This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.

R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy.

Artificial Neural Network for Drug Design, Delivery and Disposition provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research. With its ability to learn and self-correct in a highly complex environment, this predictive tool has tremendous potential to help researchers more effectively design, develop, and deliver successful drugs. This book illustrates how to use ANN methodologies and models with the intent to treat diseases like breast cancer, cardiac disease, and more. It contains the latest cutting-edge research, an analysis of the benefits of ANN, and relevant industry examples. As such, this book is an essential resource for academic and industry researchers across the pharmaceutical and biomedical sciences. - Written by leading academic and industry scientists who have contributed significantly to the field and are at the forefront of artificial neural network (ANN) research - Focuses on ANN in drug design, discovery and delivery, as well as adopted methodologies and their applications to the treatment of various diseases and disorders - Chapters cover important topics across the pharmaceutical process, such as ANN in structure-based drug design and the application of ANN in modern drug discovery - Presents the future potential of ANN-based strategies in biomedical image analysis and much more

This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental material. For those that already have a solid grasp of how to create a neural network application, this work can provide a wide range of examples of nuances in network design, data set design, testing strategy, and error analysis.Computational, rather than artificial, modifiers are used for neural networks in this book to make a distinction between networks that are implemented in hardware and those that are implemented in software. The term artificial neural network covers any implementation that is inorganic and is the most general term. Computational neural networks are only implemented in software but represent the vast majority of applications.While this book cannot provide a blue print for every conceivable geophysics application, it does outline a basic approach that has been used successfully.

Entity Identification to Virtual Reality in Driving Simulation

The investigation of the interactions between human and physical systems poses unique conceptual, methodological, and practical challenges. This book establishes a spatial science framework for policymakers, social scientists, and environmental researchers as they explore and analyze complex problems. The authors provide guidance for scientists, writers, and students across a broad range of fields on how to tackle discipline-specific issues of space, place, and scale as they propose and conduct research in the spatial sciences. This practical textbook and overview blends plenty of concrete examples of spatial research and case studies to familiarize readers with the research process, demystifying and illustrating how it is actually done. The appendix contains both completed and in-progress proposals for MA and PhD theses and dissertations, as well as successful research grants. By emphasizing research as a learning and experiential process, while providing students with the encouragement and skills needed for success in proposal writing, "Research Design and Proposal Writing in Spatial Science" can serve as a textbook for research-design or project-based courses at the upper-division undergraduate and graduate level.

Global Biomass Burning provides a convenient and current reference on such topics as the remote sensing of biomass burning from space, the geographical distribution of burning; the combustion products of burning in tropical, temperate, and boreal ecosystems; burning as a global source of atmospheric gases and particulates; the impact of biomass burning gases and particulates on global climate; and the role of biomass burning on biodiversity and past global extinctions."--Pub. desc.