Revolutionizing Industrial Automation Through The Convergence Of Artificial Intelligence And The Internet Of Things
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Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things by Divya Mishra,Shanu Sharma Pdf
"This book's aim is to include recent advancements in exploring and developing Artificial Intelligence of Things -powered strategies and mechanisms for future industrial automation and transforming industrial functions and architectures to help and improve various industrial operations"--
Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things by Mishra, Divya Upadhyay,Sharma, Shanu Pdf
With the development of innovative technologies, the study of industrial automation is obtaining extraordinary attention from academia, governments, researchers, and various industrial communities. The technical innovations in advanced technologies such as deep learning, blockchain, artificial intelligence of things (AIOT), and more have unlocked the potential for bringing intelligent automation and efficiency to the industries to control their various operations. These new technologies provide an efficient and secure way for industries to manage and develop products intelligently. Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things includes the recent advancements in exploring and developing AIOT-powered strategies and mechanisms for future industrial automation and transforming industrial functions and architectures to help improve various industrial operations. Covering topics such as convolutional neural networks, smart urban logistics, and industrial automation, this premier reference source is an excellent resource for computer scientists, IT managers, engineers, business leaders and executives, logistics managers, students and faculty of higher education, libraries, researchers, and academicians.
Convergence of Deep Learning and Internet of Things: Computing and Technology by Kavitha, T.,Senbagavalli, G.,Koundal, Deepika,Guo, Yanhui,Jain, Deepak Pdf
Digital technology has enabled a number of internet-enabled devices that generate huge volumes of data from different systems. This large amount of heterogeneous data requires efficient data collection, processing, and analytical methods. Deep Learning is one of the latest efficient and feasible solutions that enable smart devices to function independently with a decision-making support system. Convergence of Deep Learning and Internet of Things: Computing and Technology contributes to technology and methodology perspectives in the incorporation of deep learning approaches in solving a wide range of issues in the IoT domain to identify, optimize, predict, forecast, and control emerging IoT systems. Covering topics such as data quality, edge computing, and attach detection and prediction, this premier reference source is a comprehensive resource for electricians, communications specialists, mechanical engineers, civil engineers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.
Global Perspectives on Social Media Usage Within Governments by Chavadi, Chandan,Thangam, Dhanabalan Pdf
Social media applications have emerged in the last 20 years to meet the different needs of individuals, and private sector and public organizations have not been indifferent to these technologies. Social media tools help public institutions and organizations communicate directly with citizens as well as enable two-way communication and enable citizens to participate in all stages from agenda setting to evaluation of policy processes. Central and local governments, which use innovative methods to involve citizens in this process, attach significance to the development of e-participation tools. Ensuring the participation of citizens in policy processes not only determines the wishes and priorities of citizens but also uses scarce resources effectively and efficiently. Global Perspectives on Social Media Usage Within Governments reveals the best practices of various countries regarding the use of social media by central and local governments according to public administration models. The book presents various case studies on the impact of public administration models on social media use in order to contribute to public administration and social media use. Covering topics such as climate action, knowledge behaviors, and citizen participation, this premier reference source is an essential resource for government officials, public administrators, public policy scholars, social media experts, public affairs scholars, students and educators of higher education, librarians, researchers, and academicians.
Semantic Web Technologies and Applications in Artificial Intelligence of Things by Ortiz-Rodriguez, Fernando,Leyva-Mederos, Amed,Tiwari, Sanju,Hernandez-Quintana, Ania R.,Martinez-Rodriguez, Jose L. Pdf
The confluence of Artificial Intelligence of Things (AIoT) and Semantic Web technologies is nothing short of revolutionary. The profound impact of this synergy extends far beyond the realms of industry, research, and society; it shapes the very fabric of our future. Semantic Web Technologies and Applications in Artificial Intelligence of Things is a meticulously crafted reference that not only acknowledges this significance but also serves as a guide for those navigating the complexities of Industry 4.0 and AIoT. This curated compendium of cutting-edge technologies acts as a veritable knowledge base for future developments. As academics, scholars, and industry professionals, the ideal audience of this book, will find meticulously curated content that caters to their diverse interests and expertise, covering topics ranging from smart agriculture, manufacturing, industry, health sciences, and government. Seasoned academics, students, and visionary industry leaders, will find this book to be an indispensable guide that paves the way for innovation and progress.
Convergence of Artificial Intelligence and Internet of Things for Industrial Automation by Divya Mishra,Alok Kumar Verma,Shanu Sharma Pdf
This book begins by discussing the fundamentals of artificial intelligence, the internet of things, and their convergence. It then covers techniques, algorithms, and methods of analysing and processing data over the artificial intelligence of things. The text elaborates on important concepts such as body sensor networks for safety in smart factories, smart energy management, smart robotic assistive systems, and service-oriented smart manufacturing. This book: Discusses the security and privacy aspect of artificial intelligence of things (AIoT) for smart real-time applications. Explores challenges and issues of artificial intelligence and the internet of things in the field of industrial automation. Includes case studies in artificial intelligence of things (AIoT) convergence for data processing. Showcases algorithms, techniques, and methods of analysing and processing data over the artificial intelligence of things. Highlights operation management using human-robot, smart maintenance, and autonomous production. It will serve as an ideal reference text for senior undergraduate, graduate students, and professionals in fields including industrial engineering, production engineering, manufacturing engineering, operations research, and computer engineering.
