This concise but comprehensive textbook reviews the most popular neural-network methods and their associated techniques. Each chapter provides state-of-the-art descriptions of important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms - powerful tools for neural-network learning - are introduced. The systematic survey of neural-network models and exhaustive references list will point readers toward topics for future research. The algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.

Metaheuristic optimization is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. This is usually applied when two or more objectives are to be optimized simultaneously. This book is presented with two major objectives. Firstly, it features chapters by eminent researchers in the field providing the readers about the current status of the subject. Secondly, algorithm-based optimization or advanced optimization techniques, which are applied to mostly non-engineering problems, are applied to engineering problems. This book will also serve as an aid to both research and industry. Usage of these methodologies would enable the improvement in engineering and manufacturing technology and support an organization in this era of low product life cycle. Features: Covers the application of recent and new algorithms Focuses on the development aspects such as including surrogate modeling, parallelization, game theory, and hybridization Presents the advances of engineering applications for both single-objective and multi-objective optimization problems Offers recent developments from a variety of engineering fields Discusses Optimization using Evolutionary Algorithms and Metaheuristics applications in engineering

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.

This book covers the issues related to optimization of engineering and management problems using soft computing techniques with an industrial outlook. It covers a broad area related to real life complex decision making problems using a heuristics approach. It also explores a wide perspective and future directions in industrial engineering research on a global platform/scenario. The book highlights the concept of optimization, presents various soft computing techniques, offers sample problems, and discusses related software programs complete with illustrations. Features Explains the concept of optimization and relevance to soft computing techniques towards optimal solution in engineering and management Presents various soft computing techniques Offers problems and their optimization using various soft computing techniques Discusses related software programs, with illustrations Provides a step-by-step tutorial on how to handle relevant software for obtaining the optimal solution to various engineering problems

Businesses today are faced with a highly competitive market and fast-changing technologies. In order to meet demanding customers' needs, they rely on high quality software. A new field of study, soft computing techniques, is needed to estimate the efforts invested in component-based software. Component-Based Systems: Estimating Efforts Using Soft Computing Techniques is an important resource that uses computer-based models for estimating efforts of software. It provides an overview of component-based software engineering, while addressing uncertainty involved in effort estimation and expert opinions. This book will also instruct the reader how to develop mathematical models. This book is an excellent source of information for students and researchers to learn soft computing models, their applications in software management, and will help software developers, managers, and those in the industry to apply soft computing techniques to estimate efforts.

We create technology enabling us to do things never before possible and it ultimately changes the way we live, work, play, and interact with each other. Throughout human history, the democratization of technology making a technology available to the masses, has brought about sweeping cultural, social, political, and societal changes. In the last half-century, the democratization of computers, information, the Internet, and social media have revolutionized and transformed our lives. We now stand at the beginning of a new era sure to bring about waves of new revolutions, the cognitive systems era. Until now, humans have done all of the thinking. However, our lives are about to be infused with artificial entities capable of performing high-level cognitive processing previously possible only in the human mind. Systems capable of this kind of "synthetic cognition" will achieve and surpass the level of human experts in almost every field of endeavor. Far from replacing humans, these cognitive systems will be our collaborators, teachers, confidants, colleagues, and companions. The future will belong to those who can better partner with these cognitive systems. Made available to the average person via the Internet, handheld devices, and through ordinary objects all around us, expertise will become democratized. Everything will change when anyone has access to expertise in any field and new things will be possible. The democratization of expertise is the foundation on which our society's revolutions will be built over the next half-century. This book discusses societal and cultural revolutions throughout history brought about by the adoption of new technology and gives brief histories of human cognitive augmentation and artificial intelligence. In the coming cognitive systems era, humans, by collaboratively partnering with cognitive systems, will together achieve expert-level performance-synthetic expertise-with humans performing some of the cognitive processing and cognitive systems performing some. As the capabilities of cognitive systems improve over time, the balance of thinking will shift from being mostly human to mostly artificial. This book introduces the Levels of Cognitive Augmentation to describe this shift. Drawing from previous research in cognitive systems and intelligent agent theory, the knowledge stores required for expertise are identified in a Knowledge Level description of expertise. This book introduces a new abstract level, called the Expertise Level to describe the skills needed for expertise. Combining the knowledge-level and expertise-level descriptions, this book introduces the Model of Expertise. This book demonstrates use of the Model of Expertise by presenting several synthetic expert architectures: a synthetic teacher (Synthia), a synthetic friend/therapist (Sy), a synthetic elderly companion (Lois), a synthetic research companion (Synclair), and an automated scientific hypothesis explorer (Ashe). This book is intended for anyone interested in the fields of cognitive systems, cognitive computing, cognitive augmentation, or artificial intelligence or the impact of technologies from these fields on society. Anyone doing research and development in the area of cognitive systems or artificial intelligence will find this book particularly useful.

