule known as 2-methoxyethanol. 

“There are a number of 'methoxy' molecules in space, like dimethyl ether, methoxymethanol, ethyl methyl ether, and methyl formate, but 2-methoxyethanol would be the largest and most complex ever seen,” says Fried. To detect this molecule using radiotelescope observations, the group first needed to measure and analyze its rotational spectrum on Earth. The researchers combined experiments from the University of Lille (Lille, France), the New College of Florida (Sarasota, Florida), and the McGuire lab at MIT to measure this spectrum over a broadband region of frequencies ranging from the microwave to sub-millimeter wave regimes (approximately 8 to 500 gigahertz). 

The data gleaned from these measurements permitted a search for the molecule using Atacama Large Millimeter/submillimeter Array (ALMA) observations toward two separate star-forming regions: NGC 6334I and IRAS 16293-2422B. Members of the McGuire group analyzed these telescope observations alongside researchers at the National Radio Astronomy Observatory (Charlottesville, Virginia) and the University of Copenhagen, Denmark. 

“Ultimately, we observed 25 rotational lines of 2-methoxyethanol that lined up with the molecular signal observed toward NGC 6334I (the barcode matched!), thus resulting in a secure detection of 2-methoxyethanol in this source,” says Fried. “This allowed us to then derive physical parameters of the molecule toward NGC 6334I, such as its abundance and excitation temperature. It also enabled an investigation of the possible chemical formation pathways from known interstellar precursors.”

Looking forward

Molecular discoveries like this one help the researchers to better understand the development of molecular complexity in space during the star formation process. 2-methoxyethanol, which contains 13 atoms, is quite large for interstellar standards — as of 2021, only six species larger than 13 atoms were detected outside the solar system , many by McGuire’s group, and all of them existing as ringed structures.  

“Continued observations of large molecules and subsequent derivations of their abundances allows us to advance our knowledge of how efficiently large molecules can form and by which specific reactions they may be produced,” says Fried. “Additionally, since we detected this molecule in NGC 6334I but not in IRAS 16293-2422B, we were presented with a unique opportunity to look into how the differing physical conditions of these two sources may be affecting the chemistry that can occur.”

Share this news article on:

Related links.

• McGuire Lab
• Department of Chemistry

Related Topics

• Space, astronomy and planetary science
• Astrophysics

Related Articles

Found in space: Complex carbon-based molecules

Previous item Next item

More MIT News

Seven from MIT elected to American Academy of Arts and Sciences for 2024

Read full story →

Two MIT teams selected for NSF sustainable materials grants

3 Questions: A shared vocabulary for how infectious diseases spread

Study demonstrates efficacy of MIT-led Brave Behind Bars program

Bringing an investigator’s eye to complex social challenges

MIT announces 2024 Bose Grants

• More news on MIT News homepage →

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

• Map (opens in new window)
• Events (opens in new window)
• People (opens in new window)
• Careers (opens in new window)
• Accessibility
• Social Media Hub
• MIT on Facebook
• MIT on YouTube
• MIT on Instagram

Balancing Federalism: The Impact of Decentralizing School Accountability

Education policy, while primarily the responsibility of the state governments, involves complicated decision making at the local, state, and federal levels. The federal involvement dramatically increased with the introduction of test-based accountability under the No Child Left Behind Act of 2001. But, reflecting resistance to various parts of this law, the involvement of federal policy making was substantially reduced when Congress passed the Every Student Succeeds Act in 2015. This change in policy allows estimation of the impact of altered federalism. By looking at how states reacted to their enhanced decision-making role, we see a retreat from the use of output-based policy toward teachers, and this retreat was associated with significantly lower student achievement growth. As a result, this readjustment of federalism to decision making by lower levels appeared to lower national achievement. The snapshot of federalism impacts here is a lower bound on the effects as more states will very likely react to the flexibility of ESSA and as more school districts change their teacher force.

Paper prepared for the annual meetings of the Association for Public Policy Analysis and Management in Atlanta. We benefitted from the overall support of the National Council on Teacher Quality and the assistance of Kelli Lakis and Lisa Staresina in developing the cross-walk between the elements of teacher policies and the provisions of NCLB and ESSA. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

Download Citation Data

More from NBER

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .