#Machine Learning Methods For Ecological Applications PDF

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Machine Learning Methods for Ecological Applications
Language: en
Pages: 261
Authors: Alan H. Fielding
Categories: Science
Type: BOOK - Published: 2012-12-06 - Publisher: Springer Science & Business Media

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.
Artificial Intelligence Methods in the Environmental Sciences
Language: en
Pages: 424
Authors: Sue Ellen Haupt, Antonello Pasini, Caren Marzban
Categories: Science
Type: BOOK - Published: 2008-11-28 - Publisher: Springer Science & Business Media

How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.
Machine Learning for Ecology and Sustainable Natural Resource Management
Language: en
Pages: 441
Authors: Grant Humphries, Dawn R. Magness, Falk Huettmann
Categories: Science
Type: BOOK - Published: 2018-11-05 - Publisher: Springer

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.
Encyclopedia of Ecology
Language: en
Pages: 3120
Authors: Grant Humphries, Dawn R. Magness, Falk Huettmann
Categories: Science
Type: BOOK - Published: 2014-11-03 - Publisher: Newnes

The groundbreaking Encyclopedia of Ecology provides an authoritative and comprehensive coverage of the complete field of ecology, from general to applied. It includes over 500 detailed entries, structured to provide the user with complete coverage of the core knowledge, accessed as intuitively as possible, and heavily cross-referenced. Written by an international team of leading experts, this revolutionary encyclopedia will serve as a one-stop-shop to concise, stand-alone articles to be used as a point of entry for undergraduate students, or as a tool for active researchers looking for the latest information in the field. Entries cover a range of topics, including: Behavioral Ecology Ecological Processes Ecological Modeling Ecological Engineering Ecological Indicators Ecological Informatics Ecosystems Ecotoxicology Evolutionary Ecology General Ecology Global Ecology Human Ecology System Ecology The first reference work to cover all aspects of ecology, from basic to applied Over 500 concise, stand-alone articles are written by prominent leaders in the field Article text is supported by full-color photos, drawings, tables, and other visual material Fully indexed and cross referenced with detailed references for further study Writing level is suited to both the expert and non-expert Available electronically on ScienceDirect shortly upon publication
Machine Learning Applications
Language: en
Pages: 153
Authors: Rik Das, Siddhartha Bhattacharyya, Sudarshan Nandy
Categories: Computers
Type: BOOK - Published: 2020-04-20 - Publisher: Walter de Gruyter GmbH & Co KG

The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.
Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering
Language: en
Pages: 629
Authors: Tarek Sobh, Khaled Elleithy
Categories: Technology & Engineering
Type: BOOK - Published: 2014-11-07 - Publisher: Springer

Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering This book includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Informatics, and Systems Sciences, and Engineering. It includes selected papers from the conference proceedings of the Eighth and some selected papers of the Ninth International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 2012 & CISSE 2013). Coverage includes topics in: Industrial Electronics, Technology & Automation, Telecommunications and Networking, Systems, Computing Sciences and Software Engineering, Engineering Education, Instructional Technology, Assessment, and E-learning. · Provides the latest in a series of books growing out of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering; · Includes chapters in the most advanced areas of Computing, Informatics, Systems Sciences, and Engineering; · Accessible to a wide range of readership, including professors, researchers, practitioners and students.
Machine Learning: ECML 2001
Language: en
Pages: 620
Authors: Luc de Raedt, Peter Flach
Categories: Computers
Type: BOOK - Published: 2003-06-30 - Publisher: Springer

This book constitutes the refereed proceedings of the 12th European Conference on Machine Learning, ECML 2001, held in Freiburg, Germany, in September 2001. The 50 revised full papers presented together with four invited contributions were carefully reviewed and selected from a total of 140 submissions. Among the topics covered are classifier systems, naive-Bayes classification, rule learning, decision tree-based classification, Web mining, equation discovery, inductive logic programming, text categorization, agent learning, backpropagation, reinforcement learning, sequence prediction, sequential decisions, classification learning, sampling, and semi-supervised learning.
Computational Intelligence in Intelligent Data Analysis
Language: en
Pages: 306
Authors: Christian Moewes, Andreas Nürnberger
Categories: Technology & Engineering
Type: BOOK - Published: 2012-08-23 - Publisher: Springer

Complex systems and their phenomena are ubiquitous as they can be found in biology, finance, the humanities, management sciences, medicine, physics and similar fields. For many problems in these fields, there are no conventional ways to mathematically or analytically solve them completely at low cost. On the other hand, nature already solved many optimization problems efficiently. Computational intelligence attempts to mimic nature-inspired problem-solving strategies and methods. These strategies can be used to study, model and analyze complex systems such that it becomes feasible to handle them. Key areas of computational intelligence are artificial neural networks, evolutionary computation and fuzzy systems. As only a few researchers in that field, Rudolf Kruse has contributed in many important ways to the understanding, modeling and application of computational intelligence methods. On occasion of his 60th birthday, a collection of original papers of leading researchers in the field of computational intelligence has been collected in this volume.
Machine Learning in Radiation Oncology
Language: en
Pages: 336
Authors: Issam El Naqa, Ruijiang Li, Martin J. Murphy
Categories: Medical
Type: BOOK - Published: 2015-06-19 - Publisher: Springer

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Ecological Informatics
Language: en
Pages: 398
Authors: Friedrich Recknagel
Categories: Science
Type: BOOK - Published: 2013-06-29 - Publisher: Springer Science & Business Media

Ecological Informatics is defined as the design and application of computational techniques for ecological analysis, synthesis, forecasting and management. The book provides an introduction to the scope, concepts and techniques of this newly emerging discipline. It illustrates numerous applications of Ecological Informatics for stream systems, river systems, freshwater lakes and marine systems as well as image recognition at micro and macro scale. Case studies focus on applications of artificial neural networks, genetic algorithms, fuzzy logic and adaptive agents to current ecological management issues such as toxic algal blooms, eutrophication, habitat degradation, conservation of biodiversity and sustainable fishery.
Ecological Modelling for Sustainable Development (Penerbit USM)
Language: en
Authors: Koh Hock Lye, Teh Su Yean, Shahrul Anuar Mohd Sah, Zary Shariman Yahaya, Anita Talib
Categories: Science
Type: BOOK - Published: 2015-08-21 - Publisher: Penerbit USM

In view of the current global scenario, which highlighted the importance of sustainable development and sustaining natural resources, the theme selected for the 2nd Regional ECOMOD 2007 Conference was indeed appropriate. This conference has generated overwhelming interest and I am sure the participants have focussed diligently on the serious issues concerning important environmental issues and steps needed to be taken towards a sustainable development and management of our natural resources and environment. As governments in the Asian region introduce new initiatives and development policies to rejuvenate and protect their environment and natural resources, it is imperative that universities and research institutions play a fundamental role in ensuring that the objectives of these policies are realized. Such institutions can complement government proposals by embarking on research that is relevant and valuable to the needs of respective nations and pursuing extensive research so that the outcome and technology generated can be transferred effectively to the end users. This concerted effort by all the researchers from different fields to improve and manage our natural resources should be lauded. I strongly believe that this conference is an extraordinary testimony to our capacity building at regional and local levels. I believe USM has something interesting to share with all of you in this area. Finally, on behalf of the Organizing Committee, I hope readers will find this book of proceedings useful, informative and stimulating.
Machine and Deep Learning in Oncology, Medical Physics and Radiology
Language: en
Authors: Issam El Naqa, Martin J. Murphy (Ph. D.)
Categories: Electronic books
Type: BOOK - Published: 2022 - Publisher: Springer Nature

This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities. .