Humans are remarkable in processing speech, audio, image and some biomedical signals. Artificial neural networks are proved to be successful in performing several cognitive, industrial and scientific tasks. This peer reviewed book presents some recent advances and surveys on the applications of artificial neural networks in the areas of speech, audio, image and biomedical signal processing. It chapters are prepared by some reputed researchers and practitioners around the globe.

Softcomputing techniques play a vital role in the industry. This book presents several important papers presented by some of the well-known scientists from all over the globe. The application domains discussed in this book include: agroecology, bioinformatics, branched fluid-transport network layout design, dam scheduling, data analysis and exploration, detection of phishing attacks, distributed terrestrial transportation, fault detection of motors, fault diagnosis of electronic circuits, fault diagnosis of power distribution systems, flood routing, hazard sensing, health care, industrial chemical processes, knowledge management in software development, local multipoint distribution systems, missing data estimation, parameter calibration of rainfall intensity models, parameter identification for systems engineering, petroleum vessel mooring, query answering in P2P systems, real-time strategy games, robot control, satellite heat pipe design, monsoon rainfall forecasting, structural design, tool condition monitoring, vehicle routing, water network design, etc.

The softcomputing techniques presented in this book are on (or closely related to): ant-colony optimization, artificial immune systems, artificial neural networks, Bayesian models, case-based reasoning, clustering techniques, differential evolution, fuzzy classification, fuzzy neural networks, genetic algorithms, harmony search, hidden Markov models, locally weighted regression analysis, probabilistic principal component analysis, relevance vector machines, self-organizing maps, other machine learning and statistical techniques, and the combinations of the above techniques.

Soft computing techniques are widely used in most businesses. This book consists of several important papers on the applications of soft computing techniques for the business field. The soft computing techniques used in this book include (or very closely related to): Bayesian networks, biclustering methods, case-based reasoning, data mining, Dempster-Shafer theory, ensemble learning, evolutionary programming, fuzzy decision trees, hidden Markov models, intelligent agents, k-means clustering, maximum likelihood Hebbian learning, neural networks, opportunistic scheduling, probability distributions combined with Monte Carlo methods, rough sets, self organizing maps, support vector machines, uncertain reasoning, other statistical and machine learning techniques, and combinations of these techniques.

The businesses or business problems addressed in this book include (or very closely related to): analysis of correlations between currency exchange rates, analysis of USA banks and Moody s bank financial strength rating, arrears management, business risk identification, company audit fee evaluation, dental treatments, business internal control, intelligent tutoring systems and educational assessment, modeling agent behavior, motor insurance industry, personal loan defaults, pricing strategies for increasing the market share, pricing strategies in supply chain management, probabilistic sales forecasting, user relevance feedback analysis for online text retrieval, and world crude oil spot price forecasting."

Softcomputing techniques play a vital role in the industry. This book presents several important papers presented by some of the well-known scientists from all over the globe. The application domains discussed in this book include: agroecology, bioinformatics, branched fluid-transport network layout design, dam scheduling, data analysis and exploration, detection of phishing attacks, distributed terrestrial transportation, fault detection of motors, fault diagnosis of electronic circuits, fault diagnosis of power distribution systems, flood routing, hazard sensing, health care, industrial chemical processes, knowledge management in software development, local multipoint distribution systems, missing data estimation, parameter calibration of rainfall intensity models, parameter identification for systems engineering, petroleum vessel mooring, query answering in P2P systems, real-time strategy games, robot control, satellite heat pipe design, monsoon rainfall forecasting, structural design, tool condition monitoring, vehicle routing, water network design, etc.

The softcomputing techniques presented in this book are on (or closely related to): ant-colony optimization, artificial immune systems, artificial neural networks, Bayesian models, case-based reasoning, clustering techniques, differential evolution, fuzzy classification, fuzzy neural networks, genetic algorithms, harmony search, hidden Markov models, locally weighted regression analysis, probabilistic principal component analysis, relevance vector machines, self-organizing maps, other machine learning and statistical techniques, and the combinations of the above techniques.

Humans are remarkable in processing speech, audio, image and some biomedical signals. Artificial neural networks are proved to be successful in performing several cognitive, industrial and scientific tasks. This peer reviewed book presents some recent advances and surveys on the applications of artificial neural networks in the areas of speech, audio, image and biomedical signal processing. It chapters are prepared by some reputed researchers and practitioners around the globe.

Soft computing techniques are widely used in most businesses. This book consists of several important papers on the applications of soft computing techniques for the business field. The soft computing techniques used in this book include (or very closely related to): Bayesian networks, biclustering methods, case-based reasoning, data mining, Dempster-Shafer theory, ensemble learning, evolutionary programming, fuzzy decision trees, hidden Markov models, intelligent agents, k-means clustering, maximum likelihood Hebbian learning, neural networks, opportunistic scheduling, probability distributions combined with Monte Carlo methods, rough sets, self organizing maps, support vector machines, uncertain reasoning, other statistical and machine learning techniques, and combinations of these techniques.

The businesses or business problems addressed in this book include (or very closely related to): analysis of correlations between currency exchange rates, analysis of USA banks and Moody s bank financial strength rating, arrears management, business risk identification, company audit fee evaluation, dental treatments, business internal control, intelligent tutoring systems and educational assessment, modeling agent behavior, motor insurance industry, personal loan defaults, pricing strategies for increasing the market share, pricing strategies in supply chain management, probabilistic sales forecasting, user relevance feedback analysis for online text retrieval, and world crude oil spot price forecasting."

The best way to learn software engineering is by understanding its core and peripheral areas. Foundations of Software Engineering provides in-depth coverage of the areas of software engineering that are essential for becoming proficient in the field. The book devotes a complete chapter to each of the core areas. Several peripheral areas are also explained by assigning a separate chapter to each of them. Rather than using UML or other formal notations, the content in this book is explained in easy-to-understand language. Basic programming knowledge using an object-oriented language is helpful to understand the material in this book. The knowledge gained from this book can be readily used in other relevant courses or in real-world software development environments. This textbook educates students in software engineering principles. It covers almost all facets of software engineering, including requirement engineering, system specifications, system modeling, system architecture, system implementation, and system testing. Emphasizing practical issues, such as feasibility studies, this book explains how to add and develop software requirements to evolve software systems. This book was written after receiving feedback from several professors and software engineers. What resulted is a textbook on software engineering that not only covers the theory of software engineering but also presents real-world insights to aid students in proper implementation. Students learn key concepts through carefully explained and illustrated theories, as well as concrete examples and a complete case study using Java. Source code is also available on the book's website. The examples and case studies increase in complexity as the book progresses to help students build a practical understanding of the required theories and applications.