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Journal articles on the topic 'Soft computing'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Zadeh, L. A. "Soft Computing." Journal of Japan Society for Fuzzy Theory and Systems 7, no. 2 (1995): 262–69. http://dx.doi.org/10.3156/jfuzzy.7.2_262.

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2

Zadeh, L. A. "Soft Computing." Journal of Japan Society for Fuzzy Theory and Systems 7, no. 3 (1995): 530–36. http://dx.doi.org/10.3156/jfuzzy.7.3_530.

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3

Vasilakos, Dr Thanos, and Dr Marco Painho. "Soft Computing." Soft Computing 7, no. 7 (June 2003): 489. http://dx.doi.org/10.1007/s00500-003-0300-4.

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4

Pandey, Ritesh Kumar, and Dr Asha Ambhaikar. "Data Imputation by Soft Computing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 808–10. http://dx.doi.org/10.31142/ijtsrd14112.

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5

Steyaert, Jan. "Soft Computing for Soft Technologies." Computers in Human Services 10, no. 4 (August 5, 1994): 55–67. http://dx.doi.org/10.1300/j407v10n04_04.

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6

M. Darwish, Saad, Adel A. El-Zoghabi, and Marwan H. Hassan. "Soft Computing for Database Deadlock Resolution." International Journal of Modeling and Optimization 5, no. 1 (February 2015): 15–21. http://dx.doi.org/10.7763/ijmo.2015.v5.429.

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7

Hassan, Mohammad Mehedi, Md Rafiul Hassan, Victor Hugo C. de Albuquerque, and Witold Pedrycz. "Soft Computing for Intelligent Edge Computing." Applied Soft Computing 128 (October 2022): 109628. http://dx.doi.org/10.1016/j.asoc.2022.109628.

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8

Sadiku, Matthew N. O., Yonghui Wang, Suxia Cui, and Sarhan M. Musa. "SOFT COMPUTING: AN INTRODUCTION." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 6 (June 30, 2018): 63. http://dx.doi.org/10.23956/ijarcsse.v8i6.615.

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Soft computing (SC) is a newly emerging multidisciplinary field. It is a collection of computational techniques, such as expert systems, fuzzy logic, neural networks, and evolutionary algorithms, which provide information processing capabilities to solve complex practical problems. The major benefit of SC lies in its ability to tolerate imprecision, uncertainty, partial truth, and approximation in processing imprecise and inaccurate information and simulating human decision making at low cost. This paper provides a brief introduction on soft computing.

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9

Mitra, S., and Y. Hayashi. "Bioinformatics with soft computing." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 36, no. 5 (September 2006): 616–35. http://dx.doi.org/10.1109/tsmcc.2006.879384.

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10

Zadeh, Lotfi A. "Applied Soft Computing – Foreword." Applied Soft Computing 1, no. 1 (June 2001): 1–2. http://dx.doi.org/10.1016/s1568-4946(01)00003-5.

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11

Liang, Yun, and Tian-ping He. "Survey on soft computing." Soft Computing 24, no. 2 (November 13, 2019): 761–70. http://dx.doi.org/10.1007/s00500-019-04508-z.

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12

Garzon, Max H., and Michael Conrad. "Soft computing with biomolecules." Soft Computing 5, no. 1 (February 2001): 1. http://dx.doi.org/10.1007/s005000000059.

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13

Pal, Sankar K., and Ashish Ghosh. "Soft computing data mining." Information Sciences 163, no. 1-3 (June 2004): 1–3. http://dx.doi.org/10.1016/j.ins.2003.03.012.

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14

Yardimci, Ahmet. "Soft computing in medicine." Applied Soft Computing 9, no. 3 (June 2009): 1029–43. http://dx.doi.org/10.1016/j.asoc.2009.02.003.

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15

LOIA, V. "Soft computing meets agents." Information Sciences 176, no. 9 (May 8, 2006): 1101–2. http://dx.doi.org/10.1016/j.ins.2005.07.008.

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16

Sundari, S. M. Sivakama, and Dr A. Rijuvana Begum. "Soft Computing Healthcare Intelligence through Multisensory Activity Recognition." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 294–300. http://dx.doi.org/10.31142/ijtsrd5879.

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17

CHEETHAM, WILLIAM, SIMON SHIU, and ROSINA O. WEBER. "Soft case-based reasoning." Knowledge Engineering Review 20, no. 3 (September 2005): 267–69. http://dx.doi.org/10.1017/s0269888906000579.

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The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.

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18

Ruspini, Enrique H. "Soft Computing: Coping with Complexity." International Journal of Computational Intelligence Systems 3, no. 2 (2010): 190. http://dx.doi.org/10.2991/ijcis.2010.3.2.6.

