Advanced Computing
Internet of Things
Artificial Intelligence
50
Scopus Publications
529
Scholar Citations
11
Scholar h-index
12
Scholar i10-index
Scopus Publications
Enhancing security in IoMT using federated TinyGAN for lightweight and accurate malware detection Durga S, M. Gobi Shankar, Esther Daniel, Bright Gee Varghese R Scientific Reports, 2026 The internet of medical things (IoMT) ecosystem is highly vulnerable to malware attacks due to the vast number of connected devices and their continuous collection, transmission, and processing of sensitive data. Inadequate device management often makes each device a potential entry point, enabling malware to spread rapidly across networks with minimal detection. Given the resource constraints, privacy concerns, and distributed nature of IoT devices, there is a pressing need for lightweight and adaptive intrusion detection models. This paper proposes a federated learning (FL) based framework enhanced with TinyGAN, where the generator produces synthetic data to improve malware detection. The federated approach enables continuous, decentralized learning, allowing the model to adapt to emerging threats without requiring centralized retraining, thereby preserving privacy and reducing computational overhead. Experimental evaluations demonstrate significant improvements in both detection accuracy and efficiency compared to conventional centralized techniques. After 20 training rounds, the proposed model achieved a precision of 99.30%, a recall of 100%, and an F1-score of 99.52%. These results highlight the scalability, privacy-preserving nature, and effectiveness of the framework, offering a practical advancement in securing IoT environments against malware attacks. An experimental analysis of the IoT-23 dataset reveals that FL with TinyGAN consistently outperforms traditional models, such as MLP and FNN/LSTM, in terms of accuracy, convergence rate, and resource consumption, thereby establishing its effectiveness for practical IoT malware detection.
Medical Image Captioning using Deep Learning: A Vision-Language Approach Rohan Paul C, Esther Daniel, S. Seetha, S. Durga Proceedings of the 7th International Conference on Intelligent Sustainable Systems Iciss 2025, 2025 Medical Image Captioning aims to obtain an accurate and textual description from medical images, supporting healthcare professionals in diagnostic processes and reducing manual workload. This paper presents an automated solution using a VisionEncoderDecoder model that combines Vision Transformers for the extraction of visual features and BERT for natural language processing. The proposed model was trained and evaluated on the Indiana University Chest X-ray dataset, the system can produce concise and accurate captions for radiological images. Using beam search for decoding, the model achieves a score of 0.65, emphasizing its capability to generate captions closely aligned with ground-truth reports. Future work includes dataset expansion and applications to other medical imaging modalities such as MRIs and CT scans.
Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach Akash K V, Esther Daniel, S. Seetha, S. Durga 3rd International Conference on Electronics and Renewable Systems Icears 2025 Proceedings, 2025 The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older models.so that this model act as an effective tool for detecting the breast cancer in the early stages.
Bridging CNNs and Transformers: A Comparative Study of EfficientNet, ResNet, and ViT for Plant Disease Detection Ajel Henry, Esther Daniel, S. Seetha, S. Durga 2025 6th International Conference on Data Intelligence and Cognitive Informatics Icdici 2025, 2025 The research is to study and compare Vision Transformers (ViT), EfficientNet, ResNet Deep learning algorithms for identifying plant diseases. Advanced Neural Networks based model for plant disease diagnosis. In order to facilitate automated illness identification, the work uses the EfficientNet framework for classify photos of Crop leaves into several disease categories. We use a large dataset of augmented photos of several plant diseases that we got from Kaggle. To improve model generalization, the dataset is subjected to preprocessing procedures such downsizing, normalization, and data augmentation. The implementation makes use of the PyTorch and TensorFlow frameworks to guarantee reliable training and assessment. To increase accuracy and decrease overfitting, the model is trained batch-wise utilizing optimization strategies including regularization and adaptive learning rate modifications. The classification efficiency is Examined through a range of metrics and indicators, and the conclusions show that the model can correctly identify plant illnesses. The suggested system is a useful tool for initial illness intervention through delivering an automated and scalable technique for agricultural disease surveillance. The system's use of deep learning reduces the need for human interaction while guaranteeing quick and accurate disease diagnosis, which helps farmers manage their crops more successfully. For wider accessibility and useful application in smart agriculture, future developments might incorporate real-time image capturing and cloud-based deployment.
Explainable Multi-Stage Churn Prediction using Graph Neural Network in Telecom Sector Neeraj M, Esther Daniel, S. Durga, S. Seetha Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025, 2025 Customer churn prediction is a key task for telecom companies, as retaining customer is more effective than getting new customers. Traditional churn prediction model often lacks the ability to provide insights, making it hard to understand the factors leading for customer churn. This project proposes an explainable multi-stage churn prediction model using Graph Neural Networks (GNNs) to enhance both accuracy and efficiency of predictions made in the telecom industry. This approach models customer interactions as graph, capturing complex relations and dependencies between customers. Using techniques like LIME and SHAP, the model provides clear, actionable explanations for decision making. Experimental results demonstrate GNN based model maintained high level of explainability, making it an effective tool for telecom companies to manage churn and improve customer retain strategies.
