Time Space Spiking Neural Networks And Brain Inspired Artificial Intelligence
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Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence by Nikola K. Kasabov Pdf
Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.
Artificial Intelligence in the Age of Neural Networks and Brain Computing by Robert Kozma,Cesare Alippi,Yoonsuck Choe,Francesco Carlo Morabito Pdf
Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation by Igor V. Tetko,Věra Kůrková,Pavel Karpov,Fabian Theis Pdf
The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
Handbook On Computer Learning And Intelligence (In 2 Volumes) by Plamen Parvanov Angelov Pdf
The Handbook on Computer Learning and Intelligence is a second edition which aims to be a one-stop-shop for the various aspects of the broad research area of computer learning and intelligence. This field of research evolved so much in the last five years that it necessitates this new edition of the earlier Handbook on Computational Intelligence.This two-volume handbook is divided into five parts. Volume 1 covers Explainable AI and Supervised Learning. Volume 2 covers three parts: Deep Learning, Intelligent Control, and Evolutionary Computation. The chapters detail the theory, methodology and applications of computer learning and intelligence, and are authored by some of the leading experts in the respective areas. The fifteen core chapters of the previous edition have been written and significantly refreshed by the same authors. Parts of the handbook have evolved to keep pace with the latest developments in computational intelligence in the areas that span across Machine Learning and Artificial Intelligence. The Handbook remains dedicated to applications and engineering-orientated aspects of these areas over abstract theories.Related Link(s)
Artificial Intelligence Applications and Innovations by Ilias Maglogiannis,John Macintyre,Lazaros Iliadis Pdf
This book constitutes the refereed proceedings of the 17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021, held virtually and in Hersonissos, Crete, Greece, in June 2021. The 50 full papers and 11 short papers presented were carefully reviewed and selected from 113 submissions. They cover a broad range of topics related to technical, legal, and ethical aspects of artificial intelligence systems and their applications and are organized in the following sections: adaptive modeling/ neuroscience; AI in biomedical applications; AI impacts/ big data; automated machine learning; autonomous agents; clustering; convolutional NN; data mining/ word counts; deep learning; fuzzy modeling; hyperdimensional computing; Internet of Things/ Internet of energy; machine learning; multi-agent systems; natural language; recommendation systems; sentiment analysis; and smart blockchain applications/ cybersecurity. Chapter “Improving the Flexibility of Production Scheduling in Flat Steel Production Through Standard and AI-based Approaches: Challenges and Perspective” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Artificial Neural Networks and Machine Learning – ICANN 2022 by Elias Pimenidis,Plamen Angelov,Chrisina Jayne,Antonios Papaleonidas,Mehmet Aydin Pdf
The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications.
Biosignal Processing and Classification Using Computational Learning and Intelligence by Alejandro A. Torres-García,Carlos Alberto Reyes Garcia,Luis Villasenor-Pineda,Omar Mendoza-Montoya Pdf
Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms and Applications posits an approach for biosignal processing and classification using computational learning and intelligence, highlighting that the term biosignal refers to all kinds of signals that can be continuously measured and monitored in living beings. The book is composed of five relevant parts. Part One is an introduction to biosignals and Part Two describes the relevant techniques for biosignal processing, feature extraction and feature selection/dimensionality reduction. Part Three presents the fundamentals of computational learning (machine learning). Then, the main techniques of computational intelligence are described in Part Four. The authors focus primarily on the explanation of the most used methods in the last part of this book, which is the most extensive portion of the book. This part consists of a recapitulation of the newest applications and reviews in which these techniques have been successfully applied to the biosignals’ domain, including EEG-based Brain-Computer Interfaces (BCI) focused on P300 and Imagined Speech, emotion recognition from voice and video, leukemia recognition, infant cry recognition, EEGbased ADHD identification among others. Provides coverage of the fundamentals of signal processing, including sensing the heart, sending the brain, sensing human acoustic, and sensing other organs Includes coverage biosignal pre-processing techniques such as filtering, artifiact removal, and feature extraction techniques such as Fourier transform, wavelet transform, and MFCC Covers the latest techniques in machine learning and computational intelligence, including Supervised Learning, common classifiers, feature selection, dimensionality reduction, fuzzy logic, neural networks, Deep Learning, bio-inspired algorithms, and Hybrid Systems Written by engineers to help engineers, computer scientists, researchers, and clinicians understand the technology and applications of computational learning to biosignal processing
Neural Information Processing by Biao Luo,Long Cheng,Zheng-Guang Wu,Hongyi Li,Chaojie Li Pdf
The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.
