Faculty Publications
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Item Assessing mobile health applications with twitter analytics(Elsevier Ireland Ltd, 2018) Pai, R.R.; Alathur, S.Introduction: Advancement in the field of information technology and rise in the use of Internet has changed the lives of people by enabling various services online. In recent times, healthcare sector which faces its service delivery challenges started promoting and using mobile health applications with the intention of cutting down the cost making it accessible and affordable to the people. Objectives: The objective of the study is to perform sentiment analysis using the Twitter data which measures the perception and use of various mobile health applications among the citizens. Methods: The methodology followed in this research is qualitative with the data extracted from a social networking site “Twitter” through a tool RStudio. This tool with the help of Twitter Application Programming Interface requested one thousand tweets each for four different phrases of mobile health applications (apps) such as “fitness app” “diabetes app” “meditation app” and “cancer app”. Depending on the tweets, sentiment analysis was carried out, and its polarity and emotions were measured. Results: Except for cancer app there exists a positive polarity towards the fitness, diabetes, and meditation apps among the users. Following a system thinking approach for our results, this paper also explains the causal relationships between the accessibility and acceptability of mobile health applications which helps the healthcare facility and the application developers in understanding and analyzing the dynamics involved the adopting a new system or modifying an existing one. © 2018 Elsevier B.V.Item Analysis of cache behaviour and software optimizations for faster on-chip network simulations(Springer, 2019) Prasad, B.M.P.; Parane, K.; Talawar, B.Fast simulations are critical in reducing time to market in chip multiprocessors and system-on-chips. Several simulators have been used to evaluate the performance and power consumed by network-on-chips (NoCs). To speedup the simulations, it is necessary to investigate and optimize the hotspots in the simulator source code. Among several simulators available, Booksim2.0 has been chosen for the experimentation as it is being extensively used in the NoC community. In this paper, the cache and memory system behavior of Booksim2.0 have been analyzed to accurately monitor input dependent performance bottlenecks. The measurements show that cache and memory usage patterns vary widely based on the input parameters given to Booksim2.0. Based on these measurements, the cache configuration having the least misses has been identified. To further reduce the cache misses, software optimization techniques such as removal of unused functions, loop interchanging and replacing post-increment operator with pre-increment operator for non-primitive data types have been employed. The cache misses were reduced by 18.52%, 5.34% and 3.91% by employing above technology respectively. Thread parallelization and vectorization have been employed to improve the overall performance of Booksim2.0. The OpenMP programming model and SIMD are used for parallelizing and vectorizing the more time-consuming portions of Booksim2.0. Speedups of 2.93× and 3.97× were observed for the Mesh topology with 30 × 30 network size by employing thread parallelization and vectorization respectively. © 2019, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.Item Experimental analysis of Android malware detection based on combinations of permissions and API-calls(Springer-Verlag France 22, Rue de Palestro Paris 75002, 2019) Singh, A.K.; Jaidhar, C.D.; M.a, M.A.A.Android-based smartphones are gaining popularity, due to its cost efficiency and various applications. These smartphones provide the full experience of a computing device to its user, and usually ends up being used as a personal computer. Since the Android operating system is open-source software, many contributors are adding to its development to make the interface more attractive and tweaking the performance. In order to gain more popularity, many refined versions are being offered to customers, whose feedback will enable it to be made even more powerful and user-friendly. However, this has attracted many malicious code-writers to gain anonymous access to the user’s private data. Moreover, the malware causes an increase of resource consumption. To prevent this, various techniques are currently being used that include static analysis-based detection and dynamic analysis-based detection. But, due to the enhancement in Android malware code-writing techniques, some of these techniques are getting overwhelmed. Therefore, there is a need for an effective Android malware detection approach for which experimental studies were conducted in the present work using the static features of the Android applications such as Standard Permissions with Application Programming Interface (API) calls, Non-standard Permissions with API-calls, API-calls with Standard and Nonstandard Permissions. To select the prominent features, Feature Selection Techniques (FSTs) such as the BI-Normal Separation (BNS), Mutual Information (MI), Relevancy Score (RS), and the Kullback-Leibler (KL) were employed and their effectiveness was measured using the Linear-Support Vector Machine (L-SVM) classifier. It was observed that this classifier achieved Android malware detection accuracy of 99.6% for the combined features as recommended by the BI-Normal Separation FST. © 2019, Springer-Verlag France SAS, part of Springer Nature.Item Organising the knowledge from stack overflow about location-sensing of android applications(Institution of Engineering and Technology jbristow@theiet.org, 2020) Marimuthu, M.; Palisetti, S.; Chandrasekaran, K.The number of Android applications using location information has increased significantly in recent years. Over time, there have been many improvements made to the location application programme interfaces (APIs), providing newer challenges and difficulties to the developers. Therefore, there is a need to summarise the existing knowledge and to highlight the unsolved issues to bring them to the attention of expert developers. The authors used the non-negative matrix factorisation (NMF) method to identify the topics discussed by the developers on stack overflow. They found the