'Advanced Artificial Intelligence' consists of 16 chapters. The content of the book is novel, reflects the research updates in this field, and especially summarises the author's scientific efforts over many years.

BOOK SUMMARY The main topics in this book are; • Natural Language Processing • Computer Vision • Machine Learning Algorithms • Deep Learning • Reinforcement Learning • Robotics and Autonomous Systems • Generative Adversarial Networks • Explainable AI Advanced Artificial Intelligence is a comprehensive a forward-looking book that delves into the most cutting-edge advancements in the field of AI. With real-life examples the book showcases how AI is revolutionizing industries, driving innovation,and shaping the future of technology.

The book deals with digital technology which is transforming the landscape of dispute resolution. It illustrates the application of AI in the legal field and shows the future prospect of robo-justice for an AAI society in the advanced artificial intelligence era. In other words, the present justice system and the influence of current AI upon courts and arbitration are investigated. The transforming role of AI on all legal fields is examined thoroughly by giving answers concerning AI legal personality and liability. The analysis shows that digital technology is generating an ever-growing number of disputes and at the same time is challenging the effectiveness and reach of traditional dispute resolution avenues. To that extent, the book presents in tandem the impact of AI upon courts and arbitration, and reveals the role of AAI in generating a new robo-justice system. Finally, the end of the perplexing relation of courts and arbitration is evidenced methodically and comprehensively.

Advances in artificial intelligence and automation have the potential to be labor-saving and to increase inequality and poverty around the globe. They also give rise to winner-takes-all dynamics that advantage highly skilled individuals and countries that are at the forefront of technological progress. We analyze the economic forces behind these developments and delineate domestic economic policies to mitigate the adverse effects while leveraging the potential gains from technological advances. We also propose reforms to the global system of governance that make the benefits of advances in artificial intelligence more inclusive.

This book shows you how to develop a hybrid mm-wave chipless Radio Frequency Identification (RFID) system, which includes chip-less tag, reader hardware, and detection algorithm that use image processing and machine learning (ML) techniques. It provides the background and information you need to apply the concepts of AI into detection and chip-less tag signature printable on normal plastic substrates, instead of the conventional peak/nulls in the frequency tags. You’ll learn how to incorporate new AI detection techniques along with cloud computing to lower costs. You’ll also be shown a cost-effective means of image construction, which can lower detection errors. The book focuses on side-looking-aperture-radar (SLAR) with a combination of deep learning to provide a much safer means of chipless detection than the current iSAR technique. Each chapter includes practical examples of design. With its emphasis on mm-waveband and the practical side of design and engineering of the chipless tags, reader and detection algorithms, this is an excellent resource for industry engineers, design engineers and university researchers.

We stand at the precipice of a technological revolution; the entrepreneurial landscape is undergoing a metamorphosis. In the academic corridors of today, a pressing challenge emerges - the need to comprehend and dissect the profound transformations underway in the world of entrepreneurship. The fusion of emerging technologies with the age-old spirit of entrepreneurship is creating seismic shifts, ushering in new possibilities that beg exploration. Improving Entrepreneurial Processes Through Advanced AI emerges as a beacon of insight and innovation in this new entrepreneurial realm of possibility. This book embarks on a captivating journey, tailored to the discerning minds of PhD students, university educators, independent researchers, and scholars in related fields, guiding them through the intricacies of technology integration and the transformation of entrepreneurial processes. As technology continues to advance at an unprecedented pace, traditional paradigms are being upended, leaving researchers grappling with complex questions. The emergence of Artificial Intelligence (AI) as a game-changer in entrepreneurship introduces a host of intricate issues and uncertainties. Amidst this sea of change, the fundamental challenge lies in understanding how these advancing AI systems can address core entrepreneurial challenges and open new horizons of opportunity in the era of digital transformation.

Plant diseases and pests cause significant losses to farmers and threaten food security worldwide. Monitoring the growing conditions of crops and detecting plant diseases is critical for sustainable agriculture. Traditionally, crop inspection has been carried out by people with expert knowledge in the field. However, regarding any activity carried out by humans, this activity is prone to errors, leading to possible incorrect decisions. Innovation is, therefore, an essential fact of modern agriculture. In this context, deep learning has played a key role in solving complicated applications with increasing accuracy over time, and recent interest in this type of technology has prompted its potential application to address complex problems in agriculture, such as plant disease and pest recognition. Although substantial progress has been made in the area, several challenges still remain, especially those that limit systems to operate in real-world scenarios.