Data-Driven Farming by Syed Nisar Hussain Bukhari Pdf
In the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies. Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.
Computational Intelligence for Green Cloud Computing and Digital Waste Management by Kumar, K. Dinesh,Varadarajan, Vijayakumar,Nasser, Nidal,Poluru, Ravi Kumar Pdf
In the digital age, the relentless growth of data centers and cloud computing has given rise to a pressing dilemma. The power consumption of these facilities is spiraling out of control, emitting massive amounts of carbon dioxide, and contributing to the ever-increasing threat of global warming. Studies show that data centers alone are responsible for nearly eighty million metric tons of CO2 emissions worldwide, and this figure is poised to skyrocket to a staggering 8000 TWh by 2030 unless we revolutionize our approach to computing resource management. The root of this problem lies in inefficient resource allocation within cloud environments, as service providers often over-provision computing resources to avoid Service Level Agreement (SLA) violations, leading to both underutilization of resources and a significant increase in energy consumption. Computational Intelligence for Green Cloud Computing and Digital Waste Management stands as a beacon of hope in the face of the environmental and technological challenges we face. It introduces the concept of green computing, dedicated to creating an eco-friendly computing environment. The book explores innovative, intelligent resource management methods that can significantly reduce the power consumption of data centers. From machine learning and deep learning solutions to green virtualization technologies, this comprehensive guide explores innovative approaches to address the pressing challenges of green computing. Whether you are an educator teaching about green computing, an environmentalist seeking sustainability solutions, an industry professional navigating the digital landscape, a resolute researcher, or simply someone intrigued by the intersection of technology and sustainability, this book offers an indispensable resource.
Advances in Data and Information Sciences by Shailesh Tiwari,Munesh C. Trivedi,Mohan L. Kolhe,Brajesh Kumar Singh Pdf
This book gathers a collection of high-quality peer-reviewed research papers presented at the 5th International Conference on Data and Information Sciences (ICDIS 2023), held at Raja Balwant Singh Engineering Technical Campus, Agra, India, on June 16–17, 2023. The book covers all aspects of computational sciences and information security, including central topics like artificial intelligence, cloud computing, and big data. Highlighting the latest developments and technical solutions, it shows readers from the computer industry how to capitalize on key advances in next-generation computer and communication technology.
Emerging Technologies for Securing the Cloud and IoT by Ahmed Nacer, Amina,Abdmeziem, Mohammed Riyadh Pdf
In an age defined by the transformative ascent of cloud computing and the Internet of Things (IoT), our technological landscape has undergone a revolutionary evolution, enhancing convenience and connectivity in unprecedented ways. This convergence, while redefining how we interact with data and devices, has also brought to the forefront a pressing concern – the susceptibility of these systems to security breaches. As cloud services integrate further into our daily lives and the IoT saturates every aspect of our routines, the looming potential for cyberattacks and data breaches necessitates immediate and robust solutions to fortify the protection of sensitive information, ensuring the privacy and integrity of individuals, organizations, and critical infrastructure. Emerging Technologies for Securing the Cloud and IoT emerges as a comprehensive and timely solution to address the multifaceted security challenges posed by these groundbreaking technologies. Edited by Amina Ahmed Nacer from the University of Lorraine, France, and Mohammed Riyadh Abdmeziem from Ecole Nationale Supérieur d’Informatique, Algeria, this book serves as an invaluable guide for both academic scholars and industry experts. Its content delves deeply into the intricate web of security concerns, elucidating the potential ramifications of unaddressed vulnerabilities within cloud and IoT systems. With a pragmatic focus on real-world applications, the book beckons authors to explore themes like security frameworks, integration of AI and machine learning, data safeguarding, threat modeling, and more. Authored by esteemed researchers, practitioners, and luminaries, each chapter bridges the divide between theory and implementation, aiming to be an authoritative reference empowering readers to adeptly navigate the complexities of securing cloud-based IoT systems. A crucial resource for scholars, students, professionals, and policymakers striving to comprehend, confront, and surmount contemporary and future security challenges, this book stands as the quintessential guide for ushering in an era of secure technological advancement.
