Skip to main content
Elsevier
View Cart
Your Account

Books in Neural networks

BooksJournals

Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing

  • 1st Edition
  • June 12, 2024
  • Rajesh Kumar Tripathy + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 4 1 4 1 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 4 1 4 0 - 9
Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based methods for cardiovascular data analysis. Topics in this book include machine learning methods such as supervised learning, unsupervised learning, semi-supervised learning, and meta-learning combined with different signal processing techniques such as multivariate data analysis, time-frequency analysis, multiscale analysis, and feature extraction techniques for the detection of cardiovascular diseases, heart valve disorders, hypertension, and activity monitoring using ECG, PPG, and PCG signals.In addition, this book also includes the applications of digital signal processing (time-frequency analysis, multiscale decomposition, feature extraction, non-linear analysis, and transform domain methods), machine learning and deep learning (convolutional neural network (CNN), recurrent neural network (RNN), transformer and attention-based models, etc.) techniques for the analysis of cardiac signals. The interpretable machine learning and deep learning models combined with signal processing for cardiovascular data analysis are also covered.

Towards Neuromorphic Machine Intelligence

  • 1st Edition
  • June 5, 2024
  • Hong Qu
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 2 8 2 0 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 2 8 2 1 - 3
Towards Neuromorphic Machine Intelligence: Spike-Based Representation, Learning, and Applications provides readers with in-depth understanding of Spiking Neural Networks (SNNs), which is a burgeoning research branch of Artificial Neural Networks (ANNs), AI, and Machine Learning that sits at the heart of the integration between Computer Science and Neural Engineering. In recent years, neural networks have re-emerged in relation to AI, representing a well-grounded paradigm rooted in disciplines from physics and psychology to information science and engineering.This book represents one of the established cross-over areas where neurophysiology, cognition, and neural engineering coincide with the development of new Machine Learning and AI paradigms. There are many excellent theoretical achievements in neuron models, learning algorithms, network architecture, and so on. But these achievements are numerous and scattered, with a lack of straightforward systematic integration, making it difficult for researchers to assimilate and apply. As the third generation of Artificial Neural Networks (ANNs), Spiking Neural Networks (SNNs) simulate the neuron dynamics and information transmission in a biological neural system in more detail, which is a cross-product of computer science and neuroscience. The primary target audience of this book is divided into two categories: artificial intelligence researchers who know nothing about SNNs, and researchers who know a lot about SNNs. The former needs to acquire fundamental knowledge of SNNs, but the challenge is that much of the existing literature on SNNs only slightly mentions the basic knowledge of SNNs, or is too superficial, and this book gives a systematic explanation from scratch. The latter needs learning about some novel research achievements in the field of SNNs, and this book introduces the latest research results on different aspects of SNNs and provides detailed simulation processes to facilitate readers' replication. In addition, the book introduces neuromorphic hardware architecture as a further extension of the SNN system.The book starts with the birth and development of SNNs, and then introduces the main research hotspots, including spiking neuron models, learning algorithms, network architectures, and neuromorphic hardware. Therefore, the book provides readers with easy access to both the foundational concepts and recent research findings in SNNs.

Connectomic Medicine

  • 1st Edition
  • December 1, 2023
  • Michael E. Sughrue + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 9 0 8 9 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 9 0 9 0 - 2
Connectomic Medicine: A Guide to Brain AI in Treatment Decision Planning examines how to apply connectomics to clinical medicine, including discussions on techniques, applications, novel ideas, and in case examples that highlight the state-of-the-art. Written by pioneers, this volume serves as the foundation for all neuroscience clinicians/researchers venturing into the field of AI medicine, its realistic applications, and how to integrate AI connectomics into clinical practice. With widespread applications in neurology, neurosurgery and psychiatry, this book is appropriate for anyone interested in cerebral network anatomy, imaging techniques, and insights into this emerging field.

