Details

<p>.- Predictive Modeling for Patient Outcomes in Smart Healthcare Systems.</p>

<p>.- Machine Learning for Personalized Treatment Recommendations.</p>

<p>.- Smart Health Monitoring: Data Collection and Analysis with Machine Learning.</p>

<p>.- Enhancing Medical Imaging through Dimension Reduction Learning.</p>

<p>.- Natural Language Processing for Health Record Management.</p>

<p>.- Machine Learning-driven Drug Discovery and Development.</p>

<p>Mousmi Ajay Chaurasia is working as a Professor in the Department of Computer Science and Engineering at Koneru Lakshmaiah Education Foundation, Hyderabad. She is a Senior Member of IEEE and serves in different capacities in the IEEE section, region, and global level. She is a recipient of the 2020 Significant Volunteer Award from the IEEE Hyderabad Section. She has four patents to her name. She has worked in South Korea and Saudi Arabia and handled various projects funded by respective governments. She has been part of several International Conferences and Workshops. Her research interests are in Artificial Intelligence, Big Data, and Evolutionary Computation.</p>

<p>&nbsp;</p>

<p>Dr. Prasanalakshmi Balaji is Professor at the College of Computer Science, King Khalid University, Saudi Arabia. She was Postdoctoral Fellow in the Data Science Laboratory at the Industrial University of Ho Chi Minh City, Vietnam, working on AI-based Cancer project. She was Project Consultant in Industrial Optimisation and Applications at Maejo University, Thailand, working in Dynamics and Applications of Quaternion Artificial Neural Networks. She received her Ph.D. at the Research and Development Center, Bharathiar University, India. Her industrial research affiliations include being a research member of the Center for Artificial Intelligence, King Khalid University, Cancer Immunology Project -USERN, and Mentor of Fatima Fellowship, California. Prasanalakshmi is broadly interested in Neural Networks for Medical Imaging, Deep learning, Machine learning, Data Science, and the Internet of Things (IoT).</p>

<p>&nbsp;</p>

<p>Dr. Alejandro Frery received his Ph.D. in applied computing from the Instituto Nacional de Pesquisas Espaciais, São José dos Campos, Brazil. He is currently with the Instituto de Computação, Universidade Federal de Alagoas, Maceió, Brazil. His research interests are statistical computing and stochastic modeling. Dr. Frery received the IEEE GRSS Regional Leader Award in 2018. He was Editor-in-Chief of IEEE Geoscience and Remote Sensing Letters from 2014 to 2018, and he is currently Vice-President for Publications of the IEEE Geoscience and Remote Sensing Society</p>

<p>The book explores the convergence of healthcare and cutting-edge technology, making it a captivating subject for readers interested in future research. Smart healthcare with machine learning techniques offers a transformative paradigm that utilizes the power of new technology, data analytics, and interconnected devices to enhance the quality, efficiency, and accessibility of healthcare services. This involves leveraging Internet of Things (IoT) devices, wearable technology, and machine learning algorithms to monitor patient health, predict medical conditions, and offer personalized treatment recommendations. This innovative combination not only enhances diagnostics and treatment but also addresses the research challenges of healthcare access and delivery in an increasingly connected world. By exploring the synergy between smart healthcare and machine learning, the book helps to understand how these technologies can collaborate to revolutionize patient care and healthcare delivery. This book is an outcome with applications of future technologies to overcome the toughest humanitarian challenges from an engineering approach.</p>

Highlights how ML, data analytics, and interconnected devices help to enhance the quality of healthcare services Addresses the challenges of healthcare access and delivery in an increasingly connected world Explores the synergy between smart healthcare and machine learning to revolutionize patient care