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Relevant bibliographies by topics / SOFT COMPUTING TECHNIQUE

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Journal articles

Academic literature on the topic 'SOFT COMPUTING TECHNIQUE'

Author: Grafiati

Published: 11 September 2023

Last updated: 12 June 2025

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Journal articles on the topic "SOFT COMPUTING TECHNIQUE"

1

Yu, Yingbing. "Anomaly Intrusion Detection Based upon Anomalous Events and Soft Computing Technique." International Journal of Machine Learning and Computing 5, no. 6 (2015): 450–53. http://dx.doi.org/10.18178/ijmlc.2015.5.6.550.

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Sabri, Norlina M., Mazidah Puteh, and Mohamad Rusop Mahmood. "Utilization of Soft Computing Techniques in Sputtering Processes: A Review." Advanced Materials Research 832 (November 2013): 260–65. http://dx.doi.org/10.4028/www.scientific.net/amr.832.260.

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This paper presents an overview of research works on the utilizing of soft computing in the optimization of process parameters and in the prediction of thin film properties in sputtering processes. The papers from this review were obtained from relevant databases and from various scientific journals. The papers collected were published from 2008 to 2012. The focus of the review is to provide an outlook on the utilization of soft computing techniques in sputtering processes. Based on the review, the soft computing techniques which have been applied so far are ANN, GA and Fuzzy Logic. The first finding of this review is that soft computing technique is a promising and more reliable approach to optimize and predict process parameters compared to the traditional methods. The second finding is that the utilizing of soft computing techniques in sputtering processes are still limited and still in exploratory phase as they have not yet been extensively and stably applied. The techniques applied are also limited to ANN, GA and Fuzzy, whereas the exploration into other techniques is also necessary to be conducted in order to seek the most reliable technique and so as to expand the application of soft computing approach. Future research could focus on the exploration of other soft computing techniques for optimization in order to find the best optimization techniques based on the specific processes.

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3

Wong, K. W., P. M. Wong, T. D. Gedeon, and C. C. Fung. "Rainfall prediction model using soft computing technique." Soft Computing - A Fusion of Foundations, Methodologies and Applications 7, no. 6 (2003): 434–38. http://dx.doi.org/10.1007/s00500-002-0232-4.

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4

Kaur, Supreet, and Rajiv Mahajan. "Energy efficient clustering protocol for wireless sensor networks." Modern Physics Letters B 32, no. 32 (2018): 1850400. http://dx.doi.org/10.1142/s0217984918504006.

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In this paper, a hybrid soft computing technique-based energy efficient protocol is proposed to improve the inter-cluster data aggregation in clustering based general self-organized tree based energy balance (GSTEB) routing protocol. Initially, improved ant colony optimization-based technique is used to select optimal cluster heads. Afterwards, a hybrid soft computing technique is utilized to communicate the data from cluster heads to sink. Extensive experiments have been done by considering the existing and proposed technique. Experimental results indicate that the proposed technique provides better network lifetime as compared to existing techniques.

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5

Singh, Dharmpal. "An Effort to Design an Integrated System to Extract Information Under the Domain of Metaheuristics." International Journal of Applied Evolutionary Computation 8, no. 3 (2017): 13–52. http://dx.doi.org/10.4018/ijaec.2017070102.

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The main objective of this work is to develop an integrated system that is capable of extracting precise information (knowledge) based on any stored information using the techniques of data mining and soft computing. For the purpose of extracting precise information based on some stored information, it has been further observed that the research work related to the area of knowledge discovery based on certain information with the help of a particular data mining or soft computing model has been done, but the performance based on the particular soft computing or data mining model has not been reviewed as compared to the other models. The comparison of performance of various models in the area of soft computing domain or statistical domain or data mining area have been remained unattended with limitation of the survey. This absence leads to the necessity and carrying out research work for effective knowledge discovery based on a particular set of information on utilizing the versatility and potential view generation soft computing tools. The modified harmony search technique has been proposed in this paper and it has been observed that it has outperformed the other soft computing technique in case of training and tested data. The result of the modified harmony search technique has also been cross checked by the residual error. The concept of harmony search is also applied to other data set to check the optimality of the models.

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6

Sesadri, U., B. Siva Sankar, and C. Nagaraju. "Type2 Fuzzy Soft Computing Technique for Image Enhancement." IAES International Journal of Artificial Intelligence (IJ-AI) 4, no. 3 (2015): 97. http://dx.doi.org/10.11591/ijai.v4.i3.pp97-104.

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<p class="Default">The mainpurpose of Image enhancement is to process an image so that outcome is more appropriate than original image for definite application. The fuzzy logic isone of the soft computing techniques to enhance the images by eliminating uncertainty.In this paper efficient type2 fuzzy logic technique is used to get betterquality image. This method consists of two steps. In the First step fisher criterion function is useful to generate type1 fuzzy membership value. In the second step based on type1 membership value fuzzy rules are derived to enhance the image. The type2 fuzzy method is compared with type1 fuzzy. The table values and graphs provethat the proposed method gives better results compared with fuzzy type1 method.</p>

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7

KumarSaini, Dinesh, and Jabar H Yousif. "Environmental Scrutinizing System based on Soft Computing Technique." International Journal of Computer Applications 62, no. 13 (2013): 45–50. http://dx.doi.org/10.5120/10143-4952.

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8

Manh, Linh Ho, Francesco Grimaccia, Marco Mussetta, and Riccardo E. Zich. "A Soft Computing Hybridization Technique for Antenna Optimization." International Journal on Communications Antenna and Propagation (IRECAP) 5, no. 1 (2015): 16. http://dx.doi.org/10.15866/irecap.v5i1.4899.

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9

Sudipta Sen, Sudipta Sen. "Hydro-Thermal Scheduling: Using Soft Computing Technique Approch." IOSR Journal of Electrical and Electronics Engineering 5, no. 6 (2013): 55–80. http://dx.doi.org/10.9790/1676-0565580.

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10

Ghadiyali, Tejaskumar, and Kalpesh Lad. "Risk Minimization in Agribusiness using Soft Computing Technique." IOSR Journal of Computer Engineering 18, no. 05 (2016): 20–25. http://dx.doi.org/10.9790/0661-1805042025.

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