EdgeIoTics: leveraging edge cloud computing and IoT for intelligent monitoring of logistics container volumes Streaming Analytics Concepts Architectures Platforms Use Cases and Applications, 2022
A hybrid streaming analytic model for detection and classification of malware using Artificial Intelligence techniques Streaming Analytics Concepts Architectures Platforms Use Cases and Applications, 2022
Enhancing security in IoMT using federated TinyGAN for lightweight and accurate malware detection S Durga, MG Shankar, E Daniel Scientific Reports 16, 7116 , 2026 2026
Hybrid Deep Learning-Powered Secure Browser Extension for Real-Time Malicious Website Detection E Daniel, T Neeba, S Seetha, S Durga 2025 International Conference on NexGen Networks and Cybernetics (IC2NC … , 2025 2025
Artificial Intelligence based Solar Energy Prediction using Deep Learning Models V Sasikumar, E Daniel, S Seetha, S Durga 2025 6th International Conference on Smart Electronics and Communication … , 2025 2025
FLEM-XAI: Federated Learning based real time Ensemble Model with explainable AI framework for an efficient diagnosis of Lung Diseases S Durga, E Daniel, S Seetha, VK Reshma, V Sachnev Frontiers in Computer Science 7 (139) , 2025 2025 Citations: 3
Deep learning-based workload prediction and resource provisioning for mobile edge-cloud computing in healthcare applications S Durga, E Daniel, S Deepakanmani, VK Reshma Sustainable Computing: Informatics and Systems, 101176 , 2025 2025 Citations: 3
Bridging CNNs and Transformers: A Comparative Study of EfficientNet, ResNet, and ViT for Plant Disease Detection A Henry, E Daniel, S Seetha, S Durga 2025 6th International Conference on Data Intelligence and Cognitive … , 2025 2025 Citations: 1
Explainable Multi-Stage Churn Prediction using Graph Neural Network in Telecom Sector M Neeraj, E Daniel, S Durga, S Seetha 2025 8th International Conference on Trends in Electronics and Informatics … , 2025 2025 Citations: 3
Medical Image Captioning using Deep Learning: A Vision-Language Approach E Daniel, S Seetha, S Durga 2025 7th International Conference on Intelligent Sustainable Systems (ICISS … , 2025 2025
Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach KV Akash, E Daniel, S Seetha, S Durga 2025 International Conference on Electronics and Renewable Systems (ICEARS … , 2025 2025
The Influence of Financial Leverage on Firm Value Through Investment Decisions: Empirical Evidence from Oman's Food Sector S DURGA 2025
Financial Performance of selected IT companies in India: A comparative analysis M Raju, VR Podile, S Durga International Journal of Accounting and Economics Studies 12 (1) , 2025 2025 Citations: 2
2D convolution neural network-based efficient model for brain tumor detection using 5G Edge Cloud. E Durga, S., Deepakanmani, S., & Daniel Advances in Computers, 533-450 , 2025 2025 Citations: 2
Optimizing trust-based routing with a hybrid tcn-ann model for iot rpl networks S Vipparthi, E Daniel, S Durga, S Seetha 2024 9th International Conference on Communication and Electronics Systems … , 2024 2024 Citations: 2
SmartCardio: Advancing cardiac risk prediction through Internet of Things and edge cloud intelligence S Durga, E Daniel, J Andrew, R Bhat IET Wireless Sensor Systems 14 (6), 348-362 , 2024 2024 Citations: 13
Examining Cyber Security Curricular Frameworks for Business Schools Using Artificial Intelligence 28 S Durga, KL Sujitha, V Manusha, V Narasimha, K Rajeshwar, A Firos Computer Science Engineering and Emerging Technologies, 177-181 , 2024 2024
Smart Finance: Evaluating AI and Machine Learning's Impact on Investment Strategies and Financial Management SK Nanda, SH Krishna, ST Ram, SK Jena, MF Hasan, S Durga 2024 International Conference on Trends in Quantum Computing and Emerging … , 2024 2024 Citations: 4
Analyze digital images by improving the methods of advanced big data analytics and its impact on finance management during Covid-19 outbreaks S Tanwar, M Mittal, R Jebitta, Sreenivasulu, D Mohanty, S Durga AIP Conference Proceedings 2816 (1), 110007 , 2024 2024
Enhancing storage efficiency for health data records through block chain-based storj mechanism J Niranjana, E Daniel, S Durga, V Kathiresan 2024 Third International Conference on Intelligent Techniques in Control … , 2024 2024 Citations: 6
An EEG-Based Brain-Computer Interface Approach for Enhanced Interaction with Digital Devices JC Jacob, E Daniel, S Durga, S Seetha International Conference On Health Informatics, Intelligent Systems And … , 2024 2024
