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Статті в журналах з теми "Similarity measure and multi-Criteria"
Yang, Qing Bo, and Ruo Juan Xue. "Similarity Measure between Vague Sets Based on Products." Applied Mechanics and Materials 667 (October 2014): 85–88. http://dx.doi.org/10.4028/www.scientific.net/amm.667.85.
Повний текст джерелаNisha, Dr B., and Dr S. Vijayalaksmi. "Hesitant Fuzzy Soft Sets with Similarity Measure." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (January 31, 2024): 1549–54. http://dx.doi.org/10.22214/ijraset.2024.58205.
Повний текст джерелаDuong, Truong Thi Thuy, and Nguyen Xuan Thao. "TOPSIS model based on entropy and similarity measure for market segment selection and evaluation." Asian Journal of Economics and Banking 5, no. 2 (June 22, 2021): 194–203. http://dx.doi.org/10.1108/ajeb-12-2020-0106.
Повний текст джерелаWang, Lunyan, Qing Xia, Huimin Li, and Yongchao Cao. "Multi-criteria decision making method based on improved cosine similarity measure with interval neutrosophic sets." International Journal of Intelligent Computing and Cybernetics 12, no. 3 (August 12, 2019): 414–23. http://dx.doi.org/10.1108/ijicc-05-2019-0047.
Повний текст джерелаTalukdar, Pranjal, and Palash Dutta. "An Advanced Entropy Measure of IFSs via Similarity Measure." International Journal of Fuzzy System Applications 12, no. 1 (March 10, 2023): 1–23. http://dx.doi.org/10.4018/ijfsa.319712.
Повний текст джерелаPeng, Xindong, and Huiyong Yuan. "Pythagorean Fuzzy Multi-Criteria Decision Making Method Based on Multiparametric Similarity Measure." Cognitive Computation 13, no. 2 (January 17, 2021): 466–84. http://dx.doi.org/10.1007/s12559-020-09781-x.
Повний текст джерелаWagh, Rupali S., and Deepa Anand. "Legal document similarity: a multi-criteria decision-making perspective." PeerJ Computer Science 6 (March 23, 2020): e262. http://dx.doi.org/10.7717/peerj-cs.262.
Повний текст джерелаMohamed, Saida, Areeg Abdalla, and Robert John. "New Entropy-Based Similarity Measure between Interval-Valued Intuitionstic Fuzzy Sets." Axioms 8, no. 2 (June 18, 2019): 73. http://dx.doi.org/10.3390/axioms8020073.
Повний текст джерелаGvozdev, O. G., A. V. Materuhin, and A. A. Maiorov. "Adaptive geofields similarity measure based on binary similarity measures generalization." Geodesy and Cartography 1002, no. 12 (January 20, 2024): 38–48. http://dx.doi.org/10.22389/0016-7126-2023-1002-12-38-48.
Повний текст джерелаDong, Yuanxiang, Xiaoting Cheng, Weijie Chen, Hongbo Shi, and Ke Gong. "A cosine similarity measure for multi-criteria group decision making under neutrosophic soft environment." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 7863–80. http://dx.doi.org/10.3233/jifs-201328.
Повний текст джерелаДисертації з теми "Similarity measure and multi-Criteria"
Serrai, Walid. "Évaluation de performances de solutions pour la découverte et la composition des services web." Electronic Thesis or Diss., Paris Est, 2020. http://www.theses.fr/2020PESC0032.
Повний текст джерелаSoftware systems accessible via the web are built using existing and distributed web services that interact by sending messages. The web service exposes its functionalities through an interface described in a computer-readable format. Other systems interact, without human intervention, with the web service according to a prescribed procedure using the messages of a protocol. Web services can be deployed on cloud platforms. This type of deployment causes a large number of services to be managed at the level of the same directories raising different problems: How to manage these services effectively to facilitate their discovery for a possible composition. Indeed, given a directory, how to define an architecture or even a data structure to optimize the discovery of services, their composition, and their management. Service discovery involves finding one or more services that meet the client’s criteria. The service composition consists of finding many services that can be executed according to a scheme and that satisfy the client’s constraints. As the number of services is constantly increasing, the demand for the design of architectures to provide not only quality service but also rapid responsetime for discovery, selection, and composition, is getting more intense. These architectures must also be easily manageable and maintainable over time. The exploration of communities and index structures correlated with the use of multi-criteria measures could offer an effective solution provided that the data structures, the types of measures, are chosen correctly, and the appropriate techniques. In this thesis, solutions are proposed for the discovery, the selection of services and their composition in such a way as to optimizethe search in terms of response time and the relevance of the results. The performance evaluation of the proposed solutions is carried out using simulation platforms
Chaibou, Salaou Mahaman Sani. "Segmentation d'image par intégration itérative de connaissances." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2019. http://www.theses.fr/2019IMTA0140.
