Dr.S.Gandhimathi, Assistant Professor, Department of Computer Science, Valluvar College of Science and Management, Karur, Tamil Nadu, India. Dr.K.Sivakami, Associate Professor, Department of Computer Science, Nadar Saraswathi College of Arts and Science, Theni, Tamil Nadu, India. Dr.B.Senthilkumaran, Assistant Professor, Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai,Tamil Nadu, India. Dr.John T Mesia Dhas, Associate Professor, Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai,Tamil Nadu, India. Mrs.S.Saranya, Assistant Professor, Department of Computer Science, Valluvar College of Science and Management, Karur, Tamil Nadu, India.

In the field of finance, the pervasive influence of algorithms has transformed the very fabric of the industry. Today, over 75% of trades are orchestrated by algorithms, making them the linchpin for trade automation, predictions, and decision-making. This algorithmic reliance, while propelling financial services into unprecedented efficiency, has also ushered in a host of challenges. As the financial sector becomes increasingly algorithm-driven, concerns about risk assessment, market manipulation, and the ethical implications of automated decision-making have taken center stage. Artificial Intelligence and Machine Learning-Powered Smart Finance, meticulously examines the intersection of computational finance and advanced algorithms and the challenges associated with this technology. As algorithms permeate various facets of financial services, the book takes a deep dive into their applications, spanning forecasting, portfolio optimization, market trends analysis, and cryptoanalysis. It sheds light on the role of AI-based algorithms in personnel selection, implementing trusted financial services, developing recommendation systems for financial platforms, and detecting fraud, presenting a compelling case for the integration of innovative solutions in the financial sector. As the book unravels the intricate tapestry of algorithmic applications in finance, it also illuminates the ethical considerations and governance frameworks essential for navigating the delicate balance between technological innovation and responsible financial practices.

Artificial Intelligence and Machine Learning for Sustainable Development is a comprehensive exploration of how artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing the field of sustainable development. The book examines cutting-edge innovations, practical applications, and potential challenges in harnessing AI and ML to address global sustainability issues. It offers insights into how these technologies can optimize resource management, improve environmental monitoring, enhance decision-making processes, and promote equitable, eco-friendly solutions. This book would be of special interest to researchers, policymakers, and practitioners seeking to leverage cutting-edge technology for a more sustainable future.

Artificial intelligence adoption is accelerating across industries, but many learning teams may not be moving the needle fast enough. Now is the prime opportunity for learning teams to prioritize its usage. However, although some L&D professionals have experimented with generative AI tools, few know what the game-changing technology really is. In this issue of TD at Work, Myra Roldan shares: A simplified explanation of how AI works The difference between traditional AI and generative AI Sample use cases for L&D professionals to think about A brief overview of how to get started using AI Some notes on privacy and ethics The Tools & Resources in this issue are an Artificial Intelligence Tool Evaluation Worksheet and an Artificial Intelligence Project Planning Checklist.

The aim of this book is to highlight the most promising lines of research, using new enabling technologies and methods based on AI/ML techniques to solve issues and challenges related to intelligent and computing systems. Intelligent computing easily collects data using smart technological applications like IoT-based wireless networks, digital healthcare, transportation, blockchain, 5.0 industry and deep learning for better decision making. AI enabled networks will be integrated in smart cities' concept for interconnectivity. Wireless networks will play an important role. The digital era of computational intelligence will change the dynamics and lifestyle of human beings. Future networks will be introduced with the help of AI technology to implement cognition in real-world applications. Cyber threats are dangerous to encode information from network. Therefore, AI-Intrusion detection systems need to be designed for identification of unwanted data traffic. This book: Provides a better understanding of artificial intelligence-based applications for future smart cities Presents a detailed understanding of artificial intelligence tools for intelligent technologies Showcases intelligent computing technologies in obtaining optimal solutions using artificial intelligence Discusses energy-efficient routing protocols using artificial intelligence for Flying ad-hoc networks (FANETs) Covers machine learning-based Intrusion detection system (IDS) for smart grid It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

This volume brings together eminent scholars from various parts of the world, representing different fields of knowledge in order to explore the social, cultural, political and economic effects of the development of new technologies. On the one hand, the book contextualises the discussion of algorithms and artificial intelligence (AI) within the broader framework of the digital revolution, on the other it also examines individual experiences and practices. Moreover, in light of the speed at which algorithms and AI are being incorporated into various aspects of life, contributors also question the ethical implications of their development. The widespread development of AI and algorithmic solutions is one of the most important contemporary phenomena. It has an overwhelming impact on the social and cultural life of the 21st century. In this context, one can point to both exciting examples of the application of algorithms and AI in business and popular culture, as well as the challenges of widening social inequality or the expanding scope of surveillance. The scope of the impact of algorithms and AI makes the formation of new theoretical frameworks vital. This is the aim of this book, which will be of interest to academics within the humanities and social sciences with an interest in technology and the impact of algorithms and AI on society and culture.

This book tackles the recent research trends on the role of AI in advancing automotive manufacturing, augmented reality, sustainable development in smart cities, telemedicine, and robotics. It sheds light on the recent AI innovations in classical machine learning, deep learning, Internet of Things (IoT), Blockchain, knowledge representation, knowledge management, big data, and natural language processing (NLP). The edited book covers empirical and reviews studies that primarily concentrate on the aforementioned issues, which would assist scholars in pursuing future research in the domain and identifying the possible future developments of AI applications.

This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.

This book presents innovative research works to automate, innovate, design, and deploy AI fo real-world applications. It discusses AI applications in major cutting-edge technologies and details about deployment solutions for different applications for sustainable development. The application of Blockchain techniques illustrates the ways of optimisation algorithms in this book. The challenges associated with AI deployment are also discussed in detail, and edge computing with machine learning solutions is explained. This book provides multi-domain applications of AI to the readers to help find innovative methods towards the business, sustainability, and customer outreach paradigms in the AI domain. • Focuses on virtual machine placement and migration techniques for cloud data centres • Presents the role of machine learning and meta-heuristic approaches for optimisation in cloud computing services • Includes application of placement techniques for quality of service, performance, and reliability improvement • Explores data centre resource management, load balancing and orchestration using machine learning techniques • Analyses dynamic and scalable resource scheduling with a focus on resource management The reference work is for postgraduate students, professionals, and academic researchers in computer science and information technology.

This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning and cloud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.