Industry 4.0 and the subsequent automation and digitalization of processes, including the tighter integration of machine-machine and human-machine intercommunication and collaboration, is adding additional complexity to future systems design and the capability to simulate, optimize, and adapt. Current solutions lack the ability to capture knowledge, techniques, and methods to create a sustainable and intelligent nerve system for enterprise systems. With the ability to innovate new designs and solutions, as well as automate processes and decision-making capabilities with heterogenous and holistic views of current and future challenges, there can be an increase in productivity and efficiency through sustainable automation. Therefore, better understandings of the underpinning knowledge and expertise of sustainable automation that can create a sustainable cycle that drives optimal automation and innovation in the field is needed Driving Innovation and Productivity Through Sustainable Automation enhances the understanding and the knowledge for the new ecosystems emerging in the Fourth Industrial Revolution. The chapters provide the knowledge and understanding of current challenges and new capabilities and solutions having been researched, developed, and applied within the industry to drive sustainable automation for innovation and productivity. This book is ideally intended for managers, executives, IT specialists, practitioners, stakeholders, researchers, academicians, and students who are interested in the current research on sustainable automation.

In the ever-increasing landscape of industry and technology, companies worldwide face an unprecedented challenge. The relentless march of progress, epitomized by the revolution of Industry 4.0, demands adaptation for survival and competitiveness. The integration of technologies such as the Internet of Things (IoT), blockchain, artificial intelligence, additive manufacturing, and robotics has irrevocably altered manufacturing and supply chain operations. What was initially a quest for augmented quality and production has now become an inexorable pursuit of sustainability. The United Nations Sustainable Development Goals (UNSDG) 2030 have left no room for exemptions, making sustainability an imperative at the heart of every business strategy. The answer to this pressing challenge lies within the pages of the book, Convergence of Industry 4.0 and Supply Chain Sustainability. It serves a meticulously curated collection of research that illuminates the intricacies of implementing Industry 4.0 and the ramifications for sustainable supply chains. Our work focuses on the associated challenges and opportunities encountered by the adoption of Industry 4.0 in supply chain management (SCM).

In recent decades, the industrial revolution has increased economic growth despite its immersion in global environmental issues such as climate change. Researchers emphasize the adoption of circular economy practices in global supply chains and businesses for better socio-environmental sustainability without compromising economic growth. Integrating blockchain technology into business practices could promote the circular economy as well as global environmental sustainability. Integrating Blockchain Technology Into the Circular Economy discusses the technological advancements in circular economy practices, which provide better results for both economic growth and environmental sustainability. It provides relevant theoretical frameworks and the latest empirical research findings in the applications of blockchain technology. Covering topics such as big data analytics, financial market infrastructure, and sustainable performance, this book is an essential resource for managers, operations managers, executives, manufacturers, environmentalists, researchers, industry practitioners, students and educators of higher education, and academicians.

Recently, artificial intelligence (AI), the internet of things (IoT), and cognitive technologies have successfully been applied to various research domains, including computer vision, natural language processing, voice recognition, and more. In addition, AI with IoT has made a significant breakthrough and a shift in technical direction to achieve high efficiency and adaptability in a variety of new applications. On the other hand, network design and optimization for AI applications addresses a complementary topic, namely the support of AI-based systems through novel networking techniques, including new architectures, as well as performance models for IoT systems. IoT has paved the way to a plethora of new application domains, at the same time posing several challenges as a multitude of devices, protocols, communication channels, architectures, and middleware exist. Big data generated by these devices calls for advanced learning and data mining techniques to effectively understand, learn, and reason with this volume of information, such as cognitive technologies. Cognitive technologies play a major role in developing successful cognitive systems which mimic “cognitive” functions associated with human intelligence, such as “learning” and “problem solving.” Thus, there is a continuing demand for recent research in these two linked fields. The Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies discusses the latest innovations and applications of AI, IoT, and cognitive-based smart systems. The chapters cover the intersection of these three fields in emerging and developed economies in terms of their respective development situation, public policies, technologies and intellectual capital, innovation systems, competition and strategies, marketing and growth capability, and governance and relegation models. These applications span areas such as healthcare, security and privacy, industrial systems, multidisciplinary sciences, and more. This book is ideal for technologists, IT specialists, policymakers, government officials, academics, students, and practitioners interested in the experiences of innovations and applications of AI, IoT, and cognitive technologies.