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19

Ponce, Pedro, Hiram Ponce, Arturo Molina, and Brian MacCleery. "Soft Computing Based On LabVIEW." IFAC Proceedings Volumes 46, no. 9 (2013): 975–81. http://dx.doi.org/10.3182/20130619-3-ru-3018.00251.

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20

J.C, Obi, and Okpor D.M. "Soft-Computing Virus Identification System." International Journal of Fuzzy Logic Systems 3, no. 2 (April 30, 2013): 63–72. http://dx.doi.org/10.5121/ijfls.2013.3205.

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21

Pandey, Digesh. "SPEECH RECOGNITION USING SOFT COMPUTING." International Journal of Advanced Research in Computer Science 13, no. 1 (February 21, 2020): 11–15. http://dx.doi.org/10.26483/ijarcs.v13i1.6797.

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22

Gesù, Vito Di. "Artificial Vision and Soft Computing." Fundamenta Informaticae 37, no. 1,2 (1999): 101–19. http://dx.doi.org/10.3233/fi-1999-371206.

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23

OISHI, Atsuya, Daisuke TOMITA, Shinobu YOSHIMURA, and Genki YAGAWA. "Contact Search by Soft Computing." Proceedings of the JSME annual meeting 2002.1 (2002): 31–32. http://dx.doi.org/10.1299/jsmemecjo.2002.1.0_31.

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24

Mitra, Sushmita, Ranajit Das, and Yoichi Hayashi. "Genetic Networks and Soft Computing." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 1 (January 2011): 94–107. http://dx.doi.org/10.1109/tcbb.2009.39.

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25

Vassilev Lakov, Dimitar, and Mariana Vassileva Vassileva. "Decision making soft computing agents." International Journal of Systems Science 36, no. 14 (November 15, 2005): 921–30. http://dx.doi.org/10.1080/00207720500381797.

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26

Arafeh, Labib. "Soft computing auto essay grading." International Journal of Continuing Engineering Education and Life-Long Learning 23, no. 2 (2013): 147. http://dx.doi.org/10.1504/ijceell.2013.054289.

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27

Ibrahim, Dogan. "An Overview of Soft Computing." Procedia Computer Science 102 (2016): 34–38. http://dx.doi.org/10.1016/j.procs.2016.09.366.

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28

Pal, Nikhil R. "Soft computing for pattern recognition." Fuzzy Sets and Systems 103, no. 2 (April 1999): 197–200. http://dx.doi.org/10.1016/s0165-0114(98)00221-8.

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29

Pal, Nikhil Ranjan. "Soft computing for feature analysis." Fuzzy Sets and Systems 103, no. 2 (April 1999): 201–21. http://dx.doi.org/10.1016/s0165-0114(98)00222-x.

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30

Koivo, Heikki N. "Soft Computing in Ambient Intelligence." IFAC Proceedings Volumes 37, no. 16 (September 2004): 9–11. http://dx.doi.org/10.1016/s1474-6670(17)30842-x.

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31

Torra, Vicenç, and Yasuo Narukawa. "Soft Computing in decision modeling." Soft Computing 14, no. 5 (May 30, 2009): 419–20. http://dx.doi.org/10.1007/s00500-009-0456-7.

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32

Pedrycz, Witold. "Soft computing and its applications." Fuzzy Sets and Systems 144, no. 2 (June 2004): 365–66. http://dx.doi.org/10.1016/j.fss.2003.11.018.

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33

Li, Xiang, and Hongguang Ma. "Soft computing in smart logistics." Soft Computing 24, no. 9 (March 10, 2020): 6193–95. http://dx.doi.org/10.1007/s00500-020-04836-5.

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34

Wang, Peizhuang, and Shaohua Tan. "Soft computing and fuzzy logic." Soft Computing - A Fusion of Foundations, Methodologies and Applications 1, no. 1 (April 9, 1997): 35–41. http://dx.doi.org/10.1007/s005000050004.

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35

Not Available, Not Available. "Springer-Verlag and Soft Computing." Soft Computing - A Fusion of Foundations, Methodologies and Applications 2, no. 1 (April 24, 1998): 1. http://dx.doi.org/10.1007/s005000050022.

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36

Gabbay, D. M. "Soft computing, labelling and granulation." Soft Computing - A Fusion of Foundations, Methodologies and Applications 2, no. 1 (April 24, 1998): 12–13. http://dx.doi.org/10.1007/s005000050026.

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37

Zadeh, L. A. "Soft computing and fuzzy logic." IEEE Software 11, no. 6 (November 1994): 48–56. http://dx.doi.org/10.1109/52.329401.

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38

Kuznetsov, Sergei, and Dominik Slezak. "Data mining and soft computing." International Journal of General Systems 42, no. 6 (August 2013): 543–45. http://dx.doi.org/10.1080/03081079.2013.804639.

