Thematische Bibliographien / SOFT COMPUTING TECHNIQUE

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Auswahl der wissenschaftlichen Literatur zum Thema „SOFT COMPUTING TECHNIQUE“

Autor: Grafiati

Veröffentlicht am 11. September 2023

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Zeitschriftenartikel zum Thema "SOFT COMPUTING TECHNIQUE"

Yu, Yingbing. „Anomaly Intrusion Detection Based upon Anomalous Events and Soft Computing Technique“. International Journal of Machine Learning and Computing 5, Nr. 6 (Dezember 2015): 450–53. http://dx.doi.org/10.18178/ijmlc.2015.5.6.550.

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Sabri, Norlina M., Mazidah Puteh und 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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Wong, K. W., P. M. Wong, T. D. Gedeon und C. C. Fung. „Rainfall prediction model using soft computing technique“. Soft Computing - A Fusion of Foundations, Methodologies and Applications 7, Nr. 6 (01.05.2003): 434–38. http://dx.doi.org/10.1007/s00500-002-0232-4.

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Kaur, Supreet, und Rajiv Mahajan. „Energy efficient clustering protocol for wireless sensor networks“. Modern Physics Letters B 32, Nr. 32 (20.11.2018): 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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Singh, Dharmpal. „An Effort to Design an Integrated System to Extract Information Under the Domain of Metaheuristics“. International Journal of Applied Evolutionary Computation 8, Nr. 3 (Juli 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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Sesadri, U., B. Siva Sankar und C. Nagaraju. „Type2 Fuzzy Soft Computing Technique for Image Enhancement“. IAES International Journal of Artificial Intelligence (IJ-AI) 4, Nr. 3 (01.09.2015): 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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KumarSaini, Dinesh, und Jabar H Yousif. „Environmental Scrutinizing System based on Soft Computing Technique“. International Journal of Computer Applications 62, Nr. 13 (18.01.2013): 45–50. http://dx.doi.org/10.5120/10143-4952.

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Manh, Linh Ho, Francesco Grimaccia, Marco Mussetta und Riccardo E. Zich. „A Soft Computing Hybridization Technique for Antenna Optimization“. International Journal on Communications Antenna and Propagation (IRECAP) 5, Nr. 1 (28.02.2015): 16. http://dx.doi.org/10.15866/irecap.v5i1.4899.

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Sudipta Sen, Sudipta Sen. „Hydro-Thermal Scheduling: Using Soft Computing Technique Approch“. IOSR Journal of Electrical and Electronics Engineering 5, Nr. 6 (2013): 55–80. http://dx.doi.org/10.9790/1676-0565580.

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Ghadiyali, Tejaskumar, und Kalpesh Lad. „Risk Minimization in Agribusiness using Soft Computing Technique“. IOSR Journal of Computer Engineering 18, Nr. 05 (Mai 2016): 20–25. http://dx.doi.org/10.9790/0661-1805042025.

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Dissertationen zum Thema "SOFT COMPUTING TECHNIQUE"

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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Fernando, Kurukulasuriya Joseph Tilak Nihal. „Soft computing techniques in power system analysis“. Thesis, full-text, 2008. https://vuir.vu.edu.au/2025/.

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Soft computing is a concept that has come into prominence in recent times and its application to power system analysis is still more recent. This thesis explores the application of soft computing techniques in the area of voltage stability of power systems. Soft computing, as opposed to conventional “hard” computing, is a technique that is tolerant of imprecision, uncertainty, partial truth and approximation. Its methods are based on the working of the human brain and it is commonly known as artificial intelligence. The human brain is capable of arriving at valid conclusions based on incomplete and partial data obtained from prior experience. It is an approximation of this process on a very small scale that is used in soft computing. Some of the important branches of soft computing (SC) are artificial neural networks (ANNs), fuzzy logic (FL), genetic computing (GC) and probabilistic reasoning (PR). The soft computing methods are robust and low cost. It is to be noted that soft computing methods are used in such diverse fields as missile guidance, robotics, industrial plants, pattern recognition, market prediction, patient diagnosis, logistics and of course power system analysis and prediction. However in all these fields its application is comparatively new and research is being carried out continuously in many universities and research institutions worldwide. The research presented in this thesis uses the soft computing method of Artificial Neural Networks (ANN’s) for the prediction of voltage instability in power systems. The research is very timely and current and would be a substantial contribution to the present body of knowledge in soft computing and voltage stability, which by itself is a new field. The methods developed in this research would be faster and more economical than presently available methods enabling their use online.

