Journal articles: 'FUZZY TECHNIQUE'

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Dey, Aniruddha, Jamuna Kanta Sing, and Shiladitya Chowdhury. "Weighted Fuzzy Generalized 2DFLD: A Fuzzy-Based Feature Extraction Technique for Face Recognition." International Journal of Machine Learning and Computing 7, no. 6 (December 2017): 223–31. http://dx.doi.org/10.18178/ijmlc.2017.7.6.651.

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Ansari, Mohd Zeeshan, and M. M. Sufyan Beg. "Improved Fuzzy Rank Aggregation." International Journal of Rough Sets and Data Analysis 5, no. 4 (October 2018): 74–87. http://dx.doi.org/10.4018/ijrsda.2018100105.

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Rank aggregation is applied on the web to build various applications like meta-search engines, consumer reviews classification, and recommender systems. Meta-searching is the generation of a single list from a collection of the results produced by multiple search engines, together using a rank aggregation technique. It is an efficient and cost-effective technique to retrieve quality results from the internet. The quality of results produced by a meta-searching relies upon the efficiency of rank aggregation technique applied. An effective rank aggregation technique assigns the rank to a document that is closest to all its previous rankings. The newly generated list of documents may be evaluated by the measurement of Spearman footrule distance. In this article, various fuzzy logic techniques for rank aggregation are analyzed and further improvements are proposed in Modified Shimura technique. Consequently, two novel OWA operators are suggested for the calculation of membership values of document ranks in a modified Shimura technique. The performance of proposed improvements is evaluated on the Spearman footrule distance along with execution time. The results show that the anticipated improvements exhibit better performance than other fuzzy techniques.

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Zhai, Jun-hai. "Fuzzy decision tree based on fuzzy-rough technique." Soft Computing 15, no. 6 (March 10, 2010): 1087–96. http://dx.doi.org/10.1007/s00500-010-0584-0.

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S, Pradeep. "Design and FPGA Implementation of Image Compression Based Fuzzy Technique." International Journal of Electrical and Electronics Research 2, no. 2 (September 30, 2014): 1–4. http://dx.doi.org/10.37391/ijeer.020201.

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Fuzzy logic is a way to embed an engineer’s experience into the system. In recent years, many researchers have applied the fuzzy logic to develop new techniques for contrast improvement. Fuzzy logic is a well-known rather simple approach with good visual results, but proposed fuzzy operation algorithm is default nonlinear. Here proposed algorithm is a default nonlinear thus not straight forward applicable on the JPEG bit stream, it is possible when the right combination is found. Image compression is one of the major image processing techniques that are widely used in medical, automotive, consumer and military applications. In this project fuzzy technique has been used in image compression with Discrete Wavelet Transforms (DWT) technique. Discrete wavelet transforms is the most popular transformation technique adopted for image compression. Image compression has become important as storage or transmission of images requires large amount of bandwidth. In order to minimize the complexity of DWT, fuzzy technique has been proposed and implemented on FPGA.

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Ahmad, Mohammad, and Weihu Cheng. "A Novel Approach of Fuzzy Control Chart with Fuzzy Process Capability Indices Using Alpha Cut Triangular Fuzzy Number." Mathematics 10, no. 19 (September 30, 2022): 3572. http://dx.doi.org/10.3390/math10193572.

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Statistical Quality Control is a valuable strategy that applies to the statistical technique for monitoring a manufacturing system under particular situations. On the other hand, the fuzzy set theory is an ideal instrument to cope with an unclear situation. The existing studies are restricted, and there is still mystery behind the unclear data. This paper deals with technique: namely, the fuzzy control chart based on fuzzy process capability indices (FCPI) using triangular fuzzy numbers (TFNs). Alpha cut theory is applied in statistical quality control for fuzzy process control industrial application. This is a five-phase study that deals with the control chart using capability indices. The numerical example is also performed using the proposed technique. This paper would help to better assess/understand the manufacturing system data and would explore the application of the fuzzy control techniques.

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Das, Satya Kumar, and Sahidul Islam. "Multi-item a supply chain production inventory model of time dependent production rate and demand rate under space constraint in fuzzy environment." Independent Journal of Management & Production 11, no. 2 (April 1, 2020): 304. http://dx.doi.org/10.14807/ijmp.v11i2.1037.

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In this paper, we have developed an integrated production inventory model for two echelon supply chain consisting of one vendor and one retailer. Production rate and demand rate of retailer and customer are time dependent. Idle time cost of the vendor has been considered. Multi-item inventory has been considered. In integrated inventory model average cost has been calculated under limitation on stroge space. Two echelon supply chain fuzzy inventory model has been solved by various techniques like as Fuzzy programming technique with hyperbolic membership functions (FPTHMF), Fuzzy non-linear programming technique (FNLP) and Fuzzy additive goal programming technique (FAGP), weighted Fuzzy non-linear programming technique (WFNLP) and weighted Fuzzy additive goal programming technique (WFAGP). A numerical example is illustrated to test the model. Finally to make the model more realistic, sensitivity analysis has been shown.

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Taleshian, Fatemeh, and Jafar Fathali. "A Mathematical Model for Fuzzyp-Median Problem with Fuzzy Weights and Variables." Advances in Operations Research 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/7590492.

