Bøker av SN Mohanty

FUZZY COMPUTING IN DATA SCIENCEThis book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges.The book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation.AudienceResearchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.

When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment.Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning.This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers' needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

Im Zeitalter des Internet of Things (IoT) erzeugen Edge-Geräte in jedem Sekundenbruchteil gigantische Datenmengen. Dabei besteht das Hauptziel dieser Netzwerke darin, aus den gesammelten Daten sinnvolle Informationen abzuleiten. Gleichzeitig werden gewaltige Datenmengen in die Cloud übertragen, was extrem teuer und zeitaufwändig ist. Es ist somit notwendig, effiziente Mechanismen für die Verarbeitung dieser gewaltigen Datenmengen zu entwickeln, und dafür sind effiziente Datenverarbeitungstechniken erforderlich. Nachhaltige Paradigmen wie Cloud Computing und Fog Computing tragen zu einem geschickten Umgang mit Themen wie Leistung, Speicher- und Verarbeitungskapazitäten, Wartung, Sicherheit, Effizienz, Integration, Kosten, Energieverbrauch und Latenzzeiten bei. Allerdings werden ausgefeilte Analysetools benötigt, um die Anfragen in einer optimalen Zeit zu bearbeiten. Daher wird derzeit eifrig an der Entwicklung eines effektiven und effizienten Rahmens geforscht, um den größtmöglichen Nutzen zu erhalten.Bei der Verarbeitung der gewaltigen Datenmengen steht das maschinelle Lernen besonders hoch im Kurs und wird in zahlreichen Disziplinen angewandt, auch in den sozialen Medien.In Machine Learning Approach for Cloud Data Analytics in IoT werden sämtliche Aspekte des IoT, des Cloud Computing und der Datenanalyse ausführlich erläutert und aus verschiedenen Perspektiven betrachtet. Das Buch präsentiert den neuesten Stand der Forschung und fortschrittliche Themen. So erhalten die Leserinnen und Leser aktuelle Informationen und können das gesamte Spektrum der Anwendungen von IoT, Cloud Computing und Datenanalyse erfassen.

This_book_is_a_multi-disciplinary_effort_that_involves_world-wide_experts_from_diverse_fields,_such_as_artificial_intelligence,_human_computer_interaction,_information_technology,_data_mining,_statistics,_adaptive_user_interfaces,_decision_support_systems,_marketing,_and_consumer_behavior_It_comprehensively_covers_the_topic_of_recommender_systems,_which_provide_personalized_recommendations_of_items_or_services_to_the_new_users_based_on_their_past_behavior_Recommender_system_methods_have_been_adapted_to_diverse_applications_including_social_networking,_movie_recommendation,_query_log_mining,_news_recommendations,_and_computational_advertisingThis_book_synthesizes_both_fundamental_and_advanced_topics_of_a_research_area_that_has_now_reached_maturity_Recommendations_in_agricultural_or_healthcare_domains_and_contexts,_the_context_of_a_recommendation_can_be_viewed_as_important_side_information_that_affects_the_recommendation_goals_Different_types_of_context_such_as_temporal_data,_spatial_data,_social_data,_tagging_data,_and_trustworthiness_are_explored_This_book_illustrates_how_this_technology_can_support_the_user_in_decision-making,_planning_and_purchasing_processes_in_agricultural_&_healthcare_sectors