Meta-Learning

  • 1st Edition
  • November 5, 2022
  • Lan Zou
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 9 9 3 1 - 4
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 3 7 0 - 7
Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications.

Deep Network Design for Medical Image Computing

  • 1st Edition
  • August 24, 2022
  • Haofu Liao + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 4 3 8 3 - 1
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 4 4 0 3 - 6
Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more. This book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems.

Deep Learning for Robot Perception and Cognition

  • 1st Edition
  • February 4, 2022
  • Alexandros Iosifidis + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 5 7 8 7 - 1
  • eBook
    9 7 8 - 0 - 3 2 3 - 8 8 5 7 2 - 0
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks.

Adaptive Neural Networks and Robot Intelligent Control in Direct or Indirect Interaction with Humans

  • 1st Edition
  • December 1, 2019
  • Boubaker Daachi + 1 more
  • English
Adaptive Neural Networks and Robot Intelligent Control in Direct or Indirect Interaction with Humans offers a particular methodology for using neural networks to solve control problems of nonlinear systems interacting directly (mobile robot exoskeleton type) or indirectly with humans (redundant robot manipulators serial or parallel). In addition, the book provides novel perspectives and research ideas for further strengthening the presence of humans in the control loop (intention, thought, etc.). The robots used for illustration purposes were designed in collaboration with industry.

Machine Dreaming and Consciousness

  • 1st Edition
  • April 13, 2017
  • J. F. Pagel + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 3 7 2 0 - 1
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 7 4 2 - 3
Machine Dreaming and Consciousness is the first book to discuss the questions raised by the advent of machine dreaming. Artificial intelligence (AI) systems meeting criteria of primary and self-reflexive consciousness are often utilized to extend the human interface, creating waking experiences that resemble the human dream. Surprisingly, AI systems also easily meet all human-based operational criteria for dreaming. These “dreams” are far different from anthropomorphic dreaming, including such processes as fuzzy logic, liquid illogic, and integration instability, all processes that may be necessary in both biologic and artificial systems to extend creative capacity. Today, multi-linear AI systems are being built to resemble the structural framework of the human central nervous system. The creation of the biologic framework of dreaming (emotions, associative memories, and visual imagery) is well within our technical capacity. AI dreams potentially portend the further development of consciousness in these systems. This focus on AI dreaming raises even larger questions. In many ways, dreaming defines our humanity. What is humanly special about the states of dreaming? And what are we losing when we limit our focus to its technical and biologic structure, and extend the capacity for dreaming into our artificial creations? Machine Dreaming and Consciousness provides thorough discussion of these issues for neuroscientists and other researchers investigating consciousness and cognition.

Path Planning for Vehicles Operating in Uncertain 2D Environments

  • 1st Edition
  • January 28, 2017
  • Viacheslav Pshikhopov
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 2 3 0 5 - 8
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 2 3 0 6 - 5
Path Planning for Vehicles Operating in Uncertain 2D-environments presents a survey that includes several path planning methods developed using fuzzy logic, grapho-analytical search, neural networks, and neural-like structures, procedures of genetic search, and unstable motion modes.

Introduction to EEG- and Speech-Based Emotion Recognition

  • 1st Edition
  • March 22, 2016
  • Priyanka A. Abhang + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 4 4 9 0 - 2
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 4 5 3 1 - 2
Introduction to EEG- and Speech-Based Emotion Recognition Methods examines the background, methods, and utility of using electroencephalograms (EEGs) to detect and recognize different emotions. By incorporating these methods in brain-computer interface (BCI), we can achieve more natural, efficient communication between humans and computers. This book discusses how emotional states can be recognized in EEG images, and how this is useful for BCI applications. EEG and speech processing methods are explored, as are the technological basics of how to operate and record EEGs. Finally, the authors include information on EEG-based emotion recognition, classification, and a proposed EEG/speech fusion method for how to most accurately detect emotional states in EEG recordings.