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We investigate thep-median problem with fuzzy variables and weights of vertices. The fuzzy equalities and inequalities transform to crisp cases by using some technique used in fuzzy linear programming. We show that the fuzzy objective function also can be replaced by crisp functions. Therefore an auxiliary linear programming model is obtained for the fuzzyp-median problem. The results are compared with two previously proposed methods.

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M. Mousa, Hamdy. "Chaotic Genetic-fuzzy Encryption Technique." International Journal of Computer Network and Information Security 10, no. 4 (April 8, 2018): 10–19. http://dx.doi.org/10.5815/ijcnis.2018.04.02.

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Sinha, S. B., K. A. Rao, B. K. Mangaraj, and P. K. Tripathy. "Fuzzy Technique to Agricultural Planning." Journal of Information and Optimization Sciences 10, no. 1 (January 1989): 257–74. http://dx.doi.org/10.1080/02522667.1989.10698965.

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Cai, Yuliang, Huaguang Zhang, Qiang He, and Shaoxin Sun. "New classification technique: fuzzy oblique decision tree." Transactions of the Institute of Measurement and Control 41, no. 8 (June 11, 2018): 2185–95. http://dx.doi.org/10.1177/0142331218774614.

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Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put into an additional node – the only non-leaf node in this layer. Construction of the FODT consists of four major steps: (a) generation of fuzzy membership functions automatically by AFS theory according to the raw data distribution; (b) extraction of dynamically fuzzy rules in each non-leaf node by the fuzzy rule extraction algorithm (FREA); (c) construction of the FODT by the fuzzy rules obtained from step (b); and (d) determination of the optimal threshold [Formula: see text] to generate a final tree. Compared with five traditional decision trees (C4.5, LADtree (LAD), Best-first tree (BFT), SimpleCart (SC) and NBTree (NBT)) and a recently obtained fuzzy rules decision tree (FRDT) on eight UCI machine learning data sets and one biomedical data set (ALLAML), the experimental results demonstrate that the proposed algorithm outperforms the other decision trees in both classification accuracy and tree size.

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Dhiman, Nitesh, Madan M. Gupta, Dhan Pal Singh, Vandana Vandana, Vishnu Narayan Mishra, and Mukesh K. Sharma. "On Z-Intuitionistic Fuzzy Fractional Valuations for Medical Diagnosis: An Intuitionistic Fuzzy Knowledge-Based Expert System." Fractal and Fractional 6, no. 3 (March 10, 2022): 151. http://dx.doi.org/10.3390/fractalfract6030151.

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In an uncertain situation, data may present in continuous form or discrete form. We have various techniques to deal with continuous data in a realistic situation. However, when data are in discrete form, the existing techniques are inadequate to deal with these situations, and these techniques cannot provide the proper modulation for adequate analysis of the system. In order to provide the proper acceleration to discrete data, we need an appropriate modulation technique that can help us to handle unconditional boundedness on the technique and will operate like the techniques used for continuous data with fractional variables. In this work, we developed an intuitionistic fuzzy fractional knowledge-based expert system using unconditional and qualified fuzzy propositions based on the Z-intuitionistic fuzzy fractional valuation probability density function. In this proposed method, the discrete fractional variables will be converted into intuitionistic fuzzy fractional numbers and then be used in our algorithm. The proposed Z-intuitionistic fuzzy fractional valuation knowledge-based system can easily be applied in the medical field for the diagnosis of diseases in a vague environment due to the ordered-pair characteristics of the Z-intuitionistic fuzzy fractional valuation. In this study, we collected data of dengue patients, which included seven clinical findings: Temperature, sugar, Pulse Rate (PR), age, cough, and Blood Pressure (BP). A numerical example was also carried out to elaborate on the present technique. In addition, a comparative study is discussed in this work. We also provide the managerial implications of the data, with the limitations of the proposed technique presented at the end of this work.

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SIWANI, IMRAN, and MIRIAM CAPRETZ. "'fuzzy ProjectManager' — FRAMEWORK FOR SOFTWARE PROJECT MANAGEMENT USING FUZZY LOGIC." International Journal of Innovation and Technology Management 01, no. 04 (December 2004): 435–53. http://dx.doi.org/10.1142/s0219877004000301.

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Project Management is the application of knowledge, skills, tools and techniques to project activities in order to meet project requirements. The success of any project relies heavily on the initial estimation of all project parameters. The absence of reliable estimations leads to ineffective project planning, over- or under-commitment of resources and therefore an increased likelihood of a software project failure. Fuzzy Logic is a soft-computing technique used to effectively solve uncertainties due to imprecise inputs to generate linguistic or quantitative outputs. This paper presents a novel framework for project management incorporating fuzzy logic known as 'fuzzy ProjectManager'. Furthermore, this paper demonstrates the application of fuzzy logic as a feasible technique for improved estimation accuracy of all software project estimations to ensure higher software project success rates.

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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 (September 1, 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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Das, Satya Kumar, and Sahidul Islam. "Multi-Objective Two Echelon Supply Chain Inventory Model with Lot Size and Customer Demand Dependent Purchase Cost and Production Rate Dependent Production Cost." Pakistan Journal of Statistics and Operation Research 15, no. 4 (December 1, 2019): 831. http://dx.doi.org/10.18187/pjsor.v15i4.2929.

