Application Of Dsmt Icm With Adaptive Decision Rule To Supervised Classification In Multisource Remote Sensing
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A. Elhassouny, S. Idbraim, A. Bekkari, D. Mammass , D. Ducrot
Author : A. Elhassouny, S. Idbraim, A. Bekkari, D. Mammass , D. Ducrot Publisher : Infinite Study Page : 11 pages File Size : 50,5 Mb Release : 2024-06-28 Category : Electronic ISBN : 8210379456XXX
Application of DSmT-ICM with Adaptive decision rule to supervised classification in multisource remote sensing by A. Elhassouny, S. Idbraim, A. Bekkari, D. Mammass , D. Ducrot Pdf
In this paper, we introduce a new procedure called DSmT-ICM with adaptive decision rule, which is an alternative and extension of Multisource Classification Using ICM (Iterated conditional mode) and DempsterShafer theory (DST). This work confirmed the ability of the Dezert-Smarandache Theory (DSmT) used for the modeling of the classes sets of themes to significantly improve the quality of ICM classification algorithm with constraints by the fusion of the multidates images.
Improvement of multimodal images classification based on DSMT using visual saliency model fusion with SVM by Hanan Anzid,Gaetan Le Goic,Aissam Bekkari,Alamin Mansouri,Driss Mammass Pdf
Multimodal images carry available information that can be complementary, redundant information, and overcomes the various problems attached to the unimodal classification task, by modeling and combining these information together. Although, this classification gives acceptable classification results, it still does not reach the level of the visual perception model that has a great ability to classify easily observed scene thanks to the powerful mechanism of the human brain.
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5 by Florentin Smarandache,Jean Dezert ,Albena Tchamova Pdf
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 (available at fs.unm.edu/DSmT-book4.pdf or www.onera.fr/sites/default/files/297/2015-DSmT-Book4.pdf) in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well.
Advances and Applications of DSmT for Information Fusion, Vol. IV by Florentin Smarandache, Jean Dezert Pdf
The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.gallup.unm.edu/DSmT-book3.pdf) ininternational conferences, seminars, workshops and journals.
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 4 by Florentin Smarandache,Jean Dezert Pdf
The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions have been published or presented after disseminating the third volume (2009, http://fs.gallup.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals.
Machine learning in Neutrosophic Environment: A Survey by Azeddine Elhassouny ,Soufiane Idbrahim,Florentin Smarandache Pdf
Veracity in big data analytics is recognized as a complex issue in data preparation process, involving imperfection, imprecision and inconsistency. Single-valued Neutrosophic numbers (SVNs), have prodded a strong capacity to model such complex information. Many Data mining and big data techniques have been proposed to deal with these kind of dirty data in preprocessing stage. However, only few studies treat the imprecise and inconsistent information inherent in the modeling stage. However, this paper summarizes all works done about mapping machine learning algorithms from crisp number space to Neutrosophic environment. We discuss also contributions and hybridization of machine learning algorithms with Single-valued Neutrosophic numbers (SVNs) in modeling imperfect information, and then their impacts on resolving reel world problems. In addition, we identify new trends for future research, then we introduce, for the first time, a taxonomy of Neutrosophic learning algorithms, clarifying what algorithms are already processed or not, which makes it easier for domain researchers.
A Mathematical Theory of Evidence by Glenn Shafer Pdf
Both in science and in practical affairs we reason by combining facts only inconclusively supported by evidence. Building on an abstract understanding of this process of combination, this book constructs a new theory of epistemic probability. The theory draws on the work of A. P. Dempster but diverges from Depster's viewpoint by identifying his "lower probabilities" as epistemic probabilities and taking his rule for combining "upper and lower probabilities" as fundamental. The book opens with a critique of the well-known Bayesian theory of epistemic probability. It then proceeds to develop an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions. This rule, together with the idea of "weights of evidence," leads to both an extensive new theory and a better understanding of the Bayesian theory. The book concludes with a brief treatment of statistical inference and a discussion of the limitations of epistemic probability. Appendices contain mathematical proofs, which are relatively elementary and seldom depend on mathematics more advanced that the binomial theorem.
Multiple Criteria Decision Making by Y. Ilker Topcu,Özay Özaydın,Özgür Kabak,Şule Önsel Ekici Pdf
Data and its processed state 'information' have become an indispensable resource for virtually all aspects of business, education, etc. Consequently, decisions regarding the handling of this data, transforming it into meaningful information, and ultimately arriving at the best course of action have taken on a new importance. This book highlights a selection of cutting-edge research on decision making presented at the 25th International Conference on Multiple Criteria Decision Making (MCDM 2019), held in Istanbul, Turkey.
Autonomous and Intelligent Systems by Mohamed Kamel,Fakhri Karray,Wail Gueaieb,Alaa Khamis Pdf
This book constitutes the refereed proceedings of the Second International Conference on Autonomous and Intelligent Systems, AIS 2011, held in Burnaby, BC, Canada, in June 2011, colocated with the International Conference on Image Analysis and Recognition, IACIAR 2011. The 40 revised full papers presented were carefully reviewed and selected from 62 submissions. The papers are organized in topical sections on autonomous and intelligent systems, intelligent and advanced control systems, intelligent sensing and data analysis, human-machine interaction, and intelligent circuit analysis and signal processing.
On The Validity of Dempster's Rule of Combination of Evidence by Lotfi A. Zadeh Pdf
In a seminal paper published in 1967, Dempster has described a rule for combining independent sources of information. More recently, Dempster's rule has been employed as a basis for a mathematical theory of evidence. It is suggested in this note that there is a serious flaw in Dempster's rule which restricts its use in many applications.
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI by Ivan Kadar Pdf
Proceedings of SPIE present the original research papers presented at SPIE conferences and other high-quality conferences in the broad-ranging fields of optics and photonics. These books provide prompt access to the latest innovations in research and technology in their respective fields. Proceedings of SPIE are among the most cited references in patent literature.
Diagnostic Ophthalmology by Balamurali K. Ambati,Brian Stagg Pdf
Diagnostic Ophthalmology is a reference textbook that provides vital information about the 280+ most common diagnoses encountered in an ophthalmologic practice. Using the pioneered Amirsys bulleted format, the textbook presents the information you need to know in a clear, concise manner that maximizes efficiency. Each chapter distills trenchant information to the essentials, containing a highlighted key facts box that summarizes the major points of the chapter, plus detailed sections on terminology, etiology/pathogenesis, clinical issues, and management. All of the important differential diagnoses and treatment options follow so the practicing clinical ophthalmologist can find the information in the same place every time. Perhaps the most important aspect of this book is that it contains high-quality color images for every diagnosis, such as slit-lamp exam, fundus photography, ophthalmic diagnostic pictures, pathologic findings, correlative radiographic images, and richly colored graphics, all of which are fully annotated to reinforce the most important diagnostic findings. Ophthalmology is a visual specialty and images are vital. Whether you are learning for the first time or using the book as a rapid reference, this book is a valuable resource. FEATURES: Published by Amirsys, a globally recognized medical information publisher. Heavily illustrated along with hundreds of annotated images. Bulleted, easy-scan, and succinct text puts the most pertinent information at your fingertips. Comes with Amirsys eBook Advantage(tm), an online eBook featuring expanded content, additional eBook images, and fully searchable text.
