State of the Art in Neural Networks and Their Applications

State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case studies. The application of Neural Network, Artificial Intelligence, and Machine Learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more. Includes applications of neural networks, AI, machine learning, and deep learning techniques to a variety of imaging technologies Provides in-depth technical coverage of computer-aided diagnosis (CAD), with coverage of computer-aided classification, Unified Deep Learning Frameworks, mammography, fundus imaging, optical coherence tomography, cryo-electron tomography, 3D MRI, CT, and more. Covers deep learning for several medical conditions including renal, retinal, breast, skin, and dental abnormalities, Medical Image Analysis, as well as detection, segmentation, and classification via AI.

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  • Author : Ayman S. El-Baz
  • Publisher : Academic Press
  • Pages : 324 pages
  • ISBN : 0128218495
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKState of the Art in Neural Networks and Their Applications

State of the Art in Neural Networks and Their Applications

State of the Art in Neural Networks and Their Applications
  • Author : Ayman S. El-Baz,Jasjit S. Suri
  • Publisher : Academic Press
  • Release : 21 July 2021
GET THIS BOOKState of the Art in Neural Networks and Their Applications

State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case studies. The application of Neural Network, Artificial Intelligence, and Machine Learning methods in biomedical image analysis have resulted in the development of

Computer Information Systems and Industrial Management

Computer Information Systems and Industrial Management
  • Author : Khalid Saeed,Władysław Homenda
  • Publisher : Springer
  • Release : 08 September 2016
GET THIS BOOKComputer Information Systems and Industrial Management

This book constitutes the proceedings of the 15th IFIP TC8 International Conference on Computer Information Systems and Industrial Management, CISIM 2016, held in Vilnius, Lithuania, in September 2016. The 63 regular papers presented together with 1 inivted paper and 5 keynotes in this volume were carefully reviewed and selected from about 89 submissions. The main topics covered are rough set methods for big data analytics; images, visualization, classification; optimization, tuning; scheduling in manufacturing and other applications; algorithms; decisions; intelligent distributed systems; and biometrics, identification, security.

Artificial Intelligence and Soft Computing, Part II

Artificial Intelligence and Soft Computing, Part II
  • Author : Leszek Rutkowski,Rafal Scherer,Ryszard Tadeusiewicz,Lotfi A. Zadeh,Jacek M. Zurada
  • Publisher : Springer Science & Business Media
  • Release : 01 June 2010
GET THIS BOOKArtificial Intelligence and Soft Computing, Part II

This volume constitutes the proceedings of the 10th International Conference on Artificial Intelligence and Soft Computing, ICAISC’2010, held in Zakopane, Poland in June 13-17, 2010. The articles are organized in topical sections on Fuzzy Systems and Their Applications; Data Mining, Classification and Forecasting; Image and Speech Analysis; Bioinformatics and Medical Applications (Volume 6113) together with Neural Networks and Their Applications; Evolutionary Algorithms and Their Applications; Agent System, Robotics and Control; Various Problems aof Artificial Intelligence (Volume 6114).

Business Applications of Neural Networks

Business Applications of Neural Networks
  • Author : Paulo J. G. Lisboa,Bill Edisbury,Alfredo Vellido
  • Publisher : World Scientific
  • Release : 24 January 2022
GET THIS BOOKBusiness Applications of Neural Networks

Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualise complex databases for marketing segmentation. This boom in applications covers a wide range of business interests -- from finance management, through forecasting, to production. The combination of statistical, neural and fuzzy methods now enables direct quantitative studies to be

Fundamentals of Deep Learning and Computer Vision

Fundamentals of Deep Learning and Computer Vision
  • Author : Singh Nikhil
  • Publisher : BPB Publications
  • Release : 24 February 2020
GET THIS BOOKFundamentals of Deep Learning and Computer Vision

Master Computer Vision concepts using Deep Learning with easy-to-follow steps Key Featuresa- Setting up the Python and TensorFlow environmenta- Learn core Tensorflow concepts with the latest TF version 2.0a- Learn Deep Learning for computer vision applications a- Understand different computer vision concepts and use-casesa- Understand different state-of-the-art CNN architectures a- Build deep neural networks with transfer Learning using features from pre-trained CNN modelsa- Apply computer vision concepts with easy-to-follow code in Jupyter NotebookDescriptionThis book starts with setting up a Python