Author : William C. Hannas,Huey-Meei Chang Publisher : Taylor & Francis Page : 382 pages File Size : 41,6 Mb Release : 2022-07-29 Category : History ISBN : 9781000619409
Chinese Power and Artificial Intelligence by William C. Hannas,Huey-Meei Chang Pdf
This book provides a comprehensive account of Chinese AI in its various facets, based on primary Chinese-language sources. China’s rise as an AI power is an event of importance to the world and a potential challenge to liberal democracies. Filling a gap in the literature, this volume is fully documented, data-driven, and presented in a scholarly format suitable for citation and for supporting downstream research, while also remaining accessible to laypersons. It brings together 15 recognized international experts to present a full treatment of Chinese artificial intelligence. The volume contains chapters on state, commercial, and foreign sources of China’s AI power; China’s AI talent, scholarship, and global standing; the impact of AI on China’s development of cutting-edge disciplines; China’s use of AI in military, cyber, and surveillance applications; AI safety, threat mitigation, and the technology’s likely trajectory. The book ends with recommendations drawn from the authors’ interactions with policymakers and specialists worldwide, aimed at encouraging AI’s healthy development in China and preparing the rest of the world to engage with it. This book will be of much interest to students of Chinese politics, science and technology studies, security studies and international relations.
Evolving Connectionist Systems by Nikola K. Kasabov Pdf
This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.
Advances in Brain Inspired Cognitive Systems by Jinchang Ren,Amir Hussain,Jiangbin Zheng,Cheng-Lin Liu,Bin Luo,Huimin Zhao,Xinbo Zhao Pdf
This book constitutes the refereed proceedings of the 9th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2018, held in Xi’an, China, in July 2018. The 83 papers presented in this volume were carefully reviewed and selected from 137 submissions. The papers were organized in topical sections named: neural computation; biologically inspired systems; image recognition: detection, tracking and classification; data analysis and natural language processing; and applications.
Advances in Intelligent Systems Research and Innovation by Vassil Sgurev,Vladimir Jotsov,Janusz Kacprzyk Pdf
This book represents the experience of successful researchers from four continents on a broad range of intelligent systems, and it hints how to avoid anticipated conflicts and problems during multidisciplinary innovative research from Industry 4.0 and/or Internet of Things through modern machine learning, and software agent applications to open data science big data/advance analytics/visual analytics/text mining/web mining/knowledge discovery/deep data mining issues. The considered intelligent part is essential in most smart/control systems, cyber security, bioinformatics, virtual reality, robotics, mathematical modelling projects, and its significance rapidly increases in other technologies. Theoretical foundations of fuzzy sets, mathematical and non-classical logic also are rapidly developing.
Computational Thinking: How computers think, decide and learn, when human limits start and computers champ. Vol.1 by Jorge Guerra Pires Pdf
In 2013, I wrote a book[1]. At the time, I wanted to explain neural networks in simple terms, I had high school students at my mind. I have expressed my concerns that machine learning was dominating the world, and people had no idea about it, smartphones were not popular in Brazil, and started go gain attention as personal computers. Deep learning started to gain momentum on 2012, and nowadays is kind of the rule. At the time, YouTube was bad, pretty bad a must say: I used to save the links to my videos, as so I could avoid passing through the main page. . Computational thinking is synonymous of algorithms. I cannot think a single computational routine which is not an algorithm; after all, “computers are stupid”, they need to be told what to do even when it is abstract (e.g., machine learning). What is computational think, though? Think like this, a thought experiment: Suppose you give your result, from your model, to someone. Do you believe the person would be able to tell the difference between your solution, from your algorithm, and a human? If not, this is computational thinking. It is a machine (i.e., an algorithm, a routine), doing human-thinking work. As we are going to see based on Kasabov’s work, we may actually be able to send ‘thinking loads’ to computers in the future. Initially, this book supposes to be called computational intelligence. Nonetheless, I thought, we do not necessarily need ‘intelligence’ to build models, not in the sense to artificial intelligence or even human intelligence. Furthermore, as we shall learn from Daniel Kahneman and colleagues, we can achieve nice models for decision making even with simple models, when compared to humans; imagine what we can do with machine learning + cloud computing + databases (such as MongoDB and Firebase)! Possible public Web developers wanting to expand their horizon; here I am being modest, I feel any web coder should learn computational thinking, as so they can add intelligence to their “dummy” apps; People from computational intelligence, waiting to learn new tricks; Computer scientists for sure! I would recommend to computational biologists, and anyone interested in bioinformatics; Applied mathematics, and computational mathematician for sure; Anyone that is opened to new ideas, but has a minimum computer programming background; Maybe, medical doctors and biologists; one of my PhD advisors was a surgeon, with a PhD in mathematics; thus, we may have this profile in medicine and, especially, in biology; External resources and tricks My GitHub profile; Our sandbox; I have used links to my LinkedIn profile, to posts related to the discussions. Feel free to start a conversation on LinkedIn, or to connect! Just comment on the posts, and I will be noticed; I have used several external links, to articles online; this is in addition to the classical/academic reference standard; With Special release of “My selected assays from Medium on Computer programming, Artificial Intelligence” [1] Redes Neurais em termos simples: como aprendemos, pensamos e modelamos. https://www.academia.edu/18365339/Redes_Neurais_em_termos_simples_como_aprendemos_pensamos_e_modelamos?fbclid=IwAR3NLQt003L5QXZQNLSePIxJxUf7NbqsthEjj8rb1zgfpgEgzkiqoNfO0RY. Accessed on 30/06/22.