Повний текст джерелаImage processing has been a very active area of research for years. The interpretation of images is one of its most important branches because of its socio-economic and scientific applications. However, the interpretation, like most image processing processes, requires a segmentation phase to delimit the regions to be analyzed. In fact, interpretation is a process that gives meaning to the regions detected by the segmentation phase. Thus, the interpretation phase can only analyze the regions detected during the segmentation. Although the ultimate objective of automatic interpretation is to produce the same result as a human, the logic of classical techniques in this field does not marry that of human interpretation. Most conventional approaches to this task separate the segmentation phase from the interpretation phase. The images are first segmented and then the detected regions are interpreted. In addition, conventional techniques of segmentation scan images sequentially, in the order of pixels appearance. This way does not necessarily reflect the way of the expert during the image exploration. Indeed, a human usually starts by scanning the image for possible region of interest. When he finds a potential area, he analyzes it under three view points trying to recognize what object it is. First, he analyzes the area based on its physical characteristics. Then he considers the region's surrounding areas and finally he zooms in on the whole image in order to have a wider view while considering the information local to the region and those of its neighbors. In addition to information directly gathered from the physical characteristics of the image, the expert uses several sources of information that he merges to interpret the image. These sources include knowledge acquired through professional experience, existing constraints between objects from the images, and so on.The idea of the proposed approach, in this manuscript, is that simulating the visual activity of the expert would allow a better compatibility between the results of the interpretation and those ofthe expert. We retain from the analysis of the expert's behavior three important aspects of the image interpretation process that we will model in this work: 1. Unlike what most of the segmentation techniques suggest, the segmentation process is not necessarily sequential, but rather a series of decisions that each one may question the results of its predecessors. The main objective is to produce the best possible regions classification. 2. The process of characterizing an area of interest is not a one way process i.e. the expert can go from a local view restricted to the region of interest to a wider view of the area, including its neighbors and vice versa. 3. Several information sources are gathered and merged for a better certainty, during the decision of region characterisation. The proposed model of these three levels places particular emphasis on the knowledge used and the reasoning behind image segmentation
Šulc, Zdeněk. "Similarity Measures for Nominal Data in Hierarchical Clustering." Doctoral thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-261939.
Повний текст джерелаWach, Dominika, Ute Stephan, and Marjan Gorgievski. "More than money: Developing an integrative multi-factorial measure of entrepreneurial success." Sage, 2016. https://tud.qucosa.de/id/qucosa%3A35642.
Повний текст джерелаEscande, Paul. "Compression et inférence des opérateurs intégraux : applications à la restauration d’images dégradées par des flous variables." Thesis, Toulouse, ISAE, 2016. http://www.theses.fr/2016ESAE0020/document.
Повний текст джерелаThe restoration of images degraded by spatially varying blurs is a problem of increasing importance. It is encountered in many applications such as astronomy, computer vision and fluorescence microscopy where images can be of size one billion pixels. Variable blurs can be modelled by linear integral operators H that map a sharp image u to its blurred version Hu. After discretization of the image on a grid of N pixels, H can be viewed as a matrix of size N x N. For targeted applications, matrices is stored with using exabytes on the memory. This simple observation illustrates the difficulties associated to this problem: i) the storage of a huge amount of data, ii) the prohibitive computation costs of matrix-vector products. This problems suffers from the challenging curse of dimensionality. In addition, in many applications, the operator is usually unknown or only partially known. There are therefore two different problems, the approximation and the estimation of blurring operators. They are intricate and have to be addressed with a global overview. Most of the work of this thesis is dedicated to the development of new models and computational methods to address those issues
Yu, Jodie Wei. "Investigation of New Forward Osmosis Draw Agents and Prioritization of Recent Developments of Draw Agents Using Multi-criteria Decision Analysis." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2185.
Повний текст джерелаHeyns, Werner. "Urban congestion charging : road pricing as a traffic reduction measure / W. Heyns." Thesis, North-West University, 2005. http://hdl.handle.net/10394/523.
Повний текст джерелаThesis (M.Art. et Scien. (Town and Regional Planning))--North-West University, Potchefstroom Campus, 2005.
Saksrisathaporn, Krittiya. "A multi-criteria decision support system using knowledge management and project life cycle approach : application to humanitarian supply chain management." Thesis, Lyon 2, 2015. http://www.theses.fr/2015LYO22016/document.
