Immunocomputing Based Optimization For Shallow And Deep Neural Networks
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Immunocomputing Based Optimization For Shallow And Deep Neural Networks
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Author : Ali Al Bataineh
language : en
Publisher:
Release Date : 2021
Immunocomputing Based Optimization For Shallow And Deep Neural Networks written by Ali Al Bataineh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Artificial immune systems categories.
This dissertation presents efficient immunocomputing methods based on the clonal selection algorithms (CSA), a class of optimization procedures inspired by the clonal selection theory of adaptive immunity to optimize the learning ability of three main classes of artificial neural networks (ANNs): (1) multi-layer perceptrons (MLPs), (2) convolutional neural networks (CNNs), and (3) long short-term memory (LSTM)-based recurrent neural networks (RNNs). The first approach of this research is an application of CSA to train MLPs with predefined shallow architectures to optimize the model parameters (weights and biases). Gradient descent is the most widely used method to train MLPs because it is flexible and mathematically elegant. However, gradient descent requires the loss functions to be differentiable, and in some cases, it might converge to a set of sub-optimal weights and biases. Thus, this dissertation proposes a CSA-based approach as a competitive alternative to address the described problems. The performance of our proposed approach is compared with other training methods: genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and gradient descent with momentum. The comparison is benchmarked using five classification datasets: Iris Flower, Sonar, Wheat Seeds, Breast Cancer Wisconsin, and Haberman's Survival. The comparative study results show that CSA outperforms other training methods in all datasets; hence it can be employed as an effective method for training MLPs. The second proposed approach of our study is the application of CSA to discover the optimal hyperparameters of a deep CNN architecture in a fully automated manner to address image classification tasks without the necessity of expert knowledge. Most of the state-of-the-art CNN architectures are manually designed, which requires in-depth knowledge and can be time-consuming. In our second approach, the proposed methodology is evaluated on the EMNIST-Digits dataset. The results show that CSA can discover high-performance and less expensive CNN architectures in terms of the number of trainable parameters. Moreover, the EMNIST Digits dataset's optimized architecture is evaluated on other EMNIST datasets with increased data instances and classes. The results are impressive and demonstrate that the CSA finds efficient, reusable CNN architecture that can work for multiple datasets and still achieve competitive performance with the state-of-the-art. The third and final approach of the study proposes a CSA application to automatically design the architecture with optimal hyperparameters of the LSTM model for text classification tasks such as sentiment analysis and SMS spam classification. Similar to CNNs, designing LSTM's architectures requires expert domain knowledge and can be very time-consuming. The proposed methodology is evaluated on the large movie review dataset (IMDB). Furthermore, the architecture discovered by CSA for the IMDB dataset is also evaluated on the other datasets viz, Twitter US Airline Sentiment, and SMS Spam Collection. Additionally, the optimized LSTM architecture is combined with pre-determined CNN layers to achieve the same or better performance in less time and with fewer trainable parameters. For further verification and evaluation of the generalization ability and effectiveness of the proposed approach, it is compared with four machine learning algorithms widely used for text classification tasks: (1) random forest, (2) logistic regression, (3) support vector machine (SVM), (4) and multinomial naive Bayes. The results of our experiments show that the LSTM architecture automatically designed by our CSA method is more efficient, reusable and outperforms the machine learning algorithms and other models in the literature evaluated on the same three dataset. With the proposed CSA-based methods, the best CNN and LSTM architectures can be self-determined without any human intervention, making our CSA-based methods a promising approach to automatically discover optimal deep neural network architectures that provide the best performance for a given task.
Neural Networks In Optimization
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Author : Xiang-Sun Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-10-31
Neural Networks In Optimization written by Xiang-Sun Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-10-31 with Business & Economics categories.
The book consists of three parts. The first part introduces concepts and algorithms in optimization theory, which have been used in neural network research. The second part covers main neural network models and their theoretical analysis. The third part of the book introduces various neural network models for solving nonlinear programming problems and combinatorial optimization problems. Audience: Graduate students and researchers who are interested in the intersection of optimization theory and artificial neural networks. The book is appropriate for graduate courses.
Optimization Landscape Of Deep Neural Networks
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Author : Ngoc Quynh Nguyen
language : en
Publisher:
Release Date : 2019
Optimization Landscape Of Deep Neural Networks written by Ngoc Quynh Nguyen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Artificial Intelligence In The 21st Century
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Author : Stephen Lucci
language : en
Publisher: Mercury Learning and Information
Release Date : 2015-12-10
Artificial Intelligence In The 21st Century written by Stephen Lucci and has been published by Mercury Learning and Information this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-10 with Computers categories.
This new edition provides a comprehensive, colorful, up-to-date, and accessible presentation of AI without sacrificing theoretical foundations. It includes numerous examples, applications, full color images, and human interest boxes to enhance student interest. New chapters on robotics and machine learning are now included. Advanced topics cover neural nets, genetic algorithms, natural language processing, planning, and complex board games. A companion DVD is provided with resources, applications, and figures from the book. Numerous instructors’ resources are available upon adoption. eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [email protected]. FEATURES: • Includes new chapters on robotics and machine learning and new sections on speech understanding and metaphor in NLP • Provides a comprehensive, colorful, up to date, and accessible presentation of AI without sacrificing theoretical foundations • Uses numerous examples, applications, full color images, and human interest boxes to enhance student interest • Introduces important AI concepts e.g., robotics, use in video games, neural nets, machine learning, and more thorough practical applications • Features over 300 figures and color images with worked problems detailing AI methods and solutions to selected exercises • Includes DVD with resources, simulations, and figures from the book • Provides numerous instructors’ resources, including: solutions to exercises, Microsoft PP slides, etc.
