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Author: Jun Li
Publisher: Frontiers Media SA
ISBN: 2889747808
Category : Science
Languages : en
Pages : 174
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Book Description
Author: Jun Li
Publisher: Frontiers Media SA
ISBN: 2889747808
Category : Science
Languages : en
Pages : 174
Get Book Here
Book Description
Author: Pijush Samui
Publisher: Academic Press
ISBN: 0128113197
Category : Technology & Engineering
Languages : en
Pages : 660
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Book Description
Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. - Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more - Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing - Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods
Author: Kukatlapalli Pradeep Kumar
Publisher: John Wiley & Sons
ISBN: 1119841879
Category : Mathematics
Languages : en
Pages : 469
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Book Description
DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.
Author: William W. Hsieh
Publisher: Cambridge University Press
ISBN: 1107065550
Category : Computers
Languages : en
Pages : 649
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Book Description
A comprehensive guide to machine learning and statistics for students and researchers of environmental data science.
Author: Amit Kumar Tyagi
Publisher: Academic Press
ISBN: 0323985769
Category : Science
Languages : en
Pages : 314
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Book Description
Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes. Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR. - Provides a detailed explanation of data science concepts, methods and algorithms, all reinforced by practical examples that are applied to genomics - Presents a roadmap of future trends suitable for innovative Data Science research and practice - Includes topics such as Blockchain technology for securing data at end user/server side - Presents real world case studies, open issues and challenges faced in Genomics, including future research directions and a separate chapter for Ethical Concerns
Author: Alan H. Fielding
Publisher: Springer Science & Business Media
ISBN: 1461552893
Category : Science
Languages : en
Pages : 265
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Book Description
This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem.
Author: Xin Ning
Publisher: Frontiers Media SA
ISBN: 2832539564
Category : Technology & Engineering
Languages : en
Pages : 273
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Book Description
Smart grid (SG) is considered a form of intelligent system that allows the electric grid to perform its functions efficiently. The SG is a network that allows for the flow of electrical energy and data, where the data is used to make intelligent decisions in the operation of the electric grid. Artificial intelligence (AI) techniques, such as expert system (ES), Machine Learning (ML), and deep Learning (DL) have brought an advancing frontier in power electronics and power engineering with their powerful data processing capabilities. The SG relies on the flow of data to make its intelligent control; therefore, AI technology is a perfect fit for the SG. The application of AI technology in the SG has the potential to improve the intelligence of the SG. This research topic is focused on ways of improving the data analysis and control of SG by leveraging technologies. Manuscripts with the progress made in solving a range of miscellaneous and critical problems in SG by leveraging AI methods such as ES, ML, and DL methods are welcome. Reviews and original research that describe the latest developments in this field are considered for publication in this research topic. The scope of this Research Topic will include the following themes, but are not limited to: 1. Data-driven and artificial intelligence approaches to enhancing flexibility and resilience of SG. 2. Expert system, Machine Learning and Deep Learning, reinforcement learning and transfer learning for applications in SG. 3. AI for development in ensuring high reliability and stability of electric power system with high penetration of renewable energy. 4. AI for studies in operation protection, integrated planning, and control of SG systems. 5. AI for development in diagnostics and diagnostics for SG. 6. Health monitoring of a modern wind generation system using an adaptive neuro-fuzzy system. 7. Space vector fault pattern identification of a smart grid subsystem by neural mapping. 8. Control techniques, mathematical programming methods, optimization techniques and metaheuristics applied in SG. 9. AI and optimization techniques for green energy and carbon footprint. 10. Novel applications of AI-based smart grids in smart cities, smart transportation, smart healthcare, and smart manufacturing.
Author: Lisa A. Kerr
Publisher: Elsevier
ISBN: 0080470432
Category : Technology & Engineering
Languages : en
Pages : 735
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Book Description
Stock Identification Methods provides a comprehensive review of the various disciplines used to study the population structure of fishery resources. It represents the worldwide experience and perspectives of experts on each method, assembled through a working group of the International Council for the Exploration of the Sea. The book is organized to foster interdisciplinary analyses and conclusions about stock structure, a crucial topic for fishery science and management. Technological advances have promoted the development of stock identification methods in many directions, resulting in a confusing variety of approaches. Based on central tenets of population biology and management needs, Stock Identification Methods offers a unified framework for understanding stock structure by promoting an understanding of the relative merits and sensitivities of each approach.* Describes eighteen distinct approaches to stock identification grouped into sections on life history traits, environmental signals, genetic analyses, and applied marks* Features experts' reviews of benchmark case studies, general protocols, and the strengths and weaknesses of each identification method* Reviews statistical techniques for exploring stock patterns, testing for differences among putative stocks, stock discrimination, and stock composition analysis* Focuses on the challenges of interpreting data and managing mixed-stock fisheries
Author: Thomas, J. Joshua
Publisher: IGI Global
ISBN: 1799811948
Category : Computers
Languages : en
Pages : 355
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Book Description
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Author: Nitin Mittal
Publisher: CRC Press
ISBN: 1000985865
Category : Technology & Engineering
Languages : en
Pages : 321
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Book Description
The book comprehensively covers a wide range of evolutionary computer vision methods and applications, feature selection and extraction for training and classification, and metaheuristic algorithms in image processing. It further discusses optimized image segmentation, its analysis, pattern recognition, and object detection. Features: Discusses machine learning-based analytics such as GAN networks, autoencoders, computational imaging, and quantum computing. Covers deep learning algorithms in computer vision. Showcases novel solutions such as multi-resolution analysis in imaging processing, and metaheuristic algorithms for tackling challenges associated with image processing. Highlight optimization problems such as image segmentation and minimized feature design vector. Presents platform and simulation tools for image processing and segmentation. The book aims to get the readers familiar with the fundamentals of computational intelligence as well as the recent advancements in related technologies like smart applications of digital images, and other enabling technologies from the context of image processing and computer vision. It further covers important topics such as image watermarking, steganography, morphological processing, and optimized image segmentation. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in fields including electrical engineering, electronics, communications engineering, and computer engineering.
