11 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    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 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

    Collected Papers (on Neutrosophics, Plithogenics, Hypersoft Set, Hypergraphs, and other topics), Volume X

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    This tenth volume of Collected Papers includes 86 papers in English and Spanish languages comprising 972 pages, written between 2014-2022 by the author alone or in collaboration with the following 105 co-authors (alphabetically ordered) from 26 countries: Abu Sufian, Ali Hassan, Ali Safaa Sadiq, Anirudha Ghosh, Assia Bakali, Atiqe Ur Rahman, Laura Bogdan, Willem K.M. Brauers, Erick González Caballero, Fausto Cavallaro, Gavrilă Calefariu, T. Chalapathi, Victor Christianto, Mihaela Colhon, Sergiu Boris Cononovici, Mamoni Dhar, Irfan Deli, Rebeca Escobar-Jara, Alexandru Gal, N. Gandotra, Sudipta Gayen, Vassilis C. Gerogiannis, Noel Batista Hernández, Hongnian Yu, Hongbo Wang, Mihaiela Iliescu, F. Nirmala Irudayam, Sripati Jha, Darjan Karabašević, T. Katican, Bakhtawar Ali Khan, Hina Khan, Volodymyr Krasnoholovets, R. Kiran Kumar, Manoranjan Kumar Singh, Ranjan Kumar, M. Lathamaheswari, Yasar Mahmood, Nivetha Martin, Adrian Mărgean, Octavian Melinte, Mingcong Deng, Marcel Migdalovici, Monika Moga, Sana Moin, Mohamed Abdel-Basset, Mohamed Elhoseny, Rehab Mohamed, Mohamed Talea, Kalyan Mondal, Muhammad Aslam, Muhammad Aslam Malik, Muhammad Ihsan, Muhammad Naveed Jafar, Muhammad Rayees Ahmad, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Mujahid Abbas, Mumtaz Ali, Radu I. Munteanu, Ghulam Murtaza, Munazza Naz, Tahsin Oner, ‪Gabrijela Popović‬‬‬‬‬, Surapati Pramanik, R. Priya, S.P. Priyadharshini, Midha Qayyum, Quang-Thinh Bui, Shazia Rana, Akbara Rezaei, Jesús Estupiñán Ricardo, Rıdvan Sahin, Saeeda Mirvakili, Said Broumi, A. A. Salama, Flavius Aurelian Sârbu, Ganeshsree Selvachandran, Javid Shabbir, Shio Gai Quek, Son Hoang Le, Florentin Smarandache, Dragiša Stanujkić, S. Sudha, Taha Yasin Ozturk, Zaigham Tahir, The Houw Iong, Ayse Topal, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Rizha Vitania, Luige Vlădăreanu, Victor Vlădăreanu, Ștefan Vlăduțescu, J. Vimala, Dan Valeriu Voinea, Adem Yolcu, Yongfei Feng, Abd El-Nasser H. Zaied, Edmundas Kazimieras Zavadskas.‬

    Collected Papers (on Physics, Artificial Intelligence, Health Issues, Decision Making, Economics, Statistics), Volume XI

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    This eleventh volume of Collected Papers includes 90 papers comprising 988 pages on Physics, Artificial Intelligence, Health Issues, Decision Making, Economics, Statistics, written between 2001-2022 by the author alone or in collaboration with the following 84 co-authors (alphabetically ordered) from 19 countries: Abhijit Saha, Abu Sufian, Jack Allen, Shahbaz Ali, Ali Safaa Sadiq, Aliya Fahmi, Atiqa Fakhar, Atiqa Firdous, Sukanto Bhattacharya, Robert N. Boyd, Victor Chang, Victor Christianto, V. Christy, Dao The Son, Debjit Dutta, Azeddine Elhassouny, Fazal Ghani, Fazli Amin, Anirudha Ghosha, Nasruddin Hassan, Hoang Viet Long, Jhulaneswar Baidya, Jin Kim, Jun Ye, Darjan Karabašević, Vasilios N. Katsikis, Ieva Meidutė-Kavaliauskienė, F. Kaymarm, Nour Eldeen M. Khalifa, Madad Khan, Qaisar Khan, M. Khoshnevisan, Kifayat Ullah,, Volodymyr Krasnoholovets, Mukesh Kumar, Le Hoang Son, Luong Thi Hong Lan, Tahir Mahmood, Mahmoud Ismail, Mohamed Abdel-Basset, Siti Nurul Fitriah Mohamad, Mohamed Loey, Mai Mohamed, K. Mohana, Kalyan Mondal, Muhammad Gulfam, Muhammad Khalid Mahmood, Muhammad Jamil, Muhammad Yaqub Khan, Muhammad Riaz, Nguyen Dinh Hoa, Cu Nguyen Giap, Nguyen Tho Thong, Peide Liu, Pham Huy Thong, Gabrijela Popović‬‬‬‬‬‬‬‬‬‬, Surapati Pramanik, Dmitri Rabounski, Roslan Hasni, Rumi Roy, Tapan Kumar Roy, Said Broumi, Saleem Abdullah, Muzafer Saračević, Ganeshsree Selvachandran, Shariful Alam, Shyamal Dalapati, Housila P. Singh, R. Singh, Rajesh Singh, Predrag S. Stanimirović, Kasan Susilo, Dragiša Stanujkić, Alexandra Şandru, Ovidiu Ilie Şandru, Zenonas Turskis, Yunita Umniyati, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Binyamin Yusoff, Edmundas Kazimieras Zavadskas, Zhao Loon Wang.‬‬‬

    Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

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    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.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

    Towards generic domain-specific information retrieval

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    Ph.DDOCTOR OF PHILOSOPH

    Data Fusion for Materials Location Estimation in Construction

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    Effective automated tracking and locating of the thousands of materials on construction sites improves material distribution and project performance and thus has a significant positive impact on construction productivity. Many locating technologies and data sources have therefore been developed, and the deployment of a cost-effective, scalable, and easy-to-implement materials location sensing system at actual construction sites has very recently become both technically and economically feasible. However, considerable opportunity still exists to improve the accuracy, precision, and robustness of such systems. The quest for fundamental methods that can take advantage of the relative strengths of each individual technology and data source motivated this research, which has led to the development of new data fusion methods for improving materials location estimation. In this study a data fusion model is used to generate an integrated solution for the automated identification, location estimation, and relocation detection of construction materials. The developed model is a modified functional data fusion model. Particular attention is paid to noisy environments where low-cost RFID tags are attached to all materials, which are sometimes moved repeatedly around the site. A portion of the work focuses partly on relocation detection because it is closely coupled with location estimation and because it can be used to detect the multi-handling of materials, which is a key indicator of inefficiency. This research has successfully addressed the challenges of fusing data from multiple sources of information in a very noisy and dynamic environment. The results indicate potential for the proposed model to improve location estimation and movement detection as well as to automate the calculation of the incidence of multi-handling

    Indexation de l'information médicale. Application à la recherche d'images et de vidéos par le contenu