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This paper develops an integrated production inventory model for a two echelon supply chain consisting of one manufacturer and one retailer. Purchase cost for the manufacturer is dependent on inventory lot size, production cost of the manufacturer is dependent on production rate. Purchase cost of the retailer is dependent on demand rate of the customer. Idle time cost has been considered. Multi-item has been considered in this supply chain inventory model. Average cost in integrated inventory model has been calculated per unit time. The formulated problem has been solved by various techniques like as Fuzzy programming technique with hyperbolic membership functions (FPTHMF), Fuzzy non-linear programming technique (FNLP) and Fuzzy additive goal programming technique (FAGP), weighted Fuzzy non-linear programming technique (WFNLP) and weighted Fuzzy additive goal programming technique (WFAGP). A numerical example is provided to justify the proposed model. Finally graphical illustrations are considered and its sensitivity analysis is provided to test feasibility of the model.

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Shuaibu, Ali Maianguwa, Maryam Nuraini Muhammad, and Nasir Rabiu. "Utilization of fuzzy critical path method and fuzzy program evaluation and review technique for building a hydroelectric power station." Dutse Journal of Pure and Applied Sciences 8, no. 2b (June 25, 2022): 21–32. http://dx.doi.org/10.4314/dujopas.v8i2b.3.

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In this paper, fuzzy critical path method and fuzzy program evaluation and review technique are used to calculate the earliest project completion time for constructing a hydroelectric power plant project. Fuzzy trapezoidal numbers are used to estimate the activity time and determine the range of pessimistic to optimistic variation of time. Furthermore, the minimum and maximum times of project completion duration were calculated by using arithmetic operations and ranking of fuzzy trapezoidal numbers. These hybrid methods are able to deal with the limitations associated with classical critical path method and program evaluation and review technique. The fuzzy techniques were applied to network activities in a manner similar to the classical methods for optimizing the project completion duration, thereby minimizing the cost of the project. Analysis was carried out to determine the critical path through the use of fuzzy critical path method. The fuzzy program evaluation and review technique was also used to determine the probability of completing the project at a scheduled time. These two methods were then compared and the most probable scenarios were analyzed. Finally, it was concluded that fuzzy program evaluation and review technique is better than fuzzy critical path method and more efficient in terms of early project completion time.

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Dhivya, J., K. Meena, and M. N. Saroja. "A New Technique for solving Picture Fuzzy Differential Equation." Journal of Physics: Conference Series 2070, no. 1 (November 1, 2021): 012021. http://dx.doi.org/10.1088/1742-6596/2070/1/012021.

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Abstract Picture Fuzzy set (PFS) is an extension of fuzzy set (FS) and intuitionistic fuzzy set (IFS) that can model the uncertainty by integrating the concept of positive, negative and neutral membership degree of an element. In this paper, the solution of Picture Fuzzy ordinary differential equation of first order by means of picture fuzzy number is exemplified and intend to define the picture fuzzy number for (∝, δ, β)-cut. Finally, we illustrate the numerical example for drug distribution in human body for different drug levels is discussed for determining its effectiveness and practicality of the first order differential equation involving picture fuzzy numbers.

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A., Mohammed Raheel Basha. "Data Analytics Using Intelligent Optimization Technique: Profitability Analysis of Banks Using Fuzzy Logic Decision Making Technique." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (February 28, 2020): 349–73. http://dx.doi.org/10.5373/jardcs/v12sp3/20201271.

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Ranjan, Rakesh, and Dr Vinay Avasthi. "Enhanced Edge Detection Technique in Digital Images Using Optimised Fuzzy Operation." Webology 19, no. 1 (January 20, 2022): 5402–16. http://dx.doi.org/10.14704/web/v19i1/web19362.

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In image processing, edge detection is a critical issue. Edge detection is a key approach for evaluating the edge of various objects in a digital image. These edges are found using the gradients, which are present in the image. The intensity and value of pixels determine the gradients. In digital images, edge detection lowers the quantity of data and filters out irrelevant data while maintaining the image's key structural features. In this paper, a new edge detection approach based on a fuzzy rule-based system is proposed. In digital image processing, the proposed method typically depends on fuzzy logic systems. The main goal of this system is to show how fuzzy logic may be used in image processing. This paper provides a fuzzy logic-based edge detection technique that uses a sharpening Gabor filter to regulate edge quality and a Gaussian filter to reduce noise caused by sharpening. This is determined by utilizing applications such as “Peak Signal to Noise Ratio (PSNR) F-Measure, and Hausdorff distance (HoD) to prove that fuzzy logic outperforms the proposed system. The findings for edge detection approaches are included in high quality. The proposed approach outperforms most commonly used traditional edge detection methods. The proposed method also reduces the number of noisy features and may be used for a wide range of images.

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Nilashi, Mehrbakhsh, Fausto Cavallaro, Abbas Mardani, Edmundas Zavadskas, Sarminah Samad, and Othman Ibrahim. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique." Sustainability 10, no. 8 (August 1, 2018): 2707. http://dx.doi.org/10.3390/su10082707.

