• Invited as a guest of honor to deliver an interactive talk, sharing insights on the latest developments in Internet of Things technology.
• Presented research on "MODELING AND ANALYSIS – IoT Enabled Healthcare Monitoring Systems," focusing on the integration of IoT in remote health monitoring.
• Engaged with researchers and students, facilitating discussions on innovative solutions and challenges in IoT healthcare applications.
• Contributed to the conference objectives by enhancing research discussions and fostering collaborative activities in the field of networking and communications. 

The present invention introduces an innovative machine learning approach for anomaly detection within the Internet of Things (IoT) landscape. As IoT continues to burgeon, the need for robust and adaptable anomaly detection mechanisms becomes paramount. Our system combines tailored data preprocessing with cutting-edge machine learning algorithms to provide real-time, dynamic anomaly detection. This approach not only optimizes IoT data for analysis but also allows for userconfigurable thresholds and parameters, ensuring versatility across various IoT applications. With its capacity to swiftly identify anomalies in dynamic data streams, this invention offers a pivotal contribution to the security and performance of IoT ecosystems.

Feature article on Dr. Rahul Pulimamidi's contributions to patient care through innovative IT solutions.

An in-depth look at Dr. Rahul Pulimamidi's influence on the healthcare sector through technological advancements.

Article discussing the integration of AI and IoT in healthcare and its potential to revolutionize patient care.