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39

Yu, Ker-Wei, Yang Xu, and Jer-Guang Hsieh. "Mathematical Tools of Soft Computing." Mathematical Problems in Engineering 2014 (2014): 1–3. http://dx.doi.org/10.1155/2014/904014.

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40

Ruspini, Enrique H. "Soft Computing: Coping with Complexity." International Journal of Computational Intelligence Systems 3, no. 2 (June 2010): 190–96. http://dx.doi.org/10.1080/18756891.2010.9727689.

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41

Reformat, Marek Z. "Soft Computing for Intelligent Web." International Journal of Intelligent Systems 28, no. 1 (November 29, 2012): 1–3. http://dx.doi.org/10.1002/int.21571.

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42

Moraga, Claudio. "Spectral techniques and soft computing." Approximation Theory and its Applications 14, no. 4 (December 1998): 1–11. http://dx.doi.org/10.1007/bf02856144.

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43

Sheikhzade, Shokooh, Hamed Vahdat-Nejad, and Ramazan Havangi. "Pothole Detection by Soft Computing." Signal and Data Processing 19, no. 2 (September 1, 2022): 1–12. http://dx.doi.org/10.52547/jsdp.19.2.1.

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44

Pawar, Rajeshwari H. "Soft Computing in Advanced Robotics." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 1263–66. http://dx.doi.org/10.22214/ijraset.2024.59963.

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Abstract: Advanced robotics has witnessed significant advancements in recent years, enabling robots to perform complex tasks in various domains such as manufacturing, healthcare, and space exploration. Soft computing techniques have emerged as powerful tools to address the challenges associated with uncertainty, imprecision, and complexity inherent in robotic systems. This paper presents a comprehensive review of the application of soft computing techniques in advanced robotics. The review encompasses various soft computing paradigms including fuzzy logic, neural networks, evolutionary algorithms, and swarm intelligence. The paper discusses the integration of these techniques in different aspects of robotic systems such as perception, planning, control, and learning. Furthermore, it highlights the strengths, limitations, and future directions of soft computing in advancing robotics technology.

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45

Peters, Georg, and Richard Weber. "Dynamic clustering with soft computing." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2, no. 3 (March 14, 2012): 226–36. http://dx.doi.org/10.1002/widm.1050.

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46

Agah, Arvin. "Intelligent Automation and Soft Computing Special Section on Soft Computing Applications to Mobile Robots." Intelligent Automation & Soft Computing 14, no. 1 (January 2008): 45–46. http://dx.doi.org/10.1080/10798587.2008.10642981.

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47

Chakraborty, Santanu, Ramesh Kumar Sharma, and Pushpa Tewari. "APPLICATION OF SOFT COMPUTING TECHNIQUES OVER HARD COMPUTING TECHNIQUES: A SURVEY." International Journal of Students' Research in Technology & Management 5, no. 4 (November 25, 2017): 08–17. http://dx.doi.org/10.18510/ijsrtm.2017.542.

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Soft computing is the fusion of different constituent elements. The main aim of this fusion to solve real-world problems, which are not solve by traditional approach that is hard computing. Actually, in our daily life maximum problem having uncertainty and vagueness information. So hard computing fail to solve this problems, because it give exact solution. To overcome this situation soft computing techniques plays a vital role, because it has capability to deal with uncertainty and vagueness and produce approximate result. This paper focuses on application of soft computing techniques over hard computing techniques.

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48

Rath, Mamata, and Bibudhendu Pati. "Appraisal of Soft Computing Methods in Collaboration With Smart City Applications and Wireless Network." International Journal of e-Collaboration 14, no. 1 (January 2018): 27–42. http://dx.doi.org/10.4018/ijec.2018010102.

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Applications of soft computing methods are spread in fields that deal with intelligent analysis. As the human intelligence can survey the likelihood of some occasions in possibilities, comparatively soft computing systems additionally utilize some smart-based strategies to evaluate ongoing issues with diagnostic models. Fundamental segments of soft computing incorporate machine learning, probabilistic thinking, swarm intelligence, and ANN algorithms. In this research article, there is a broad analysis of these intelligence-based soft computing strategies connected as different operational parts of a wireless network and there is a scheme of a soft computing-based method for smart and safe health care systems.

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49

Gupta, B. B., Dharma P. Agrawal, Shingo Yamaguchi, and Michael Sheng. "Soft computing techniques for big data and cloud computing." Soft Computing 24, no. 8 (March 6, 2020): 5483–84. http://dx.doi.org/10.1007/s00500-020-04766-2.

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50

Dorronsoro, Bernabe, and Sergio Nesmachnow. "Parallel Soft Computing Techniques in High-Performance Computing Systems." Computer Journal 59, no. 6 (November 4, 2015): 775–76. http://dx.doi.org/10.1093/comjnl/bxv087.

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