Proceedings Of International Conference On Artificial Intelligence And Networks
DOWNLOAD
Download Proceedings Of International Conference On Artificial Intelligence And Networks PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Proceedings Of International Conference On Artificial Intelligence And Networks book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Proceedings Of International Conference On Artificial Intelligence And Networks
DOWNLOAD
Author : Bal Virdee
language : en
Publisher:
Release Date : 2025-06-09
Proceedings Of International Conference On Artificial Intelligence And Networks written by Bal Virdee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-09 with Computers categories.
This book presents selected papers from International Conference on Artificial Intelligence and Networks (ICAIN 2024), held on 24 - 25 September 2024, in Guru Tegh Bahadur Institute of Technology (GTBIT), GGSIPU, Delhi, India. The topics covered in the book are deep learning, machine learning, natural language processing, data science and analytics, cybersecurity and privacy, cloud computing, and wireless and mobile networks.
Proceedings Of International Conference On Artificial Intelligence And Networks
DOWNLOAD
Author : Bal Virdee
language : en
Publisher: Springer Nature
Release Date : 2025-08-02
Proceedings Of International Conference On Artificial Intelligence And Networks written by Bal Virdee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-02 with Computers categories.
This book presents selected papers from International Conference on Artificial Intelligence and Networks (ICAIN 2024), held on 24 – 25 September 2024, in Guru Tegh Bahadur Institute of Technology (GTBIT), GGSIPU, Delhi, India. The topics covered in the book are deep learning, machine learning, natural language processing, data science and analytics, cybersecurity and privacy, cloud computing, and wireless and mobile networks.
Artificial Intelligence And Quantum Computing For Advanced Wireless Networks
DOWNLOAD
Author : Savo G. Glisic
language : en
Publisher: John Wiley & Sons
Release Date : 2022-04-13
Artificial Intelligence And Quantum Computing For Advanced Wireless Networks written by Savo G. Glisic 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 2022-04-13 with Technology & Engineering categories.
ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKS A comprehensive presentation of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency. In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few. The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from: A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines An exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and more Discussions of explainable neural networks and XAI Examinations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology Perfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.
Machine Learning
DOWNLOAD
Author : Sergios Theodoridis
language : en
Publisher: Academic Press
Release Date : 2020-02-19
Machine Learning written by Sergios Theodoridis and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-19 with Technology & Engineering categories.
Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models. New to this edition: - Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs). - Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes. - Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method - Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling - Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more
Ecai 2020
DOWNLOAD
Author : G. De Giacomo
language : en
Publisher: IOS Press
Release Date : 2020-09-11
Ecai 2020 written by G. De Giacomo and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-11 with Computers categories.
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.
Cognitive Science
DOWNLOAD
Author : Harald Maurer
language : en
Publisher: CRC Press
Release Date : 2021-07-08
Cognitive Science written by Harald Maurer and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-08 with Medical categories.
The Mind and Brain are usually considered as one and the same nonlinear, complex dynamical system, in which information processing can be described with vector and tensor transformations and with attractors in multidimensional state spaces. Thus, an internal neurocognitive representation concept consists of a dynamical process which filters out statistical prototypes from the sensorial information in terms of coherent and adaptive n-dimensional vector fields. These prototypes serve as a basis for dynamic, probabilistic predictions or probabilistic hypotheses on prospective new data (see the recently introduced approach of "predictive coding" in neurophilosophy). Furthermore, the phenomenon of sensory and language cognition would thus be based on a multitude of self-regulatory complex dynamics of synchronous self-organization mechanisms, in other words, an emergent "flux equilibrium process" ("steady state") of the total collective and coherent neural activity resulting from the oscillatory actions of neuronal assemblies. In perception it is shown how sensory object informations, like the object color or the object form, can be dynamically related together or can be integrated to a neurally based representation of this perceptual object by means of a synchronization mechanism ("feature binding"). In language processing it is shown how semantic concepts and syntactic roles can be dynamically related together or can be integrated to neurally based systematic and compositional connectionist representations by means of a synchronization mechanism ("variable binding") solving the Fodor-Pylyshyn-Challenge. Since the systemtheoretical connectionism has succeeded in modeling the sensory objects in perception as well as systematic and compositional representations in language processing with this vector- and oscillation-based representation format, a new, convincing theory of neurocognition has been developed, which bridges the neuronal and the cognitive analysis level. The book describes how elementary neuronal information is combined in perception and language, so it becomes clear how the brain processes this information to enable basic cognitive performance of the humans.
Clinical Image Based Procedures Distributed And Collaborative Learning Artificial Intelligence For Combating Covid 19 And Secure And Privacy Preserving Machine Learning
DOWNLOAD
Author : Cristina Oyarzun Laura
language : en
Publisher: Springer Nature
Release Date : 2021-11-13
Clinical Image Based Procedures Distributed And Collaborative Learning Artificial Intelligence For Combating Covid 19 And Secure And Privacy Preserving Machine Learning written by Cristina Oyarzun Laura and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-13 with Computers categories.
This book constitutes the refereed proceedings of the 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, Second MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, First MICCAI Workshop, LL-COVID19, First Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice. For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority. LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice. And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community.
Nature Inspired Computing Concepts Methodologies Tools And Applications
DOWNLOAD
Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2016-07-26
Nature Inspired Computing Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-26 with Computers categories.
As technology continues to become more sophisticated, mimicking natural processes and phenomena also becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for man-made computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications takes an interdisciplinary approach to the topic of natural computing, including emerging technologies being developed for the purpose of simulating natural phenomena, applications across industries, and the future outlook of biologically and nature-inspired technologies. Emphasizing critical research in a comprehensive multi-volume set, this publication is designed for use by IT professionals, researchers, and graduate students studying intelligent computing.
Machine Learning For Speaker Recognition
DOWNLOAD
Author : Man-Wai Mak
language : en
Publisher: Cambridge University Press
Release Date : 2020-11-19
Machine Learning For Speaker Recognition written by Man-Wai Mak and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-19 with Computers categories.
Learn fundamental and advanced machine learning techniques for robust speaker recognition and domain adaptation with this useful toolkit.
Knowledge Mining Using Intelligent Agents
DOWNLOAD
Author : Satchidananda Dehuri
language : en
Publisher: World Scientific
Release Date : 2011
Knowledge Mining Using Intelligent Agents written by Satchidananda Dehuri and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Business & Economics categories.
Knowledge Mining Using Intelligent Agents explores the concept of knowledge discovery processes and enhances decision-making capability through the use of intelligent agents like ants, termites and honey bees. In order to provide readers with an integrated set of concepts and techniques for understanding knowledge discovery and its practical utility, this book blends two distinct disciplines data mining and knowledge discovery process, and intelligent agents-based computing (swarm intelligence and computational intelligence). For the more advanced reader, researchers, and decision/policy-makers are given an insight into emerging technologies and their possible hybridization, which can be used for activities like dredging, capturing, distributions and the utilization of knowledge in their domain of interest (i.e. business, policy-making, etc.). By studying the behavior of swarm intelligence, this book aims to integrate the computational intelligence paradigm and intelligent distributed agents architecture to optimize various engineering problems and efficiently represent knowledge from the large gamut of data.