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State Of The Art In Neural Networks And Their Applications

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State of the Art in Neural Networks and Their Applications

State of the Art in Neural Networks and Their Applications Book
Author : Ayman S. El-Baz,Jasjit S. Suri
Publisher : Academic Press
Release : 2021-07-21
ISBN : 0128218495
Language : En, Es, Fr & De

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Book Description :

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.

Business Applications of Neural Networks

Business Applications of Neural Networks Book
Author : Bill Edisbury
Publisher : World Scientific
Release : 2000
ISBN : 9812813314
Language : En, Es, Fr & De

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Book Description :

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 carried out without the need for rocket-science expertise. This is a review of the state-of-the-art in applications of neural-network methods in three important areas of business analysis. It includes a tutorial chapter to introduce new users to the potential and pitfalls of this new technology.

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 Book
Author : Jing Dai
Publisher : Unknown
Release : 2013
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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 research is to develop a biologically inspired artificial neural network (BIANN), which is not only biologically meaningful, but also computationally powerful. The BIANN can serve as a novel computational intelligence tool in monitoring, modeling and control of the power systems. A comprehensive survey of ANNs applications in power system is presented. It is shown that novel types of reservoir-computing-based ANNs, such as echo state networks (ESNs) and liquid state machines (LSMs), have stronger modeling capability than conventional ANNs. The feasibility of using ESNs as modeling and control tools is further investigated in two specific power system applications, namely, power system nonlinear load modeling for true load harmonic prediction and the closed-loop control of active filters for power quality assessment and enhancement. It is shown that in both applications, ESNs are capable of providing satisfactory performances with low computational requirements. A novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. A comprehensive survey of the spiking models of living neurons as well as the coding approaches is presented to review the state-of-the-art in BIANN research. The proposed BIANNs are based on spiking models of living neurons with adoption of reservoir-computing approaches. It is shown that the proposed BIANNs have strong modeling capability and low computational requirements, which makes it a perfect candidate for online monitoring and control applications in power systems. BIANN-based modeling and control techniques are also proposed for power system applications. The proposed modeling and control schemes are validated for the modeling and control of a generator in a single-machine infinite-bus system under various operating conditions and disturbances. It is shown that the proposed BIANN-based technique can provide better control of the power system to enhance its reliability and tolerance to disturbances. To sum up, a novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. It is clearly shown that the proposed BIANN-based modeling and control schemes can provide faster and more accurate control for power system applications. The conclusions, the recommendations for future research, as well as the major contributions of this research are presented at the end.

Understanding Neural Networks and Fuzzy Logic

Understanding Neural Networks and Fuzzy Logic Book
Author : Stamatios V. Kartalopoulos
Publisher : Wiley-IEEE Press
Release : 1996
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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 Neural Networks Council

Complex Networks and Their Applications VIII

Complex Networks and Their Applications VIII Book
Author : Hocine Cherifi,Sabrina Gaito,José Fernendo Mendes,Esteban Moro,Luis Mateus Rocha
Publisher : Springer Nature
Release : 2019-11-26
ISBN : 3030366839
Language : En, Es, Fr & De

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Book Description :

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students, and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the Eighth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019), which took place in Lisbon, Portugal, on December 10–12, 2019. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, and network dynamics; diffusion, epidemics, and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Complex Networks and Their Applications VII

Complex Networks and Their Applications VII Book
Author : Luca Maria Aiello,Chantal Cherifi,Hocine Cherifi,Renaud Lambiotte,Pietro Lió,Luis M. Rocha
Publisher : Springer
Release : 2018-12-05
ISBN : 3030054144
Language : En, Es, Fr & De

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Book Description :

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Reconfigurable Cellular Neural Networks and Their Applications

Reconfigurable Cellular Neural Networks and Their Applications Book
Author : Müştak E. Yalçın,Tuba Ayhan,Ramazan Yeniçeri
Publisher : Springer
Release : 2019-04-15
ISBN : 3030178404
Language : En, Es, Fr & De

