Relevant bibliographies by topics / Soft computing

Contents

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

Academic literature on the topic 'Soft computing'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Soft computing"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Soft computing"

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Keukelaar, J. H. D. "Topics in Soft Computing." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3294.

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Medaglia, Andres L. "Simulation Optimization Using Soft Computing." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010124-233615.

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To date, most of the research in simulation optimization has been focused on single response optimization on the continuous space of input parameters. However, the optimization of more complex systems does not fit this framework. Decision makers often face the problem of optimizing multiple performance measures of systems with both continuous and discrete input parameters. Previously acquired knowledge of the system by experts is seldomincorporated into the simulation optimization engine. Furthermore, when the goals of the system design are stated in natural language or vague terms, current techniques are unable to deal with this situation. For these reasons, we define and study the fuzzy single response simulation optimization (FSO) and fuzzy multiple response simulation optimization (FMSO) problems.

The primary objective of this research is to develop an efficient and robust method for simulation optimization of complex systems with multiple vague goals. This method uses a fuzzy controller to incorporate existing knowledge to generate high quality approximate Pareto optimal solutions in a minimum number of simulation runs.

For comparison purposes, we also propose an evolutionary method for solving the FMSO problem. Extensive computational experiments on the design of a flow line manufacturing system (in terms of tandem queues with blocking) have been conducted. Both methods are able to generate high quality solutions in terms of Zitzlerand Thiele's "dominated space" metric. Both methods are also able to generate an even sample of the Pareto front. However, the fuzzy controlled method is more efficient, requiring fewer simulation runs than the evolutionary method to achieve the same solution quality.

To accommodate the complexity of natural language, this research also provides a new Bezier curve-based mechanism to elicit knowledge and express complex vague concepts. To date, this is perhaps the most flexible and efficient mechanism for both automatic and interactive generation of membership functions for convex fuzzy sets.

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Di, Tomaso Enza. "Soft computing for Bayesian networks." Thesis, University of Bristol, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409531.

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Thomas, Anna. "Error detection for soft computing applications." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44811.

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Hardware errors are on the rise with reducing chip sizes, and power constraints have necessitated the involvement of software in hardware error detection. At the same time, emerging workloads in the form of soft computing applications, (e.g., multimedia applications) can tolerate most hardware errors as long as the erroneous outputs do not deviate significantly from error-free outcomes. We term outcomes that deviate significantly from the error-free outcomes as Egregious Data Corruptions (EDCs). In this thesis, we propose a technique to place detectors for selectively detecting EDC causing errors in an application. Our technique identifies program locations for placing high coverage detectors for EDCs using static analysis and runtime profiling. We evaluate our technique on six benchmarks to measure the EDC coverage under given performance overhead bounds. Our technique achieves an average EDC coverage of 82%, under performance overheads of 10%, while detecting only 10% of the Non-EDC and benign faults. We also explore the performance-resilience tradeoff space, by studying the effect of compiler optimizations on the error resilience of soft computing applications, both with and without our technique.
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Machaka, Pheeha. "Situation recognition using soft computing techniques." Master's thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/11225.

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Includes bibliographical references.
The last decades have witnessed the emergence of a large number of devices pervasively launched into our daily lives as systems producing and collecting data from a variety of information sources to provide different services to different users via a variety of applications. These include infrastructure management, business process monitoring, crisis management and many other system-monitoring activities. Being processed in real-time, these information production/collection activities raise an interest for live performance monitoring, analysis and reporting, and call for data-mining methods in the recognition, prediction, reasoning and controlling of the performance of these systems by controlling changes in the system and/or deviations from normal operation. In recent years, soft computing methods and algorithms have been applied to data mining to identify patterns and provide new insight into data. This thesis revisits the issue of situation recognition for systems producing massive datasets by assessing the relevance of using soft computing techniques for finding hidden pattern in these systems.
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Mitra, Malay. "Medical diognosis using soft computing technology." Thesis, University of North Bengal, 2018. http://hdl.handle.net/123456789/2722.