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Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this technique has been rarely explored when developing sustainability assessment methods. In addition, most of the methods developed in the literature solely rely on fuzzy logic. The main shortcoming of solely using the fuzzy logic rule-based technique is that it cannot automatically learn from the data. This problem of fuzzy logic has been solved by the use of neural networks in many real-world problems. The combination of these two techniques will take the advantages of both to precisely predict the output of a system. In addition, combining the outputs of several predictors can result in an improved accuracy in complex systems. This study accordingly aims to propose an accurate method for measuring countries’ sustainability performance using a set of real-world data of the sustainability indicators. The adaptive neuro-fuzzy inference system (ANFIS) technique was used for discovering the fuzzy rules from data from 128 countries, and ensemble learning was used for measuring the countries’ sustainability performance. The proposed method aims to provide the country rankings in term of sustainability. The results of this research show that the method has potential to be effectively implemented as a decision-making tool for measuring countries’ sustainability performance.

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Yi, Jae-Eung, and Chang-Won Choi. "Flood Forecasting and Warning Using Neuro-Fuzzy Inference Technique." Journal of Korea Water Resources Association 41, no. 3 (March 15, 2008): 341–51. http://dx.doi.org/10.3741/jkwra.2008.41.3.341.

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Kaur, Harneet, and Neeru Singla. "Video De-Noising Using Fuzzy Technique." American Journal of Electrical and Electronic Engineering 1, no. 3 (October 21, 2013): 46–51. http://dx.doi.org/10.12691/ajeee-1-3-3.

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An, Ruoming, and Wei Liang. "Unobservable fuzzy Petri net diagnosis technique." Aircraft Engineering and Aerospace Technology 85, no. 3 (May 3, 2013): 215–21. http://dx.doi.org/10.1108/00022661311313650.

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Arnold, Bernhard F., and Peter Stahlecker. "Minimax adjustment technique and fuzzy information." Linear Algebra and its Applications 289, no. 1-3 (March 1999): 25–39. http://dx.doi.org/10.1016/s0024-3795(97)10005-2.

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Olaru, Cristina, and Louis Wehenkel. "A complete fuzzy decision tree technique." Fuzzy Sets and Systems 138, no. 2 (September 2003): 221–54. http://dx.doi.org/10.1016/s0165-0114(03)00089-7.

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Boisa, Ahiauzu. "Rainfall-Runoff modeling using fuzzy technique." International Journal of Cloud Computing and Database Management 2, no. 1 (January 1, 2021): 01–03. http://dx.doi.org/10.33545/27075907.2021.v2.i1a.21.

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Komal, Komal. "Fuzzy reliability analysis of the washing system in a paper plant using the TBFLT technique." International Journal of Quality & Reliability Management 34, no. 8 (September 4, 2017): 1352–72. http://dx.doi.org/10.1108/ijqrm-06-2016-0097.

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Purpose The purpose of this paper is to analyze the reliability of the washing system in a paper plant in a more promising way under vague environment by reducing the accumulating phenomenon of fuzziness and accelerating the computation process using the Tω (weakest t-norm) based fuzzy lambda-tau (TBFLT) technique. Design/methodology/approach This paper presents a unified approach for analyzing the fuzzy reliability of the washing system under vague environment. This approach applies the TBFLT technique which uses triangular fuzzy numbers for incorporating data uncertainty, fault tree and lambda-tau method for finding system failure rate and repair time mathematical expressions while simplified Tω-based arithmetic operations are applied for computing various reliability parameters of the system. The effectiveness of the TBFLT technique has been demonstrated by analyzing fuzzy reliability of the system using five different techniques including TBFLT. Moreover, this paper applies extended Tanaka’s (1983) approach to rank the critical components of the system. Findings The TBFLT technique has the advantage of low computation complexity in comparison to other techniques and effectively reduces the accumulating phenomenon of fuzziness. This main finding verifies the conclusion made by Chen (1994). Originality/value The author has suggested a simple and more applicable technique for analyzing the fuzzy reliability of any complex process industrial system under vague environment. The effectiveness of the technique has been demonstrated by computing various reliability parameters of the washing system of a paper plant.

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Ramadan, Ayad M., and Goran Hidayat Kareem. "A New Method for Ranking Fuzzy Numbers Based on Principal Component Analysis." International Journal of Science, Engineering and Management 9, no. 9 (September 13, 2022): 1–5. http://dx.doi.org/10.36647/ijsem/09.09.a001.

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It is difficult to rank fuzzy numbers because of their ambiguous values. A few numbers of ranking techniques have been encountered in last few decades. However, existing techniques are situation-dependent which have drawbacks. In this paper we introduced a statistical technique to rank two types of fuzzy numbers, triangular and trapezoidal fuzzy numbers. This technique is the multi-dimensional scaling, more precisely the principal component analysis. Here, we presented each fuzzy numbers as a row in a matrix, then found the scale points in a low dimensions. The scale points are then reconfigured to have a unique representation. The results from this approach are obtained by comparison to the other ranking methods. The validity has been established by comparison with existing works.