PDSCM: Packet Delivery Assured Secure Channel Selection for Multicast Routing in Wireless Mesh Networks E Daniel, S Durga, J Eunice R Technologies 11 (5), 130 , 2023 2023
MOST CITED SCHOLAR PUBLICATIONS
Survey on machine learning and deep learning algorithms used in internet of things (IoT) healthcare S Durga, R Nag, E Daniel 2019 3rd international conference on computing methodologies and … , 2019 2019 Citations: 150
A survey on energy efficient server consolidation through vm live migration J Sekhar, G Jeba, S Durga International Journal of Advances in Engineering & Technology 5 (1), 515 , 2012 2012 Citations: 54
Big data analytics in healthcare: A survey S Gemson Andrew Ebenezer , Durga ARPN Journal of Engineering and Applied Sciences 10 (issue 8, MAY 2015 ISSN … , 2015 2015 Citations: 37
Advanced and effective baby care monitoring Smart cradle system using Internet of Things S Durga, S Itnal, K Soujanya, CZ Basha, C Saxena 2021 2nd international conference on smart electronics and communication … , 2021 2021 Citations: 26
SmartMobiCam: Towards a new paradigm for leveraging smartphone cameras and IaaS cloud for smart city video surveillance S Durga, S Surya, E Daniel 2018 2nd International Conference on Trends in Electronics and Informatics … , 2018 2018 Citations: 18
A state-of-art review on antenna designs for ingestible application TM Neebha, AD Andrushia, S Durga Electromagnetic Biology and Medicine 39 (4), 387-402 , 2020 2020 Citations: 15
Proximity‐based cloud resource provisioning for deep learning applications in smart healthcare D Sivan, M Sellappa Expert Systems 39 (7), e12524 , 2022 2022 Citations: 14
Deep learning application in iot health care: A survey JM Philip, S Durga, D Esther Intelligence in Big Data Technologies—Beyond the Hype: Proceedings of … , 2020 2020 Citations: 14
SmartCardio: Advancing cardiac risk prediction through Internet of Things and edge cloud intelligence S Durga, E Daniel, J Andrew, R Bhat IET Wireless Sensor Systems 14 (6), 348-362 , 2024 2024 Citations: 13
Mobile cloud media computing applications: A survey S Durga, M S Proceedings of the Fourth International Conference on Signal and Image … , 2013 2013 Citations: 12
Context-aware adaptive resource provisioning for mobile clients in intra-cloud environment S Durga, S Mohan, JD Peter, S Surya Cluster Computing 22 (Suppl 4), 9915-9928 , 2019 2019 Citations: 11
WITHDRAWN: Utilization of IoT for a secured clinical information transmission and remote monitoring of patients V Vedanarayanan, S Durga, A Sharma, T Gomathi, S Poonguzhali, ... Materials Today: Proceedings , 2020 2020 Citations: 10
Blockchain-based privacy preservation technique for digital forensics records S Durga, E Daniel, S Deepakanmani, TM Neeba, V Ravi Artificial intelligence and blockchain in digital forensics, 211-229 , 2023 2023 Citations: 9
Panoramic view of cloud storage security attacks: an insight and security approaches E Daniel, S Durga, S Seetha 2019 3rd international conference on computing methodologies and … , 2019 2019 Citations: 9
A conceptual analysis on the impact of big data analytics toward on digital marketing transformation H Ramirez-Asis, M Silva-Zapata, E Ramirez-Asis, T Sharma, S Durga, ... 2022 2nd International Conference on Advance Computing and Innovative … , 2022 2022 Citations: 8
IoT-based ensemble method on PCG signal classification to predict heart diseases E Daniel, S Durga, S Iwin Thanakumar Joseph, D Angelin, SBE Raj Secure communication for 5G and IoT networks, 101-116 , 2021 2021 Citations: 8
Resource Provisioning Techniques in Multi‐Access Edge Computing Environments: Outlook, Expression, and Beyond S Durga, E Daniel, JA Onesimu, Y Sei Mobile Information Systems 2022 (1), 7283516 , 2022 2022 Citations: 7
Cuckoo based resource allocation for mobile cloud environments S Durga, S Mohan, J Dinesh, A Aneena Computational Intelligence, Cyber Security and Computational Models … , 2015 2015 Citations: 7
Testing the validity of CAPM in Indian stock markets MS Reddy, S Durga International Journal of Multidisciplinary Research and Development 2 (2), 56-60 , 2015 2015 Citations: 7
Enhancing storage efficiency for health data records through block chain-based storj mechanism J Niranjana, E Daniel, S Durga, V Kathiresan 2024 Third International Conference on Intelligent Techniques in Control … , 2024 2024 Citations: 6