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Fernando, Kurukulasuriya Joseph Tilak Nihal. „Soft computing techniques in power system analysis“. full-text, 2008. http://eprints.vu.edu.au/2025/1/thesis.pdf.

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Esteves, João Trevizoli. „Climate and agrometeorology forecasting using soft computing techniques. /“. Jaboticabal, 2018. http://hdl.handle.net/11449/180833.

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Orientador: Glauco de Souza Rolim
Resumo: Precipitação, em pequenas escalas de tempo, é um fenômeno associado a altos níveis de incerteza e variabilidade. Dada a sua natureza, técnicas tradicionais de previsão são dispendiosas e exigentes em termos computacionais. Este trabalho apresenta um modelo para prever a ocorrência de chuvas em curtos intervalos de tempo por Redes Neurais Artificiais (RNAs) em períodos acumulados de 3 a 7 dias para cada estação climática, mitigando a necessidade de predizer o seu volume. Com essa premissa pretende-se reduzir a variância, aumentar a tendência dos dados diminuindo a responsabilidade do algoritmo que atua como um filtro para modelos quantitativos, removendo ocorrências subsequentes de valores de zero(ausência) de precipitação, o que influencia e reduz seu desempenho. O modelo foi desenvolvido com séries temporais de 10 regiões agricolamente relevantes no Brasil, esses locais são os que apresentam as séries temporais mais longas disponíveis e são mais deficientes em previsões climáticas precisas, com 60 anos de temperatura média diária do ar e precipitação acumulada. foram utilizados para estimar a evapotranspiração potencial e o balanço hídrico; estas foram as variáveis utilizadas como entrada para as RNAs. A precisão média para todos os períodos acumulados foi de 78% no verão, 71% no inverno 62% na primavera e 56% no outono, foi identificado que o efeito da continentalidade, o efeito da altitude e o volume da precipitação normal , tem um impacto direto na precisão das RNAs. Os... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Precipitation, in short periods of time, is a phenomenon associated with high levels of uncertainty and variability. Given its nature, traditional forecasting techniques are expensive and computationally demanding. This paper presents a model to forecast the occurrence of rainfall in short ranges of time by Artificial Neural Networks(ANNs) in accumulated periods from 3 to 7 days for each climatic season, mitigating the necessity of predicting its amount. With this premise it is intended to reduce the variance, rise the bias of data and lower the responsibility of the model acting as a filter for quantitative models by removing subsequent occurrences of zeros values of rainfall which leads to bias the and reduces its performance. The model were developed with time series from 10 agriculturally relevant regions in Brazil, these places are the ones with the longest available weather time series and and more deficient in accurate climate predictions, it was available 60 years of daily mean air temperature and accumulated precipitation which were used to estimate the potential evapotranspiration and water balance; these were the variables used as inputs for the ANNs models. The mean accuracy of the model for all the accumulated periods were 78% on summer, 71% on winter 62% on spring and 56% on autumn, it was identified that the effect of continentality, the effect of altitude and the volume of normal precipitation, have a direct impact on the accuracy of the ANNs. The models have ... (Complete abstract click electronic access below)
Mestre

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Erman, Maria. „Applications of Soft Computing Techniques for Wireless Communications“. Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17314.

