Sakthivel Ramalingam | Computer Science | Editorial Board Member

Assist. Prof. Dr. Sakthivel Ramalingam | Computer Science | Editorial Board Member

Vellore Institute of Technology Chennai | India

Assist. Prof. Dr. Sakthivel Ramalingamthe researcher’s work spans advanced control theory, nonlinear systems, and complex dynamical networks with a strong emphasis on cyber-physical security, resilient control design, and intelligent fuzzy systems. Their contributions focus on developing robust, finite-time, and event-triggered control and filtering strategies for Takagi–Sugeno fuzzy models, Markovian jump systems, networked control systems, and multi-agent networks subjected to uncertainties, delays, cyber attacks, actuator faults, and communication constraints. Their research advances include designing synchronization mechanisms for fractional-order systems, creating hybrid-triggered and observer-based state estimation methods, and proposing fault-tolerant and non-fragile control algorithms for large-scale intelligent systems. With more than thirty-eight SCIE-indexed publications in high-impact journals such as IEEE Transactions on Fuzzy Systems, Neural Networks, Communications in Nonlinear Science and Numerical Simulation, Applied Mathematics and Computation, Nonlinear Dynamics, and the Journal of the Franklin Institute, their work significantly contributes to resilient autonomous systems, intelligent vehicles, stochastic complex networks, and distributed optimization. Their research extends to sampled-data control, interval type-2 fuzzy systems, polynomial fuzzy models, semi-Markovian jump systems, and fractional-order complex networks. They also engage in experimental validation, synchronization analysis, and stability theory, aiming to enhance the reliability, safety, and robustness of modern intelligent systems in uncertain and adversarial environments.

Featured Publications

Sakthivel, R., Sakthivel, R., Kaviarasan, B., & Alzahrani, F. (2018). Leader-following exponential consensus of input saturated stochastic multi-agent systems with Markov jump parameters. Neurocomputing, 287, 84–92.

Sakthivel, R., Sakthivel, R., Kaviarasan, B., Lee, H., & Lim, Y. (2019). Finite-time leaderless consensus of uncertain multi-agent systems against time-varying actuator faults. Neurocomputing, 325, 159–171.

Sakthivel, R., Sakthivel, R., Nithya, V., Selvaraj, P., & Kwon, O. M. (2018). Fuzzy sliding mode control design of Markovian jump systems with time-varying delay. Journal of the Franklin Institute, 1–15.

Sakthivel, R., Kwon, O. M., Park, M. J., Choi, S. G., & Sakthivel, R. (2021). Robust asynchronous filtering for discrete-time T–S fuzzy complex dynamical networks against deception attacks. IEEE Transactions on Fuzzy Systems, 30(8), 3257–3269.

Rounak Raman | Information Technology | Young Scientist Award

Mr. Rounak Raman | Information Technology | Young Scientist Award

Netaji Subhas University of Technology | India

Mr. Rounak Raman research journey reflects a diverse and impactful engagement across multidisciplinary domains including Artificial Intelligence, Wireless Sensor Networks, IoT Security, and Neuroinformatics. At the forefront of innovation, multiple projects demonstrate a strong alignment with real-world challenges and technical excellence. The development of the EEG Data Analysis System and multimodal neurofeedback loop underlines expertise in biomedical signal processing and applied AI for cognitive enhancement, contributing to measurable improvements in attentiveness and engagement. In IoT and network optimization, the implementation of the Energy Aware Hybrid Clustering Protocol (EAHCP) showcased advancements in energy-efficient communication, achieving notable performance gains in scalability and mobility management. Research on secure IoT frameworks, such as the Hierarchical Key Rotation and Isolation Protocol (HKRISRP), reinforced resilience in wireless networks through lightweight cryptography and dynamic key rotation, enhancing both security and energy performance. Additional contributions in opportunistic networks introduced context-aware, trust-based, and collision-avoidant models that optimized data aggregation and reliability through intelligent decision mechanisms and trajectory optimization. The pursuit of Generative AI culminated in the development of SyntheX, a document analysis system integrating semantic search and transcription models to streamline enterprise knowledge management. Collectively, these works exemplify a strong commitment to advancing applied research in AI, cybersecurity, and intelligent network systems

Featured Publication

Raman, R., Yadav, A., Kukreja, D., & Sharma, D. K. (2025). CONTEXT-NET: A context-aware nexus-based aggregation protocol for opportunistic networks. Internet of Things, 25, 101809. https://doi.org/10.1016/j.iot.2025.101809