Bio-inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies by Bhowmick, Parijat,Das, Sima,Arvin, Farshad Pdf
The academic community is currently facing the challenge of navigating the complexities of swarm robotics. This field demands understanding the design, control, and coordination of autonomous robotic swarms. The intricacies of developing algorithms that facilitate communication, cooperation, and adaptation among simple individual agents remain a formidable obstacle. Addressing issues like task allocation, formation control, path planning, and decentralized decision-making are pivotal to unlocking the true potential of swarm robotics. Bio-inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies immerses readers in the cutting-edge realm of swarm robotics, a discipline inspired by the intricate choreography observed in biological systems like insect colonies, bird flocks, and fish schools. Encompassing a rich array of bio-inspired algorithms, mechanisms, and strategies, the text elucidates how robots can communicate, cooperate, and adapt within dynamic environments. The book propels robotics, automation, and artificial intelligence advancements by fostering interdisciplinary connections and charting a course toward more efficient and resilient multi-robot systems. This book is ideal for biologists, engineers, and computer scientists to join forces in unlocking the full potential of swarm robotics.
Technological Advancements in Data Processing for Next Generation Intelligent Systems by Sharma, Shanu,Prakash, Ayushi,Sugumaran, Vijayan Pdf
Technological Advancements in Data Processing for Next Generation Intelligent Systems presents an in-depth exploration of cutting-edge data processing technologies that drive the development of next-generation intelligent systems in the context of the digital transformation era. This comprehensive book delves into the role data plays as a critical asset for organizations across diverse industries, and how recent technological breakthroughs have unlocked unprecedented potential for handling vast data volumes and real-time analysis. The book begins by providing a thorough overview of novel technologies such as artificial intelligence (AI) or machine learning (ML), edge computing, federated learning, quantum computing, and more. These revolutionary technologies, when integrated with big data frameworks, in-memory computing, and AI/ML algorithms, have transformed data processing capabilities, enabling the creation of intelligent systems that fuel innovation, optimize operations, and deliver personalized experiences. The ultimate aim of this integration is to empower devices with the ability to make autonomous intelligent decisions, maximizing computing power. This book serves as a valuable resource for research scholars, academicians, and industry professionals working towards the future advancement of optimized intelligent systems and intelligent data processing approaches. The chapters encompass a wide range of topics, including architecture and frameworks for intelligent systems, applications in diverse domains, cloud-based solutions, quantum processing, federated learning, in-memory data processing, real-time stream processing, trustworthy AI for Internet of Things (IoT) sensory data, and more.
Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence by Chowdhary, Chiranji Lal Pdf
Emotional intelligence has emerged as an important area of research in the artificial intelligence field as it covers a wide range of real-life domains. Though machines may never need all the emotional skills that people need, there is evidence to suggest that machines require at least some of these skills to appear intelligent when interacting with people. To understand how deep learning-based emotional intelligence can be applied and utilized across industries, further study on its opportunities and future directions is required. Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence explores artificial intelligence applications, such as machine and deep learning, in emotional intelligence and examines their use towards attaining emotional intelligence acceleration and augmentation. It provides research on tools used to simplify and streamline the formation of deep learning for system architects and designers. Covering topics such as data analytics, deep learning, knowledge management, and virtual emotional intelligence, this reference work is ideal for computer scientists, engineers, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.
Convergence of Big Data Technologies and Computational Intelligent Techniques by Gupta, Govind P. Pdf
Advanced computational intelligence techniques have been designed and developed in recent years to cope with various big data challenges and provide fast and efficient analytics that assist in making critical decisions. With the rapid evolution and development of internet-based services and applications, this technology is receiving attention from researchers, industries, and academic communities and requires additional study. Convergence of Big Data Technologies and Computational Intelligent Techniques considers recent advancements in big data and computational intelligence across fields and disciplines and discusses the various opportunities and challenges of adoption. Covering topics such as deep learning, data mining, smart environments, and high-performance computing, this reference work is crucial for computer scientists, engineers, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.
Multimodal Biometric and Machine Learning Technologies by Sandeep Kumar,Deepika Ghai,Arpit Jain,Suman Lata Tripathi,Shilpa Rani Pdf
MULTIMODAL BIOMETRIC AND MACHINE LEARNING TECHNOLOGIES With an increasing demand for biometric systems in various industries, this book on multimodal biometric systems, answers the call for increased resources to help researchers, developers, and practitioners. Multimodal biometric and machine learning technologies have revolutionized the field of security and authentication. These technologies utilize multiple sources of information, such as facial recognition, voice recognition, and fingerprint scanning, to verify an individual???s identity. The need for enhanced security and authentication has become increasingly important, and with the rise of digital technologies, cyber-attacks and identity theft have increased exponentially. Traditional authentication methods, such as passwords and PINs, have become less secure as hackers devise new ways to bypass them. In this context, multimodal biometric and machine learning technologies offer a more secure and reliable approach to authentication. This book provides relevant information on multimodal biometric and machine learning technologies and focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity. The book provides content on the theory of multimodal biometric design, evaluation, and user diversity, and explains the underlying causes of the social and organizational problems that are typically devoted to descriptions of rehabilitation methods for specific processes. Furthermore, the book describes new algorithms for modeling accessible to scientists of all varieties. Audience Researchers in computer science and biometrics, developers who are designing and implementing biometric systems, and practitioners who are using biometric systems in their work, such as law enforcement personnel or healthcare professionals.