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Singh, Harmandeep, and Baljit Singh Khehra. "Visibility enhancement of color images using Type-II fuzzy membership function." Modern Physics Letters B 32, no. 11 (April 18, 2018): 1850130. http://dx.doi.org/10.1142/s0217984918501300.

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Images taken in poor environmental conditions decrease the visibility and hidden information of digital images. Therefore, image enhancement techniques are necessary for improving the significant details of these images. An extensive review has shown that histogram-based enhancement techniques greatly suffer from over/under enhancement issues. Fuzzy-based enhancement techniques suffer from over/under saturated pixels problems. In this paper, a novel Type-II fuzzy-based image enhancement technique has been proposed for improving the visibility of images. The Type-II fuzzy logic can automatically extract the local atmospheric light and roughly eliminate the atmospheric veil in local detail enhancement. The proposed technique has been evaluated on 10 well-known weather degraded color images and is also compared with four well-known existing image enhancement techniques. The experimental results reveal that the proposed technique outperforms others regarding visible edge ratio, color gradients and number of saturated pixels.

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Shukla, Ashutosh, and Asst Prof Sudeep Mohaney. "A Complete review of FLC MPPT Technique and Comparison with INC Technique." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1245–53. http://dx.doi.org/10.22214/ijraset.2022.44031.

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Abstract: In recent years, all around the world, considerable technological growth has been observed to improve the availability of electrical energy in the most ecological way. Under partial shading conditions, maximum power point tracking techniques track the point at which full power can be taken out. Thus the net efficiency of a photovoltaic system is improved. This paper evaluates, methods such as incremental conductance (INC) and fuzzy logic controller (FLC) are evaluated. The simulation results obtained are developed under the software MATLAB / Simulink. Both techniques (INC) and (FLC) are used with a boost DC / DC converter and a load. Theseresults show that the fuzzy logic controller is superior to and faster than the conventional incremental conductance (INC) technique in dynamic response and steady-state in regular operation. Keywords: MPPT; PV; technique INC; technique FLC; Boost DC/DC.

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Du, Shi Ping, Jian Wang, and Yu Ming Wei. "The Training Algorithm of Fuzzy Coupled Hidden Markov Models." Applied Mechanics and Materials 568-570 (June 2014): 254–59. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.254.

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A variety of coupled hidden Markov models (CHMMs) have recently been proposed as extensions of HMM to better characterize multiple interdependent sequences. The resulting models have multiple state variables that are temporally coupled via matrices of conditional probabilities. A generalised fuzzy approach to statistical modelling techniques is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to coupled hidden Markov models. The CHMM based on the fuzzy c-means (FCM) and fuzzy entropy (FE) is referred to as FCM-FE-CHMM in this paper. By building up a generalised fuzzy objective function, several new formulae solving Training algorithms are theoretically derived for FCM-FE-CHMM. The fuzzy modelling techniques are very flexible since the degree of fuzziness, the degree of fuzzy entropy.

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Kanzawa, Yuchi. "Fuzzy Co-Clustering Algorithms Based on Fuzzy Relational Clustering and TIBA Imputation." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 2 (March 20, 2014): 182–89. http://dx.doi.org/10.20965/jaciii.2014.p0182.

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In this paper, two types of fuzzy co-clustering algorithms are proposed. First, it is shown that the base of the objective function for the conventional fuzzy co-clustering method is very similar to the base for entropy-regularized fuzzy nonmetric model. Next, it is shown that the non-sense clustering problem in the conventional fuzzy co-clustering algorithms is identical to that in fuzzy nonmetric model algorithms, in the case that all dissimilarities among rows and columns are zero. Based on this discussion, a method is proposed applying entropy-regularized fuzzy nonmetric model after all dissimilarities among rows and columns are set to some values using a TIBA imputation technique. Furthermore, since relational fuzzy cmeans is similar to fuzzy nonmetricmodel, in the sense that both methods are designed for homogeneous relational data, a method is proposed applying entropyregularized relational fuzzyc-means after imputing all dissimilarities among rows and columns with TIBA. Some numerical examples are presented for the proposed methods.

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Sethi, Rabinarayan, S. K. Senapati, and Dayal R. Parhi. "Structural Damage Detection by Fuzzy Logic Technique." Applied Mechanics and Materials 592-594 (July 2014): 1175–79. http://dx.doi.org/10.4028/www.scientific.net/amm.592-594.1175.

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In this paper, a novel approach for detecting crack location and its intensity in cantilever beam by Fuzzy logic techniques is established. The analysis has been done by using ANSYS FE software. The fuzzy controller with Bell shaped membership functions are used here which consists of three input parameters are relative deviation of first three natural frequencies and two output parameters are relative crack depth and relative crack location respectively. A series of fuzzy rules are resulting from vibration parameters which are finally used for prediction of crack location and its intensity. This method provides the knowledge towards the detection, location and characterization of the damage in the cantilever beam.

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Sahu, Sanat Kumar, and A. K. Shrivas. "Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm." International Journal of Computer Vision and Image Processing 8, no. 4 (October 2018): 16–25. http://dx.doi.org/10.4018/ijcvip.2018100102.

