In businesses and organizations, understanding the social reality of individuals, groups, and cultures allows for in-depth understanding and rich analysis of multiple research areas to improve practices. Qualitative research provides important insight into the interactions of the workplace. Qualitative Techniques for Workplace Data Analysis is an essential reference source that discusses the qualitative methods used to analyze workplace data, as well as what measures should be adopted to ensure the credibility and dependability of qualitative findings in the workplace. Featuring research on topics such as collection methods, content analysis, and sampling, this book is ideally designed for academicians, development practitioners, business managers, and analytic professionals seeking coverage on quality measurement techniques in the occupational settings of emerging markets.

Digital identity and access management (DIAM) systems are essential to security frameworks for their ability to rapidly and consistently confirm identities and to control individuals access to resources and services. However, administering digital identities and system access rights can be challenging even under stable conditions. 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.

Data collection allows today's businesses to cater to each customer's individual needs and provides a necessary edge in a competitive market. However, any breach in confidentiality can cause serious consequences for both the consumer and the company. The Handbook of Research on Emerging Developments in Data Privacy brings together new ideas on how to deal with potential leaks of valuable customer information. Highlighting the legal aspects of identity protection, trust and security, and detection techniques, this comprehensive work is a valuable resource for any business, legal, or technology professional looking to improve information security within their organization.

Organizations are increasingly relying on electronic information to conduct business, which has caused the amount of personal information to grow exponentially. Threats, Countermeasures, and Advances in Applied Information Security addresses the fact that managing information security program while effectively managing risks has never been so critical. This book contains 24 chapters on the most relevant and important issues and advances in applied information security management. The chapters are authored by leading researchers and practitioners in the field of information security from across the globe. The chapters represent emerging threats and countermeasures for effective management of information security at organizations.

"More often than not, it is becoming increasingly evident that the weakest links in the information-security chain are the people. Due an increase in information security threats, it is imperative for organizations and professionals to learn more on the human nature and social interactions behind those creating the problem. Social and Human Elements of Information Security: Emerging Trends and Countermeasures provides insightful, high-quality research into the social and human aspects of information security. A comprehensive source of the latest trends, issues, and findings in the field, this book fills the missing gap in existing literature by bringing together the most recent work from researchers in the fast and evolving field of information security."

As the diffusion and use of technology applications have accelerated in organizational and societal domains, behavioral and social dynamics have inevitably created the potential for negative as well as positive consequences and events associated with technology. A pressing need within organizations and societies has therefore emerged for robust, proactive information security measures that can prevent as well as ameliorate breaches, attacks, and abuses.""The Handbook of Research on Social and Organizational Liabilities in Information Security"" offers a critical mass of insightful, authoritative articles on the most salient contemporary issues of managing social and human aspects of information security. Aimed at providing immense scholarly value to researchers, academicians, and practitioners in the area of information technology and security, this landmark reference collection provides estimable coverage of pertinent issues such as employee surveillance, information security policies, and password authentication.

Attacks on information systems and applications have become more prevalent with new advances in technology. Management of security and quick threat identification have become imperative aspects of technological applications. Information Technology Risk Management and Compliance in Modern Organizations is a pivotal reference source featuring the latest scholarly research on the need for an effective chain of information management and clear principles of information technology governance. Including extensive coverage on a broad range of topics such as compliance programs, data leak prevention, and security architecture, this book is ideally designed for IT professionals, scholars, researchers, and academicians seeking current research on risk management and compliance.

Highlights the importance and applications of Swarm Intelligence and Machine learning in Healthcare industry. Elaborates Swarm Intelligence and Machine Learning for Cancer Detection. Focuses on applying Swarm Intelligence and Machine Learning for Heart Disease detection and diagnosis. Explores of the concepts of machine learning along with swarm intelligence techniques, along with recent research developments in healthcare sectors. Investigates how healthcare companies can leverage the tapestry of big data to discover new business values. Provides a strong foundation for Diabetic Retinopathy detection using Swarm and Evolutionary algorithms.