We create technology enabling us to do things never before possible and it ultimately changes the way we live, work, play, and interact with each other. Throughout human history, the democratization of technology making a technology available to the masses, has brought about sweeping cultural, social, political, and societal changes. In the last half-century, the democratization of computers, information, the Internet, and social media have revolutionized and transformed our lives. We now stand at the beginning of a new era sure to bring about waves of new revolutions, the cognitive systems era. Until now, humans have done all of the thinking. However, our lives are about to be infused with artificial entities capable of performing high-level cognitive processing previously possible only in the human mind. Systems capable of this kind of "synthetic cognition" will achieve and surpass the level of human experts in almost every field of endeavor. Far from replacing humans, these cognitive systems will be our collaborators, teachers, confidants, colleagues, and companions. The future will belong to those who can better partner with these cognitive systems. Made available to the average person via the Internet, handheld devices, and through ordinary objects all around us, expertise will become democratized. Everything will change when anyone has access to expertise in any field and new things will be possible. The democratization of expertise is the foundation on which our society's revolutions will be built over the next half-century. This book discusses societal and cultural revolutions throughout history brought about by the adoption of new technology and gives brief histories of human cognitive augmentation and artificial intelligence. In the coming cognitive systems era, humans, by collaboratively partnering with cognitive systems, will together achieve expert-level performance-synthetic expertise-with humans performing some of the cognitive processing and cognitive systems performing some. As the capabilities of cognitive systems improve over time, the balance of thinking will shift from being mostly human to mostly artificial. This book introduces the Levels of Cognitive Augmentation to describe this shift. Drawing from previous research in cognitive systems and intelligent agent theory, the knowledge stores required for expertise are identified in a Knowledge Level description of expertise. This book introduces a new abstract level, called the Expertise Level to describe the skills needed for expertise. Combining the knowledge-level and expertise-level descriptions, this book introduces the Model of Expertise. This book demonstrates use of the Model of Expertise by presenting several synthetic expert architectures: a synthetic teacher (Synthia), a synthetic friend/therapist (Sy), a synthetic elderly companion (Lois), a synthetic research companion (Synclair), and an automated scientific hypothesis explorer (Ashe). This book is intended for anyone interested in the fields of cognitive systems, cognitive computing, cognitive augmentation, or artificial intelligence or the impact of technologies from these fields on society. Anyone doing research and development in the area of cognitive systems or artificial intelligence will find this book particularly useful.

Businesses today are faced with a highly competitive market and fast-changing technologies. In order to meet demanding customers' needs, they rely on high quality software. A new field of study, soft computing techniques, is needed to estimate the efforts invested in component-based software. Component-Based Systems: Estimating Efforts Using Soft Computing Techniques is an important resource that uses computer-based models for estimating efforts of software. It provides an overview of component-based software engineering, while addressing uncertainty involved in effort estimation and expert opinions. This book will also instruct the reader how to develop mathematical models. This book is an excellent source of information for students and researchers to learn soft computing models, their applications in software management, and will help software developers, managers, and those in the industry to apply soft computing techniques to estimate efforts.

