This book can be regarded as 'Soft computing for physicists and chemists self-taught'. It prepares the readers with a solid background of soft computing and how to adapt soft computing techniques to problem solving in physical and chemical research. Soft computing methods have been little explored by researchers in physical and chemical sciences primarily because of the absence of books that bridge the gap between the traditional computing paradigm pursued by researchers in science and the new soft computing paradigm that has emerged in computer science. This book is the interface between these primary sources and researchers in physics and chemistry.

The contributions to this book cover a wide range of applications of Soft Computing to the chemical domain. The early roots of Soft Computing can be traced back to Lotfi Zadeh's work on soft data analysis [1] published in 1981. 'Soft Computing' itself became fully established about 10 years later, when the Berkeley Initiative in Soft Computing (SISC), an industrial liaison program, was put in place at the University of California - Berkeley. Soft Computing applications are characterized by their ability to: • approximate many different kinds of real-world systems; • tolerate imprecision, partial truth, and uncertainty; and • learn from their environment. Such characteristics commonly lead to a better ability to match reality than other approaches can provide, generating solutions of low cost, high robustness, and tractability. Zadeh has argued that soft computing provides a solid foundation for the conception, design, and application of intelligent systems employing its methodologies symbiotically rather than in isolation. There exists an implicit commitment to take advantage of the fusion of the various methodologies, since such a fusion can lead to combinations that may provide performance well beyond that offered by any single technique.

Computer Simulation in Chemical Physics contains the proceedings of a NATO Advanced Study Institute held at CORISA, Alghero, Sardinia, in September 1992. In the five years that have elapsed since the field was last summarized there have been a number of remarkable advances which have significantly expanded the scope of the methods. Good examples are the Car--Parrinello method, which allows the study of materials with itinerant electrons; the Gibbs technique for the direct simulation of liquid--vapor phase equilibria; the transfer of scaling concepts from simulations of spin models to more complex systems; and the development of the configurational--biased Monte-Carlo methods for studying dense polymers. The field has also been stimulated by an enormous increase in available computing power and the provision of new software. All these exciting developments, an more, are discussed in an accessible way here, making the book indispensable reading for graduate students and research scientists in both academic and industrial settings.

The Chemical Sciences Roundtable provides a forum for discussing chemically related issues affecting government, industry and government. The goal is to strengthen the chemical sciences by foster communication among all the important stakeholders. At a recent Roundtable meeting, information technology was identified as an issue of increasing importance to all sectors of the chemical enterprise. This book is the result of a workshop convened to explore this topic.

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.

Hard boundaries have traditionally existed between such fields as fuzzy systems, neural networks, genetic algorithms, chaotic systems and expert systems. Gradually those boundaries are tending to vanish and "soft computing"-based systems that mix these different approaches have begun to emerge. Soft Computing Techniques in Human-Related Sciences focuses on the use of novel techniques such as artificial neural networks, fuzzy logic and genetic algorithms to solve practical problems related to humans: their activities, health and social needs. This volume illustrates and presents in an organized manner some of the recent progress in the applications of soft computing to fields related to social science, medical science, psychology, psychiatry , management of health and community services, and humanoid robots. Soft Computing Techniques in Human-Related Sciences begins with an introductory chapter to aid newcomers with the basic concepts, and progresses to the methodology of the use of soft computing in robotics, prosthetics, medicine, psycchology and man-machine interaction.

This book meets the present and future needs for the interaction between various science and technology/engineering areas on the one hand and different branches of soft computing on the other. Soft computing is the recent development about the computing methods which include fuzzy set theory/logic, evolutionary computation (EC), probabilistic reasoning, artificial neural networks, machine learning, expert systems, etc. Soft computing refers to a partnership of computational techniques in computer science, artificial intelligence, machine learning, and some other engineering disciplines, which attempt to study, model, and analyze complex problems from different interdisciplinary problems. This, as opposed to traditional computing, deals with approximate models and gives solutions to complex real-life problems. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty, partial truth, and approximations. Interdisciplinary sciences include various challenging problems of science and engineering. Recent developments in soft computing are the bridge to handle different interdisciplinary science and engineering problems. In recent years, the correspondingly increased dialog between these disciplines has led to this new book. This is done, firstly, by encouraging the ways that soft computing may be applied in traditional areas, as well as point towards new and innovative areas of applications and secondly, by encouraging other scientific disciplines to engage in a dialog with the above computation algorithms outlining their problems to both access new methods as well as to suggest innovative developments within itself.

The vigorous development of the internet and other information technologies have significantly expanded the amount and variety of sources of information available on decision making. This book presents the current trends of soft computing applications to the fields of measurements and information acquisition. Main topics are the production and presentation of information including multimedia, virtual environment, and computer animation as well as the improvement of decisions made on the basis of this information in various applications ranging from engineering to business. In order to make high-quality decisions, one has to fuse information of different kinds from a variety of sources with differing degrees of reliability and uncertainty. The necessity to use intelligent methodologies in the analysis of such systems is demonstrated as well as the inspiring relation of computational intelligence to its natural counterpart. This book includes several contributions demonstrating a further movement towards the interdisciplinary collaboration of the biological and computer sciences with examples from biology and robotics.

Comprehensive knowledge on the preparation, characterization, and applications of polymer nanocomposites Chemical Physics of Polymer Nanocomposites examines the state of the art in preparation, processing, characterizing, and applying a wide range of polymer nanocomposites, elucidating nanofiller/polymer interactions, nanofiller dispersion, distribution, filler-filler interactions, and interface properties, with a particular focus on the rheology of this important class of materials. The dependence of the rheological properties on the preparation techniques is discussed in detail, complemented by an overview of the processing approaches using conventional and micro injection molding, extrusion, compression molding, film blowing, pultrusion, and resin transfer molding. The book covers the latest understanding and accomplishments on polymer composites and presents the huge variety of this materials class. Practice-oriented with industry relevance, it also reviews preparation, characterization, morphology, properties, applications, sustainability, and recyclability. The topics covered in Chemical Physics of Polymer Nanocomposites include: Classification of nano fillers, nano-objects, nanomaterials, and polymer nanocomposites based on chemical nature and identity, and synthesis and characterization of nanoparticles General manufacturing methods and processes, including melt and shear mixing manufacturing of polymer nanocomposites 1D nano fillers and polymer nanocomposites, including polymer nanocomposites based on graphite nanoplatelets (GNP) and amphiphilic graphene platelets Polymer nanocomposites based on nano chitin, starch, and lignin, gold nanowires, titanium dioxide, and graphene and graphene oxide Chemical Physics of Polymer Nanocomposites is an essential resource for materials scientists, polymer chemists, chemical engineers, and engineering scientists in industry.

Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as