Computational Intelligence Paradigms
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Author : S.. PANEERSELVAM SUMATHI (SUREKHA.)
language : en
Publisher: CRC Press
Release Date : 2019-08-30
Computational Intelligence Paradigms written by S.. PANEERSELVAM SUMATHI (SUREKHA.) and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-30 with categories.
Offering a wide range of programming examples implemented in MATLAB(R), Computational Intelligence Paradigms: Theory and Applications Using MATLAB(R) presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research. The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi-Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization. Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.
Preclinical Development Handbook
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Author : Shayne Cox Gad
language : en
Publisher: John Wiley & Sons
Release Date : 2008-03-21
Preclinical Development Handbook written by Shayne Cox Gad and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-21 with Medical categories.
A clear, straightforward resource to guide you through preclinical drug development Following this book's step-by-step guidance, you can successfully initiate and complete critical phases of preclinical drug development. The book serves as a basic, comprehensive reference to prioritizing and optimizing leads, dose formulation, ADME, pharmacokinetics, modeling, and regulations. This authoritative, easy-to-use resource covers all the issues that need to be considered and provides detailed instructions for current methods and techniques. Each chapter is written by one or more leading experts in the field. These authors, representing the many disciplines involved in preclinical toxicology screening and testing, give you the tools needed to apply an effective multidisciplinary approach. The editor has carefully reviewed all the chapters to ensure that each one is thorough, accurate, and clear. Among the key topics covered are: * Modeling and informatics in drug design * Bioanalytical chemistry * Absorption of drugs after oral administration * Transporter interactions in the ADME pathway of drugs * Metabolism kinetics * Mechanisms and consequences of drug-drug interactions Each chapter offers a full exploration of problems that may be encountered and their solutions. The authors also set forth the limitations of various methods and techniques used in determining the safety and efficacy of a drug during the preclinical stage. This publication should be readily accessible to all pharmaceutical scientists involved in preclinical testing, enabling them to perform and document preclinical safety tests to meet all FDA requirements before clinical trials may begin.
Natural Computing Algorithms
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Author : Anthony Brabazon
language : en
Publisher: Springer
Release Date : 2015-10-08
Natural Computing Algorithms written by Anthony Brabazon and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-08 with Computers categories.
The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design. This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science.
Introduction To Computer Science Using Python
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Author : Charles Dierbach
language : en
Publisher:
Release Date : 2015
Introduction To Computer Science Using Python written by Charles Dierbach and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Immunocomputing
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Author : Alexander O. Tarakanov
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-06-03
Immunocomputing written by Alexander O. Tarakanov and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-03 with Computers categories.
Overview This book introduces immunocomputing (Ie) as a new computing approach that replicates the principles of information processing by proteins and immune networks. It establishes a rigorous mathematical basis for IC, consistent with recent findings in immunology, and it presents various applications of IC to specific computationally intensive real-life problems. The hardware implementation aspects of the IC concept in an immunocomputer as a new kind of computing medium and its potential connections with modem biological microchips (biochips) and future biomolecular computers (biocomputers) are also discussed. All biological systems at the cellular and biomolecular levels are sophisticated mechanisms honed to perfection by millions of years of evolution, and their exploration provides inspiration for various novel concepts in science and engineering. Of these systems, however, only two types, the neural system and the immune system of the vertebrates, possess the extraordinary capabilities of "intellectual" information processing, which include memory, the ability to learn, to recognize, and to make decisions with respect to unknown situations. The potential of the natural neural system as a biological prototype of a computing scheme has already been utilized intensively in computer science through the mathematical and software models of artificial neural networks (ANN) and their hardware implementation in neural computers (see, e.g., Haykin, 1999; Wasserman, 1990).
Artificial Immune Systems A New Computational Intelligence Approach
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Author : Leandro Nunes de Castro
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-09-23
Artificial Immune Systems A New Computational Intelligence Approach written by Leandro Nunes de Castro and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-09-23 with Computers categories.
Artificial Immune Systems (AIS) are adaptive systems inspired by the biological immune system and applied to problem solving. This book provides an accessible introduction that will be suitable for anyone who is beginning to study or work in this area. It gives a clear definition of an AIS, sets out the foundations of the topic (including basic algorithms), and analyses how the immune system relates to other biological systems and processes. No prior knowledge of immunology is needed - all the essential background information is covered in the introductory chapters. Key features of the book include: - A discussion of AIS in the context of Computational Intelligence; - Case studies in Autonomous Navigation, Computer Network Security, Job-Shop Scheduling and Data Analysis =B7 An extensive survey of applications; - A framework to help the reader design and understand AIS; - A web site with additional resources including pseudocodes for immune algorithms, and links to related sites. Written primarily for final year undergraduate and postgraduate students studying Artificial Intelligence, Evolutionary and Biologically Inspired Computing, this book will also be of interest to industrial and academic researchers working in related areas.