Повний текст джерелаThis thesis aims to contribute to the understanding of HOLC in context of the HSCM and to propose a decision model which applies to the phases of HOLC the decision making regarding a real situation . This include the implementation of the proposed model to design and develop a decision support tool in order to improve the performance of humanitarian logistics in both national and international relief operations.This research is divided into three phases; the first phase is to clarify and define HL among HSCM, commercial supply chain management (CSCM) and SCM and their relationship. Project Life Cycle Management (PLCM) approaches are also presented. The difference between project life cycle management (PLM) and PLCM is also required to distinguish a clear understanding which can be addressed in the phase of humanitarian operation life cycle. Additionally, the literature of Multiple-Criteria Decision Making (MCDM) models and existing decision aid system for HL are analyzed to establish the research gap. The MCDM approaches which implement the decision support system (DSS) and lastly how DSS has been used in the HSCM context.The second phase is to propose a decision model based on MCDM approaches to support the decision of the decision maker before he/she takes action. This model provides the ranking alternatives to warehouse, supplier and transportation over the phases of HOLC. The proposed decision model is conducted in 3 scenarios; I. The decision in 4-phase HOLC, international relief operation of French Red Cross (FRC). II. The decision on 3-phase HOLC, national operation by the Thai Red Cross (TRC). III. The decision on response phase HOLC, international operation by the FRC in four countries. In this phase, the scenario I and II are performed step by step though numerical calculation and mathematical formulas. The scenario III will be presented in the third phase.In the third phase, an application of web-based multi-criteria decision support system (WB-MCDSS) which implement the proposed model is developed. The web-based multi-criteria decision support system is developed based on the integration of analytical hierarchy process (AHP) and TOPSIS approaches. In order to achieve an appropriate decision in a real time response, the WB-MCDSS is developed based on server-client protocol and is simple to operate. Last but not least, a validation application of the model is performed using the sensitivity analysis approach
Igoulalene, Idris. "Développement d'une approche floue multicritère d'aide à la coordination des décideurs pour la résolution des problèmes de sélection dans les chaines logistiques." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM4357/document.
Повний текст джерелаThis thesis presents a development of a multi-criteria group decision making approach to solve the selection problems in supply chains. Indeed, we start in the context where a group of k decision makers/experts, is in charge of the evaluation and the ranking of a set of potential m alternatives. The alternatives are evaluated in fuzzy environment while taking into consideration both subjective (qualitative) and objective (quantitative) n conflicting criteria. Each decision maker is brought to express his preferences for each alternative relative to each criterion through a fuzzy matrix called preference matrix. We have developed three new approaches for manufacturing strategy, information system and robot selection problem:1. Fuzzy consensus-based possibility measure and goal programming approach.2. Fuzzy consensus-based neat OWA and goal programming approach.3. Fuzzy consensus-based goal programming and TOPSIS approach.Finally, a comparison of these three approaches is conducted and thus was able to give recommendations to improve the approaches and provide decision aid to the most satisfying decision makers
Dang, Vinh Q. "Evolutionary approaches for feature selection in biological data." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2014. https://ro.ecu.edu.au/theses/1276.
Повний текст джерелаКниги з теми "Similarity measure and multi-Criteria"
Howell, Simon J. Clinical trial designs in anaesthesia. Edited by Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0030.
Повний текст джерелаSobczyk, Eugeniusz Jacek. Uciążliwość eksploatacji złóż węgla kamiennego wynikająca z warunków geologicznych i górniczych. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 2022. http://dx.doi.org/10.33223/onermin/0222.
Повний текст джерелаЧастини книг з теми "Similarity measure and multi-Criteria"
Abbas, Rizwan, Qaisar Abbas, Gehad Abdullah Amran, Abdulaziz Ali, Majed Hassan Almusali, Ali A. AL-Bakhrani, and Mohammed A. A. Al-qaness. "A New Similarity Measure for Multi Criteria Recommender System." In Advances in Intelligent Systems and Computing, 29–52. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28106-8_3.
Повний текст джерелаChatterjee, R., P. Majumdar, and S. K. Samanta. "Similarity Measures in Neutrosophic Sets-I." In Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets, 249–94. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00045-5_11.
Повний текст джерелаChatterjee, R., P. Majumdar, and S. K. Samanta. "Similarity Measures in Neutrosophic Sets-II." In Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets, 295–325. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00045-5_12.
Повний текст джерелаNguyen, Duc Thang, Lihui Chen, and Chee Keong Chan. "Multi-viewpoint Based Similarity Measure and Optimality Criteria for Document Clustering." In Information Retrieval Technology, 49–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_5.
Повний текст джерелаKaracapilidis, Nikos, and Lefteris Hatzieleftheriou. "Exploiting Similarity Measures in Multi-criteria Based Recommendations." In E-Commerce and Web Technologies, 424–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45229-4_41.