Organizations, worldwide, have adopted practical and applied approaches for mitigating risks and managing information security program. Considering complexities of a large-scale, distributed IT environments, security should be proactively planned for and prepared ahead, rather than as used as reactions to changes in the landscape. Strategic and Practical Approaches for Information Security Governance: Technologies and Applied Solutions presents high-quality research papers and practice articles on management and governance issues in the field of information security. The main focus of the book is to provide an organization with insights into practical and applied solutions, frameworks, technologies and practices on technological and organizational factors. The book aims to be a collection of knowledge for professionals, scholars, researchers and academicians working in this field that is fast evolving and growing as an area of information assurance.

This book is a collection of peer-reviewed best selected research papers presented at the First International Conference on Machine Intelligence and Smart Systems 2020 (MISS 2020), organized during September 24-25, 2020, in Gwalior, India. The book presents new advances and research results in the fields of machine intelligence, artificial intelligence and smart systems. It includes main paradigms of machine intelligence algorithms, namely (1) neural networks, (2) evolutionary computation, (3) swarm intelligence, (4) fuzzy systems and (5) immunological computation.

This book is a collection of best selected research papers presented at the International Conference on Communication and Artificial Intelligence (ICCAI 2020), held in the Department of Electronics & Communication Engineering, GLA University, Mathura, India, during 17-18 September 2020. The primary focus of the book is on the research information related to artificial intelligence, networks, and smart systems applied in the areas of industries, government sectors, and educational institutions worldwide. Diverse themes with a central idea of sustainable networking solutions are discussed in the book. The book presents innovative work by leading academics, researchers, and experts from industry.

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, neural networks), 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

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 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)

This book is a collection of best selected research papers presented at the International Conference on Communication and Artificial Intelligence (ICCAI 2021), held in the Department of Electronics & Communication Engineering, GLA University, Mathura, India, during 19-20 November 2021. The primary focus of the book is on the research information related to artificial intelligence, networks, and smart systems applied in the areas of industries, government sectors, and educational institutions worldwide. Diverse themes with a central idea of sustainable networking solutions are discussed in the book. The book presents innovative work by leading academics, researchers, and experts from industry.

In businesses and organizations, understanding the social reality of individuals, groups, and cultures allows for in-depth understanding and rich analysis of multiple research areas to improve practices. Qualitative research provides important insight into the interactions of the workplace. Qualitative Techniques for Workplace Data Analysis is an essential reference source that discusses the qualitative methods used to analyze workplace data, as well as what measures should be adopted to ensure the credibility and dependability of qualitative findings in the workplace. Featuring research on topics such as collection methods, content analysis, and sampling, this book is ideally designed for academicians, development practitioners, business managers, and analytic professionals seeking coverage on quality measurement techniques in the occupational settings of emerging markets.

Information retrieval with verbose natural language queries has been generating a lot of interest in recent years. The focus of many novel search applications has shifted from short keyword queries to verbose queries. Examples include question answering systems and dialogue systems, voice search on mobile devices, and entity search engines like Facebook's Graph Search or Google's Knowledge Graph. However, the performance of textbook information retrieval techniques for such verbose queries is not as good as that for their shorter counterparts. Thus, effective handling of verbose queries has become a critical factor for adoption of information retrieval techniques in this new breed of search applications. Over the past decade, the information retrieval community has deeply explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations. This is the first monograph to provide a coherent and organized survey on this topic. It puts together the various research pieces of the puzzle, provides a comprehensive and structured overview of diverse proposed methods, and lists several application scenarios where effective verbose query processing can make a significant difference. Information Retrieval with Verbose Queries is a very timely reference on this important topic. It is entirely based on previously published research and publicly available datasets and as such, it should prove useful for both practitioners and academic researchers interested in reproducing the reported results.

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. The book presents new advances and research results in the fields of machine intelligence, artificial intelligence and smart systems. It includes main paradigms of machine intelligence algorithms, namely (1) neural networks, (2) evolutionary computation, (3) swarm intelligence, (4) fuzzy systems and (5) immunological computation. Scientists, engineers, academicians, technology developers, researchers, students and government officials will find this book useful in handling their complicated real-world issues by using machine intelligence methodologies.

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.