Relevant bibliographies by topics / Granular soft computing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Granular soft computing'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Granular soft computing"

1

Panda, Mrutyunjaya, Ajith Abraham, and B. K. Tripathy. "Soft granular computing based classification using hybrid fuzzy-KNN-SVM." Intelligent Decision Technologies 10, no. 2 (2016): 115–28. http://dx.doi.org/10.3233/idt-150243.

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Beldjehem, Mokhtar. "A unified granular hybrid soft computing framework for vision engineering." International Journal of Advanced Intelligence Paradigms 3, no. 3/4 (2011): 189. http://dx.doi.org/10.1504/ijaip.2011.043426.

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Pal, Sankar K., and Saroj K. Meher. "Special issue on granular soft computing for pattern recognition and mining." Applied Soft Computing 13, no. 9 (2013): 3942–43. http://dx.doi.org/10.1016/j.asoc.2013.06.025.

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A, Sathesh. "PERFORMANCE ANALYSIS OF GRANULAR COMPUTING MODEL IN SOFT COMPUTING PARADIGM FOR MONITORING OF FETAL ECHOCARDIOGRAPHY." Journal of Soft Computing Paradigm 2019, no. 1 (2019): 14–23. http://dx.doi.org/10.36548/jscp.2019.1.002.

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The monitoring of fetal heart being essential in the second trimester of the prenatal periods. The abnormalities in the child heart rate has to be identified in the early stages, so as to take essential remedies for the babies in the womb, or would enable the physician to be ready for he complication on the delivery and the further treatment after the baby is received. The traditional methodologies being ineffective in detecting the abnormalities leading to fatalities, paves way for the granular computing based fuzzy set, that requires only a limited set of data for training, and helps in the
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Ali, Muhammad, Muhammad Shabir, and Feng Feng. "Two kinds of orthopair soft sets as novel approaches to granular computing based on parametrization." Journal of Algebraic Hyperstructures and Logical Algebras 2, no. 3 (2021): 41–59. http://dx.doi.org/10.52547/hatef.jahla.2.3.4.

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Zadeh, L. A. "Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems." Soft Computing - A Fusion of Foundations, Methodologies and Applications 2, no. 1 (1998): 23–25. http://dx.doi.org/10.1007/s005000050030.

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Chen, Xiangtang, Bingzhen Sun, Xinrui Zhang, et al. "An approach to multiple attribute decision making based on linguistic value soft rough set and VIKOR method." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 9609–26. http://dx.doi.org/10.3233/jifs-202085.

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Linguistic variable is an effective method of representation the preferences of a decision-maker for inaccuracy available information in decision making under uncertainty. This article investigates a multiple attribute ranking decision making problem with linguistic preference by using linguistic value soft rough set. Firstly, we present the definition of linguistic value fuzzy set by introducing the concept of linguistic variable into the original Zadeh’s fuzzy set. We then define the concept of linguistic value soft set and the pseudo linguistic value soft set over the alternative set and pa
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Alvarado, Oscar, Vero Vanden Abeele, David Geerts, and Katrien Verbert. "Towards Tangible Algorithms: Exploring the Experiences of Tangible Interactions with Movie Recommender Algorithms." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022): 1–30. http://dx.doi.org/10.1145/3555757.

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Artificial Intelligence (AI) supports many of our everyday activities and decisions. However, personalized algorithmic recommendations often produce adverse experiences due to a lack of awareness, control, or transparency. While research has directed solutions on graphical user interfaces (GUIs), there are no explorations of Tangible User Interfaces (TUIs) to improve the experience with such systems, despite the valid existing academic arguments in favor of this exploration. Therefore, centering on transparency and control, we analyzed how 18 users of movie recommender systems perceived four d
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Bernal, Jose, John Willmer Escobar, and Rodrigo Linfati. "A granular tabu search algorithm for a real case study of a vehicle routing problem with a heterogeneous fleet and time windows." Journal of Industrial Engineering and Management 10, no. 4 (2017): 646. http://dx.doi.org/10.3926/jiem.2159.

