Machine Learning for Planetary Science

Machine Learning for Planetary Science
Author: Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
Publsiher: Elsevier
Total Pages: 400
Release: 2021-03-15
ISBN 10: 9780128187210
ISBN 13: 0128187212
Language: EN, FR, DE, ES & NL

Machine Learning for Planetary Science Book Review:

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather
Author: Enrico Camporeale,Simon Wing,Jay Johnson
Publsiher: Elsevier
Total Pages: 454
Release: 2018-05-31
ISBN 10: 0128117893
ISBN 13: 9780128117897
Language: EN, FR, DE, ES & NL

Machine Learning Techniques for Space Weather Book Review:

Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences
Author: Anonim
Publsiher: Academic Press
Total Pages: 316
Release: 2020-09-25
ISBN 10: 0128216840
ISBN 13: 9780128216842
Language: EN, FR, DE, ES & NL

Machine Learning and Artificial Intelligence in Geosciences Book Review:

Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. Provides high-level reviews of the latest innovations in geophysics Written by recognized experts in the field Presents an essential publication for researchers in all fields of geophysics

Earth Observation Open Science and Innovation

Earth Observation Open Science and Innovation
Author: Pierre-Philippe Mathieu,Christoph Aubrecht
Publsiher: Springer
Total Pages: 330
Release: 2018-01-23
ISBN 10: 3319656333
ISBN 13: 9783319656335
Language: EN, FR, DE, ES & NL

Earth Observation Open Science and Innovation Book Review:

This book is published open access under a CC BY 4.0 license. Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites.

Machine Learning Methods in the Environmental Sciences

Machine Learning Methods in the Environmental Sciences
Author: William W. Hsieh
Publsiher: Cambridge University Press
Total Pages: 349
Release: 2009-07-30
ISBN 10: 0521791928
ISBN 13: 9780521791922
Language: EN, FR, DE, ES & NL

Machine Learning Methods in the Environmental Sciences Book Review:

A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

Machine Learning

Machine Learning
Author: Tom M. Mitchell,Jaime G. Carbonell,Ryszard S. Michalski
Publsiher: Springer Science & Business Media
Total Pages: 429
Release: 2012-12-06
ISBN 10: 1461322790
ISBN 13: 9781461322795
Language: EN, FR, DE, ES & NL

Machine Learning Book Review:

One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation
Author: Petr Skoda,Fathalrahman Adam
Publsiher: Elsevier
Total Pages: 472
Release: 2020-04-10
ISBN 10: 0128191554
ISBN 13: 9780128191552
Language: EN, FR, DE, ES & NL

Knowledge Discovery in Big Data from Astronomy and Earth Observation Book Review:

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin Brazil

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin  Brazil
Author: Elezhan Zhakiya
Publsiher: Unknown
Total Pages: 28
Release: 2016
ISBN 10:
ISBN 13: OCLC:1031985504
Language: EN, FR, DE, ES & NL

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin Brazil Book Review:

Machine Learning techniques are being widely used in Social Sciences to find connections amongst various variables. Machine Learning connects features across different fields that do not seem to have known mathematical relationships with each other. In natural resource prospecting, machine learning can be applied to connect geochemical, geophysical, and geological variables. However, the biggest challenge in machine learning remains obtaining the data to train the ML algorithms. Here, we have applied machine learning on data extracted from maps via image processing. While the overall accuracy of prediction remains as low as 33% at this stage, we see places where the algorithm can be improved and the accuracy increased.

The Sun Interplanetary Medium Earth s Magnetosphere and Planetary Sciences

The Sun  Interplanetary Medium  Earth s Magnetosphere and Planetary Sciences
Author: Anonim
Publsiher: Unknown
Total Pages: 233
Release: 2008
ISBN 10:
ISBN 13: STANFORD:36105131918646
Language: EN, FR, DE, ES & NL

The Sun Interplanetary Medium Earth s Magnetosphere and Planetary Sciences Book Review:

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy
Author: Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publsiher: CRC Press
Total Pages: 744
Release: 2012-03-29
ISBN 10: 143984173X
ISBN 13: 9781439841730
Language: EN, FR, DE, ES & NL

Advances in Machine Learning and Data Mining for Astronomy Book Review:

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
Author: Petra Perner
Publsiher: Springer Science & Business Media
Total Pages: 824
Release: 2009-07-21
ISBN 10: 364203070X
ISBN 13: 9783642030703
Language: EN, FR, DE, ES & NL

Machine Learning and Data Mining in Pattern Recognition Book Review:

There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.

Machine Learning and Biometrics

Machine Learning and Biometrics
Author: Jucheng Yang,Dong Sun Park,Sook Yoon,Yarui Chen,Chuanlei Zhang
Publsiher: BoD – Books on Demand
Total Pages: 146
Release: 2018-08-29
ISBN 10: 1789235901
ISBN 13: 9781789235906
Language: EN, FR, DE, ES & NL

Machine Learning and Biometrics Book Review:

We are entering the era of big data, and machine learning can be used to analyze this deluge of data automatically. Machine learning has been used to solve many interesting and often difficult real-world problems, and the biometrics is one of the leading applications of machine learning. This book introduces some new techniques on biometrics and machine learning, and new proposals of using machine learning techniques for biometrics as well. This book consists of two parts: "Biometrics" and "Machine Learning for Biometrics." Parts I and II contain four and three chapters, respectively. The book is reviewed by editors: Prof. Jucheng Yang, Prof. Dong Sun Park, Prof. Sook Yoon, Dr. Yarui Chen, and Dr. Chuanlei Zhang.

