Books in Neural networks

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 + 2 more
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.

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.

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.

Artificial Neural Network for Drug Design, Delivery and Disposition

1st Edition
October 15, 2015
Munish Puri + 4 more
English
Hardback
9 7 8 0 1 2 8 0 1 5 5 9 9
eBook
9 7 8 0 1 2 8 0 1 7 4 4 9

Artificial Neural Network for Drug Design, Delivery and Disposition provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research. With its ability to learn and self-correct in a highly complex environment, this predictive tool has tremendous potential to help researchers more effectively design, develop, and deliver successful drugs. This book illustrates how to use ANN methodologies and models with the intent to treat diseases like breast cancer, cardiac disease, and more. It contains the latest cutting-edge research, an analysis of the benefits of ANN, and relevant industry examples. As such, this book is an essential resource for academic and industry researchers across the pharmaceutical and biomedical sciences.

Neural Networks and Genome Informatics

1st Edition
Volume 1
September 21, 2000
C.H. Wu + 1 more
English
eBook
9 7 8 0 0 8 0 5 3 7 3 7 5

This book is a comprehensive reference in the field of neural networks and genome informatics. The tutorial of neural network foundations introduces basic neural network technology and terminology. This is followed by an in-depth discussion of special system designs for building neural networks for genome informatics, and broad reviews and evaluations of current state-of-the-art methods in the field. This book concludes with a description of open research problems and future research directions.

Kohonen Maps

1st Edition
July 2, 1999
E. Oja + 1 more
English
eBook
9 7 8 0 0 8 0 5 3 5 2 9 6

The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm.The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.

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