الصفحة 5
الصفحة 5
Computational methods for protein structure prediction and modeling ; Vol.1 : Basic Characterization
Volume one of this two volume sequence focuses on the basic characterization of known protein structures as well as structure prediction from protein sequence information. The 11 chapters provide an overview of the field, covering key topics in modeling, force fields, classification, computational methods, and struture prediction.
Computational Intelligence in Reliability Engineering : New Metaheuristics, Neural and Fuzzy Techniques in Reliability
This volume contains chapters presenting applications of different metaheuristics (ant colony optimization, great deluge algorithm, cross-entropy method and particle swarm optimization) in reliability engineering. It also includes chapters devoted to cellular automata and support vector machines and different applications of artificial neural networks, a powerful adaptive technique that can be used for learning, prediction and optimization. Several chapters describe different aspects of imprecise reliability and applications of fuzzy and vague set theory.
Computational intelligence for modelling and prediction
This book contains recent advances in Computational Intelligence methods for modeling, optimization and prediction and covers a large number of applications. The book presents new Computational Intelligence theory and methods for modeling and prediction. The range of the various applications is captured with 5 chapters in image processing, 2 chapters in audio processing, 3 chapters in commerce and finance, 2 chapters in communication networks and 6 chapters containing other applications.
Computational earthquake physics ; Part II
Exciting developments in earthquake science have benefited from new observations, improved computational technologies, and improved modeling capabilities. Designing realistic supercomputer simulation models for the complete earthquake generation process is a grand scientific challenge due to the complexity of phenomena and range of scales involved from microscopic to global. The present volume - Part II - incorporates computational environment and algorithms, data assimilation and understanding, model applications and iSERVO. Topics covered range from iSERVO and QuakeSim: implementing the international solid earth research virtual observatory by integrating computational grid and geographical information web services; LURR (Load-Unload Response Ratio) described in six papers involving this promising earthquake forecasting model; pattern informatics and phase dynamics and their applications, which was also a highlight in the Workshop; computational algorithms, including continuum damage models and visualization and analysis of geophysical datasets; evolution of mantle material; the state vector approach; and assimilation of data such as geodetic data, GPS data, and seismicity and laboratory experimental data.
Computational drug discovery and design
Provides new and updated methods and techniques for identification of drug target, binding sites prediction, high- throughput virtual screening, lead discovery and optimization, conformational sampling, prediction of pharmacokinetic properties using computer-based methodologies. Chapters also focus on the application of the latest artificial intelligence technologies for computer aided drug discovery.
Computational drug discovery : methods and application
Covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design. Additionally, it covers key technological advancements in computing such as quantum and cloud computing that are driving innovations in drug discovery.
Collective Phenomena in Synchrotron Radiation Sources : Prediction, Diagnostics, Countermeasures
This book helps to dispel the notion that collective phenomena, which have become increasingly important in modern storage rings, are an obscure and inaccessible topic. Despite an emphasis on synchrotron light sources, the basic concepts presented here are valid for other facilities as well. Graduate students, scientists and engineers working in an accelerator environment will find this to be a systematic exposition of the principles behind collective instabilities and lifetime-limiting effects. Experimental methods to identify and characterize collective effects are also surveyed. Among other measures to improve the performance of a projected or existing facility, a detailed account of feedback control of instabilities is given.
Machine learning challenges : Evaluating predictive uncertainty, Visual Object Classification, and Recognizing Textual Entailment, 1st Pascal Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers
Constitutes the refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.
Machine learning and deep learning in medical data analytics and healthcare applications
Introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.
Machine learning and data mining for sports analytics ; 7th international workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings
Constitutes the refereed post-conference proceedings of the 7th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2020, colocated with ECML/PKDD 2020, in Ghent, Belgium, in September 2020. The 11 papers presented were carefully reviewed and selected from 22 submissions. The papers present a variety of topics within the area of sports analytics, including tactical analysis, outcome predictions, data acquisition, performance optimization, and player evaluation.
Machine learning and big data : Concepts, algorithms, tools and applications
Showcase novel use-cases and applications, present empirical research results from user-centered qualitative and quantitative experiments of these new applications, and facilitate a discussion forum to explore the latest trends in big data and machine learning by providing algorithms which can be trained to perform interdisciplinary techniques such as statistics, linear algebra, and optimization and also create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention
Machine Learning : The Basics
Approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. Trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.
Linearity, Symmetry, and Prediction in the Hydrogen Atom
The predictive power of mathematics in quantum phenomena is one of the great intellectual successes of the 20th century. This textbook, aimed at undergraduate or graduate level students (depending on the college or university), concentrates on how to make predictions about the numbers of each kind of basic state of a quantum system from only two ingredients: the symmetry and the linear model of quantum mechanics. This method, involving the mathematical area of representation theory or group theory, combines three core mathematical subjects, namely, linear algebra, analysis and abstract algebra. Wide applications of this method occur in crystallography, atomic structure, classification of manifolds with symmetry, and other areas.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
Lifetime Spectroscopy : A Method of Defect Characterization in Silicon for Photovoltaic Applications
Lifetime spectroscopy is one of the most sensitive diagnostic tools for the identification and analysis of impurities in semiconductors. Since it is based on the recombination process, it provides insight into precisely those defects that are relevant to semiconductor devices such as solar cells. This book introduces a transparent modeling procedure that allows a detailed theoretical evaluation of the spectroscopic potential of the different lifetime spectroscopic techniques. The various theoretical predictions are verified experimentally with the context of a comprehensive study on different metal impurities. The quality and consistency of the spectroscopic results, as explained here, confirms the excellent performance of lifetime spectroscopy.
Learning Theory ; Vol. 4005 ; 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings
Constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA in June 2006. The 43 revised full papers presented together with 2 articles on open problems and 3 invited lectures were carefully reviewed and selected from a total of 102 submissions. The papers cover a wide range of topics including clustering, un- and semisupervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, learning algorithms and limitations on learning, online aggregation, online prediction and reinforcement learning.
Learning in Economic Systems with Expectations Feedback
Recently economists have more and more focussed on scenarios in which agents' views of the world may be erroneous. These notes introduce the concept of perfect forecasting rules which provide best least-squares predictions along the evolution of an economic system.
Land-Change Science in the Tropics : Changing Agricultural Landscapes
Land use and land-cover change research over the past decade has focused mainly on contemporary primary land-cover conversions in the tropics and sub-tropics, with considerable resources dedicated to the explanation and prediction of tropical deforestation and often ignoring the dynamism in the world’s agro-pastoral landscapes. This collection integrates cutting-edge research in the social, biogeophysical, and geographical information sciences to understand the human and environmental dynamics that change the type, magnitude and location of land uses and land covers in the changing countryside.This book describes the monitoring of land-cover changes, explains the processes through which land is altered, and describes the development of spatially-explicit models to predict land change. This book illustrates how practitioners have integrated knowledge from the three scientific realms - social, biophysical, and GIScience - that underpin land-change science.
LabVIEW based Advanced Instrumentation Systems
Information is a valuable resource to an organization. User-friendly, computer-controlled instrumentation and data analysis techniques are revolutionizing the way measurements are being made, allowing nearly instantaneous comparison between theoretical predictions, simulations, and actual experimental results. This book provides comprehensive coverage of fundamentals of advanced instrumentation systems based on LabVIEW concepts. This book is for those who wish a better understanding of virtual instrumentation concepts, its purpose, its nature, and the applications developed using the National Instrument’s LabVIEW software.
