List of Articles Fuzzy c-means

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  1 - Image Processing of steel sheets for Defect Detection by using Gabor Wavelet
  masoud shafiee mostafa sadeghi
  In different steps of steel production, various defects appear on the surface of the sheet. Putting aside the causes of defects, precise identification of their kinds helps classify steel sheet correctly, thereby it allocates a high percentage of quality control process More

  In different steps of steel production, various defects appear on the surface of the sheet. Putting aside the causes of defects, precise identification of their kinds helps classify steel sheet correctly, thereby it allocates a high percentage of quality control process. QC of steel sheet for promotion of product quality and maintaining the competitive market is of great importance. In this paper, in addition to quick review of image process techniques used, using image process by means of Gabor wavelet, a fast and precise solution for detection of texture defects in steel sheet is presented. In first step, the approach extracts considerable texture specification from image by using Gabor wavelet. The specification includes both different directions and different frequencies. Then using statistical methods, images are selected that have more obvious defects, and location of defects is determined. Offering the experimental samples, the accuracy and speed of the method is indicated. Manuscript profile
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  2 - The study of the accuracy of real estate experts' evaluations using a data mining model (Case study of Mellat Bank)
  fatemeh davar
  As the main part of the financial system, banks always face different risks, the most important of which is the credit scoring risk and property valuation. One of the issues faced by property valuation experts is how to evaluate property prices. In general, court expert More

  As the main part of the financial system, banks always face different risks, the most important of which is the credit scoring risk and property valuation. One of the issues faced by property valuation experts is how to evaluate property prices. In general, court experts assess real estate based on price indices. In this research, the researcher aimed to verify the accuracy of valuation experts by using data mining models. This action has been taken to help bank managers and audit reporters to make better decisions about experts and their valuations. Using property valuation indexes and data mining, a predictive model has been developed to predict property prices, and a combination of FCM and K-NN algorithms has been used to achieve a high performance prediction model. This measure was able to greatly increase the predictive accuracy and increase the efficiency of the proposed model. The accuracy level in predicting valuated prices was 84.21% and the RMSE rate in its forecast was 0.43. The proposed approach was tested on real estate valuation data of the Mellat Bank. Manuscript profile
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  3 - Efficient Land-cover Segmentation Using Meta Fusion
  Morteza Khademi Hadi Sadoghi Yazdi
  10.7508/jist.2016.03.003
  Most popular fusion methods have their own limitations; e.g. OWA (order weighted averaging) has “linear model” and “summation of inputs proportions in fusion equal to 1” limitations. Considering all possible models for fusion, proposed fusion method involve input data c More

  Most popular fusion methods have their own limitations; e.g. OWA (order weighted averaging) has “linear model” and “summation of inputs proportions in fusion equal to 1” limitations. Considering all possible models for fusion, proposed fusion method involve input data confusion in fusion process to segmentation. Indeed, limitations in proposed method are determined adaptively for each input data, separately. On the other hand, land-cover segmentation using remotely sensed (RS) images is a challenging research subject; due to the fact that objects in unique land-cover often appear dissimilar in different RS images. In this paper multiple co-registered RS images are utilized to segment land-cover using FCM (fuzzy c-means). As an appropriate tool to model changes, fuzzy concept is utilized to fuse and integrate information of input images. By categorizing the ground points, it is shown in this paper for the first time, fuzzy numbers are need and more suitable than crisp ones to merge multi-images information and segmentation. Finally, FCM is applied on the fused image pixels (with fuzzy values) to obtain a single segmented image. Furthermore mathematical analysis and used proposed cost function, simulation results also show significant performance of the proposed method in terms of noise-free and fast segmentation. Manuscript profile
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  4 - Proposing an FCM-MCOA Clustering Approach Stacked with Convolutional Neural Networks for Analysis of Customers in Insurance Company
  Motahareh Ghavidel meisam Yadollahzadeh tabari Mehdi Golsorkhtabaramiri
  10.61186/jist.41465.12.45.62
  To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimension More

  To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimensional with too many instances and include both supervised and unsupervised ones. For this reason, companies today are trying to satisfy their customers as much as possible. This issue requires careful consideration of customers from several aspects. Data mining algorithms are one of the practical methods in businesses to find the required knowledge from customer’s both demographic and behavioral. This paper presents a hybrid clustering algorithm using the Fuzzy C-Means (FCM) method and the Modified Cuckoo Optimization Algorithm (MCOA). Since customer data analysis has a key role in ensuring a company's profitability, The Insurance Company (TIC) dataset is utilized for the experiments and performance evaluation. We compare the convergence of the proposed FCM-MCOA approach with some conventional optimization methods, such as Genetic Algorithm (GA) and Invasive Weed Optimization (IWO). Moreover, we suggest a customer classifier using the Convolutional Neural Networks (CNNs). Simulation results reveal that the FCM-MCOA converges faster than conventional clustering methods. In addition, the results indicate that the accuracy of the CNN-based classifier is more than 98%. CNN-based classifier converges after some couples of iterations, which shows a fast convergence in comparison with the conventional classifiers, such as Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighborhood (KNN), and Naive Bayes (NB) classifiers. Manuscript profile