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International Conference on Trends in Electronics and Informatics (ICEI)

作者： M. Jothi T. Veeramakali Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Nowadays, generative models with deep learning have a focus and give enormous success in various real-time experience domains. Deep learning (DL) is a subdivision of machine learning that is inspired by the structure and function of human brain activity, known as artificial neural networks. It is a data-driven technique that achieves the best accuracy while using a large number of input samples. Generative Adversarial Networks (GANs) are a robust class of neural networks used for unsupervised learning under DL categories. GAN is one of the most influential architectures demonstrating remarkable success in generating high-quality data. The reusable feature learning process from a huge number of unlabelled data paves the way for research. This survey article discusses an overview of the GAN model, such as the original GAN, Conditional GANs, and resolution GANs, along with their specific adaptations for various tasks. Additionally, we examine the situations faced by Generative Adversarial Networks depending on various applications such as image and video generation, medical image analysis, text-to-image synthesis, agriculture, and creative industries such as music and art. This survey explores diverse applications of recently developed frameworks and benchmark datasets used for development, along with their architecture and metric evaluations.

关键词： Deep learning Surveys Measurement Image analysis Accuracy Generative adversarial networks Agriculture Real-time systems Medical diagnostic imaging Farming

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MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-Computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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Hybrid Visual Verification 14

Hybrid Visual Verification

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14th International Conference on Cloud computing, data science and Engineering, Confluence 2024

作者： Bushra, S.Nikkath Syed Ismail, A. Sasigresa, P. Edwin Singh, C. Srm Institute of Scienece and Technology Department of Computing Technologies Chengalpattu Kattankulathur India Srm Institute of Scienece and Technology Department of Data Science and Business Systems Chengalpattu Kattankulathur India Veltech University Department: Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department: Computer Science and Engineering India

ISBN: (纸本)9798350344837

data security is becoming increasingly important as cloud computing advances. data security is the fundamental problem of all distributed computing systems. Cloud computing enables access to distributed applications and services belonging to various organizations spread across different locations. Cloud computing raises security concerns, because it shares data with distributed users via network in an open terrain. In this system, some of the most important security services like image-based security are handled by the cloud computing system. The proposed scheme takes care of several attacks like botnets, malware and guessing attacks. This scheme is contrasted withseveral established schemesand a new form of authentication is used for the purpose of storing files in a secure manner. © 2024 IEEE.

关键词： Authentication

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Performance Evaluation of Intrusion Detection Systems on the TON_IoT datasets Using a Feature Selection Method 24

Performance Evaluation of Intrusion Detection Systems on the...

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8th International Conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Maseno, Elijah Mwandata Wang, Zenghui Sun, Yanxia Department of Computing and Information Technology Mama Ngina University College Gatundu Nairobi Kenya Department of Electrical and Smart Systems Engineering Centre for Augmented Intelligence and Data Science University of South Africa Florida Gauteng South Africa Department of Electrical and Electronic Engineering Science University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400718182

As Internet of Things (IoT) technology develops so quickly, security issues with IoT devices have come to light. IoT is an array of intelligent devices connected via a network to provide various services. The amount of data generated by these devices has an impact on how well the current intrusion detection systems (IDS) function. The generated dataset consists of irrelevant features which reduces the performance of IDS, making IoT ecosystem vulnerable to cyberattacks. The researchers have suggested the feature reduction technique as a potential solution to the current problem. The proposed method seeks to reduce the feature count by removing the redundant feature subset. Several machine learning methods have been successfully implemented in this discipline. This study proposed the application of hybrid feature reduction technique. The research combined Convolutional neural network (CNN) and Long short-term memory (LSTM);CNN extracted local features and decreased dimensionality, while LSTM identified long-term relationships in the data. SVM and Random Forest classifiers models were used to classify the chosen feature subset. This study employed the TON_IoT datasets, an up-to-date dataset, to evaluate the model. During data preprocessing, the study applied SMOTETomek data pre-processing technique to address class imbalance in the dataset. With the decreased feature subset, the classification models fared reasonably well;RF had a 98% accuracy rate while SVM had a 91% accuracy rate, showcasing the suggested methodology’s potential for creating efficient IDS. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Interpretable Deep Learning for Solar Flare Prediction 36

Interpretable Deep Learning for Solar Flare Prediction

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Gazula, Vinay Ram Herbert, Katherine G. Abduallah, Yasser Wang, Jason T. L. New Jersey Institute of Technology Department of Data Science NewarkNJ07102 United States School of Computing Montclair State University MontclairNJ07043 United States New Jersey Institute of Technology Department of Computer Science NewarkNJ07102 United States

ISBN: (纸本)9798331527235

We propose to incorporate three interpretable methods, namely SHAP (SHapley Additive exPlanations), PDP (partial dependence plots) and Anchors, into a deep learning-based model, called SolarFlareNet, for operational flare forecasting. SolarFlareNet takes as input a sample of SHARP (Space-weather HMI Active Region Patches) magnetic parameters and predicts as output whether a solar flare would occur within the next 24 hours. We analyze flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite's X-ray flare catalogs and construct a database of flares with identified active regions in the catalogs. This database, together with the SHARP magnetic parameters, is used to train and test the SolarFlareNet model. Our experimental results describe the use of the three proposed methods (SHAP, PDP, and Anchors) to interpret the SolarFlareNet model and demonstrate the effectiveness of the methods. © 2024 IEEE.