Lunar and Planetary Science

Lunar and Planetary Science
Author: Anonim
Publsiher: Unknown
Total Pages: 329
Release: 1995
ISBN 10:
ISBN 13: UIUC:30112026542677
Language: EN, FR, DE, ES & NL

Lunar and Planetary Science Book Review:

Machine Learning with TensorFlow Second Edition

Machine Learning with TensorFlow  Second Edition
Author: Mattmann A. Chris
Publsiher: Manning Publications
Total Pages: 456
Release: 2021-02-02
ISBN 10: 1617297712
ISBN 13: 9781617297717
Language: EN, FR, DE, ES & NL

Machine Learning with TensorFlow Second Edition Book Review:

Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape

Earth and Space Science Information Systems

Earth and Space Science Information Systems
Author: Arthur Zygielbaum
Publsiher: A I P Press
Total Pages: 924
Release: 1998
ISBN 10:
ISBN 13: PSU:000023926400
Language: EN, FR, DE, ES & NL

Earth and Space Science Information Systems Book Review:

The ISY Conference was organized to promote and enhance international scientific communication and co-operation for the collection, processing, archiving, distribution and analysis of earth and space science data. These are the proceedings of this conference.

Statistics of Earth Science Data

Statistics of Earth Science Data
Author: Graham J. Borradaile
Publsiher: Springer Science & Business Media
Total Pages: 351
Release: 2013-11-11
ISBN 10: 3662052237
ISBN 13: 9783662052235
Language: EN, FR, DE, ES & NL

Statistics of Earth Science Data Book Review:

From the reviews: "All in all, Graham Borradaile has written and interesting and idiosyncratic book on statistics for geoscientists that will be welcome among students, researchers, and practitioners dealing with orientation data. That should include engineering geologists who work with things like rock fracture orientation measurements or clast alignment in paleoseismic trenches. It won’t replace the collection of statistics and geostatistics texts in my library, but it will have a place among them and will likely be one of several references to which I turn when working with orientation data.... The text is easy to follow and illustrations are generally clear and easy to read..."(William C. Haneberg, Haneberg Geoscience)

Explainable AI Interpreting Explaining and Visualizing Deep Learning

Explainable AI  Interpreting  Explaining and Visualizing Deep Learning
Author: Wojciech Samek,Grégoire Montavon,Andrea Vedaldi,Lars Kai Hansen,Klaus-Robert Müller
Publsiher: Springer Nature
Total Pages: 439
Release: 2019-10-23
ISBN 10: 3030289540
ISBN 13: 9783030289546
Language: EN, FR, DE, ES & NL

Explainable AI Interpreting Explaining and Visualizing Deep Learning Book Review:

The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

Planetary Remote Sensing and Mapping

Planetary Remote Sensing and Mapping
Author: Bo Wu,Kaichang Di,Jürgen Oberst,Irina Karachevtseva
Publsiher: CRC Press
Total Pages: 332
Release: 2018-10-29
ISBN 10: 0429000502
ISBN 13: 9780429000508
Language: EN, FR, DE, ES & NL

Planetary Remote Sensing and Mapping Book Review:

The early 21st century marks a new era in space exploration. The National Aeronautics and Space Administration (NASA) of the United States, The European Space Agency (ESA), as well as space agencies of Japan, China, India, and other countries have sent their probes to the Moon, Mars, and other planets in the solar system. Planetary Remote Sensing and Mapping introduces original research and new developments in the areas of planetary remote sensing, photogrammetry, mapping, GIS, and planetary science resulting from the recent space exploration missions. Topics covered include: Reference systems of planetary bodies Planetary exploration missions and sensors Geometric information extraction from planetary remote sensing data Feature information extraction from planetary remote sensing data Planetary remote sensing data fusion Planetary data management and presentation Planetary Remote Sensing and Mapping will serve scientists and professionals working in the planetary remote sensing and mapping areas, as well as planetary probe designers, engineers, and planetary geologists and geophysicists. It also provides useful reading material for university teachers and students in the broader areas of remote sensing, photogrammetry, cartography, GIS, and geodesy.

Overcoming Data Scarcity in Earth Science

Overcoming Data Scarcity in Earth Science
Author: Angela Gorgoglione,Alberto Castro Casales,Christian Chreties Ceriani,Lorena Etcheverry Venturini
Publsiher: MDPI
Total Pages: 94
Release: 2020-05-22
ISBN 10: 3039282107
ISBN 13: 9783039282104
Language: EN, FR, DE, ES & NL

Overcoming Data Scarcity in Earth Science Book Review:

heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification
Author: Anil Kumar,Priyadarshi Upadhyay,A. Senthil Kumar
Publsiher: CRC Press
Total Pages: 194
Release: 2020-08-30
ISBN 10: 1000091546
ISBN 13: 9781000091540
Language: EN, FR, DE, ES & NL

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification Book Review:

This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.