Special Issue Modern Dimension Reduction Methods For Big Data Problems In Ecology
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Big Data Optimization: Recent Developments and Challenges
| Author | : Ali Emrouznejad |
| Publisher | : Springer |
| Total Pages | : 492 |
| Release | : 2016-05-26 |
| Genre | : Technology & Engineering |
| ISBN | : 3319302655 |
The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
Modern Dimension Reduction
| Author | : Philip D. Waggoner |
| Publisher | : Cambridge University Press |
| Total Pages | : 98 |
| Release | : 2021-08-05 |
| Genre | : Political Science |
| ISBN | : 1108991645 |
Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github.
Machine Learning, Advances in Computing, Renewable Energy and Communication
| Author | : Anuradha Tomar |
| Publisher | : Springer Nature |
| Total Pages | : 651 |
| Release | : 2021-08-19 |
| Genre | : Technology & Engineering |
| ISBN | : 9811623546 |
This book gathers selected papers presented at International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC 2020), held in Krishna Engineering College, Ghaziabad, India, during December 17–18, 2020. This book discusses key concepts, challenges, and potential solutions in connection with established and emerging topics in advanced computing, renewable energy, and network communications.
The Ecological Brain
| Author | : Luis H. Favela |
| Publisher | : Taylor & Francis |
| Total Pages | : 212 |
| Release | : 2023-12-22 |
| Genre | : Psychology |
| ISBN | : 1003830358 |
The Ecological Brain is the first book of its kind, using complexity science to integrate the seemingly disparate fields of ecological psychology and neuroscience. The book develops a unique framework for unifying investigations and explanations of mind that span brain, body, and environment: the NeuroEcological Nexus Theory (NExT). Beginning with an introduction to the history of the fields, the author provides an assessment of why ecological psychology and neuroscience are commonly viewed as irreconcilable methods for investigating and explaining cognition, intelligent behavior, and the systems that realize them. The book then progresses to its central aim: presenting a unified investigative and explanatory framework offering concepts, methods, and theories applicable across neural and ecological scales of investigation. By combining the core principles of ecological psychology, neural population dynamics, and synergetics under a unified complexity science approach, NExT offers a compressive investigative framework to explain and understand neural, bodily, and environmental contributions to perception-action and other forms of intelligent behavior and thought. The book progresses the conversation around the role of brains in ecological psychology, as well as bodies and environments in neuroscience. It is essential reading for all students of ecological psychology, perception, cognitive sciences, and neuroscience, as well as anyone interested in the history and philosophy of the brain/mind sciences and their state-of-the-art methods and theories.
Knowledge Graphs and Big Data Processing
| Author | : Valentina Janev |
| Publisher | : Springer Nature |
| Total Pages | : 212 |
| Release | : 2020-07-15 |
| Genre | : Computers |
| ISBN | : 3030531996 |
This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
Data Science for Genomics
| Author | : Amit Kumar Tyagi |
| Publisher | : Academic Press |
| Total Pages | : 314 |
| Release | : 2022-11-27 |
| Genre | : Science |
| ISBN | : 0323985769 |
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
Predictive Intelligence in Biomedical and Health Informatics
| Author | : Rajshree Srivastava |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 180 |
| Release | : 2020-10-12 |
| Genre | : Computers |
| ISBN | : 3110676125 |
Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.
Management of Emerging Public Health Issues and Risks
| Author | : Benoit Roig |
| Publisher | : Academic Press |
| Total Pages | : 316 |
| Release | : 2018-11-13 |
| Genre | : Medical |
| ISBN | : 0128132914 |
Management of Emerging Public Health Issues and Risks: Multidisciplinary Approaches to the Changing Environment addresses the threats facing the rapidly changing world and provides guidance on how to manage risks to population health. Unlike conventional and recognized risks (major, industrial, and natural), emerging risks are characterized by low or non-existent scientific knowledge, high levels of uncertainty, and different levels of acceptability by the relevant authorities and exposed populations. Emerging risk must be analyzed through multiple and crossed approaches identifying the phenomenon linked to the emergence of risk but also by combining scientific, policy and social data in order to provide more enlightened decision making. Management of Emerging Public Health Issues and Risks: Multidisciplinary Approaches to the Changing Environment provides examples of transdisciplinary approaches used to characterize, analyze, and manage emerging risks. This book will be useful for public health researchers, policy makers, and students as well as those working in emergency management, risk management, security, environmental health, nanomaterials, and food science. Presents emerging risks from the technological, environmental, health, and energy sectors, as well as their social impacts Contextualizes emerging risks as new threats, existing threats in new locations, and known issues, which are newly recognized as risks due to increased scientific knowledge Includes case studies from around the world to reinforce concepts
