"This book is a timely compendium of key elements that are crucial for the study of machine learning in chemoinformatics, giving an overview of current research in machine learning and their applications to chemoinformatics tasks"--Provided by publisher.
"This book is a timely compendium of key elements that are crucial for the study of machine learning in chemoinformatics, giving an overview of current research in machine learning and their applications to chemoinformatics tasks"--Provided by publisher.
The fields of computer vision and image processing are constantly evolving as new research and applications in these areas emerge. Staying abreast of the most up-to-date developments in this field is necessary in order to promote further research and apply these developments in real-world settings. Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies features timely and informative research on the design and development of computer vision and image processing applications in intelligent agents as well as in multimedia technologies. Covering a diverse set of research in these areas, this publication is ideally designed for use by academicians, technology professionals, students, and researchers interested in uncovering the latest innovations in the field.
Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely. - Outlines latent variable based hypothesis testing fault detection techniques to enhance monitoring processes represented by linear or nonlinear input-space models (such as PCA) or input-output models (such as PLS) - Explains multiscale latent variable based hypothesis testing fault detection techniques using multiscale representation to help deal with uncertainty in the data and minimize its effect on fault detection - Includes interval PCA (IPCA) and interval PLS (IPLS) fault detection methods to enhance the quality of fault detection - Provides model-based detection techniques for the improvement of monitoring processes using state estimation-based fault detection approaches - Demonstrates the effectiveness of the proposed strategies by conducting simulation and experimental studies on synthetic data
A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.
How to Design and Implement Powder-to-Tablet Continuous Manufacturing Systems provides a comprehensive overview on the considerations necessary for the design of continuous pharmaceutical manufacturing processes. The book covers both the theory and design of continuous processing of associated unit operations, along with their characterization and control. In addition, it discusses practical insights and strategies that the editor and chapter authors have learned. Chapters cover Process Analytical Technology (PAT) tools and the application of PAT data to enable distributed process control. With numerous case studies throughout, this valuable guide is ideal for those engaged in, or learning about, continuous processing in pharmaceutical manufacturing. - Discusses the development of strategy blueprints in the design of continuous processes - Shows how to create process flowsheet models from individual unit operation models - Includes a chapter on characterization methods for materials, the use of statistical methods to analyze material property data, and the use of material databases - Covers the evolving regulatory expectations for continuous manufacturing - Provides readers with ways to more effectively navigate these expectations
"This 10-volume compilation of authoritative, research-based articles contributed by thousands of researchers and experts from all over the world emphasized modern issues and the presentation of potential opportunities, prospective solutions, and future directions in the field of information science and technology"--Provided by publisher.
The prevailing global environmental crisis is primarily because of non-standardized parameters for environmental regulation. Unplanned expansion of economic activities, consideration for environmental conservation and several associated problems are emerging due to degradation in quality of ambient environment such as clean air, safe drinking water and quality of food, particularly in developing nations. Due to poor/casual execution of EIA protocol, newly developing countries are preferred destination for establishing pollution emitting industries, which results in degradation and depletion of natural resources. Lack of environmental policy intervention is another major attraction for establishing such industries in these nations. In order to ensure sustainable development, the highest priority issues include the monitoring and eradication of environmental problems which arise due to economic development. Initiation of any form of economic development primarily results in loss of forests and thus biodiversity, followed by deterioration in quality of air and contamination of natural resources. The worst impact of non-standardized economic development is the contamination of air, water and soil. Sustainable development ensures responsible interface with the environment to minimize the depletion or degradation of natural resources and ensure long term environmental quality. It involves integrated approaches in understanding the importance of environmental management systems and policy inventions leading to improved environmental performance. The present book is proposed to address the environmental concerns associated with economic development and approaches involved to attain sustainable economic development, which include monitoring of the quality of air, deforestation, quality of water resources, soil erosion and degradation of the natural environment.
Mass production companies have become obliged to reduce their production costs and sell more products with lower profit margins in order to survive in competitive market conditions. The complexity and automation level of machinery are continuously growing. This development calls for some of the most critical issues that are reliability and dependability of automatic systems. In the future, machines will be monitored remotely, and computer-aided techniques will be employed to detect faults in the future, and also there will be unmanned factories where machines and systems communicate to each other, detect their own faults, and can remotely intercept their faults. The pioneer studies of such systems are fault diagnosis studies. Thus, we hope that this book will contribute to the literature in this regard.
The four-volume set LNCS 7724--7727 constitutes the thoroughly refereed post-conference proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012, held in Daejeon, Korea, in November 2012. The total of 226 contributions presented in these volumes was carefully reviewed and selected from 869 submissions. The papers are organized in topical sections on object detection, learning and matching; object recognition; feature, representation, and recognition; segmentation, grouping, and classification; image representation; image and video retrieval and medical image analysis; face and gesture analysis and recognition; optical flow and tracking; motion, tracking, and computational photography; video analysis and action recognition; shape reconstruction and optimization; shape from X and photometry; applications of computer vision; low-level vision and applications of computer vision.