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Purpose: We consider a real case study of a vehicle routing problem with a heterogeneous fleet and time windows (HFVRPTW) for a franchise company bottling Coca-Cola products in Colombia. This study aims to determine the routes to be performed to fulfill the demand of the customers by using a heterogeneous fleet and considering soft time windows. The objective is to minimize the distance traveled by the performed routes.Design/methodology/approach: We propose a two-phase heuristic algorithm. In the proposed approach, after an initial phase (first phase), a granular tabu search is applied during
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Sakai, Hiroshi, Hiroaki Ishii, and Junzo Watada. "Special Issue on Forum for Interdisciplinary Mathematics 2013." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 6 (2014): 927–28. http://dx.doi.org/10.20965/jaciii.2014.p0927.

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This special issue focuses on recent research in interdisciplinary mathematics and mathematical sciences. For the last four decades, the Forum for Interdisciplinary Mathematics (FIM), a society for researchers in mathematical sciences, has focused on mathematics, combinatorics, statistics, operations research, computer science, fuzzy sets, rough sets, bioinformatics, etc. The 22nd International Conference of FIM on Interdisciplinary Mathematics, Statistics and Computational Techniques (IMSCT 2013-FIM XXII) was held in Kitakyushu, Japan, on November 10-12, 2013. This conference was organized by
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Dissertations / Theses on the topic "Granular soft computing"

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Tang, Yuchun. "Granular Support Vector Machines Based on Granular Computing, Soft Computing and Statistical Learning." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/5.

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With emergence of biomedical informatics, Web intelligence, and E-business, new challenges are coming for knowledge discovery and data mining modeling problems. In this dissertation work, a framework named Granular Support Vector Machines (GSVM) is proposed to systematically and formally combine statistical learning theory, granular computing theory and soft computing theory to address challenging predictive data modeling problems effectively and/or efficiently, with specific focus on binary classification problems. In general, GSVM works in 3 steps. Step 1 is granulation to build a sequence
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Books on the topic "Granular soft computing"

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Bargiela, A. Granular Computing: An Introduction. Springer US, 2003.

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JingTao, Yao, ed. Novel developments in granular computing: Applications for advanced human reasoning and soft computation. Information Science Reference, 2010.

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Yao, JingTao. Novel developments in granular computing: Applications for advanced human reasoning and soft computation. Information Science Reference, 2010.

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Pedrycz, Witold. Knowledge-Based Clustering. John Wiley & Sons, Ltd., 2005.

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Zhong, Ning, Andrzej Skowron, and Setsuo Ohsuga, eds. New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/b72293.

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PhD, Mitra Pabitra, ed. Pattern recognition algorithms for data mining: Scalability, knowledge discovery and soft granular computing. Chapman & Hall/CRC, 2004.

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7

RSFDGrC 2007 (2007 Toronto, Ont.). Rough sets, fuzzy sets, data mining and granular computing: 11th international conference, RSFDGrC 2007, Toronto, Canada, May 14-16, 2007 : proceedings. Springer, 2007.

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Kuznetsov, Sergei O. Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 13th International Conference, RSFDGrC 2011, Moscow, Russia, June 25-27, 2011. Proceedings. Springer-Verlag GmbH Berlin Heidelberg, 2011.

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1956-, Zhong Ning, Skowron Andrzej, and Ōsuga Setsuo 1934-, eds. New directions in rough sets, data mining, and granular-soft computing: 7th International Workshop, RSFDGrC'99, Yamaguchi, Japan, November 1999 : proceedings. Springer, 1999.

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RSFDGrC 2009 (2009 Delhi, India). Rough sets, fuzzy sets, data mining and granular computing: 12th international conference, RSFDGrC 2009, Delhi, India, December 16-18, 2009 : proceedings. Springer, 2009.

