Technology is used in various forms within today’s modern market. Businesses and companies, specifically, are beginning to manage their effectiveness and performance using intelligent systems and other modes of digitization. The rise of artificial intelligence and automation has caused organizations to re-examine how they utilize their personnel and how to train employees for new skillsets using these technologies. These responsibilities fall on the shoulders of human resources, creating a need for further understanding of autonomous systems and their capabilities within organizational progression. Transforming Human Resource Functions With Automation is a collection of innovative research on the methods and applications of artificial intelligence and autonomous systems within human resource management and modern alterations that are occurring. While highlighting topics including cloud-based systems, robotics, and social media, this book is ideally designed for managers, practitioners, researchers, executives, policymakers, strategists, academicians, and students seeking current research on advancements within human resource strategies through the implementation of information technology and automation.

The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications. One of the major challenges is the fetching of data from computing memory and writing it back without experiencing a memory-wall bottleneck. To address such concerns, in-memory computing (IMC) and supporting frameworks have been introduced. In-memory computing methods have ultra-low power and high-density embedded storage. Resistive Random-Access Memory (ReRAM) technology seems the most promising IMC solution due to its minimized leakage power, reduced power consumption and smaller hardware footprint, as well as its compatibility with CMOS technology, which is widely used in industry. In this book, the authors introduce ReRAM techniques for performing distributed computing using IMC accelerators, present ReRAM-based IMC architectures that can perform computations of ML and data-intensive applications, as well as strategies to map ML designs onto hardware accelerators. The book serves as a bridge between researchers in the computing domain (algorithm designers for ML and DL) and computing hardware designers.

Software engineering has surfaced as an industrial field that is continually evolving due to the emergence of advancing technologies and innovative methodologies. Scrum is the most recent revolution that is transforming traditional software procedures, which has researchers and practitioners scrambling to find the best techniques for implementation. The continued development of this agile process requires an extensive level of research on up-to-date findings and applicable practices. Agile Scrum Implementation and Its Long-Term Impact on Organizations is a collection of innovative research on the methods and applications of scrum practices in developing agile software systems. The book combines perspectives from both the academic and professional communities as the challenges and solutions expressed by each group can create a better understanding of how practice must be applied in the real world of software development. While highlighting topics including scrum adoption, iterative deployment, and human impacts, this book is ideally designed for researchers, developers, engineers, practitioners, academicians, programmers, students, and educators seeking current research on practical improvements in agile software progression using scrum methodologies.

Emerging scientific and industrial applications in today's world require significant computing power. Modern software tools are available for such platforms but are relatively complex and require the use of innovative programming models. One promising area in modern software design is the development, analysis, and implementation of algorithms and adaptive methods. These advancements in programming are promising but lack relevant research and understanding. Formal and Adaptive Methods for Automation of Parallel Programs Construction: Emerging Research and Opportunities is an essential reference source that solves the problem of the development of efficient models, methods, and tools for parallel programming automation based on the algebra of algorithms, term rewriting, and auto-tuning paradigms. The results of this book will help to further develop and improve existing research on design, synthesis, and optimization of sequential and parallel algorithms and programs. Featuring research on topics such as auto-tuning methods, graphics processing, and algorithmic language, this book is ideally designed for mathematicians, software engineers, data scientists, researchers, academicians, and students seeking coverage on developing tools for automated design and parallel programs.

There is no doubt that there has been much excitement regarding the pioneering contributions of artificial intelligence (AI), the internet of things (IoT), and blockchain technologies and tools in visualizing and realizing smarter as well as sophisticated systems and services. However, researchers are being bombarded with various machine and deep learning algorithms, which are categorized as a part and parcel of the enigmatic AI discipline. The knowledge discovered gets disseminated to actuators and other concerned systems in order to empower them to intelligently plan and insightfully execute appropriate tasks with clarity and confidence. The IoT processes in conjunction with the AI algorithms and blockchain technology are bound to lay out a stimulating foundation for producing and sustaining smarter systems for society. The Handbook of Research on Smarter and Secure Industrial Applications Using AI, IoT, and Blockchain Technology articulates and accentuates various AI algorithms, fresh innovations in the IoT, and blockchain spaces. The domain of transforming raw data to information and to relevant knowledge is gaining prominence with the availability of data ingestion, processing, mining, analytics algorithms, platforms, frameworks, and other accelerators. Covering topics such as blockchain applications, Industry 4.0, and cryptography, this book serves as a comprehensive guide for AI researchers, faculty members, IT professionals, academicians, students, researchers, and industry professionals.

The internet of things (IoT) revolution has given rise to smart cities and villages all over the world. With technology advancements such as cloud computing, fog computing, and software-defined networking, it is necessary to examine ways that these environments can implement innovation for cost-effective citizen services and e-governance. Also, as cyber-physical systems are becoming more vulnerable with IoT attacks threatening their security and privacy, there is an even greater need for solutions that offer protection for all of these advancing technologies. The Handbook of Research on Implementation and Deployment of IoT Projects in Smart Cities is an essential research publication that combines theory and practice, reflecting on advancing technologies for the automation, protection, and sustainability of urban environments. Highlighting a wide range of topics such as blockchain, smart grid, and sustainability, this book is ideal for researchers, academicians, scientists, engineers, programmers, IT consultants, professionals, and policymakers.

In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering. Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians.

By specializing in a vertical market, companies can better understand their customers and bring more insight to clients in order to become an integral part of their businesses. This approach requires dedicated tools, which is where artificial intelligence (AI) and machine learning (ML) will play a major role. By adopting AI software and services, businesses can create predictive strategies, enhance their capabilities, better interact with customers, and streamline their business processes. This edited book explores novel concepts and cutting-edge research and developments towards designing these fully automated advanced digital systems. Fostered by technological advances in artificial intelligence and machine learning, such systems potentially have a wide range of applications in robotics, human computing, sensing and networking. The chapters focus on models and theoretical approaches to guarantee automation in large multi-scale implementations of AI and ML systems; protocol designs to ensure AI systems meet key requirements for future services such as latency; and optimisation algorithms to leverage the trusted distributed and efficient complex architectures. The book is of interest to researchers, scientists, and engineers working in the fields of ICTs, networking, AI, ML, signal processing, HCI, robotics and sensing. It could also be used as supplementary material for courses on AI, machine and deep learning, ICTs, networking signal processing, robotics and sensing.

Most technologies have been harnessed to enable educators to conduct their business remotely. However, the social context of technology as a mediating factor needs to be examined to address the perceptions of barriers to learning due to the lack of social interaction between a teacher and a learner in such a setting. Developing Technology Mediation in Learning Environments is an essential reference source that widens the scene of STEM education with an all-encompassing approach to technology-mediated learning, establishing a context for technology as a mediating factor in education. Featuring research on topics such as distance education, digital storytelling, and mobile learning, this book is ideally designed for teachers, IT consultants, educational software developers, researchers, administrators, and professionals seeking coverage on developing digital skills and professional knowledge using technology.