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This thesis presents methods and applications of Fuzzy Logic and Rough Sets in the domain of Telecommunications at both the network and physical layers. Specifically, the use of a new class of functions, the truncated π functions, for classifying IP traffic by matching datagram size histograms is explored. Furthermore, work on adapting the payoff matrix in multiplayer games by using fuzzy entries as opposed to crisp values that are hard to quantify, is presented. Additionally, applications of fuzzy logic in wireless communications are presented, comprised by a comprehensive review of current trends and applications, followed by work directed towards using it in spectrum sensing and power control in cognitive radio networks. This licentiate thesis represents parts of my work in the fields of Fuzzy Systems and Wireless Communications. The work was done in collaboration between the Departments of Applied Signal Processing and Mathematics at Blekinge Institute of Technology.

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Perez, Ruben E. „Soft Computing techniques and applications in aircraft design optimization“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63122.pdf.

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Wang, Lijuan. „Multiphase flow measurement using Coriolis flowmeters incorporating soft computing techniques“. Thesis, University of Kent, 2017. https://kar.kent.ac.uk/63877/.

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This thesis describes a novel measurement methodology for two-phase or multiphase flow using Coriolis flowmeters incorporating soft computing techniques. A review of methodologies and techniques for two-phase and multiphase flow measurement is given, together with the discussions of existing problems and technical requirements in their applications. The proposed measurement system is based on established sensors and data-driven models. Detailed principle and implementation of input variable selection methods for data-driven models and associated data-driven modelling process are reported. Three advanced input variable selection methods, including partial mutual information, genetic algorithm-artificial neural network and tree-based iterative input selection, are implemented and evaluated with experimental data. Parametric dependency between input variables and their significance and sensitivity to the desired output are discussed. Three soft computing techniques, including artificial neural network, support vector machine and genetic programming, are applied to data-driven modelling for two-phase flow measurement. Performance comparisons between the data-driven models are carried out through experimental tests and data analysis. Performance of Coriolis flowmeters with air-water, air-oil and gas-liquid two-phase carbon dioxide flows is presented through experimental assessment on one-inch and two-inch bore test rigs. Effects of operating pressure, temperature, installation orientation and fluid properties (density and viscosity) on the performance of Coriolis flowmeters are quantified and discussed. Experimental results suggest that the measurement system using Coriolis flowmeters together with the developed data-driven models has significantly reduced the original errors of mass flow measurement to within ±2%. The system also has the capability of predicting gas volume fraction with the relative errors less than ±10%.

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Yang, Yingjie. „Investigation on soft computing techniques for airport environment evaluation systems“. Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/35015.

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Spatial and temporal information exist widely in engineering fields, especially in airport environmental management systems. Airport environment is influenced by many different factors and uncertainty is a significant part of the system. Decision support considering this kind of spatial and temporal information and uncertainty is crucial for airport environment related engineering planning and operation. Geographical information systems and computer aided design are two powerful tools in supporting spatial and temporal information systems. However, the present geographical information systems and computer aided design software are still too general in considering the special features in airport environment, especially for uncertainty. In this thesis, a series of parameters and methods for neural network-based knowledge discovery and training improvement are put forward, such as the relative strength of effect, dynamic state space search strategy and compound architecture.

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Amina, Mahdi. „Dynamic non-linear system modelling using wavelet-based soft computing techniques“. Thesis, University of Westminster, 2011. https://westminsterresearch.westminster.ac.uk/item/8zwwz/dynamic-non-linear-system-modelling-using-wavelet-based-soft-computing-techniques.