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Book Description :

This book explores how neural networks can be designed to analyze sensory data in a way that mimics natural systems. It introduces readers to the cellular neural network (CNN) and formulates it to match the behavior of the Wilson–Cowan model. In turn, two properties that are vital in nature are added to the CNN to help it more accurately deliver mimetic behavior: randomness of connection, and the presence of different dynamics (excitatory and inhibitory) within the same network. It uses an ID matrix to determine the location of excitatory and inhibitory neurons, and to reconfigure the network to optimize its topology. The book demonstrates that reconfiguring a single-layer CNN is an easier and more flexible solution than the procedure required in a multilayer CNN, in which excitatory and inhibitory neurons are separate, and that the key CNN criteria of a spatially invariant template and local coupling are fulfilled. In closing, the application of the authors’ neuron population model as a feature extractor is exemplified using odor and electroencephalogram classification.

Complex Networks Their Applications IX

Complex Networks   Their Applications IX Book
Author : Rosa M. Benito
Publisher : Springer Nature
Release : 2021
ISBN : 303065351X
Language : En, Es, Fr & De

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Book Description :

Download Complex Networks Their Applications IX book written by Rosa M. Benito, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Proceedings of the 7th IEEE International Workshop on Cellular Neural Networks and Their Applications

Proceedings of the 7th IEEE International Workshop on Cellular Neural Networks and Their Applications Book
Author : Ronald Tetzlaff
Publisher : World Scientific
Release : 2002
ISBN : 9789812776792
Language : En, Es, Fr & De

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Book Description :

This volume covers the fundamental theory of Cellular Neural Networks as well as their applications in various fields such as science and technology. It contains all 83 papers of the 7th International Workshop on Cellular Neural Networks and their Applications. The workshop follows a biennial series of six workshops consecutively hosted in Budapest (1990), Munich, Rome, Seville, London and Catania (2000).

Advances in Neural Networks ISNN 2019

Advances in Neural Networks     ISNN 2019 Book
Author : Huchuan Lu,Huajin Tang,Zhanshan Wang
Publisher : Springer
Release : 2019-06-26
ISBN : 3030228088
Language : En, Es, Fr & De

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Book Description :

This two-volume set LNCS 11554 and 11555 constitutes the refereed proceedings of the 16th International Symposium on Neural Networks, ISNN 2019, held in Moscow, Russia, in July 2019. The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; Time Series Analysis, Dynamic Prediction, and Uncertain Estimation; Model Optimization, Bayesian Learning, and Clustering; Game Theory, Stability Analysis, and Control Method; Signal Processing, Industrial Application, and Data Generation; Image Recognition, Scene Understanding, and Video Analysis; Bio-signal, Biomedical Engineering, and Hardware.

Artificial Neural Networks

Artificial Neural Networks Book
Author : Ali Roghani
Publisher : Createspace Independent Publishing Platform
Release : 2016-08-09
ISBN : 9781536976830
Language : En, Es, Fr & De

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Book Description :

Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."

Fundamentals of Deep Learning and Computer Vision

Fundamentals of Deep Learning and Computer Vision Book
Author : Singh Nikhil
Publisher : BPB Publications
Release : 2020-02-24
ISBN : 9388176618
Language : En, Es, Fr & De

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Book Description :