Artificial Intelligence in the Age of Neural Networks and Brain Computing

Artificial Intelligence in the Age of Neural Networks and Brain Computing
  • Author : Robert Kozma,Cesare Alippi,Yoonsuck Choe,Francesco Carlo Morabito
  • Publisher : Academic Press
  • Release : 30 October 2018
GET THIS BOOKArtificial Intelligence in the Age of Neural Networks and Brain Computing

Artificial Intelligence in the Age of Neural Networks and Brain Computing demonstrates that existing 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 future alternatives. The success of AI-based commercial products

Efficient Processing of Deep Neural Networks

Efficient Processing of Deep Neural Networks
  • Author : Vivienne Sze,Yu-Hsin Chen,Tien-Ju Yang,Joel S. Emer
  • Publisher : Morgan & Claypool Publishers
  • Release : 24 June 2020
GET THIS BOOKEfficient Processing of Deep Neural Networks

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or

Introduction to Graph Neural Networks

Introduction to Graph Neural Networks
  • Author : Zhiyuan Liu,Jie Zhou
  • Publisher : Morgan & Claypool Publishers
  • Release : 20 March 2020
GET THIS BOOKIntroduction to Graph Neural Networks

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation

Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management

Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management
  • Author : R. N. G. Naguib,G. V. Sherbet
  • Publisher : CRC Press
  • Release : 22 June 2001
GET THIS BOOKArtificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management

The potential value of artificial neural networks (ANN) as a predictor of malignancy has begun to receive increased recognition. Research and case studies can be found scattered throughout a multitude of journals. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management brings together the work of top researchers - primaril

Reservoir-computing-based, Biologically Inspired Artificial Neural Networks and Their Applications in Power Systems

Reservoir-computing-based, Biologically Inspired Artificial Neural Networks and Their Applications in Power Systems
  • Author : Jing Dai
  • Publisher : Unknown Publisher
  • Release : 24 January 2022
GET THIS BOOKReservoir-computing-based, Biologically Inspired Artificial Neural Networks and Their Applications in Power Systems

Computational intelligence techniques, such as artificial neural networks (ANNs), have been widely used to improve the performance of power system monitoring and control. Although inspired by the neurons in the brain, ANNs are largely different from living neuron networks (LNNs) in many aspects. Due to the oversimplification, the huge computational potential of LNNs cannot be realized by ANNs. Therefore, a more brain-like artificial neural network is highly desired to bridge the gap between ANNs and LNNs. The focus of this

Graph Representation Learning

Graph Representation Learning
  • Author : William L. Hamilton
  • Publisher : Morgan & Claypool Publishers
  • Release : 16 September 2020
GET THIS BOOKGraph Representation Learning

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning

Understanding Neural Networks and Fuzzy Logic

Understanding Neural Networks and Fuzzy Logic
  • Author : Stamatios V. Kartalopoulos
  • Publisher : Wiley-IEEE Press
  • Release : 24 January 1996
GET THIS BOOKUnderstanding Neural Networks and Fuzzy Logic

Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutorials, the simple numerical examples presented in this book provide excellent tools for progressive learning. UNDERSTANDING NEURAL NETWORKS AND FUZZY LOGIC offers a simple presentation and bottom-up approach that is ideal for working professional engineers, undergraduates, medical/biology majors, and anyone with a nonspecialist background. Sponsored by: IEEE

Handbook of Deep Learning in Biomedical Engineering

Handbook of Deep Learning in Biomedical Engineering
  • Author : Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
  • Publisher : Academic Press
  • Release : 23 November 2020
GET THIS BOOKHandbook of Deep Learning in Biomedical Engineering

Deep learning (DL) is a method of machine learning, running over artificial neural networks, that uses multiple layers to extract high-level features from large amounts of raw data. DL methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of DL and its applications in the field of biomedical engineering. DL has been rapidly developed in

Neural Networks for Optimization and Signal Processing

Neural Networks for Optimization and Signal Processing
  • Author : Andrzej Cichocki,R. Unbehauen
  • Publisher : Wiley-Blackwell
  • Release : 07 June 1993
GET THIS BOOKNeural Networks for Optimization and Signal Processing

Neural Networks for Optimization and Signal Processing A. Cichocki Warsaw University of Technology Poland R. Unbehauen Universität Erlangen-Nürnberg Germany Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing. Taking a computational approach, this book explains how ANNs provide solutions in real time, and allow the visualization and development of new techniques and architectures. Features include: * A guide to the fundamental mathematics of neurocomputing. * A review

An Introduction to Neural Networks

An Introduction to Neural Networks
  • Author : Kevin Gurney
  • Publisher : CRC Press
  • Release : 08 October 2018
GET THIS BOOKAn Introduction to Neural Networks

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several