Artificial Intelligence In Models Methods And Applications
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Artificial Intelligence In Models Methods And Applications
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Author : Olga Dolinina
language : en
Publisher: Springer Nature
Release Date : 2023-04-24
Artificial Intelligence In Models Methods And Applications written by Olga Dolinina and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-24 with Technology & Engineering categories.
This book is based on the accepted research papers presented in the International Conference "Artificial Intelligence in Engineering & Science" (AIES-2022). The aim of the AIES Conference is to bring together researchers involved in the theory of computational intelligence, knowledge engineering, fuzzy systems, soft computing, machine learning and related areas and applications in engineering, bioinformatics, industry, medicine, energy, smart city, social spheres and other areas. This book presents new perspective research results: models, methods, algorithms and applications in the field of Artificial Intelligence (AI). Particular emphasis is given to the medical applications - medical images recognition, development of the expert systems which could be interesting for the AI researchers as well for the physicians looking for the new ideas in medicine. The central audience of the book are researchers, industrial practitioners, students specialized in the Artificial Intelligence.
Artificial Intelligence Models Algorithms And Applications
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Author : Terje Solsvik Kristensen
language : en
Publisher: Bentham Science Publishers
Release Date : 2021-05-31
Artificial Intelligence Models Algorithms And Applications written by Terje Solsvik Kristensen and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-31 with Computers categories.
Artificial Intelligence: Models, Algorithms and Applications presents focused information about applications of artificial intelligence (AI) in different areas to solve complex problems. The book presents 8 chapters that demonstrate AI based systems for vessel tracking, mental health assessment, radiology, instrumentation, business intelligence, education and criminology. The book concludes with a chapter on mathematical models of neural networks. The book serves as an introductory book about AI applications at undergraduate and graduate levels and as a reference for industry professionals working with AI based systems.
Computational Neuroscience For Advancing Artificial Intelligence Models Methods And Applications
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Author : Alonso, Eduardo
language : en
Publisher: IGI Global
Release Date : 2010-11-30
Computational Neuroscience For Advancing Artificial Intelligence Models Methods And Applications written by Alonso, Eduardo and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-30 with Computers categories.
"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--
Risk Modeling
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Author : Terisa Roberts
language : en
Publisher: John Wiley & Sons
Release Date : 2022-09-20
Risk Modeling written by Terisa Roberts and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-20 with Business & Economics categories.
A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.
Explainable Ai Foundations Methodologies And Applications
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Author : Mayuri Mehta
language : en
Publisher: Springer Nature
Release Date : 2022-10-19
Explainable Ai Foundations Methodologies And Applications written by Mayuri Mehta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Technology & Engineering categories.
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
Artificial Intelligence Concepts Techniques And Applications
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Author : Alexis Keller
language : en
Publisher: States Academic Press
Release Date : 2021-11-16
Artificial Intelligence Concepts Techniques And Applications written by Alexis Keller and has been published by States Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-16 with Computers categories.
The ability of a digital computer to perform complex tasks which are associated with humans is termed as artificial intelligence. It is a multi-disciplinary field which employs the principles of computer science, information engineering, psychology, mathematics, philosophy and linguistics. The primary goals of research in artificial intelligence are knowledge representation, reasoning, learning, planning, perception, and the ability to move and manipulate objects. It uses statistical approaches and computational modeling methods to achieve its long term goal of general intelligence. Artificial intelligence can be divided into machine learning, deep learning, natural language processing and robotics. It finds extensive application in the fields of military simulation, delivery and distribution networks, strategic game systems and self-driving cars. The topics included in this book on artificial intelligence are of utmost significance and bound to provide incredible insights to readers. Different approaches, evaluations and methodologies on artificial intelligence have been included herein. This book is an essential guide for both academicians and those who wish to pursue this discipline further.
Artificial Intelligence Methods And Applications
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Author : N G Bourbakis
language : en
Publisher: World Scientific
Release Date : 1992-12-31
Artificial Intelligence Methods And Applications written by N G Bourbakis and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-12-31 with Computers categories.
This volume is the first in a series which deals with the challenge of AI issues, gives updates of AI methods and applications, and promotes high quality new ideas, techniques and methodologies in AI. This volume contains articles by 38 specialists in various AI subfields covering theoretical and application issues. Contents:Introduction to Advanced Series on Artificial Intelligence (N G Bourbakis)Fundamental Methods for Horn Logic and Artificial Intelligence Applications (E Kounalis & P Marquis)Applications of Genetic Algorithms to Permutation Problems (F E Petry & B P Buckles)Extracting Procedural Knowledge from Software Systems Using Inductive Learning in the PM System (R G Reynolds et al.)Resource-Oriented Parallel Planning (S Lee & K Chung)Advanced Parsing Technology for Knowledge-Based Shells (J R Kipps)The Analysis and Synthesis of Intelligent Systems (W Arden)Document Image Analysis and Recognition (S N Srihari et al.)Signal Understanding: An Artificial Intelligence Approach to Modulation Classification (J E Whelchel et al.)and other papers Readership: Computer scientists, researchers and professionals in artificial intelligence. keywords:
Machine Learning
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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2021-12-22
Machine Learning written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-22 with Computers categories.
Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.
Federated Learning
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Author : Heiko Ludwig
language : en
Publisher: Springer Nature
Release Date : 2022-07-07
Federated Learning written by Heiko Ludwig and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-07 with Computers categories.
Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods. Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
Decision Theory Models For Applications In Artificial Intelligence Concepts And Solutions
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Author : Sucar, L. Enrique
language : en
Publisher: IGI Global
Release Date : 2011-10-31
Decision Theory Models For Applications In Artificial Intelligence Concepts And Solutions written by Sucar, L. Enrique and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-31 with Computers categories.
One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design rational agents that must take the best action given the information available and their goals. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence. This book provides insights into the advantages and challenges of using decision theory models for developing intelligent systems.