Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound impact in the elucidation of complex biological, chemical, and environmental processes. Artificial Neural Networks in Biological and Environmental Analysis provides an in-depth and timely perspective on the fundamental, technological, and applied aspects of computational neural networks. Presenting the basic principles of neural networks together with applications in the field, the book stimulates communication and partnership among scientists in fields as diverse as biology, chemistry, mathematics, medicine, and environmental science. This interdisciplinary discourse is essential not only for the success of independent and collaborative research and teaching programs, but also for the continued interest in the use of neural network tools in scientific inquiry. The book covers: A brief history of computational neural network models in relation to brain function Neural network operations, including neuron connectivity and layer arrangement Basic building blocks of model design, selection, and application from a statistical perspective Neurofuzzy systems, neuro-genetic systems, and neuro-fuzzy-genetic systems Function of neural networks in the study of complex natural processes Scientists deal with very complicated systems, much of the inner workings of which are frequently unknown to researchers. Using only simple, linear mathematical methods, information that is needed to truly understand natural systems may be lost. The development of new algorithms to model such processes is needed, and ANNs can play a major role. Balancing basic principles and diverse applications, this text introduces newcomers to the field and reviews recent developments of interest to active neural network practitioners.

Multivariate, heterogeneous data has been traditionally analyzed using the "one at a time" variable approach, often missing the main objective of discovering the relationships among multiple variables and samples. Enter chemometrics, with its powerful tools for design, analysis, and data interpretation of complex environmental systems. Delineating the rigors of modern environmental analysis and how to effectively solve limitations through multivariate approaches, Environmental Chemometrics: Principles and Modern Applications provides an introduction and practical guide to chemometric methods used in environmental chemical analysis. The text begins with an overview of chemometrics in relation to quantitative environmental analysis and a review of descriptive statistical concepts. Building on this, the author covers environmental sampling considerations, experimental design and optimization techniques, multivariate analysis of environmental and chemical data sets, time series analysis, and quality assurance and method validation. Each chapter contains problem-oriented exercises and research applications from the author's own work and from other experts in the field. The author's presentation of the basic principles of these methods together with real applications in the field of environmental chemistry makes the comprehension of complex environmental problems and chemically-related concepts more accessible. He covers all major areas of environmental analysis backed by studies from experts in the field. The book is a valuable tool for understanding the rapidly developing world of chemometric methods in environmental analysis.

Multivariate, heterogeneous data has been traditionally analyzed using the "one at a time" variable approach, often missing the main objective of discovering the relationships among multiple variables and samples. Enter chemometrics, with its powerful tools for design, analysis, and data interpretation of complex environmental systems. Delineating the rigors of modern environmental analysis and how to effectively solve limitations through multivariate approaches, Environmental Chemometrics: Principles and Modern Applications provides an introduction and practical guide to chemometric methods used in environmental chemical analysis.

The text begins with an overview of chemometrics in relation to quantitative environmental analysis and a review of descriptive statistical concepts. Building on this, the author covers environmental sampling considerations, experimental design and optimization techniques, multivariate analysis of environmental and chemical data sets, time series analysis, and quality assurance and method validation. Each chapter contains problem-oriented exercises and research applications from the author's own work and from other experts in the field.

The author's presentation of the basic principles of these methods together with real applications in the field of environmental chemistry makes the comprehension of complex environmental problems and chemically-related concepts more accessible. He covers all major areas of environmental analysis backed by studies from experts in the field. The book is a valuable tool for understanding the rapidly developing world of chemometric methods in environmental analysis.

Key Concepts in Environmental Chemistry provides a modern and concise introduction to environmental chemistry principles and the dynamic nature of environmental systems. It offers an intense, one-semester examination of selected concepts encountered in this field of study and provides integrated tools in explaining complex chemical problems of environmental importance. Principles typically covered in more comprehensive textbooks are well integrated into general chapter topics and application areas. The goal of this textbook is to provide students with a valuable resource for learning the basic concepts of environmental chemistry from an easy to follow, condensed, application and inquiry-based perspective. Additional statistical, sampling, modeling and data analysis concepts and exercises will be introduced for greater understanding of the underlying processes of complex environmental systems and fundamental chemical principles. Each chapter will have problem-oriented exercises (with examples throughout the body of the chapter) that stress the important concepts covered and research applications/case studies from experts in the field. Research applications will be directly tied to theoretical concepts covered in the chapter. Overall, this text provides a condensed and integrated tool for student learning and covers key concepts in the rapidly developing field of environmental chemistry.

Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound impact in the elucidation of complex biological, chemical, and environmental processes. Artificial Neural Networks in Biological and Environmental Analysis provides an in-depth and timely perspective on the fundamental, technological, and applied aspects of computational neural networks. Presenting the basic principles of neural networks together with applications in the field, the book stimulates communication and partnership among scientists in fields as diverse as biology, chemistry, mathematics, medicine, and environmental science. This interdisciplinary discourse is essential not only for the success of independent and collaborative research and teaching programs, but also for the continued interest in the use of neural network tools in scientific inquiry. The book covers: A brief history of computational neural network models in relation to brain function Neural network operations, including neuron connectivity and layer arrangement Basic building blocks of model design, selection, and application from a statistical perspective Neurofuzzy systems, neuro-genetic systems, and neuro-fuzzy-genetic systems Function of neural networks in the study of complex natural processes Scientists deal with very complicated systems, much of the inner workings of which are frequently unknown to researchers. Using only simple, linear mathematical methods, information that is needed to truly understand natural systems may be lost. The development of new algorithms to model such processes is needed, and ANNs can play a major role. Balancing basic principles and diverse applications, this text introduces newcomers to the field and reviews recent developments of interest to active neural network practitioners.