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Book chapters on the topic "Granular soft computing"

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Pedrycz, Witold. "Granular Networks and Granular Learning." In New Learning Paradigms in Soft Computing. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1803-1_2.

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Pedrycz, Witold, and George Vukovich. "Intelligent Agents in Granular Worlds." In Soft Computing Agents. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1815-4_2.

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Yao, Y. Y. "Granular Computing using Neighborhood Systems." In Advances in Soft Computing. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0819-1_40.

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Wong, S. K. Michael, Y. Y. Yao, and Cory J. Butz. "Granular Information Retrieval." In Soft Computing in Information Retrieval. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1849-9_13.

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Elmore, Paul, and Frederick Petry. "Information Theory Applications in Soft Computing." In Granular, Soft and Fuzzy Approaches for Intelligent Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40314-4_5.

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Wiaderek, Krzysztof, Danuta Rutkowska, and Elisabeth Rakus-Andersson. "New Algorithms for a Granular Image Recognition System." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39384-1_67.

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Pal, Sankar K. "Soft Computing Pattern Recognition: Principles, Integrations and Data Mining." In Rough Set Theory and Granular Computing. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36473-3_3.

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Wiaderek, Krzysztof, Danuta Rutkowska, and Elisabeth Rakus-Andersson. "Linguistic Description of Color Images Generated by a Granular Recognition System." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59063-9_54.

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Kavirayani, Srikanth, and Umme Salma. "Fuzzy Granular Computing Controls Ill-Conditioned Matrix Definitions for Triple Inverted Pendulum." In Soft Computing in Data Analytics. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0514-6_52.

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Bryniarska, Anna. "Fuzzy Granular Calculations for the Sematic Web Using Some Mathematical Morphology Methods." In Recent Advances in Soft Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97888-8_20.

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Conference papers on the topic "Granular soft computing"

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Nikravesh, M. "Soft computing for reservoir characterization and management." In 2005 IEEE International Conference on Granular Computing. IEEE, 2005. http://dx.doi.org/10.1109/grc.2005.1547361.

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Lin, Renbing, and Jiyi Wang. "Soft homomophism properties on soft groups and soft normal groups." In 2011 IEEE International Conference on Granular Computing (GrC-2011). IEEE, 2011. http://dx.doi.org/10.1109/grc.2011.6122712.

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Martin, Trevor, Yun Shen, and Ben Azvine. "Finding Soft Relations in Granular Information Hierarchies." In 2007 IEEE International Conference on Granular Computing (GRC 2007). IEEE, 2007. http://dx.doi.org/10.1109/grc.2007.30.

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Martin, Trevor, Yun Shen, and Ben Azvine. "Finding Soft Relations in Granular Information Hierarchies." In 2007 IEEE International Conference on Granular Computing (GRC 2007). IEEE, 2007. http://dx.doi.org/10.1109/grc.2007.4403118.

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Zhu, Ping, and Qiaoyan Wen. "Probabilistic Soft Sets." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.51.

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Daowu Pei and Duoqian Miao. "From soft sets to information systems." In 2005 IEEE International Conference on Granular Computing. IEEE, 2005. http://dx.doi.org/10.1109/grc.2005.1547365.

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Fu-Ping Yang and Shie-Jue Lee. "Applying soft computing for forecasting chaotic time series." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664777.

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Xue Bai, Siwei Luo, and Yibiao Zhao. "Entropy based soft K-means clustering." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664627.

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Kong, Zhi, Lifu Wang, Zhaoxia Wu, and Dexuan Zou. "A new parameter reduction in fuzzy soft sets." In 2012 IEEE International Conference on Granular Computing (GrC-2012). IEEE, 2012. http://dx.doi.org/10.1109/grc.2012.6468637.

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Muda, Muhammad Zaiyad, and George Panoutsos. "An Entropy-Based Uncertainty Measure for Developing Granular Models." In 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2020. http://dx.doi.org/10.1109/iscmi51676.2020.9311589.

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