This book presents a compilation of selected papers from the 2nd International Conference on Healthcare Science and Engineering (Healthcare 2018). The work focuses on novel computing, networking, and data analytics techniques for various issues in healthcare. The book is a valuable resource for academic researchers and practitioners working in the field.

Autorentext

Chase Q. Wu is currently an Associate Professor at the Department of Computer Science and the Director of the Center for Big Data at New Jersey Institute of Technology (NJIT). He joined NJIT in fall 2015 from the University of Memphis, where he was an Associate Professor at the Department of Computer Science. His research interests include big data, high-performance networking, parallel and distributed computing, sensor networks, scientific visualization, and cyber security. Dr. Wu's work has been supported by various funding agencies, including the U.S. National Science Foundation, Department of Energy, Department of Homeland Security, Office of Naval Research, and Oak Ridge National Laboratory, where he is a member of the research staff and works on a number of high-performance networking projects and big-data computational science projects. Ming-Chien Chyu is a Professor of Mechanical Engineering and an Adjunct Professor of Medicine at Texas Tech University, USA. He is a Fellow of the American Society of Mechanical Engineers and has received numerous awards for his research achievements and his contributions to the community. He has conducted research funded by the National Institutes of Health, the National Science Foundation, US Department of Energy, US Department of Agriculture, and other national laboratories. His research activities cover a wide spectrum of specialties from epilepsy to cancer detection, from bone to joint and muscle biomechanics, and from physical exercise to dietary supplements for intervention, as well as experiments, model simulation, and animal and human studies. He has written over 180 technical publications, including more than 130 journal papers on engineering and healthcare.

Jaime Lloret received his B.Sc. and M.Sc. in Physics in 1997, his B.Sc.and M.Sc. in Electronic Engineering in 2003 and his Ph.D. in Telecommunication Engineering (Dr. Ing.) in 2006. He is a Cisco Certified Network Professional Instructor. He has worked as a network designer and administrator in several enterprises, and is currently an Associate Professor at the Polytechnic University of Valencia. His research interests include P2P networks, wireless local area networks, sensor networks and routing protocols. He also conducts research on educational approaches and strategies. He has authored 22 book chapters and has published more than 450 research papers at national and international conferences and in international journals (more than 200 with ISI Thomson JCR).

Xianxian Li is a Professor at the School of Computer Science and Information Technology, Guangxi Normal University. He is also a Distinguished Professor in national, high-level special support programs. He received his Ph.D. degree in Computer Software and Theory from Beihang University in 2002 and worked as a Professor at Beihang University from 2003 to 2010. His current research interests include big data, data privacy, and the security of distributed systems.

Inhalt

Viral Quasispecies Spectrum Reconstruction via Coloring the Vertex in the Weighted Read Conflict Graph.- Robust feature selection method of Radiomics for grading glioma.- Cardiovascular Disease Risk Prediction based on Random Forest.- ECG Beat Classification based on Deep Bi-directional Long Short-term Memory Recurrent Neural Network.- Deep Convolutional Neural Networks for Electrocardiogram Classification.- Transfer Learning based Behavior Task Decoding from Brain Activity.- Fine-tuning ResNet for Breast Cancer Classification from Mammography.- Lung Sounds Diagnosis with Deep Convolutional Neural Network and Two-Stage Pipeline Model.- An Improved Data Anonymization Algorithm for Incomplete Medical Dataset Publishing.- ECG Classification Based on Long Short-Term Memory Networks.