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Mitra, Malay. "Medical diognosis using soft computing technologies." Thesis, University of North Bengal, 2018. http://ir.nbu.ac.in/handle/123456789/3659.

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Rajkhowa, Priyanka. "Exploiting soft computing for real time performance." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3928.

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Thesis (M.S.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Dept. of Electrical and Computer Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Hirschen, Kai. "Soft computing methods for applied shape optimization." Phd thesis, [S.l. : s.n.], 2004. http://elib.tu-darmstadt.de/diss/000499.

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Abraham, Ajith 1968. "Hybrid soft computing : architecture optimization and applications." Monash University, Gippsland School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8676.

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Books on the topic "Soft computing"

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Fortuna, Luigi, Gianguido Rizzotto, Mario Lavorgna, Giuseppe Nunnari, M. Gabriella Xibilia, and Riccardo Caponetto. Soft Computing. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0357-8.

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Tettamanzi, Andrea, and Marco Tomassini. Soft Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04335-6.

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Chaturvedi, D. K. Soft Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77481-5.

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Debnath, Pradip, Oscar Castillo, and Poom Kumam. Soft Computing. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003312017.

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Borah, Samarjeet, and Ranjit Panigrahi. Applied Soft Computing. Boca Raton: Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003186885.

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Balas, Valentina Emilia, Lakhmi C. Jain, and Branko Kovačević, eds. Soft Computing Applications. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-18296-4.

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Balas, Valentina Emilia, Lakhmi C. Jain, and Branko Kovačević, eds. Soft Computing Applications. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-18416-6.

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Balas, Valentina Emilia, János Fodor, Annamária R. Várkonyi-Kóczy, Joszef Dombi, and Lakhmi C. Jain, eds. Soft Computing Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33941-7.

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Balas, Valentina Emilia, Lakhmi C. Jain, Marius Mircea Balas, and Shahnaz N. Shahbazova, eds. Soft Computing Applications. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51992-6.

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Balas, Valentina Emilia, Lakhmi C. Jain, Marius Mircea Balas, and Shahnaz N. Shahbazova, eds. Soft Computing Applications. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-52190-5.

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Book chapters on the topic "Soft computing"

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Sick, B. "Soft-Computing." In Messen, Steuern und Regeln mit ICONNECT, 469–590. Wiesbaden: Vieweg+Teubner Verlag, 2003. http://dx.doi.org/10.1007/978-3-322-87251-7_8.

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Deo, M. C. "Soft Computing." In Encyclopedia of Estuaries, 605. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-8801-4_361.

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Kale, Vivek. "Soft Computing." In Digital Transformation of Enterprise Architecture, 457–77. Boca Raton, Florida : CRC Press, [2020]: CRC Press, 2019. http://dx.doi.org/10.1201/9781351029148-24.

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Zhang, Wengang, Yanmei Zhang, Xin Gu, Chongzhi Wu, and Liang Han. "Soft Computing." In Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience, 7–19. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6835-7_2.

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Mamdani, E. H. "Towards soft computing." In Software Agents and Soft Computing Towards Enhancing Machine Intelligence, 242–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62560-7_48.

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Chauhan, N. C., M. V. Kartikeyan, and A. Mittal. "Soft Computing Methods." In Soft Computing Methods for Microwave and Millimeter-Wave Design Problems, 9–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25563-2_2.

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de Korvin, Andre, Hong Lin, and Plamen Simeonov. "Soft Computing Essentials." In Advanced Information and Knowledge Processing, 13–74. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-326-2_2.

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Garzon, Max, Russell Deaton, and John Rose. "Soft molecular computing." In DNA Based Computers V, 91–100. Providence, Rhode Island: American Mathematical Society, 2000. http://dx.doi.org/10.1090/dimacs/054/08.

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Moeller, Dietmar P. F. "Soft-Computing Methods." In Mathematical and Computational Modeling and Simulation, 311–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18709-4_6.

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Turunen, Esko, Kimmo Raivio, and Timo Mantere. "Soft Computing Methods." In Mathematical Modelling, 79–112. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27836-0_6.