关键词： Geostationary satellites

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Multi-Task Pointwise Mutual Information Learning for Bearing Remaining Useful Life Cross-Domain Imbalanced Regression

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IEEE Internet of Things Journal 2025年

作者： Hu, Tao Mo, Zhenling Zhang, Zijun City University of Hong Kong College of Computing Department of Data Science Kowloon Hong Kong

In modern industry, the Industrial Internet of Things (IIoT) has enabled system health analytics and monitoring through continuous data collection and networked connectivity. Bearing remaining useful life (RUL) prediction is one pivotal analytical task in preventing industrial system failures and optimizing maintenance schedules. Existing prediction methods using data face two critical engineering challenges: (1) performance degrades when deployed to unseen operational domains, and (2) imbalanced sensor data distributions cause biased predictions. Two challenges can even co-exist, which further limits the effectiveness of prediction methods using data. This study proposes a multi-task pointwise mutual information learning (MPML) based prediction model development method to tackle bearing RUL prediction under this compound challenge. MPML offers several key innovations through the following developments. First, an auxiliary task-assisted multi-task model learning scheme is devised to obtain task-wise generalizability for learning invariant latent features, and theoretical analysis is provided to explain the invariant feature learning mechanism. Furthermore, pointwise mutual information (PMI) modeling with a statistical explanation is proposed to impose adaptive biases, rectifying penalties for RUL misprediction in minority groups. Consequently, MPML addresses the unseen domain prediction through the multi-task learning scheme and effectively handles the data imbalance with the novel PMI-assisted loss. Extensive computational experiments are conducted to demonstrate the superiority of MPML, achieving a 33.12 square error compared to state-of-the-art methods. © 2014 IEEE.

关键词： Multi-task learning

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Blockchain and IoT's role in Agri Food Supply Chain Traceability 9

Blockchain and IoT's role in Agri Food Supply Chain Traceabi...

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9th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2022

作者： Subashini, B. Hemavati, D. Department of Data Science and Business Systems School of Computing. SRM IST Tamilnadu Kattankulathur603203 India

ISBN: (数字)9798350332506

ISBN: (纸本)9798350332506

In this work, we show how blockchain can be used to manage the supply chain for non-perishable agricultural food (NPAF). Keeping product quality high across the supply chain is a major challenge for the food business. Traditional foods are severely affected. The system's major purpose is to make product history accessible. At each stage of the supply chain, the product can be checked for quality and cleanliness. We created a trust less environment by utilising Blockchain, RFID, and IoT. Encryption and the network are more trusted than public or private institutions. This research suggests an execution model for the supply chain management of non-perishable agricultural foods that addresses these challenges by combining Ethereum smart contracts with IPFS, RFID, and IoT technologies. These technologies were combined in an effort to find a solution to these problems. The technology will give consumers a clear view of the complete process from raw material to the finished product and make it simple for a controlling authority to conduct operational examinations on the excellence of the goods and decent working practises. © 2022 IEEE.

关键词： Blockchain

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Comparative Analysis of CNN and SVM Machine Learning Techniques for Plant Disease Detection 4th

Comparative Analysis of CNN and SVM Machine Learning Techniq...

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4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis

arXiv

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arXiv 2024年

作者： Wang, Fuying Wu, Feng Tang, Yihan Yu, Lequan Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong

Integrating multimodal Electronic Health Records (EHR) data-such as numerical time series and free-text clinical reports-has great potential in predicting clinical outcomes. However, prior work has primarily focused on capturing temporal interactions within individual samples and fusing multimodal information, overlooking critical temporal patterns across patients. These patterns, such as trends in vital signs like abnormal heart rate or blood pressure, can indicate deteriorating health or an impending critical event. Similarly, clinical notes often contain textual descriptions that reflect these patterns. Identifying corresponding temporal patterns across different modalities is crucial for improving the accuracy of clinical outcome predictions, yet it remains a challenging task. To address this gap, we introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data. Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings. To ensure rich cross-modal temporal semantics in the learned patterns, we introduce a contrastivebased TPNCE loss for cross-modal alignment, along with two reconstruction losses to retain core information of each modality. Evaluations on two clinically critical tasks-48-hour in-hospital mortality and 24-hour phenotype classification-using the MIMIC-III database demonstrate the superiority of our method over existing approaches. © 2024, CC BY.

关键词： Electronic health record

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