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The purpose of this article is to weigh up the foremost imperative features of Chronic Kidney Disease (CKD). This study is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used evolutionary techniques like genetic algorithms (GA) to extend the performance of the clustering model. The performance of these three clusters: live parameter purity, entropy, and Adjusted Rand Index (ARI) have been contemplated. The best purity is obtained by the K-means clustering technique, 96.50%; whereas, Fuzzy C-means clustering received 93.50% and hierarchical clustering was the lowest at 92. 25%. After using evolutionary technique Genetic Algorithm as Feature selection technique, the best purity is obtained by hierarchical clustering, 97.50%, compared to K –means clustering, 96.75%, and Fuzzy C-means clustering at 94.00%.

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Ali, Abdualmtalab, Usama Heneash, Amgad Hussein, and Mohamed Eskebi. "Predicting Pavement Condition Index Using Fuzzy Logic Technique." Infrastructures 7, no. 7 (July 2, 2022): 91. http://dx.doi.org/10.3390/infrastructures7070091.

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The fuzzy logic technique is one of the effective approaches for evaluating flexible and rigid pavement distress. The process of classifying pavement distress is usually performed by visual inspection of the pavement surface or using data collected by automated distress measurement equipment. Fuzzy mathematics provides a convenient tool for incorporating subjective analysis, uncertainty in pavement condition index, and maintenance-needs assessment, and can greatly improve consistency and reduce subjectivity in this process. This paper aims to develop a fuzzy logic-based system of pavement condition index and maintenance-needs evaluation for a pavement road network by utilizing pavement distress data from the U.S. and Canada. Considering rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and raveling as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relationships between nine pavement distress parameters and PCI were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF–THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used as the performance indicator metrics to evaluate the performance of analytical models.

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Majumder, Priyanka, Valentina Emilia Balas, Arnab Paul, and Dayarnab Baidya. "Application of improved fuzzy best worst analytic hierarchy process on renewable energy." PeerJ Computer Science 7 (April 13, 2021): e453. http://dx.doi.org/10.7717/peerj-cs.453.

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In this work, a novel fuzzy decision making technique namely trapezoidal fuzzy Best-Worst method (fuzzy BWM) is developed which is based on Best-Worst method (BWM) and Trapezoidal fuzzy number. The real motive behind our work is to take a broad view of the existing fuzzy BWM based on triangular fuzzy number by trapezoidal fuzzy number. Also, we have presented a new hybrid MCDM technique called as Trapezoidal fuzzy Best Worst Analytic Hierarchy based on proposed trapezoidal fuzzy BWM and existing trapezoidal fuzzy Analytic Hierarchy Process (AHP). BWM approach is employed in evaluating the PV of considering criteria and trapezoidal fuzzy AHP is used to assess the local priority vale (PV) of considering alternatives (or indicators) of a decision problem. Moreover it used to identify the most significant alternative which is responsible for performance efficiency of a hydro power plant under climatic scenario. From the result, it is undoubtedly found that hydraulic had is most responsible indicator. Further, the CR (consistency ratio) value which is determined by our proposed trapezoidal fuzzy BWM is less than that of existing BWM and fuzzy BWM techniques. Finally, we have validated our result by comparative study, scenario analysis and sensitivity analysis.

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Dey, Samir, and Tapan Kumar Roy. "Multi-objective Structural Optimization Using Fuzzy and Intuitionistic Fuzzy Optimization Technique." International Journal of Intelligent Systems and Applications 7, no. 5 (April 8, 2015): 57–65. http://dx.doi.org/10.5815/ijisa.2015.05.08.

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Zhai, Junhai, Mengyao Zhai, and Xiaomeng Kang. "Condensed fuzzy nearest neighbor methods based on fuzzy rough set technique." Intelligent Data Analysis 18, no. 3 (April 30, 2014): 429–47. http://dx.doi.org/10.3233/ida-140649.

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Zhang, Xiaolu, Zeshui Xu, and Xiaoming Xing. "Hesitant fuzzy programming technique for multidimensional analysis of hesitant fuzzy preferences." OR Spectrum 38, no. 3 (October 12, 2015): 789–817. http://dx.doi.org/10.1007/s00291-015-0420-0.

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Wang, Xizhao, Eric C. C. Tsang, Suyun Zhao, Degang Chen, and Daniel S. Yeung. "Learning fuzzy rules from fuzzy samples based on rough set technique." Information Sciences 177, no. 20 (October 2007): 4493–514. http://dx.doi.org/10.1016/j.ins.2007.04.010.

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Nefti, S., M. Oussalah, and U. Kaymak. "A New Fuzzy Set Merging Technique Using Inclusion-Based Fuzzy Clustering." IEEE Transactions on Fuzzy Systems 16, no. 1 (February 2008): 145–61. http://dx.doi.org/10.1109/tfuzz.2007.902011.

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Shams, Mudassir, Nasreen Kausar, Naveed Khan, and Mohd Asif Shah. "Modified Block Homotopy Perturbation Method for solving triangular linear Diophantine fuzzy system of equations." Advances in Mechanical Engineering 15, no. 3 (March 2023): 168781322311595. http://dx.doi.org/10.1177/16878132231159519.

