Manish Gupta - Böcker

Today the healthcare sector is facing challenges such as detecting the cause of ailments, disease prevention, high operating costs, availability of skilled technicians and infrastructure bottlenecks. Intelligent healthcare management technologies are needed to manage these challenges. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data being generated for saving lives, reducing medical errors, enhancing efficiency, reducing costs and making the whole world a healthy place.The book introduces techniques that developed using machine learning along with swarm intelligence in healthcare informatics. It also discusses one of the major applications of artificial intelligence: using machine learning to extract useful information from multimodal data optimally by using swarm intelligence. It reviews optimization methods that help to minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The objective of this book is to use swarm intelligence and machine learning techniques for various medical issues such as diagnosing cancer, brain tumor, diabetic retinopathy, heart diseases as well as drug design and development. The book will act as one-stop reference to think and explore swarm intelligence and machine learning algorithms seriously for real-time patient diagnosis.

Today the healthcare sector is facing challenges such as detecting the cause of ailments, disease prevention, high operating costs, availability of skilled technicians and infrastructure bottlenecks. Intelligent healthcare management technologies are needed to manage these challenges. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data being generated for saving lives, reducing medical errors, enhancing efficiency, reducing costs and making the whole world a healthy place.The book introduces techniques that developed using machine learning along with swarm intelligence in healthcare informatics. It also discusses one of the major applications of artificial intelligence: using machine learning to extract useful information from multimodal data optimally by using swarm intelligence. It reviews optimization methods that help to minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The objective of this book is to use swarm intelligence and machine learning techniques for various medical issues such as diagnosing cancer, brain tumor, diabetic retinopathy, heart diseases as well as drug design and development. The book will act as one-stop reference to think and explore swarm intelligence and machine learning algorithms seriously for real-time patient diagnosis.

This book addresses the topic of people analytics focusing especially on demystifying analytics for managers, from both statistical and computing points of view. It presents the evolution and the importance of people analytics in today’s workforce management and highlights the motivation for studying business analytics, shows the different levels of analytic maturity and discusses real world success stories of business analytics in action through case studies. The volume provides the readers with a step-by-step guide to analyze data and interpret the results for strategy formulation. Each chapter of the book includes a vignette, data-driven exercises provided in a companion website, and real-world case studies. The authors also introduce the readers to data visualization software Microsoft Power BI as well as elaborates on how to use Tableau Desktop.This book would be useful to the students of human resource management, organizational behaviour, marketing management, business research methods, applied psychology, social work and sociology. It would also serve as very good handbook for practicing business executives helping them to implement data analytics for effective decision making in all kinds of business situations around the world.

This book addresses the topic of people analytics focusing especially on demystifying analytics for managers, from both statistical and computing points of view. It presents the evolution and the importance of people analytics in today’s workforce management and highlights the motivation for studying business analytics, shows the different levels of analytic maturity and discusses real world success stories of business analytics in action through case studies. The volume provides the readers with a step-by-step guide to analyze data and interpret the results for strategy formulation. Each chapter of the book includes a vignette, data-driven exercises provided in a companion website, and real-world case studies. The authors also introduce the readers to data visualization software Microsoft Power BI as well as elaborates on how to use Tableau Desktop.This book would be useful to the students of human resource management, organizational behaviour, marketing management, business research methods, applied psychology, social work and sociology. It would also serve as very good handbook for practicing business executives helping them to implement data analytics for effective decision making in all kinds of business situations around the world.

Digital Identity and Access Management: Technologies and Frameworks explores important and emerging advancements in DIAM systems. The book helps researchers and practitioners in digital identity management to generate innovative answers to an assortment of problems, as system managers are faced with major organizational, economic and market changes and are also expected to increase reach and ease of access to users across cyberspace while guaranteeing the reliability and privacy of highly sensitive data.

This unique volume shows how to tackle the challenges of diversity in the workplace. It addresses the need to keep the workforce engaged while taking into consideration the diverse backgrounds of employees. The book explores 12 themes of workforce diversity and culture, including differences of race, religion, gender, sexuality, income class, education level, marital status, generation/age, physical ability, and more. Focusing on the benefits of engaging a diverse workforce, the volume considers the issue through the different stages of the human resource process, including recruitment, selection, performance appraisal, demand forecasting, supply forecasting, job description and specification, job analysis and evaluation, training and development, career planning and development, succession planning, etc.Employing an abundance of case studies, the volume enables readers to comprehend what it means to have a diverse workforce and how to engage such a workforce for the betterment of the employees as well as the employer. The volume acts as a textbook for courses on diversity in human resource management as well as a valuable resource for HRM and other management professionals. The discussions and questions sections will be useful for faculty, and the short case studies are designed to keep students interested and engaged.

In today’s rapidly evolving work landscape, the integration of diverse perspectives, backgrounds, and identities is crucial for organizational success and societal progress. As businesses strive to create inclusive environments, they face multifaceted challenges in engaging and empowering their diverse workforce. From gender biases in artificial intelligence to the complexities of managing geographically dispersed and linguistically and culturally diverse teams, organizations encounter myriad obstacles in fostering collaboration, innovation, and well-being among their employees.Drawing upon a rich tapestry of research and real-world cases, this volume offers insights, methodologies, and practical solutions for leveraging gamification to enhance engagement, teamwork, and performance across diverse workforce demographics. The book explores the various dimensions of diversity, ranging from gender and sexual orientation to career stage, linguistic diversity, religious affiliation, and geographic dispersion. From the exploration of biases embedded in AI programming to the promotion of cultural understanding through gamification initiatives, the book provides a comprehensive framework for navigating the complexities of today’s diverse workplace. Through thematic analysis, systematic literature reviews, and primary case studies, the chapters elucidate the significance of gamification in promoting inclusion, fostering collaboration, and driving organizational success.

This book constitutes the refereed proceedings of five workshops that were held in conjunction with the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021, in Delhi, India, in May 2021.The 17 revised full papers presented were carefully reviewed and selected from a total of 39 submissions..The five workshops were as follows:  Workshop on Smart and Precise Agriculture (WSPA 2021) PAKDD 2021 Workshop on Machine Learning for Measurement Informatics (MLMEIN 2021) The First Workshop and Shared Task on Scope Detection of the Peer Review Articles (SDPRA 2021) The First International Workshop on Data Assessment and Readiness for AI (DARAI 2021) The First International Workshop on Artificial Intelligence for Enterprise Process Transformation (AI4EPT 2021)

Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data.In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neuralnetworks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies

?The two-volume set CCIS 1951 and 1952 constitutes the refereed post-conference proceedings of the Third International Conference on Machine Intelligence and Smart Systems, MISS 2023, Bhopal, India, during January 24-25, 2023. The 58 full papers included in this book were carefully reviewed and selected from 203 submissions.

?The two-volume set CCIS 1951 and 1952 constitutes the refereed post-conference proceedings of the Third International Conference on Machine Intelligence and Smart Systems, MISS 2023, Bhopal, India, during January 24-25, 2023. Deduction and prevention models.

This book constitutes the thoroughly refereed post-proceedings of the 13th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2000, held in Yorktown Heights, NY, USA, in August 2000. The 22 revised full papers presented together with 5 posters were carefully selected during two rounds of reviewing and improvement. All current aspects of parallel processing are addressed with emphasis on issues in optimizing compilers, languages, and software environments in high-performance computing.

This book is a collection of peer-reviewed best selected research papers presented at the Second International Conference on Machine Intelligence and Smart Systems (MISS 2021), organized during September 24–25, 2021, in Gwalior, India.