Advances in artificial life ; 8th European Conference, ECAL 2005 , Canterbury, UK, September 5-9, 2005, Proceedings
The Artificial Life term appeared more than 20 years ago . Since then the area has developed dramatically, many researchersjoining enthusiastically and research groups sprouting everywhere.a conceptual track, where papers were judged on criteria like importance and/or novelty of the concepts proposed rather than the experimental / theoretical results, has been introduced this year. A conference on a theme as broad as Artificial Life is bound to be very di-verse, but a few tendencies emerged. First, fields like ‘Robotics and Autonomous Agents’ or ‘Evolutionary Computation’are still extremely active and keep onbringing a wealth of results to the A-Life community. Even there, however, new tendencies appear, like collective robotics, and more specifically self-assembling robotics, which represent now a large subsection. Second, new areas appear.‘Morphogenesis and Development’ which used to be the subject of only a fewpapers, is now one of the largest subsections, and seems to be on the brinkof becoming a field of its own. Finally, most classical themes of A-Life re-search like ‘Artificial Chemistry’, ‘Ant-Inspired Systems’, ‘Cellular Automata’,‘Self-Replication’, ‘Social Simulations’ or ‘Bio-realist Simulations’ are still goingstrong and are well represented within this volume.
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New challenges in software engineering ; Vol 1
Explores the key challenges shaping the future of software development, including automation, AI-driven development, security-focused engineering, resilient and autonomous architectures, business process optimization, cloud computing, microservices, high-performance distributed systems, and sustainable technologies. Software engineering is undergoing a constant transformation, driven by rapid technological advances and evolving market demands. additionally, it delves into the ethical considerations of AI, the evolution of intuitive user interfaces, and the importance of multidisciplinary collaboration.
Fundamentals of manufacturing engineering using digital visualization
Offers a guide to core principles and practices of manufacturing engineering. It covers the design of, together with technological and measurement issues for, technical systems. Locating charts and setup schemes describing different machining processes are included. Concepts of product quality, with a focus on accuracy indicators, machining accuracy, roughness, and the impact of surface quality on exploitation properties are also explained. Furthermore, key machining methods, including turning, milling, hole machining, grinding, and gear machining, are analyzed in depth, covering their principles, applications, and techniques. The book is enriched by QR codes, linking to a mobile application presenting additional information about the content, for an interactive and extended learning experience. It also uses illustrations visualized with digital tools to promote a better understanding of the concepts.
AI in drug discovery
Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
AI in banking : Practical applications and case studies
Delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. it provides hands-on examples of various technologies, including automatic machine learning, integrated learning, graph computation, recommendation systems, causal inference, generative adversarial networks, supervised learning, unsupervised learning, computer vision, reinforcement learning, fuzzy control, automatic control, speech recognition, semantic understanding, bayesian networks, edge computing, and more. this book stands as a rare and practical guide to AI projects in the banking industry.
