Share

Improvement of multimodal images classification based on DSMT using visual saliency model fusion with SVM

Download Improvement of multimodal images classification based on DSMT using visual saliency model fusion with SVM PDF Online Free

Author :
Release :
Genre : Mathematics
Kind : eBook
Book Rating : /5 ( reviews)

GET EBOOK


Book Synopsis Improvement of multimodal images classification based on DSMT using visual saliency model fusion with SVM by : Hanan Anzida

Download or read book Improvement of multimodal images classification based on DSMT using visual saliency model fusion with SVM written by Hanan Anzida. This book was released on . Available in PDF, EPUB and Kindle. Book excerpt: 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

Download Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5 PDF Online Free

Author :
Release :
Genre : Mathematics
Kind : eBook
Book Rating : /5 ( reviews)

GET EBOOK


Book Synopsis Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5 by : Florentin Smarandache

Download or read book Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5 written by Florentin Smarandache. This book was released on . Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence in Healthcare

Download Artificial Intelligence in Healthcare PDF Online Free

Author :
Release : 2020-06-21
Genre : Computers
Kind : eBook
Book Rating : 396/5 ( reviews)

GET EBOOK


Book Synopsis Artificial Intelligence in Healthcare by : Adam Bohr

Download or read book Artificial Intelligence in Healthcare written by Adam Bohr. This book was released on 2020-06-21. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

Machine Vision Beyond Visible Spectrum

Download Machine Vision Beyond Visible Spectrum PDF Online Free

Author :
Release : 2011-05-30
Genre : Technology & Engineering
Kind : eBook
Book Rating : 683/5 ( reviews)

GET EBOOK


Book Synopsis Machine Vision Beyond Visible Spectrum by : Riad Hammoud

Download or read book Machine Vision Beyond Visible Spectrum written by Riad Hammoud. This book was released on 2011-05-30. Available in PDF, EPUB and Kindle. Book excerpt: The material of this book encompasses many disciplines, including visible, infrared, far infrared, millimeter wave, microwave, radar, synthetic aperture radar, and electro-optical sensors as well as the very dynamic topics of image processing, computer vision and pattern recognition. This book is composed of six parts: * Advanced background modeling for surveillance * Advances in Tracking in Infrared imagery * Methods for Pose estimation in Ultrasound and LWIR imagery * Recognition in multi-spectral and synthetic aperture radar * Fusion of disparate sensors * Smart Sensors

Conformal Prediction for Reliable Machine Learning

Download Conformal Prediction for Reliable Machine Learning PDF Online Free

Author :
Release : 2014-04-23
Genre : Computers
Kind : eBook
Book Rating : 150/5 ( reviews)

GET EBOOK


Book Synopsis Conformal Prediction for Reliable Machine Learning by : Vineeth Balasubramanian

Download or read book Conformal Prediction for Reliable Machine Learning written by Vineeth Balasubramanian. This book was released on 2014-04-23. Available in PDF, EPUB and Kindle. Book excerpt: The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection

You may also like...