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 virtual environment with the deep learning framework TensorFlow and then introduces the fundamental concepts of TensorFlow. Before moving on to Computer Vision, you will learn about neural networks and related aspects such as loss functions, gradient descent optimization, activation functions and how backpropagation works for training multi-layer perceptrons.To understand how the Convolutional Neural Network (CNN) is used for computer vision problems, you need to learn about the basic convolution operation. You will learn how CNN is different from a multi-layer perceptron along with a thorough discussion on the different building blocks of the CNN architecture such as kernel size, stride, padding, and pooling and finally learn how to build a small CNN model. Next, you will learn about different popular CNN architectures such as AlexNet, VGGNet, Inception, and ResNets along with different object detection algorithms such as RCNN, SSD, and YOLO. The book concludes with a chapter on sequential models where you will learn about RNN, GRU, and LSTMs and their architectures and understand their applications in machine translation, image/video captioning and video classification.What will you learnThis book will help the readers to understand and apply the latest Deep Learning technologies to different interesting computer vision applications without any prior domain knowledge of image processing. Thus, helping the users to acquire new skills specific to Computer Vision and Deep Learning and build solutions to real-life problems such as Image Classification and Object Detection. Who this book is forThis book is for all the Data Science enthusiasts and practitioners who intend to learn and master Computer Vision concepts and their applications using Deep Learning. This book assumes a basic Python understanding with hands-on experience. A basic senior secondary level understanding of Mathematics will help the reader to make the best out of this book. Table of Contents1. Introduction to TensorFlow2. Introduction to Neural Networks 3. Convolutional Neural Network 4. CNN Architectures5. Sequential ModelsAbout the AuthorNikhil Singh is an accomplished data scientist and currently working as the Lead Data Scientist at Proarch IT Solutions Pvt. Ltd in London. He has experience in designing and delivering complex and innovative computer vision and NLP centred solutions for a large number of global companies. He has been an AI consultant to a few companies and mentored many apprentice Data Scientists. His LinkedIn Profile: https://www.linkedin.com/in/nikhil-singh-b953ba122/Paras Ahuja is a seasoned data science practitioner and currently working as the Lead Data Scientist at Reliance Jio in Hyderabad. He has good experience in designing and deploying deep learning-based Computer Vision and NLP-based solutions. He has experience in developing and implementing state-of-the-art automatic speech recognition systems.His LinkedIn Profile: https://www.linkedin.com/in/parasahuja

Applications of Soft Computing

Applications of Soft Computing Book
Author : Erel Avineri,Mario Köppen,Keshav Dahal,Yos Sunitiyoso,Rajkumar Roy
Publisher : Springer Science & Business Media
Release : 2008-12-28
ISBN : 3540880798
Language : En, Es, Fr & De

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Book Description :

Soft Computing is a complex of methodologies that includes artificial neural networks, genetic algorithms, fuzzy logic, Bayesian networks, and their hybrids. It admits approximate reasoning, imprecision, uncertainty and partial truth in order to mimic the remarkable human capability of making decisions in real-life, ambiguous environments. Soft Computing has therefore become popular in developing systems that encapsulate human expertise. 'Applications of Soft Computing: Updating the State of Art' contains a collection of papers that were presented at the 12th On-line World Conference on Soft Computing in Industrial Applications, held in October 2007. This carefully edited book provides a comprehensive overview of the recent advances in the industrial applications of soft computing and covers a wide range of application areas, including design, intelligent control, optimization, signal processing, pattern recognition, computer graphics, production, as well as civil engineering and applications to traffic and transportation systems. The book is aimed at researchers and practitioners who are engaged in developing and applying intelligent systems principles to solving real-world problems. It is also suitable as wider reading for science and engineering postgraduate students.

Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition

Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition Book
Author : Chen-Yu Lee
Publisher : Unknown
Release : 2016
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