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The enormous number of complex systems results in the necessity of high-level and cost-efficient modelling structures for the operators and system designers. Model-based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Soft computing based models in particular, can successfully be applied in cases of highly nonlinear problems. A further reason for dealing with so called soft computational model based techniques is that in real-world cases, many times only partial, uncertain and/or inaccurate data is available. Wavelet-Based soft computing techniques are considered, as one of the latest trends in system identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based approaches to model the non-linear dynamical systems in real world problems in conjunction with possible twists and novelties aiming for more accurate and less complex modelling structure. Initially, an on-line structure and parameter design has been considered in an adaptive Neuro- Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus (Monascus ruber van Tieghem) is examined against several other approaches for further justification of the proposed methodology. By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have been introduced. Increasing the accuracy and decreasing the computational cost are both the primary targets of proposed novelties. Modifying the synoptic weights by replacing them with Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA) comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for the above challenges. These two models differ from the point of view of structure while they share the same HLA scheme. The second approach contains an additional Multiplication layer, plus its hidden layer contains several sub-WNNs for each input dimension. The practical superiority of these extensions is demonstrated by simulation and experimental results on real non-linear dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT) whole milk, and consolidated with comprehensive comparison with other suggested schemes. At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network (FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from the data by building accurate regression, but also for the identification of complex systems. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient hybrid learning approach is used to adjust the parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the above technique.

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Chen, Mingwu. „Motion planning and control of mobile manipulators using soft computing techniques“. Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266128.

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Bücher zum Thema "SOFT COMPUTING TECHNIQUE"

Chaudhuri, Arindam, und Soumya K. Ghosh. Bankruptcy Prediction through Soft Computing based Deep Learning Technique. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6683-2.

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Borah, Samarjeet, Ratika Pradhan, Nilanjan Dey und Phalguni Gupta, Hrsg. Soft Computing Techniques and Applications. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7394-1.

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Timbadiya, P. V., P. L. Patel, Vijay P. Singh und A. B. Mirajkar, Hrsg. Geospatial and Soft Computing Techniques. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1901-7.

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K, Anbumani, und Nedunchezhian R, Hrsg. Soft computing applications for database technologies: Techniques and issues. Hershey, PA: Information Science Reference, 2010.

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Patnaik, Srikanta, und Baojiang Zhong, Hrsg. Soft Computing Techniques in Engineering Applications. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04693-8.

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Patnaik, Srikanta, und Yeon-Mo Yang, Hrsg. Soft Computing Techniques in Vision Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25507-6.

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Patnaik, Srikanta. Soft Computing Techniques in Vision Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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E, Ruano A., und Institution of Electrical Engineers, Hrsg. Intelligent control systems using computational intelligence techniques. London: Institution of Electrical Engineers, 2005.

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K, Pal Sankar, Polkowski Lech und Skowron Andrzej, Hrsg. Rough-neural computing: Techniques for computing with words. Berlin: Springer, 2004.

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Chakraborty, Kabir, und Abhijit Chakrabarti. Soft Computing Techniques in Voltage Security Analysis. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2307-8.

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Buchteile zum Thema "SOFT COMPUTING TECHNIQUE"

Rotaru, Florin, Silviu-Ioan Bejinariu, Cristina Diana Niţă, Ramona Luca, Mihaela Luca und Adrian Ciobanu. „Retinal Vessel Classification Technique“. In Soft Computing Applications, 498–514. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62524-9_37.

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Jara-Maldonado, Miguel, Vicente Alarcon-Aquino und Roberto Rosas-Romero. „A Multiresolution Machine Learning Technique to Identify Exoplanets“. In Advances in Soft Computing, 50–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60884-2_4.

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Selouani, Sid-Ahmed. „Variance of the Reconstruction Error Technique“. In Speech Processing and Soft Computing, 25–32. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9685-5_4.

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Sarwar, Sohail, Zia Ul-Qayyum und Owais Ahmed Malik. „CBR and Neural Networks Based Technique for Predictive Prefetching“. In Advances in Soft Computing, 221–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16773-7_19.

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Kazikova, Anezka, Michal Pluhacek, Adam Viktorin und Roman Senkerik. „New Running Technique for the Bison Algorithm“. In Artificial Intelligence and Soft Computing, 417–26. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91253-0_39.

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Pankwar, Deepak, G. L. Saini, Pankaj Agarwal und Pardeep Singh. „Firefly Optimization Technique for Software Quality Prediction“. In Soft Computing: Theories and Applications, 263–73. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0707-4_25.