following ten topics: fundamental, background service, global positioning system (GPS) provider, application error, location updates, programming aspects, GPS alternatives, location settings, NULL location, and location testing. In addition, they performed a manual analysis to add more qualitative insights into the results. They applied the NMF method on 3165 question posts and produced ten related topics. This study aims at organising the knowledge about location-sensing strategies by answering three relevant research questions. They also analysed the most popular and unanswered topics in recent years. An important finding of this study is that the changes that occurred in the Google Location APIs have had a significant impact on the location-sensing strategies followed by the developers. © The Institution of Engineering and Technology 2020Item A Google Glass Based Real-Time Scene Analysis for the Visually Impaired(Institute of Electrical and Electronics Engineers Inc., 2021) Ali A, H.; Rao, S.U.; Ranganath, S.; Ashwin, T.S.; Guddeti, G.R.M.Blind and Visually Impaired People (BVIP) are likely to experience difficulties with tasks that involve scene recognition. Wearable technology has played a significant role in researching and evaluating systems developed for and with the BVIP community. This paper presents a system based on Google Glass designed to assist BVIP with scene recognition tasks, thereby using it as a visual assistant. The camera embedded in the smart glasses is used to capture the image of the surroundings, which is analyzed using the Custom Vision Application Programming Interface (Vision API) from Azure Cognitive Services by Microsoft. The output of the Vision API is converted to speech, which is heard by the BVIP user wearing the Google Glass. A dataset of 5000 newly annotated images is created to improve the performance of the scene description task in Indian scenarios. The Vision API is trained and tested on this dataset, increasing the mean Average Precision (mAP) score from 63% to 84%, with an IoU > 0.5. The overall response time of the proposed application was measured to be less than 1 second, thereby providing accurate results in real-time. A Likert scale analysis was performed with the help of the BVIP teachers and students at the 'Roman Catherine Lobo School for the Visually Impaired' at Mangalore, Karnataka, India. From their response, it can be concluded that the application helps the BVIP better recognize their surrounding environment in real-time, proving the device effective as a potential assistant for the BVIP. © 2013 IEEE.Item How do open source app developers perceive API changes related to Android battery optimization? An empirical study(John Wiley and Sons Ltd, 2021) Marimuthu, C.; Chimalakonda, S.; Chandrasekaran, K.There is an increasing interest shown by researchers and developers in reducing the battery consumption of Android applications. Recently, the battery optimization features such as doze mode, app standby, background execution limits, and background location limits were introduced in the form of API changes. According to the API changes, application developers have to change their source code to manage the behavioral changes caused by operating system limitations. These battery optimization features are evolving rapidly, and the apps show unexpected behaviors until updating the source code. Also, developers find it difficult to cope with the changes. Therefore, there is a need to understand the behavioral changes, application developer's perceptions, and response patterns on the API changes to plan upcoming battery optimization features. In this article, we have collected the relevant GitHub issues from 225 open-source Android repositories and performed a thematic analysis of collected data. This study analyzes the 391 related issues to answer three research questions. This study's important finding is that developers often post issues related to delayed app notifications, inconsistent background location updates, and suspended background tasks, and so on. We found that library developers are showing a quick response to API changes compared with application developers. © 2020 John Wiley & Sons LtdItem Detection of injections in API requests using recurrent neural networks and transformers(Inderscience Publishers, 2022) Sujan Reddy, A.; Rudra, B.Application programming interfaces (APIs) are playing a vital role in every online business. The objective of this study is to analyse the incoming requests to a target API and flag any malicious activity. This paper proposes a solution based on sequence models and transformers for the identification of whether an API request has SQL injections, code injections, XSS attacks, operating system (OS) command injections, and other types of malicious injections or not. In this paper, we observe that transformers outperform B-RNNs in detecting malicious activity which is present in API requests. We also propose a novel heuristic procedure that minimises the number of false positives. We observe that the RoBERTa transformer outperforms and gives an accuracy of 100% on our dataset. We observe that the heuristic procedure works well in reducing the number of false positives when a large number of false positives exist in the predictions of the models. © © 2022 Inderscience Enterprises Ltd.Item Vulnerability Testing of RESTful APIs Against Application Layer DDoS Attacks(Science and Information Organization, 2025) Sivakumar, K.; Santhi Thilagam, P.S.In recent years, modern mobile, web applications are shifting from monolithic application to microservice based application because of the issues such as scalability and ease of maintenance.These services are exposed to the clients through Application programming interface (API). APIs are built, integrated and deployed quickly.The very nature of APIs directly interact with backend server, the security is paramount important for CAP. Denial of service attacks are more serious attack which denies service to legitimate request. Rate limiting policies are used to stop the API DoS attacks. But by passing rate limit or flooding attack overload the backend server. Even sophisticated attack using http/2 multiplexing with multiple clients leads severe disruptions of service. This research shows that how sophisticated multi client attack on high workload end point leads to a dos attack. © (2025), (Science and Information Organization). All rights reserved.