This thesis presents two principled approaches to improve the performance of convolutional neural networks on visual recognition and demonstrates the effectiveness of CNNs on optical character recognition problem. First, we propose deeply-supervised nets (DSN), a method that simultaneously minimizes classification error and improves the directness and transparency of the hidden layer learning process. We focus our attention on three aspects of traditional CNN-type architectures: (1) transparency in the effect intermediate layers have on overall classification; (2) discriminativeness and robustness of learned features, especially in early layers; (3) training effectiveness in the face of "vanishing" gradients. To combat these issues, we introduce "companion" objective functions at each hidden layer, in addition to the overall objective function at the output layer. Second, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. The two primary directions lie in (1) learning a pooling function via combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. The advantages provided by the proposed methods are evident in our experimental results, showing state-of-the-art performance on MNIST, CIFAR-10, CIFAR-100, and SVHN. Finally, we present recursive recurrent neural networks with attention modeling for lexicon-free optical character recognition in natural scene images. The primary advantages of the proposed method are: (1) use of recursive convolutional neural networks (CNNs), which allow for parametrically efficient and effective image feature extraction; (2) an implicitly learned character-level language model, embodied in a recurrent neural network which avoids the need to use N-grams; and (3) the use of a soft-attention mechanism, allowing the model to selectively exploit image features in a coordinated way, and allowing for end-to-end training within a standard backpropagation framework. We validate our method with state-of-the-art performance on challenging benchmark datasets: Street View Text, IIIT5k, ICDAR and Synth90k.

Cellular Neural Networks and Their Applications

Cellular Neural Networks and Their Applications Book
Author : Ronald Tetzlaff
Publisher : World Scientific
Release : 2002-07-08
ISBN : 9814487767
Language : En, Es, Fr & De

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Book Description :

This volume covers the fundamental theory of Cellular Neural Networks as well as their applications in various fields such as science and technology. It contains all 83 papers of the 7th International Workshop on Cellular Neural Networks and their Applications. The workshop follows a biennial series of six workshops consecutively hosted in Budapest (1990), Munich, Rome, Seville, London and Catania (2000). Contents:On the Relationship Between CNNs and PDEs (M Gilli et al.)Moving Object Tracking on Panoramic Images (P Földesy et al.)Emergence of Global Patterns in Connected Neural Networks (T Shimizu)Configurable Multi-Layer CNN-UM Emulator on FPGA (Z Nagy & P Szolgay)A CNN Based System to Blind Sources Separation of MEG Signals (M Bucolo et al.)Time as Coding Space for Information Processing in the Cerebral Cortex (W Singer)Analyzing Multidimensional Neural Activity via CNN-UM (V Gál et al.)Visual Feedback by Using a CNN Chip Prototype System (P Arena et al.)Computational and Computer Complexity of Analogic Cellular Wave Computers (T Roska)Chaotic Phenomena in Quantum Cellular Neural Networks (L Fortuna & D Porto)Fingerprint Image Enhancement Using CNN Gabor-Type Filters (E Saatci & V Tavsanoglu)CNN Based Color Constancy Algorithm (L Török & Á Zarándy)Statistical Error Modeling of CNN-UM Architectures: The Grayscale Case (P Földesy)MEMS, Microsystems and Nanosystems (M E Zaghloul)Texture Segmentation by the 64x64 CNN Chip (T Szirányi)Teaching CNN and Learning by Using CNN (P Arena et al.)Novel Methods and Results in Training Universal Multi-Nested Neurons (R Dogaru et al.)Test-Bed Board for 16x64 Stereo Vision CNN Chip (M Salerno et al.)and other papers Readership: Graduate students, researchers, lecturers and industrialists. Keywords:

Deep Learning for Natural Language Processing

Deep Learning for Natural Language Processing Book
Author : Karthiek Reddy Bokka,Shubhangi Hora,Tanuj Jain
Publisher : Unknown
Release : 2019-06-07
ISBN : 9781838550295
Language : En, Es, Fr & De

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Book Description :

Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key Features Gain insights into the basic building blocks of natural language processing Learn how to select the best deep neural network to solve your NLP problems Explore convolutional and recurrent neural networks and long short-term memory networks Book Description Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you'll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learn Understand various pre-processing techniques for deep learning problems Build a vector representation of text using word2vec and GloVe Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP Build a machine translation model in Keras Develop a text generation application using LSTM Build a trigger word detection application using an attention model Who this book is for If you're an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.