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Singh, Ashwini, und A. B. Mirajkar. „Leakage Management in WDN System Using Optimization Technique“. In Geospatial and Soft Computing Techniques, 345–56. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1901-7_28.

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Soni, Ashu, und Maneesha Gupta. „Fractional Order Elliptic Filter Implemented Using Optimization Technique“. In Cognitive Informatics and Soft Computing, 181–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1056-1_16.

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Joon, Kirti, Namrata Agrawal, Hifzan Ahmad, Vikash Yadav, Dinesh Kumar Sah und Cornel Barna. „DDoS Attack Prevention Protocol Through Support Vector Machine and Fuzzy Clustering Mechanism on Traffic Flow with Harmonic Homogeneity Validation Technique“. In Soft Computing Applications, 197–214. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51992-6_17.

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Onisawa, Takehisa. „Soft Computing Technique in Kansei (Emotional) Information Processing“. In Soft Computing and Human-Centered Machines, 215–48. Tokyo: Springer Japan, 2000. http://dx.doi.org/10.1007/978-4-431-67907-3_9.

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Konferenzberichte zum Thema "SOFT COMPUTING TECHNIQUE"

Reddy, G. Deepika, N. R. Medikondu, T. Vijaya Kumar, M. B. S. Sreekara Reddy, P. Harisha und M. Murali Krishna. „Soft computing technique for jobs scheduling“. In 2ND INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION SCIENTIFIC DEVELOPMENT (ICAISD) 2021: Innovating Scientific Learning for Deep Communication. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0143070.

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Barbu, Tudor, Mihaela Costin und Adrian Ciobanu. „Content-based image recognition technique using area moments“. In 2010 4th International Workshop on Soft Computing Applications (SOFA). IEEE, 2010. http://dx.doi.org/10.1109/sofa.2010.5565603.

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Singh, J. Amar Pratap, und M. Karnan. „Intelligent Location Management Using Soft Computing Technique“. In 2010 Second International Conference on Communication Software and Networks. IEEE, 2010. http://dx.doi.org/10.1109/iccsn.2010.60.

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Goel, Shubham, Amrendra Tripathi, Tanupriya Choudhury und Vivek Kumar. „Parkinson's Disease Detection using Soft Computing Technique“. In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART). IEEE, 2019. http://dx.doi.org/10.1109/smart46866.2019.9117384.

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Mishra, Arvind Kumar, Renuka Nagpal, Kirti Seth und Rajni Sehgal. „Analyzability of SOA using Soft Computing Technique“. In 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2022. http://dx.doi.org/10.1109/icrito56286.2022.9964602.

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Barbu, Tudor, Mihaela Costin und Adrian Ciobanu. „Histogram intersection based image retrieval technique using relevance feedback“. In 2009 3rd International Workshop on Soft Computing Applications (SOFA). IEEE, 2009. http://dx.doi.org/10.1109/sofa.2009.5254878.

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Ge Zheng, Qinghua Cao und Chao Liu. „Anomaly intrusion detection based on soft computing technique“. In 2011 International Conference on System Science, Engineering Design and Manufacturing Informatization (ICSEM). IEEE, 2011. http://dx.doi.org/10.1109/icssem.2011.6081303.

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Esther, J., und M. Mohamed Sathik. „Retrieval of Brain Image Using Soft Computing Technique“. In 2014 International Conference on Intelligent Computing Applications (ICICA). IEEE, 2014. http://dx.doi.org/10.1109/icica.2014.104.

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Hota, Radhanath, Sachikanta Dash, Sujogya Mishra, Sipali Pradhan und P. K. Pattnaik. „Symptoms Prediction of Tuberculosis using Soft Computing Technique“. In 2022 OITS International Conference on Information Technology (OCIT). IEEE, 2022. http://dx.doi.org/10.1109/ocit56763.2022.00071.

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Parekh, Aarti M., und Nidhi B. Shah. „Classification of ovarian cyst using soft computing technique“. In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2017. http://dx.doi.org/10.1109/icccnt.2017.8203965.

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