Nanoscale Dynamic Voltage Comparators: A Thermal Reliability Study Sharvani Yedulapuram, J. Ajayan, L. M. I. Leo Joseph Proceedings of the 4th IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2026, 2026
Integration of 5G and 4G Communication in Battery Management Systems for Electric Vehicles: A Cloud-Based Architecture for Enhanced Performance and Analytics R. Suganya, L. M. I. Leo Joseph, Sreedhar Kollem Internet Technology Letters, 2025 The Cloud‐Based Architecture is proposed for the Integration of 4G and 5G Communication in a Battery Management System (BMS) for Electric Vehicles (EV). This study compares Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and an AI‐optimized BMS algorithm. The AI‐optimized BMS has recorded 88% State of Health (SoH), with a good old traditional BMS only managing 72%. Furthermore, the AI model reaches 85% energy efficiency, 20 ms latency, and 92% fault detection accuracy, surpassing existing approaches. Using Network performance analysis, 5G has 2.5× more throughput, and less latency (approx. 60% less than 4G), empowering real‐time monitoring. This can make Over‐the‐air (OTA) updates 98% reliable with 5G and 85% with 4G, ensuring the software updates success rate. Incorporating this AI‐based BMS system with 5G provides efficient automation of the battery management process, improving battery lifespan, energy efficiency, and enabling fault detection, predictive analysis, and remote battery update. Ideal for next‐gen EV implementations, this scalable and cloud‐based edge solution extends performance with low operational expenditure and optimal battery lifecycle management.
Sustainable urban mobility: Reducing emissions through intelligent transportation R. Suganya, L. M. I. Leo Joseph, Sreedhar Kollem Urban Mobility and Challenges of Intelligent Transportation Systems, 2025 Since prehistoric times, humans have traveled on foot, gradually developing early pathways into modern transportation networks. Initially, animals like camels and horses carried supplies over long distances, promoting trade. Canoes enabled movement across waterways, while the wheel allowed for heavier loads, reshaping trade routes. The Industrial Revolution introduced steam-powered trains and ships, increasing travel speed and reach, followed by the internal combustion engine and later air travel, which significantly cut travel times. However, the rise in vehicles has caused congestion and pollution, highlighting the need for Intelligent Transportation Systems (ITS). ITS uses data, sensors, and smart signals to optimize traffic flow, reduce congestion, and encourage sustainable practices like smart parking. Its goal is to enable autonomous vehicles to communicate with urban infrastructure, reducing collisions and emissions. Despite challenges with costs and privacy, ITS is key to creating efficient and sustainable urban transportation.
A Real-World Dataset “IDSIoT2024” for Machine Learning/Deep Learning based Cyber Attack Detection System for IoT Architecture Manasa Koppula, L.M.I. Leo Joseph 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2025, 2025 The Internet of Things (IoT) is an emerging technology evaluating its inception in all domains like industries, home automation, healthcare, agriculture, etc. The major challenge in IoT is securing the IoT devices and information associated with the IoT devices from hackers or unauthorized persons. These cyber security attacks can be detected early for timely interventions and reduce major risks. Machine Learning (ML)/Deep Learning (DL) has become an influential technique for automatic Attack or Intrusion Detection Systems (IDS), putting forward numerous advantages over traditional techniques. A well-structured, real-time IoT security dataset must be used to develop IDS using ML/DL approaches. The article proposes a Real-World dataset IDSIoT2024 consisting of 16,230,955 records to help researchers advance solutions for tackling cyber security in IoT networks. The records presented in the dataset were captured using the Wireshark tool in a real-world IoT architecture. The data collection has taken two months from the middle of June 2023 to August 2023. The proposed dataset can be employed in the field of Artificial Intelligence (AI) to develop ML/DL approaches and detect cyber-attacks in IoT networks. A large amount of data needs to be trained to an ML algorithm to get precise detection of attacks. Therefore, the proposed dataset with a vast number of records with different attacks can accommodate the researchers for the development of Intrusion Detection Systems.
