Shwet Ketu
Biography
Dr. Shwet Ketu is an Assistant Professor in the Department of Computer Science and Engineering at Madan Mohan Malaviya University of Technology (MMMUT), Gorakhpur, India. He holds a Ph.D. in Information Technology from Banaras Hindu University (BHU), Varanasi. His research interests include Internet of Things (IoT), Smart Healthcare Data Analytics, Big Data Analytics, Stream Data Processing, and Machine Learning.Dr. Ketu has been recognized among the Stanford?Elsevier Top 2% Scientists worldwide in the domain of Artificial Intelligence and Image Processing. He has published extensively in reputed journals and conferences and has actively contributed to projects focusing on real-time analytics frameworks, IoT-enabled healthcare, and AI-driven intelligent systems.He is also involved in academic leadership, laboratory development, and curriculum design, spearheading initiatives such as the establishment of an Integrated Big Data & IoT Analytics Laboratory (IBIoT-Lab) and Data Science minor programs. In addition, he regularly delivers invited talks, workshops, and faculty development programs on Big Data, AI, and Next-Generation IoT Applications.
Research Interest
Dr. Shwet Ketu?s research focuses on IoT and IoT-enabled Healthcare, Big Data Analytics, Machine Learning, Stream Data Analytics, Smart Healthcare Systems, and AI-driven Intelligent Systems?
Abstract
Data Analytics for IoT-enabled Healthcare System : The integration of the Internet of Things (IoT) with healthcare systems is revolutionizing the way patient data is collected, processed, and analyzed. IoT-enabled healthcare devices generate continuous streams of heterogeneous and high-dimensional data, which require efficient analytics frameworks for real-time monitoring, diagnosis, and decision-making. Data analytics plays a crucial role in transforming this raw sensor data into actionable insights, enabling early disease detection, personalized treatment, and predictive healthcare. By leveraging advanced techniques such as machine learning, deep learning, and big data analytics, IoT-based healthcare systems can uncover hidden patterns, optimize resource utilization, and enhance patient outcomes. Furthermore, integrating edge and cloud computing ensures scalability, low latency, and security for sensitive health information. This paper explores the architecture, challenges, and emerging trends of data analytics in IoT-enabled healthcare, highlighting its potential to build intelligent, patient-centric, and cost-effective healthcare ecosystems.