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Conference papers on the topic "Soft computing"

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"Soft computing [breaker page]." In 2013 Fifth International Conference on Advanced Computing (ICoAC). IEEE, 2013. http://dx.doi.org/10.1109/icoac.2013.6921908.

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"Session SC: Soft computing." In 2015 Tenth International Conference on Computer Engineering & Systems (ICCES). IEEE, 2015. http://dx.doi.org/10.1109/icces.2015.7393035.

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"Soft computing & application." In 2015 International Symposium on Advanced Computing and Communication (ISACC). IEEE, 2015. http://dx.doi.org/10.1109/isacc.2015.7377304.

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Briggs, Fred C. "Soft Computing in Aerospace." In AIAA Infotech @ Aerospace. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-0483.

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Jannson, Tomasz P., Dai Hyun Kim, Andrew A. Kostrzewski, and Igor V. Ternovskiy. "Soft computing and soft communications for synchronized data." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Bruno Bosacchi, David B. Fogel, and James C. Bezdek. SPIE, 1999. http://dx.doi.org/10.1117/12.367705.

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Seising, Rudolf. "“Soft concepts” for Soft Computing in “soft sciences” on 20 years of “Soft Computing”." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584385.

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"Session YC3: Soft Computing 1." In 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings. IEEE, 2005. http://dx.doi.org/10.1109/imtc.2005.1604555.

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"Session YC4: Soft Computing 2." In 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings. IEEE, 2005. http://dx.doi.org/10.1109/imtc.2005.1604584.

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Carlsson, C., and P. Walden. "Intelligent systems and soft computing." In 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the. IEEE, 2003. http://dx.doi.org/10.1109/hicss.2003.1174202.

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Carlsson, C., and P. Walden. "Intelligent systems and soft computing." In 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the. IEEE, 2004. http://dx.doi.org/10.1109/hicss.2004.1265217.

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Reports on the topic "Soft computing"

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Dixit, Arati M., Harpreet Singh, and Thomas Meitzler. Soft Computing Approach to Crack Detection and FPGA Implementation. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada570058.

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Aminzadeh, Fred, Charles Sammis, Mohammad Sahimi, and David Okaya. Characterizing Fractures in Geysers Geothermal Field by Micro-seismic Data, Using Soft Computing, Fractals, and Shear Wave Anisotropy. Office of Scientific and Technical Information (OSTI), April 2015. http://dx.doi.org/10.2172/1185274.

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Canto, Patricia, ed. The role of vocational training knowledge intensive business services. (Main conclusions). Universidad de Deusto, 2020. http://dx.doi.org/10.18543/vyqr9353.

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In the global economic model, the service sector continues to gain ground on the manufacturing sector and trends such as the integration of new technologies into production processes are advancing inexorably. Advanced economies are pushed to specialise, supported by their regional innovation systems, and cities are emerging as key and strategic centres of activity. In this context, Knowledge Intensive Business Services (KIBS) are presented as critical due to their capacity to promote innovation within the regional productive fabric and smart specialisation strategies, the promotion of advanced manufacturing, the generation of quality employment and the stimulation of economic growth, especially in urban environments. This is why many cities, prioritizing KIBS to stimulate their economy, need to create and retain talent for this sort of industry. Likewise, vocational and education training (VET) systems, such as the Basque VET system, have so far developed their greatest strengths in the field of manufacturing knowledge. Due to this, VET seems to be obliged to adapt to this new scenario, in which KIBS and cities stand out, in order to continue to maintain their level of excellence. KIBS have been extensively examined, but until now no one had posed the following questions: What is the role of vocational training in KIBS? To what extent are VET profiles (and will VET profiles be) relevant in KIBS? This study will show an emerging trend in the labour market. This is the growing relevance of technology profiles with VET background in KIBS, especially in technology-based KIBS. VET technology profiles can be consolidated as one of the main implementing agents of the digital transformation (cybersecurity, blockchain, cloud computing, UX design, artificial intelligence, scientific computing...). To this end, hybridisation with other fields of knowledge but also with studies of other kinds such as university studies may be essential.
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