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Focusing on students’ presentations and discussions in laboratory seminars, this book presents case studies on evidence-based education using artificial intelligence (AI) technologies. It proposes a system to help users complete research activities, and a machine-learning method that makes the system suitable for long-term operation by performing data mining for discussions and automatically extracting essential tasks. By illustrating the complete process – proposal, implementation, and operation – of applying machine learning techniques to real-world situations, the book will inspire researchers and professionals to develop innovative new applications for education. The book is divided into six chapters, the first of which provides an overview of AI research and practice in education. In turn, Chapter 2 describes a mechanism for applying data analytics to student discussions and utilizing the results for knowledge creation activities such as research. Based on discussion data analytics, Chapter 3 describes a creative activity support system that effectively utilizes the analytical results of the discussion for subsequent activities. Chapter 4 discusses the incorporation of a gamification method to evaluate and improve discussion skills while maintaining the motivation to participate in the discussion. Chapters 5 and 6 describe an advanced learning environment for honing students’ discussion and presentation skills. Two important systems proposed here are a presentation training system using virtual reality technologies, and an interactive presentation/discussion training system using a humanoid robot. In the former, the virtual space is constructed by measuring the three-dimensional shape of the actual auditorium, presentations are performed in the same way as in the real world, and the AI as audience automatically evaluates the presentation and provides feedback. In the latter, a humanoid robot makes some remarks on and asks questions about students’ presentations, and the students practice responding to it.
Managing Expert Systems explores the trends in expert systems development and implementation. As top authorities in the field of ES, Turban and Liebowitz examine the factors that contribute to the development of a successful expert system.
The use of cognitive science in creating stories, languages, visuals, and characters is known as narrative generation, and it has become a trending area of study. Applying artificial intelligence (AI) techniques to story development has caught the attention of professionals and researchers; however, few studies have inherited techniques used in previous literary methods and related research in social sciences. Implementing previous narratology theories to current narrative generation systems is a research area that remains unexplored. Bridging the Gap Between AI, Cognitive Science, and Narratology With Narrative Generation is a collection of innovative research on the analysis of current practices in narrative generation systems by combining previous theories in narratology and literature with current methods of AI. The book bridges the gap between AI, cognitive science, and narratology with narrative generation in a broad sense, including other content generation, such as a novels, poems, movies, computer games, and advertisements. The book emphasizes that an important method for bridging the gap is based on designing and implementing computer programs using knowledge and methods of narratology and literary theories. In order to present an organic, systematic, and integrated combination of both the fields to develop a new research area, namely post-narratology, this book has an important place in the creation of a new research area and has an impact on both narrative generation studies, including AI and cognitive science, and narrative studies, including narratology and literary theories. It is ideally designed for academicians, researchers, and students, as well as enterprise practitioners, engineers, and creators of diverse content generation fields such as advertising production, computer game creation, comic and manga writing, and movie production.
Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.
In the rapidly evolving landscape of industrial activities, artificial intelligence (AI) has emerged as a powerful force driving transformative change. Among its many applications, AI has proven to be instrumental in reducing processing costs associated with optimization challenges. The intersection of AI with optimization and multi-criteria decision making (MCDM) techniques has led to practical solutions in diverse fields such as manufacturing, transportation, finance, economics, and artificial intelligence. Using Traditional Design Methods to Enhance AI-Driven Decision Making delves into a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. The book is ideal for higher education faculty and administrators, students of higher education, librarians, researchers, graduate students, and academicians. Contributors are invited to explore a wide range of topics, including the role of AI-driven decision-making in leadership, trends in AI-driven decision-making in Industry 5.0, applications in various industries such as manufacturing, transportation, healthcare, and banking services, as well as AI-driven optimization in mechanical engineering and materials.
Recent advances in VLSI technologies have allowed larger and more complex designs. CAD systems using Artificial Intelligence (AI) techniques play the most important role in their short-term realization. This book contains 27 research papers reporting on the various technical fields in VLSI CAD. It is a coordinated collection of papers from researchers in the USA, Europe and Japan. The main topics covered are logic design using AI techniques, and expert systems, every paper offering an effective solution applying AI techniques to difficult problems in the CAD field.
This book consists of selected papers presented at the Third Conference of the Asian-Pacific Operational Research Societies. The selected papers cover broad areas of operations research, ranging from traditional to new directions in theory, algorithm and applications. Typical topics are linear and nonlinear programming, interior point method, combinatorial programming, scheduling, queueing theory, simulation, forecasting, inventory, telecommunication, finance, organizational intelligence, analytic hierarchy process, data envelopment analysis, fuzzy programming and others.
Artificial intelligence (AI) and knowledge management can create innovative digital solutions and business opportunities in Asia from circular and green economies to technological disruption, innovation, and smart cities. It is essential to understand the impact and importance of AI and knowledge management within the digital economy for future development and for fostering the best practices within 21st century businesses. The Handbook of Research on Artificial Intelligence and Knowledge Management in Asia’s Digital Economy offers conceptual frameworks, empirical studies, and case studies that help to understand the latest developments in artificial intelligence and knowledge management, as well as its potential for digital transformation and business opportunities in Asia. Covering topics such as augmented reality. Convolutional neural networks, and digital transformation, this major reference work generates enriching debate on the challenges and opportunities for economic growth and inclusion in the region among business executives and leaders, IT managers, policymakers, government officials, students and educators of higher education, researchers, and academicians.