Diabetes Mellitus (DM, commonly referred to as diabetes, is a metabolic disorder in which there are high blood sugar levels over a prolonged period. Lack of sufficient insulin causes presence of excess sugar levels in the blood. As a result the glucose levels in diabetic patients are more than normal ones. It has symptoms like frequent urination, increased hunger, increase thirst and high blood sugar. There are mainly three types of diabetes namely type-1, type-2 and gestational diabetes. Type-1 DM occurs due to immune system mistakenly attacks and destroys the beta-cells and Type-2 DM occurs due to insulin resistance. Gestational DM occurs in women during pregnancy due to insulin blocking by pregnancy harmones. Among these three types of DM, type-2 DM is more prevalence, and impacting so many millions of people across the world. Classification and predictive systems are actually reliable in the health care sector to explore hidden patterns in the patients data. These systems aid, medical professionals to enhance their diagnosis, prognosis along with remedy organizing techniques. The less percentage of improvement in classifier predictive accuracy is very important for medical diagnosis purposes where mistakes can cause a lot of damage to patient's life. Hence, we need a more accurate classification system for prediction of type-2 DM. Although, most of the above classification algorithms are efficient, they failed to provide good accuracy with low computational cost. In this book, we proposed various classification algorithms using soft computing techniques like Neural Networks (NNs), Fuzzy Systems (FS) and Swarm Intelligence (SI). The experimental results demonstrate that these algorithms are able to produce high classification accuracy at less computational cost. The contributions presented in this book shall attempt to address the following objectives using soft computing approaches for identification of diabetes mellitus. Introuducing an optimized RBFN model called Opt-RBFN. Designing a cost effective rule miner called SM-RuleMiner for type-2 diabetes diagnosis. Generating more interpretable fuzzy rules for accurate diagnosis of type2 diabetes using RST-BatMiner. Developing accurate cascade ensemble frameworks called Diabetes-Network for type-2 diabetes diagnosis. Proposing a Multi-level ensemble framework called Dia-Net for improving the classification accuracy of type-2 diabetes diagnosis. Designing an Intelligent Diabetes Risk score Model called Intelli-DRM estimate the severity of Diabetes mellitus. This book serves as a reference book for scientific investigators who need to analyze disease data and/or numerical data, as well as researchers developing methodology in soft computing field. It may also be used as a textbook for a graduate and post graduate level course in machine learning or soft computing.

Python for Scientific Computation and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI). This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling. Features: No prior experience of programming is required. Online GitHub repository available with codes for readers to practice. Covers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computing. Full solutions to exercises are available as Jupyter notebooks on the Web.

Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called "seeing then doing" as it first gives step-by-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided, allowing the reader to execute the scripts as they study the explanations given in the text. Features Gets you quickly using R as a problem-solving tool Uses RStudio's integrated development environment Shows how to interface R with SQLite Includes examples using R's Rattle graphical user interface Requires no prior knowledge of R, machine learning, or computer programming Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression Includes end-of-chapter exercises, many of which can be solved by modifying existing scripts Includes datasets from several areas, including business, health and medicine, and science About the Author Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato, where he taught and performed research in the Computer and Information Science Department for over 30 years.

Neural Nets and Chaotic Carriers develops rational principles for the design of associative memories, with a view to applying these principles to models with irregularly oscillatory operation so evident in biological neural systems, and necessitated by the meaninglessness of absolute signal levels.Design is based on the criterion that an associative memory must be able to cope with "fading data", i.e., to form an inference from the data even as its memory of that data degrades. The resultant net shows striking biological parallels. When these principles are combined with the Freeman specification of a neural oscillator, some remarkable effects emerge. For example, the commonly-observed phenomenon of neuronal bursting appears, with gamma-range oscillation modulated by a low-frequency square-wave oscillation (the "escapement oscillation"). Bridging studies and new results of artificial and biological neural networks, the book has a strong research character. It is, on the other hand, accessible to non-specialists for its concise exposition on the basics.

This book comprehensively studies fuzzy temporal and spatial information, starting from the basics on fuzzy set theory and temporal/spatial reasoning, the development of a new model to represent fuzzy temporal/spatial information, the study of efficient and complete reasoning algorithms, and their application in an information retrieval context.

This useful volume presents the first approach that goes beyond merely representing information, by thoroughly addressing a variety of reasoning tasks. It also focuses on applications in the domain of information retrieval, and demonstrating the practical importance of the proposed framework.

Provides a comprehensive introduction to multi-robot systems planning and task allocation; Explores multi robot aerial planning, flight planning, orienteering and coverage, and deployment, patrolling, and foraging; Includes real-world case studies; Treats different aspects of cooperation in multi-agent systems.

In this insightful work, Dogaru proposes a systematic framework for measuring emergence and a systematic design method to locate computationally meaningful genes in a reasonable computing time. Programs and application examples are provided so that the reader may easily understand the new concepts and develop her own specific experiments. The book 's approachability recommends it to a large audience including specialists from various interdisciplinary fields.

Featuring an international team of authors, Neural Network Perspectives on Cognition and Adaptive Robotics presents several approaches to the modeling of human cognition and language using neural computing techniques. It also describes how adaptive robotic systems can be produced using neural network architectures. Covering a wide range of mainstream area and trends, each chapter provides the latest information from a different perspective.

Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond.This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition - all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained.The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

This book provides theoretical and practical knowledge on AI and swarm intelligence. It provides a methodology for EA (evolutionary algorithm)-based approach for complex adaptive systems with the integration of several meta-heuristics, e.g., ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), and PSO (Particle Swarm Optimization), etc. These developments contribute towards better problem-solving methodologies in AI. The book also covers emerging uses of swarm intelligence in applications such as complex adaptive systems, reaction-diffusion computing, and diffusion-limited aggregation, etc. Another emphasis is its real-world applications. We give empirical examples from real-world problems and show that the proposed approaches are successful when addressing tasks from such areas as swarm robotics, silicon traffics, image understanding, Vornoi diagrams, queuing theory, and slime intelligence, etc. Each chapter begins with the background of the problem followed by the current state-of-the-art techniques of the field, and ends with a detailed discussion. In addition, the simulators, based on optimizers such as PSO and ABC complex adaptive system simulation, are described in detail. These simulators, as well as some source codes, are available online on the author's website for the benefit of readers interested in getting some hands-on experience of the subject. The concepts presented in this book aim to promote and facilitate the effective research in swarm intelligence approaches in both theory and practice. This book would also be of value to other readers because it covers interdisciplinary research topics that encompass problem-solving tasks in AI, complex adaptive systems, and meta-heuristics.

This book contains a selection of the papers presented at the XVII SIGEF Congress. It presents fuzzy logic, neural networks and other intelligent techniques applied to economic and business problems. This book is very useful for researchers and graduate students aiming to introduce themselves to the field of quantitative techniques for overcoming uncertain environments. The contributors are experienced scholars of different countries who offer real world applications of these mathematical techniques.

This book describes the functions frequently used in deep neural networks. For this purpose, 37 activation functions are explained both mathematically and visually, and given with their LaTeX implementations due to their common use in scientific articles.

This book presents an introductory coverage of fuzzy logic, including basic principles from an interdisciplinary perspective. It includes concept of evolving a fuzzy set and fuzzy set operations, fuzzification rule base design and defuzzification and simple guidelines for fuzzy sets design and selected applications. Preliminary concepts of Neural Networks and Genetic Algorithm are added features with relevant examples and exercises. It is primarily intended for undergraduate and postgraduate students and researchers to facilitate education in the ever-increasing field of fuzzy logic as medium between human intelligence and machine.

This book describes new theories and applications of artificial neural networks, with a special focus on addressing problems in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain-computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XX International Conference on Neuroinformatics, held in Moscow, Russia on October 8-12, 2018.

Simulation of brain neurons in real-time using biophysically-meaningful models is a pre-requisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experiments. In spiking neural networks (SNNs), propagated information is not just encoded by the firing rate of each neuron in the network, as in artificial neural networks (ANNs), but, in addition, by amplitude, spike-train patterns, and the transfer rate. The high level of realism of SNNs and more significant computational and analytic capabilities in comparison with ANNs, however, limit the size of the realized networks. Consequently, the main challenge in building complex and biophysically-accurate SNNs is largely posed by the high computational and data transfer demands. Real-Time Multi-Chip Neural Network for Cognitive Systems presents novel real-time, reconfigurable, multi-chip SNN system architecture based on localized communication, which effectively reduces the communication cost to a linear growth. The system use double floating-point arithmetic for the most biologically accurate cell behavior simulation, and is flexible enough to offer an easy implementation of various neuron network topologies, cell communication schemes, as well as models and kinds of cells. The system offers a high run-time configurability, which reduces the need for resynthesizing the system. In addition, the simulator features configurable on- and off-chip communication latencies as well as neuron calculation latencies. All parts of the system are generated automatically based on the neuron interconnection scheme in use. The simulator allows exploration of different system configurations, e.g. the interconnection scheme between the neurons, the intracellular concentration of different chemical compounds (ions), which affect how action potentials are initiated and propagate.

This book presents the idea that innovative ways of teaching and learning are very essential to retention and growth. Presented in 15 sections, the book starts with the common sense training on education and moves on to neural network operation. Throughout the book, the art of learning, associative, cognitive, and creative learning are stated and defined. Learning simplicity, information content as related to neural network learning are discussed. The author also discusses neural plasticity and adaptability in smarter neural networks. If we know our human brain's basic abilities and limitation then a better educational methods can be implemented. Presents the idea that innovative ways of teaching and learning are very essential to retention and growth Discusses major differences and constraints between neural network and computer Presents the significances of learning simplicity and information content as related to neural network learning are included Stresses the neural network learning capabilities and limitations and their role in developing more efficient learning techniques