Повний текст джерелаYang, Jie, Guoyin Wang, and Xukun Li. "Multi-granularity Similarity Measure of Cloud Concept." In Rough Sets, 318–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47160-0_29.
Повний текст джерелаBai, Xue, Siwei Luo, Qi Zou, and Yibiao Zhao. "Contour Grouping by Clustering with Multi-feature Similarity Measure." In Lecture Notes in Computer Science, 415–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14980-1_40.
Повний текст джерелаJoshi, Deepa, and Sanjay Kumar. "An Approach to Multi-criteria Decision Making Problems Using Dice Similarity Measure for Picture Fuzzy Sets." In Communications in Computer and Information Science, 135–40. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0023-3_13.
Повний текст джерелаJayaprada, S., Amarapini Aswani, and G. Gayathri. "Hierarchical Divisive Clustering with Multi View-Point Based Similarity Measure." In Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013, 483–91. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02931-3_55.
Повний текст джерелаSzmidt, Eulalia, and Janusz Kacprzyk. "An Application of Intuitionistic Fuzzy Set Similarity Measures to a Multi-criteria Decision Making Problem." In Artificial Intelligence and Soft Computing – ICAISC 2006, 314–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11785231_34.
Повний текст джерелаТези доповідей конференцій з теми "Similarity measure and multi-Criteria"
Sanz, Ismael, María Pérez, and Rafael Berlanga. "Measure Selection in Multi-similarity XML Applications." In 2008 19th International Conference on Database and Expert Systems Applications (DEXA). IEEE, 2008. http://dx.doi.org/10.1109/dexa.2008.46.
Повний текст джерелаMun, Duhwan, Junmyon Cho, and Karthik Ramani. "A Method for Measuring Part Similarity Using Ontology and a Multi-Criteria Decision Making Method." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87711.
Повний текст джерелаKasiri, Keyvan, Paul Fieguth, and David A. Clausi. "Self-similarity measure for multi-modal image registration." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7533211.
Повний текст джерелаDaewon Lee, M. Hofmann, F. Steinke, Y. Altun, N. D. Cahill, and B. Scholkopf. "Learning similarity measure for multi-modal 3D image registration." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5206840.
Повний текст джерелаAbdoos, Monireh, Nasser Mozayani, and Ahmad Akbari. "A new similarity difference measure in multi agent systems." In 2009 14th International CSI Computer Conference (CSICC 2009) (Postponed from July 2009). IEEE, 2009. http://dx.doi.org/10.1109/csicc.2009.5349625.
Повний текст джерелаYang Yan, Lihui Chen, and Duc Thang Nguyen. "Semi-supervised clustering with multi-viewpoint based similarity measure." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252650.
Повний текст джерелаPickering, Mark R. "A new similarity measure for multi-modal image registration." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116092.
Повний текст джерелаDaewon Lee, Matthias Hofmann, Florian Steinke, Yasemin Altun, Nathan D. Cahill, and Bernhard Scholkopf. "Learning similarity measure for multi-modal 3D image registration." In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2009. http://dx.doi.org/10.1109/cvpr.2009.5206840.
Повний текст джерелаHao Hu, Bin Liu, Weiwei Guo, Zenghui Zhang, and Wenxian Yu. "Preliminary exploration of introducing spatial correlation information into the probabilistic patch-based similarity measure." In 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). IEEE, 2017. http://dx.doi.org/10.1109/multi-temp.2017.8035223.
Повний текст джерелаSarkar, Kamal, and Sohini Roy Chowdhury. "Improving Salience-Based Multi-Document Summarization Performance using a Hybrid Sentence Similarity Measure." In 4th International Conference on AI, Machine Learning and Applications. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140202.
Повний текст джерелаЗвіти організацій з теми "Similarity measure and multi-Criteria"
Duvvuri, Sarvani, and Srinivas S. Pulugurtha. Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics. Mineta Transportation Institute, July 2021. http://dx.doi.org/10.31979/mti.2021.1946.
Повний текст джерелаDrayton, Paul, Jeffrey Panek, Tom McGrath, and James McCarthy. PR-312-12206-R01 FTIR Formaldehyde Measurement at Turbine NESHAP and Ambient Levels. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 2016. http://dx.doi.org/10.55274/r0011014.
Повний текст джерелаMcPhedran, R., K. Patel, B. Toombs, P. Menon, M. Patel, J. Disson, K. Porter, A. John, and A. Rayner. Food allergen communication in businesses feasibility trial. Food Standards Agency, March 2021. http://dx.doi.org/10.46756/sci.fsa.tpf160.
Повний текст джерелаEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Повний текст джерела