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    Dans ce travail de thèse, nous nous intéressons à l'utilisation des bases de données médicales multimédia pour l'aide à la décision diagnostique et le suivi thérapeutique. Notre objectif est de définir des méthodes, et un système, pour sélectionner dans les bases de documents multimédia des documents similaires à un document proposé en requête. Ces documents contiennent des informations sous forme texte, numérique, des images et parfois des séquences vidéos. Pour l'aide au diagnostic, l'interrogation du système s'effectue en lui présentant en requête le dossier patient, ou une partie de ce dossier. Notre travail va donc mettre en oeuvre des méthodes relatives au raisonnement à base de cas (CBR : Case Based Reasoning), à la fouille de données, à la recherche d images par le contenu (CBIR : Content Based Image Retrieval) et à la rechercher de vidéo par le contenu (CBVR : Content Based Video Retrieval). Les méthodes sont évaluées sur trois bases de données médicales multimodales. La première base de données étudiée est une base d images rétiniennes, constituée au LaTIM pour l aide au suivi de la rétinopathie diabétique. La seconde base est une base publique de mammographies (Digital Database for Screening Mammography, DDSM University of South Florida). La troisième base de données est une base de video gastro-entérologie constituée aussi au LaTIM. Nous utilisons cette base pour étudier les possibilités d'utilisation des méthodes développées dans le cadre de la recherche d images fixes, pour la recherche de séquences vidéos couleurs. Dans la première partie de notre travail, nous cherchons à caractériser individuellement chaque image du dossier patient. Nous avons poursuivi les travaux effectués dans le laboratoire sur l utilisation des méthodes globales de caractérisation des images dans le domaine compressé (quantification vectorielle, DCT, JPEG-ondelettes, ondelettes adaptées) pour la recherche d images. Les résultats obtenus avec les ondelettes, comparés aux autres méthodes de compression ont montré une grande amélioration en terme de retrouvaille. Cependant, les ondelettes nécessitent la spécification d'un noyau ou d'une fonction de base pour effectuer la décomposition. Pour pallier ce problème, nous avons proposé une méthode originale de caractérisation à partir de la décomposition BEMD des images (Bidimensionnal Empirical Mode Decomposition) : elle permet de décomposer une image en plusieurs modes BIMFs (Bidimensionnel Intrinsic Mode Functions), qui permettent d'accéder à des informations sur le contenu fréquentiel des images. Une des originalités de la méthode provient de l auto-adaptativite de la BEMD, qui ne nécessite pas une fonction de base pour effectuer la décomposition. Une fois les images caractérisées, la recherche s'effectue en calculant, au sens d'une métrique donnée, la distance entre la signature de l image requête et les signatures des images de la base. Ce calcul permet de sélectionner des images en réponse à la requête en dehors de toute signification sémantique. Pour améliorer le résultat de retrouvaille, nous introduisons une technique d optimisation pour le calcul de la distance entre signature, en utilisant les algorithmes génétiques. Nous abordons ensuite le problème de la recherche de vidéos par le contenu. Pour cela, nous introduisons une méthode pour le calcul des signatures vidéo à partir des images clefs extraites par l analyse du mouvement. La distance entre signatures video est calculée en utilisant une technique basée sur l analyse en composantes principales. Enfin, nous intégrons les travaux précédents dans la requète par dossiers patients, qui contiennent plusieurs images ainsi que des informations textuelles, sémantiques et numériques. Pour cela nous utilisons trois méthodes développées dans le cadre d une these récemment soutenue dans notre laboratoire : la première est basée sur les arbres de décision, la deuxième sur les réseaux bayésiens et la troisième sur la théorie de Dezert-Smarandache (DSmT).This PhD thesis addresses the use of multimedia medical databases for diagnostic decision and therapeutic follow-up. Our goal is to develop methods and a system to select in multimedia databases documents similar to a query document. These documents consist of text information, numeric images and sometimes videos. In the proposed diagnosis aid system, the database is queried with the patient file, or a part of it, as input. Our work therefore involves implementing methods related to Case-Based Reasoning (CBR), datamining, Content Based Image Retrieval (CBIR) and Content Based Video Retrieval (CBVR). These methods are evaluated on three multimodal medical databases. The first database consists of retinal images collected by the LaTIM laboratory for aided diabetic retinopathy follow-up. The second database is a public mammography database (Digital Database for Screening Mammography DDSM ) collected by the University of South Florida. The third database consists of gastroenterology videos also collected by the LaTIM laboratory. This database is used to discover whether methods developed for fixed image retrieval can also be used for color video retrieval. The first part of this work focuses on the characterization of each image in the patient file. We continued the work started in our laboratory to characterize images globally in the compressed domain (vector quantization, DCT-JPEG, wavelets, adapted wavelets) for image retrieval. Compared to other compression methods, the wavelet decomposition led to a great improvement in terms of retrieval performance. However, the wavelet decomposition requires the specification of a kernel or basis function. To overcome this problem, we proposed an original image characterization method based on the BEMD (Bidimensionnal Empirical Mode Decomposition). It allows decomposing an image into several BIMFs (Bidimensionnal Intrinsic Mode Functions) that provide access to frequency information of the image content. An originality of the method comes from the self-adaptivity of BEMD: it does not require the specification of a basic function. Once images are characterized, a similarity search is performed by computing the distance between the signature of the query image and the signature of each image in the database, given a metric. This process leads to the selection of similar images, without semantic meaning. An optimization process, based on genetic algorithms, is used to adapt the distance metric and thus improve retrieval performance. Then, the problem of content based video retrieval is addressed. A method to generate video signatures is presented. This method relies on key video frames extracted by movement analysis. The distance between video signatures is computed using a Principal Component Analysis (PCA) based technique. Finally, the proposed methods are integrated into the framework of patient file retrieval (each patient file consisting of several images and textual information). Three methods developed during a PhD thesis recently defended in our laboratory are used for patient file retrieval: the first approach is based on decision trees and their extensions, the second on Bayesian networks and the third on the Dezert-Smarandache theory (DSmT)..RENNES1-BU Sciences Philo (352382102) / SudocCESSON SEVIGNE-Télécom Breta (350512301) / SudocBREST-Télécom Bretagne (290192306) / SudocSudocFranceF
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