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The book explores the complete system perspective, underlying theories, modelling, and the applications of pattern recognition in Healthcare Recommender System. Considering the interest of researchers and academicians, editors here aim to present this book in a multidimensional perspective that will be covering Healthcare Recommender Systems in depth, considering pattern recognition techniques using amalgamation of emerging technologies. It aims to cover all topics ranging from discussion of recommender system to efficient management to recent research challenges and issues. Editors aim to present the book in a self-sufficient manner and in order to achieve this, the book has been organized into various chapters.
The prime focus of the book is to explore the various issues, challenges, and research directions of pattern recognition in Healthcare Recommender Systems. The table of contents is designed in a manner so as to provide the reader with a broad list of its applications. Additionally, the book addresses the transformations in the area of Healthcare Recommender Systems. Thus, the book plans to discuss the recent research trends and advanced topics in the field of healthcare automation system which will be of interest to industry experts, academicians and researchers working in this area. Hence, the editors aim is to cover diversity in the domain while achieving completeness.
Dr. Simar Preet Singh, SMIEEE, is an Associate Professor at School of Computer Science Engineering and Technology (SCSET), Bennett University, Greater Noida, Uttar Pradesh, India. He was previously affiliated with GNA University, Phagwara, and Chandigarh Engineering College, Ajitgarh, India. He also previously worked with Infosys Limited and DAV University, Jalandhar, India, and has worked on international projects. He has published papers in SCI/SCIE/Scopus-indexed journals and has presented many research papers at various national and international conferences in India and abroad. His areas of interests include cloud computing, fog computing, IoT, big data and machine learning. He earned his doctoral degree at Thapar Institute of Engineering and Technology, Patiala, India, and also holds several specialized certifications, including like Microsoft Certified System Engineer (MCSE), Microsoft Certified Technology Specialist (MCTS), and Core Java. He had also undergone a training program for VB. Net and Cisco Certified Network Associates (CCNA).
Dr. Deepak Kumar Jain is an Assistant Professor at Dalian University of Technology,Dalian , China. He received the degrees of Bachelor of Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya, India and Master of Technology from Jaypee University of Engineering and Technology, India in 2010 and 2012, respectively. He completed his Ph.D. from University of Chinese Academy of Sciences, Institute of Automation, Beijing, China. He has been appointed as Associate Editor in IEEE Consumer Electronic Magazine and Human Centric Computing and Information Sciences. He is serving as Guest editors in Pattern Recognition Letters, Computer Communications, Neural Computing and Applications, Image and Vision Computing. He has presented several papers in peer reviewed Conferences, as well as published numerous studies in science cited journals. His areas of research are deep learning, machine learning, pattern recognition, and computer vision.
Prof. Johan Debayle, FIET, SMIEEE, is a Professor at the Ecole Nationale Supérieure des Mines de Saint-Etienne (ENSM-SE), part of the Institute Mines-Telecom in France.
Chapter 1: Revolutionizing Healthcare through Blockchain and AI: A Secure Data Sharing Framework.- Chapter 2: Application Scenarios of Recommender System in Healthcare.- Chapter 3: Deep Learning Model for Histopathological Image Classification of Renal Biopsies in Diabetic Kidney Disease: A Study using Whole Slide Imaging.- Chapter 4: Personalized Healthcare in the Digital Age: Advancements and Applications in Healthcare Recommender Systems.-Chapter 5: Recommendation of Nourishment for Glycemic Patients by applying Improved K means (IKM) and Krill Herd Optimization (KHO).- Chapter 6: Classification And Segmentation In Brain Tumor Detection.- Chapter 7: Fine-tuned Convolutional Neural Network for Alzheimers Detection and Classification.- Chapter 8: Knee Osteoarthritis Severity Prediction using CNN Models and Web Application.- Chapter 9: Parkinson's Disease Prediction Using Artificial Neural Network.- Chapter 10: Securing the Cloud: Strategies for Protecting Sensitive Patient Data in Cloud-Based Healthcare Recommender Systems.- Chapter 11: Blockchain Based Security and Privacy in Digital Healthcare Recommender Systems.- Chapter 12: The Aftermath of Data Breaches in Mission-critical Healthcare Recommender Systems.- Chapter 13: Healthy Food Recommender Systems.- Chapter 15: Deep Learning with VGG-19, Inception V3 Ensembling Learning Techniques for Brain Tumor Analysis.- Chapter 16: TPKD - Ensemble Bagged Trees for Identifying Therapeutic Plants for Kidney Diseases.- Chapter 17: B2ET - Prediction of medicinal plants effective in treating Amenorrhea using Bagging-Based Ensemble Trees.- Chapter 18: Predicting the Happiness Index of the Teachers Fraternity Using a Fuzzy Model.- Chapter 19: Deep Learning for the Detection of Cardiovascular Disease: A Review.- Chapter 20: Smart Manufacturing for Better Healthcare: Integrating 3D Printing, Robotics, and IoT.- Chapter 21: An Intelligent Approach to Predict Agricultural Productivity using Artificial Neural Network Framework.- Chapter 22: Automated Sanitizer-Based Mosquito Repellent Led - Smart City Innovation.- Chapter 23: Sustainable Biopolymers: Applications and Case Studies in Pharmaceuticals, Medical, and Food Industries.
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