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Numerous real-world applications can be solved using the broadly adopted notions of intuitionistic fuzzy sets, Pythagorean fuzzy sets, and q-rung orthopair fuzzy sets. These theories, however, have their own restrictions in terms of membership and non-membership levels. Because it utilizes benchmark or control parameters relating to membership and non-membership levels, this theory is particularly valuable for modeling uncertainty in real-world problems. We propose the unique concept of linear Diophantine fuzzy set with benchmark parameters to overcome these restrictions. Different numerical, analytical, and semi-analytical techniques are used to solve linear systems of equations with several fuzzy numbers, such as intuitionistic fuzzy number, triangular fuzzy number, bipolar fuzzy number, trapezoidal fuzzy number, and hexagon fuzzy number. The purpose of this research is to solve a fuzzy linear system of equations with the most generalized fuzzy number, such as Triangular linear Diophantine fuzzy number, using an analytical technique called Homotopy Perturbation Method. The linear systems co-efficient are crisp when the right hand side vector is a triangular linear Diophantine fuzzy number. A numerical test examples demonstrates how our newly improved analytical technique surpasses other existing methods in terms of accuracy and CPU time. The triangular linear Diophantine fuzzy systems of equations’ strong and weak visual representations are explored.

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Towfik, Zaki, and Sabiha Jawad. "Proposed Method for Optimizing Fuzzy linear programming Problems by using Two-Phase Technique." Iraqi Journal for Electrical and Electronic Engineering 6, no. 2 (December 1, 2010): 89–96. http://dx.doi.org/10.37917/ijeee.6.2.2.

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Fuzzy linear programming (FLP ) is an application of fuzzy set theory in linear decision making problems and most of these problems are related to linear programming contains fuzzy constrains or crisp objectives function or contains crisp constrains with fuzzy objectives function, which called fuzzy linear programming (FLP) with triplet fuzzy numbers consist a hybrid fuzzy. The crisp constrains used in the problems of types (= or ≥) with a proposed optimization fuzzy objectives and fuzzy constrains. In this paper proposed method for solving fuzzy linear programming problem by using Two-phase technique to solve the problem and to determine the optima crisp objectives.

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Ansaf, Huda, Bahaa Kazem Ansaf, and Sanaa S. Al Samahi. "A Neuro-Fuzzy Technique for the Modeling of β-Glucosidase Activity from Agaricus bisporus." BioChem 1, no. 3 (October 19, 2021): 159–73. http://dx.doi.org/10.3390/biochem1030013.

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This paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership functions based on previously published experimental data for β-glucosidase. Second, each input’s optimized membership functions from the ANFIS technique were embedded in a new fuzzy inference system to simultaneously encompass the impact of temperature and pH level on the activity of β-glucosidase. The required base rules for the developed fuzzy inference system were created to describe the antecedent (pH and temperature) implication to the consequent (enzyme activity), using the singleton Sugeno fuzzy inference technique. The simulation results from the developed models achieved high accuracy. The neuro-fuzzy approach performed very well in predicting β-glucosidase activity through comparative analysis. The proposed approach may be used to predict enzyme kinetics for several nonlinear biosynthetic processes.

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Honda, Katsuhiro, Shunnya Oshio, and Akira Notsu. "Fuzzy Co-Clustering Induced by Multinomial Mixture Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 717–26. http://dx.doi.org/10.20965/jaciii.2015.p0717.

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A close connection between fuzzyc-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms were induced by the GMMs concept, where fuzzy partitions are proved to be more useful for revealing intrinsic cluster structures than probabilistic ones. Co-clustering is a promising technique for summarizing cooccurrence information such as document-keyword frequencies. In this paper, a fuzzy co-clustering model is induced based on the multinomial mixture models (MMMs) concept, in which the degree of fuzziness of both object and item fuzzy memberships can be properly tuned. The advantages of the dual fuzzy partition are demonstrated through several experimental results including document clustering applications.

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Thiab, Omar Sami, Sami Thiab Abdulrazaaq, Zainal Abidin Izham, and Zainul Abidin Aidil Azwin. "Fuzzy Logic Overcurrent Relay Using Sugeno Technique." Advanced Materials Research 925 (April 2014): 559–63. http://dx.doi.org/10.4028/www.scientific.net/amr.925.559.

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The overcurrent relay is the simplest and most economical type of protective relay, the relay is designed to operate when more than a predetermined amount of current flows into a particular portion of the power system. Time delay between relay's main zone and it's backup will keep the selectivity of the system, The inverse definite minimum time relay will calculate the time delay inversely proportional to the fault current level. This technique has some weaknesses where the time delay provided for a given case maybe too high and if the fault is not isolated immediately then damage could be caused to other connected equipment in the power system. This paper presents the fuzzy logic relay using Sugeno technique to determine the time delay, and using of symmetrical components application in detecting fault type, also it presents a comparison between times calculated by other techniques.

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Hemamalini, Selvamani, and Visvam Devadoss Ambeth Kumar. "Outlier Based Skimpy Regularization Fuzzy Clustering Algorithm for Diabetic Retinopathy Image Segmentation." Symmetry 14, no. 12 (November 28, 2022): 2512. http://dx.doi.org/10.3390/sym14122512.