Proceedings of the 21st EANN Engineering Applications of Neural Networks 2020 Conference

Proceedings of the 21st EANN  Engineering Applications of Neural Networks  2020 Conference Book
Author : Lazaros Iliadis,Plamen Parvanov Angelov,Chrisina Jayne,Elias Pimenidis
Publisher : Springer Nature
Release : 2020-05-27
ISBN : 3030487911
Language : En, Es, Fr & De

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Book Description :

This book gathers the proceedings of the 21st Engineering Applications of Neural Networks Conference, which is supported by the International Neural Networks Society (INNS). Artificial Intelligence (AI) has been following a unique course, characterized by alternating growth spurts and “AI winters.” Today, AI is an essential component of the fourth industrial revolution and enjoying its heyday. Further, in specific areas, AI is catching up with or even outperforming human beings. This book offers a comprehensive guide to AI in a variety of areas, concentrating on new or hybrid AI algorithmic approaches with robust applications in diverse sectors. One of the advantages of this book is that it includes robust algorithmic approaches and applications in a broad spectrum of scientific fields, namely the use of convolutional neural networks (CNNs), deep learning and LSTM in robotics/machine vision/engineering/image processing/medical systems/the environment; machine learning and meta learning applied to neurobiological modeling/optimization; state-of-the-art hybrid systems; and the algorithmic foundations of artificial neural networks.

Complex Valued Neural Networks with Multi Valued Neurons

Complex Valued Neural Networks with Multi Valued Neurons Book
Author : Igor Aizenberg
Publisher : Springer Science & Business Media
Release : 2011-06-24
ISBN : 3642203523
Language : En, Es, Fr & De

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Book Description :

Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information. These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.

Python Deep Learning

Python Deep Learning Book
Author : Ivan Vasilev,Daniel Slater,Gianmario Spacagna,Peter Roelants,Valentino Zocca
Publisher : Packt Publishing Ltd
Release : 2019-01-16
ISBN : 1789349702
Language : En, Es, Fr & De

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Book Description :

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book Description With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications. What you will learn Grasp the mathematical theory behind neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks Solve generative tasks using variational autoencoders and Generative Adversarial Networks Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models Explore reinforcement learning and understand how agents behave in a complex environment Get up to date with applications of deep learning in autonomous vehicles Who this book is for This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

Artificial Neural Networks Formal Models and Their Applications ICANN 2005

Artificial Neural Networks  Formal Models and Their Applications     ICANN 2005 Book
Author : Wlodzislaw Duch,Janusz Kacprzyk,Erkki Oja,Slawomir Zadrozny
Publisher : Springer Science & Business Media
Release : 2005-08-31
ISBN : 3540287558
Language : En, Es, Fr & De

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Book Description :

The two volume set LNCS 3696 and LNCS 3697 constitutes the refereed proceedings of the 15th International Conference on Artificial Neural Networks, ICANN 2005, held in Warsaw, Poland in September 2005. The over 600 papers submitted to ICANN 2005 were thoroughly reviewed and carefully selected for presentation. The first volume includes 106 contributions related to Biological Inspirations; topics addressed are modeling the brain and cognitive functions, development of cognitive powers in embodied systems spiking neural networks, associative memory models, models of biological functions, projects in the area of neuroIT, evolutionary and other biological inspirations, self-organizing maps and their applications, computer vision, face recognition and detection, sound and speech recognition, bioinformatics, biomedical applications, and information- theoretic concepts in biomedical data analysis. The second volume contains 162 contributions related to Formal Models and their Applications and deals with new neural network models, supervised learning algorithms, ensemble-based learning, unsupervised learning, recurent neural networks, reinforcement learning, bayesian approaches to learning, learning theory, artificial neural networks for system modeling, decision making, optimalization and control, knowledge extraction from neural networks, temporal data analysis, prediction and forecasting, support vector machines and kernel-based methods, soft computing methods for data representation, analysis and processing, data fusion for industrial, medical and environmental applications, non-linear predictive models for speech processing, intelligent multimedia and semantics, applications to natural language processing, various applications, computational intelligence in games, and issues in hardware implementation.