Sudip Kumar Sahana – författare

Grid computing, a computing infrastructure that combines computer resources spread over different geographical locations to achieve a common goal, is seen as an efficient solution to solve complex real-world computer problems in a shorter time. Grid computing makes it easier to collaborate with other organizations, makes better use of existing hardware, and reduces transmission congestion, leading to more efficient electricity markets, improved power quality, and a reduced environmental impact.Highlighting the salient features of grid computing, this new book begins with the basic definition of grid and its architecture and also discusses advances to the complex hybridized model of grid computing, grid scheduling, and grid resource management. The chapters address different problems causing grid bottlenecks, the formulation of solution strategies, implementation of grid scheduling, along with case studies of grid computing. It introduces several scheduling algorithms and evaluates the efficiency of these algorithms using comparative analysis while also discussing grid simulators. The book will be a valuable guide for new and experienced researchers and will advance their understanding of the concept of grid computing in a broader perspective. Starting from the basic definition of grid and its architecture to the complex hybridized model of scheduling, this book is a comprehensive resource on grid computing, grid scheduling, and grid resource management.

Machine learning is playing an indispensable role in framing clinical decisions and enhancing accuracy. This new book offers a comprehensive take on the field of biomedical and health informatics, discussing topics that include predictive health analytics, pandemic management, AI ethics, application and integration of Internet of Things and machine learning for effective healthcare, and more. The book covers a range of bioinformatics tools and methods and their relation to drug designing and drug screening using ML. Several chapters cover clustering techniques and other methods for analyzing human heart-related disorders. The authors also explore the use of ML in creating adaptive therapies for using chemotherapy and androgen deprivation therapy for prostate cancer and for tracking diseases such as Parkinson’s Speech, Covid-19, and others. Case studies are included that demonstrate the practical use of ML in healthcare informatics.

This volume comprises the proceedings of the International Conference on Computational Intelligence 2015 (ICCI15). This book aims to bring together work from leading academicians, scientists, researchers and research scholars from across the globe on all aspects of computational intelligence. The work is composed mainly of original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of computational intelligence. Specifically, the major topics covered include classical computational intelligence models and artificial intelligence, neural networks and deep learning, evolutionary swarm and particle algorithms, hybrid systems optimization, constraint programming, human-machine interaction, computational intelligence for the web analytics, robotics, computational neurosciences, neurodynamics, bioinspired and biomorphic algorithms, cross disciplinary topics and applications. The contents of this volume will be of use to researchers and professionals alike.