Dr.S.Alex David, Professor and Head, Department of Artificial Intelligence and Machine Learning, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India. Dr.N.K.Manikandan, Assistant Professor (Senior Grade), Department of Computer Science & Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India. Mrs.Hemalatha D, Assistant Professor, Department of Computer Science & Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India. Dr.Almas Begum, Professor, Department of Computer Science & Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.

Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniquesConsiders the merits of e

This SpringerBrief discusses how to develop intelligent systems for cyber attribution regarding cyber-attacks. Specifically, the authors review the multiple facets of the cyber attribution problem that make it difficult for “out-of-the-box” artificial intelligence and machine learning techniques to handle. Attributing a cyber-operation through the use of multiple pieces of technical evidence (i.e., malware reverse-engineering and source tracking) and conventional intelligence sources (i.e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence. This SpringerBrief not only lays out the theoretical foundations for how to handle the unique aspects of cyber attribution – and how to update models used for this purpose – but it also describes a series of empirical results, as well as compares results of specially-designed frameworks for cyber attribution to standard machine learning approaches. Cyber attribution is not only a challenging problem, but there are also problems in performing such research, particularly in obtaining relevant data. This SpringerBrief describes how to use capture-the-flag for such research, and describes issues from organizing such data to running your own capture-the-flag specifically designed for cyber attribution. Datasets and software are also available on the companion website.

The purpose of this catalogue is to promote interaction between members of the AI community. It will do this by announcing the existence of AI techniques and portable software, and acting as 30 pointer into the literature. Thus the AI community will have access to 30 common, extensional definition of the field, which will: promote 30 common terminology, discourage the reinvention of w heels, and act as 30 clearing house for ideas and software. The catalogue is 30 reference work providing 30 quick guide to the AI tools ava.ilable for different jobs. It is not intended to be 30 textbook like the Artificial Intelligence Handbook. It, intentiona11y, only provides 30 brief description of each tool, with no extended discussion of the historical origin of the tool or how it has been used in particular AI programs. The focus is on techniques abstracted from their historical origins. The original version of the catalogue, was hastily built in 1983 as part of the UK SERC-DoI, IKBS, Architecture Study. It has now been adopted by the UK Alvey Programme and is both kept as an on-line document undergoing constant revision and refinement and published as 30 paperback by Springer-Verlag. Springer-Verlag have agreed to reprint the Catalogue at frequent intervals in order to keep it up to date.

There are various factors that influence the quality and quantity of agricultural products; among them, weather conditions play the most significant role in agriculture. More reliable weather forecasting enables farmers to make important planting and harvesting decisions that can enhance agricultural yield. Thus, there is a dire need to combine all available modern technologies and agricultural science for economic and environmentally sustainable crop production. In this direction, artificial intelligence (AI) serves as a budding solution in the domain of agriculture practices. Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices discusses various tools and technologies that can be used in smart farming and agriculture practice and explores the role of different emerging technologies like the internet of things, big data, machine learning, deep learning, and AI from agricultural prospects. Covering key topics such as farming, pests, soil, and weeds, this premier reference source is ideal for environmentalists, farmers, agriculturalists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Embrace the intersection of human ingenuity and technological innovation. Gartner’s research team have predicted that by 2030, 80% of Project Management tasks will be run by AI. AI is not going to go away, so the longer project managers ignore it, the more they are losing out. Not only losing out on the benefits of AI but losing out on staying ahead in their careers. Organisations that effectively leverage AI in project management gain a competitive edge. They can deliver projects more efficiently, meet deadlines consistently, and adapt to changing circumstances with greater agility, ultimately contributing to the overall success of the organization. Are you ready to transform your project management approach? Artificial Intelligence in Project Management is the essential guide for project managers who are new to AI and want to catch up with this game-changing technology. This book shows how AI can save you time, and support you in being more efficient in your role. With real-world examples, practical use cases and actionable insights, this book bridges the gap between complex AI concepts and everyday project management tasks. It provides clear, actionable steps to help you integrate AI into your projects, even if you're starting from scratch. Don't get left behind. Equip yourself with this essential know-how to lead your projects with confidence in this new age. The efficiency gains from using AI tools in project management allows us more time to focus on the people-side of project management. AI enables project managers and teams to stay on top of their project planning and execution activities, whilst freeing up time for much needed stakeholder management. Target audience for this book: Project and Programme Managers, workstream leaders, project management officers, Business Analysts and all professionals interested in AI and Project Management. This is also a brilliant text to support graduate and post-graduate studies in Project Management. Reading this book will help you: · Understand the fundamentals of AI and its implications for project management. · Learn about key AI tools and technologies applicable to project management tasks. · Discover real-world examples of successful AI integration in project environments. · Learn how to leverage AI for decision-making, planning, and risk management. · Utilise a set if time-saving use cases where AI can assist in project management. · Understand what you need to do to stay ahead in your career as a Project Manager. As always with this author’s books, there is no waffle, page filling theory or unnecessary padding. Practical. Relevant. Useful. EMANUELA is a performance improvement coach with over 20 years’ experience in project management. She has trained thousands of people on the subject around the world and has a knack for explaining complex topics simply. Whether training in-person or virtually, she engages individuals and teams with her energy, enthusiasm, and her passion for continuous improvement.

Transforming Management Using Artificial Intelligence Techniques redefines management practices using artificial intelligence (AI) by providing a new approach. It offers a detailed, well-illustrated treatment of each topic with examples and case studies, and brings the exciting field to life by presenting a substantial and robust introduction to AI in a clear and concise manner. It provides a deeper understanding of how the relevant aspects of AI impact each other’s efficacy for better output. It’s a reliable and accessible one-step resource that introduces AI; presents a full examination of applications; provides an understanding of the foundations; examines education powered by AI, entertainment, home and service robots, healthcare re-imagined, predictive policing, space exploration; and so much more, all within the realm of AI. This book will feature: Uncovering new and innovative features of AI and how it can help in raising economic efficiency at both micro- and macro levels Both the literature and practical aspects of AI and its uses This book summarizing key concepts at the end of each chapter to assist reader comprehension Case studies of tried and tested approaches to resolutions of typical problems Ideal for both teaching and general-knowledge purposes. This book will also simply provide the topic of AI for the readers, aspiring researchers and practitioners involved in management and computer science, so they can obtain a high-level of understanding of AI and managerial applications.

Amid the dynamic growth of artificial intelligence, this book presents a collection of findings and advancements from the second edition of the A2IA-Artificial Intelligence and Industrial Applications conference. The conference, hosted by ENSAM-Meknès at Moulay Ismail University, Morocco, fosters knowledge exchange in AI, focusing primarily on its industrial applications. Covering a wide range of topics, the book highlights the adaptable nature of AI and its increasing impact on industrial sectors. It brings together contributions from an international cohort of researchers, discussing themes such as intelligent manufacturing and maintenance, intelligent supply chain management, various modes of learning including supervised, unsupervised, reinforcement, semi-supervised, and graph-based, as well as neural networks, deep learning, planning, and optimization. A defining feature of this edition is its extensive scope and emphasis on the practical applications of AI, along with its foundational elements. It facilitates an understanding of AI's current state and potential future direction, showcasing recent developments that bridge the gap between theory and practice. Designed for a diverse readership, this book is of interest to AI practitioners, academics, and enthusiasts, as well as to those new to the field. It provides an opportunity to explore AI's critical role in industrial applications, and the practical insights it offers are likely to be beneficial for decision-making within industrial settings.