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Author: Amit Kumar Bairwa
Publisher: Springer Nature
ISBN: 3031714814
Category :
Languages : en
Pages : 419
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Book Description
Author: Amit Kumar Bairwa
Publisher: Springer Nature
ISBN: 3031714814
Category :
Languages : en
Pages : 419
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Book Description
Author: Aleš Leonardis
Publisher: Springer Nature
ISBN: 3031727517
Category :
Languages : en
Pages : 575
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Book Description
Author: Jesus Carretero
Publisher: Springer Nature
ISBN: 3031695771
Category :
Languages : en
Pages : 430
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Book Description
Author: Ilias Maglogiannis
Publisher: Springer Nature
ISBN: 3031632117
Category :
Languages : en
Pages : 397
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Book Description
Author: Sławomir Nowaczyk
Publisher: Springer Nature
ISBN: 3031503961
Category : Computers
Languages : en
Pages : 469
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Book Description
This volume constitutes the refereed proceedings presented at the international workshops of the 26th European Conference on Artificial Intelligence, ECAI 2023, which was held in Kraków, Poland, in September-October 2023. The papers in this volume were presented at the following workshops: XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI.
Author: Sanju Tiwari
Publisher: Springer Nature
ISBN: 3031479971
Category : Computers
Languages : en
Pages : 234
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Book Description
This book constitutes the proceedings of the First International Conference, AI4S 2023, held in Pune, India, during September 4-5, 2023. The 14 full papers and the 2 short papers included in this volume were carefully reviewed and selected from 72 submissions. This volume aims to open discussion on trustworthy AI and related topics, trying to bring the most up to date developments around the world from researchers and practitioners.
Author: Rai, Mritunjay
Publisher: IGI Global
ISBN:
Category : Psychology
Languages : en
Pages : 332
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Book Description
In the realm of analyzing human emotions through Artificial Intelligence (AI), a myriad of challenges persist. From the intricate nuances of emotional subtleties to the broader concerns of ethical considerations, privacy implications, and the ongoing battle against bias, AI faces a complex landscape when venturing into the understanding of human emotions. These challenges underscore the intricate balance required to navigate the human psyche with accuracy. The book, Using Machine Learning to Detect Emotions and Predict Human Psychology, serves as a guide for innovative solutions in the field of emotion detection through AI. It explores facial expression analysis, where AI decodes real-time emotions through subtle cues such as eyebrow movements and micro-expressions. In speech and voice analysis, the book unveils how AI processes vocal nuances to discern emotions, considering elements like tone, pitch, and language intricacies. Additionally, the power of text analysis is of great importance, revealing how AI extracts emotional tones from diverse textual communications. By weaving these systems together, the book offers a holistic solution to the challenges faced by AI in understanding the complex landscape of human emotions.
Author: Virginia Dignum
Publisher: Springer Nature
ISBN: 3030303713
Category : Computers
Languages : en
Pages : 133
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Book Description
In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens.
Author: Peter Fong
Publisher: World Scientific Publishing Company
ISBN: 9813102004
Category : Science
Languages : en
Pages : 395
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Book Description
Quantum mechanics is a difficult subject for students to learn after years of rigorous training in classical physics. In quantum mechanics they have to abandon what they have laboriously learned and adopt a new system of thinking.In the previous edition of this book, the author reformulated classical mechanics as a classical theory with an undetermined constant. As the constant approaches zero the theory reduces to Newton's exactly, but when set equal to the Planck constant the theory reduces to the Schrödinger representation of quantum mechanics. Thus the new theory, at least in its mathematical form, can be learned without ramifications and complexity. Over the years, the book has shepherded the growth of a generation of physicists.In this expanded edition, a similar trick is applied to introduce matrix mechanics. The matrix formulation presented allows quantum theory to be generalized to new physical systems such as electron spin, which cannot be done by the Schrödinger approach.The result is a textbook which promises to provide a future generation of students a clear, usable and authoritative resource to study the fundamentals of quantum mechanics. Twenty new problems are added to existing chapters.
Author: Changliu Liu
Publisher:
ISBN: 9781680837865
Category :
Languages : en
Pages :
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Book Description
Neural networks have been widely used in many applications, such as image classification and understanding, language processing, and control of autonomous systems. These networks work by mapping inputs to outputs through a sequence of layers. At each layer, the input to that layer undergoes an affine transformation followed by a simple nonlinear transformation before being passed to the next layer. Neural networks are being used for increasingly important tasks, and in some cases, incorrect outputs can lead to costly consequences, hence validation of correctness at each layer is vital. The sheer size of the networks makes this not feasible using traditional methods. In this monograph, the authors survey a class of methods that are capable of formally verifying properties of deep neural networks. In doing so, they introduce a unified mathematical framework for verifying neural networks, classify existing methods under this framework, provide pedagogical implementations of existing methods, and compare those methods on a set of benchmark problems. Algorithms for Verifying Deep Neural Networks serves as a tutorial for students and professionals interested in this emerging field as well as a benchmark to facilitate the design of new verification algorithms.