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Blood vessels are harmed in diabetic retinopathy (DR), a condition that impairs vision. Using modern healthcare research and technology, artificial intelligence and processing units are used to aid in the diagnosis of this syndrome and the study of diagnostic procedures. The correct assessment of DR severity requires the segmentation of lesions from fundus pictures. The manual grading method becomes highly difficult and time-consuming due to the wide range of the morphologies, number, and sizes of lesions. For image segmentation, traditional fuzzy clustering techniques have two major drawbacks. First, fuzzy memberships based clustering are more susceptible to outliers. Second, because of the lack of local spatial information, these techniques often result in oversegmentation of images. In order to address these issues, this research study proposes an outlier-based skimpy regularization fuzzy clustering technique (OSR-FCA) for image segmentation. Clustering methods that use fuzzy membership with sparseness can be improved by incorporating a Gaussian metric regularisation into the objective function. The proposed study used the symmetry information contained in the image data to conduct the image segmentation using the fuzzy clustering technique while avoiding over segmenting relevant data. This resulted in a reduced proportion of noisy data and better clustering results. The classification was carried out by a deep learning technique called convolutional neural network (CNN). Two publicly available datasets were used for the validation process by using different metrics. The experimental results showed that the proposed segmentation technique achieved 97.16% and classification technique achieved 97.26% of accuracy on the MESSIDOR dataset.

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Sharma, Omdutt, Pratiksha Tiwari, and Priti Gupta. "Fuzzy Soft Matrices Entropy." International Journal of Fuzzy System Applications 7, no. 3 (July 2018): 56–75. http://dx.doi.org/10.4018/ijfsa.2018070104.

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This article describes how information technology and internet together infused organizations with huge amount of data. Consequently, accumulating, storing, understanding and analyzing data at a large scale is equally important and complex. Out of this data not all is information data, in order to extract information, one needs to discard redundant, irrelevant and unnecessary data. This article aims to introduce a data reduction technique which will be useful to discard irrelevant data. Here in data-reduction, the authors have used fuzzy-soft set techniques, namely fuzzy-soft information matrixes. Further, they have introduced a new fuzzy-soft information measure of fuzzy-soft matrixes.

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Hussein, Yahya, and ALI Sahan. "An Intelligent Ear Recognition Technique." International Journal of Advances in Soft Computing and its Applications 13, no. 3 (November 28, 2021): 13–27. http://dx.doi.org/10.15849/ijasca.211128.02.

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The human ear has unique and attractive details; therefore, human ear recognition is one of the most important fields in the biometric domains. In this work, we proposed an efficient and intelligent ear recognition technique based on particle swarm optimization, discrete wavelet transform, and fuzzy neural network. Discrete wavelet transform is used to provide comprise and effective features about the ear image, while the particle swarm optimization utilized to select more effective and attractive features. Furthermore, using particle swarm optimization leads to reduce the complexity of the classification stage since it reduces the number of the features. Fuzzy neural network used in the classification stage in order to provide strong distinguishing between the testing and training ear images. many experiments performed using two ear databases to examine the accuracy of the proposed technique. The analysis of the results refers that the presented technique gained high recognition accuracy using various data sets with less complexity. Keywords: Ear recognition; bio-metric; discrete wavelet transform, particle swarm optimization, fuzzy neural network.

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Campbell, Gwendolyn Elizabeth, Wendi Lynn Buff, and Amy Elizabeth Bolton. "The Diagnostic Utility of Fuzzy System Modeling for Application in Training Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 11 (July 2000): 370–73. http://dx.doi.org/10.1177/154193120004401111.

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While there are many different computational modeling techniques capable of predicting human decision-making outcomes, training applications require modeling techniques that are also diagnostic of human decision-making processes. Multiple linear regression, a commonly used modeling technique in Psychology, makes overly restrictive processing assumptions such as that of additivity. A relatively new modeling approach, fuzzy system modeling, bears some striking similarities to current theories of categorization and cognition. In this research, we compare the diagnostic utility of multiple linear regression to fuzzy system models. Specifically, decision-making data are modeled using either linear regression or fuzzy system models, and trainee models are compared to an expert model built with the same technique. Discrepancies between the trainee and expert models are noted and qualitative feedback is generated. The diagnostic utility of each technique is evaluated by measuring changes in performance after model-based feedback is provided to the trainees.

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Gonzales, Julirose, and Zahari Taha. "Mobile Robot Navigation Using Open Computer Vision with Fuzzy Controller." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 4 (July 20, 2008): 336–41. http://dx.doi.org/10.20965/jaciii.2008.p0336.

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This research presents a fuzzy controller technique in navigation with obstacle avoidance for a general purpose mobile robot in a given global environment with image processing technique using Open Source Computer Vision (OpenCV) library on Visual C++. Fuzzy Logic is used to control the navigation of the robot towards the goal while avoiding obstacles along the way by changing its direction of movement. The positions of the mobile robot, obstacle and the destination are taken into consideration and an overhead camera (above the robot’s environment) is used to gather these necessary information. The images captured are processed using different techniques to get the desired positions and is directly integrated with the fuzzy controller making the algorithm more efficient compared to other vision-guided navigation techniques.

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Journal articles on the topic 'FUZZY TECHNIQUE'

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