Both misinformation and disinformation lead to lasting misperceptions that can pose dangers to the public. While information manipulation has always existed, modern technology allows for misinformation to spread further and quicker than ever before. Especially during times in which misinformation has a greater potential reach and a more captivated audience, it is important to recognize its impact on society. Information Manipulation and Its Impact Across All Industries focuses on information manipulation in different areas including finance and economy, health, society, and more. It considers the effects of misinformation and disinformation on organizations, human resources, citizens, and governments. Additionally, it describes detection models and approaches of misinformation and disinformation as well as anti-misinformation actions, policies, and laws. Covering topics such as cybersecurity, online health misinformation, and social capital, this book is a dynamic resource for managers, engineers, researchers, lecturers, advanced students, professors, academicians, and professional institutions.

Discover the intricate nature of a company's production function and the comprehensive principles of planning operations in this book. Through practical applications and enriched by numerical examples, readers gain essential knowledge of elementary mathematical methods in operations planning. The inclusion of the powerful R programming language, accompanied by code scripts and real-world examples, enhances the learning experience. Blending theory with practice, this resource equips readers with the tools necessary to optimize production systems, make informed decisions, and gain a competitive edge in today's dynamic business landscape.

To sustain and stay at the top of the market and give absolute comfort to the consumers, industries are using different strategies and technologies. Natural language processing (NLP) is a technology widely penetrating the market, irrespective of the industry and domains. It is extensively applied in businesses today, and it is the buzzword in every engineer’s life. NLP can be implemented in all those areas where artificial intelligence is applicable either by simplifying the communication process or by refining and analyzing information. Neural machine translation has improved the imitation of professional translations over the years. When applied in neural machine translation, NLP helps educate neural machine networks. This can be used by industries to translate low-impact content including emails, regulatory texts, etc. Such machine translation tools speed up communication with partners while enriching other business interactions. Deep Natural Language Processing and AI Applications for Industry 5.0 provides innovative research on the latest findings, ideas, and applications in fields of interest that fall under the scope of NLP including computational linguistics, deep NLP, web analysis, sentiments analysis for business, and industry perspective. This book covers a wide range of topics such as deep learning, deepfakes, text mining, blockchain technology, and more, making it a crucial text for anyone interested in NLP and artificial intelligence, including academicians, researchers, professionals, industry experts, business analysts, data scientists, data analysts, healthcare system designers, intelligent system designers, practitioners, and students.

Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks (HONNs) play an important role in this new direction. HONNs will challenge traditional artificial neural network products and change the research methodology that people are currently using in control and recognition areas for the control signal generating, pattern recognition, nonlinear recognition, classification, and prediction. Since HONNs are open box models, they can be easily accepted and used by individuals working in information science, information technology, management, economics, and business fields. Emerging Capabilities and Applications of Artificial Higher Order Neural Networks contains innovative research on how to use HONNs in control and recognition areas and explains why HONNs can approximate any nonlinear data to any degree of accuracy, their ease of use, and how they can have better nonlinear data recognition accuracy than SAS nonlinear procedures. Featuring coverage on a broad range of topics such as nonlinear regression, pattern recognition, and data prediction, this book is ideally designed for data analysists, IT specialists, engineers, researchers, academics, students, and professionals working in the fields of economics, business, modeling, simulation, control, recognition, computer science, and engineering research.

The internet of things (IoT) has massive potential to transform current business models and enhance human lifestyles. With the current pace of research, IoT will soon find many new horizons to touch. IoT is now providing a base of technological advancement in various realms such as pervasive healthcare, smart homes, smart cities, connected logistics, automated supply chain, manufacturing units, and many more. IoT is also paving the path for the emergence of the digital revolution in industrial technology, termed Industry 4.0. Transforming the Internet of Things for Next-Generation Smart Systems focuses on the internet of things (IoT) and how it is involved in modern day technologies in a variety of domains. The chapters cover IoT in sectors such as agriculture, education, business and management, and computer science applications. The multi-disciplinary view of IoT provided within this book makes it an ideal reference work for IT specialists, technologists, engineers, developers, practitioners, researchers, academicians, and students interested in how IoT will be implemented in the next generation of smart systems and play an integral role in advancing technology in the future.

The artificial intelligence subset machine learning has become a popular technique in professional fields as many are finding new ways to apply this trending technology into their everyday practices. Two fields that have majorly benefited from this are pattern recognition and information security. The ability of these intelligent algorithms to learn complex patterns from data and attain new performance techniques has created a wide variety of uses and applications within the data security industry. There is a need for research on the specific uses machine learning methods have within these fields, along with future perspectives. The Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security is a collection of innovative research on the current impact of machine learning methods within data security as well as its various applications and newfound challenges. While highlighting topics including anomaly detection systems, biometrics, and intrusion management, this book is ideally designed for industrial experts, researchers, IT professionals, network developers, policymakers, computer scientists, educators, and students seeking current research on implementing machine learning tactics to enhance the performance of information security.