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Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorders affected to female in their reproductive cycle. PCO (Polycystic Ovaries) describes ovaries that contain many small cysts/follicles. This paper proposes an image clustering approach for follicles segmentation using Particle Swarm Optimization (PSO) with a new modified non-parametric fitness function. The new modified fitness function...
This paper presents a novel iterative Bayesian algorithm, Block Iterative Bayesian Algorithm (Block-IBA), for reconstructing block-sparse signals with unknown block structures. Unlike the other existing algorithms for block sparse signal recovery which assume the cluster structure of the non-zero elements of the unknown signal to be independent and identically distributed (i.i.d.), we use a more realistic...
Segmentation is considered as a core step for any recognition or classification method and for the text within any document to be effectively recognized it must be segmented accurately. In this paper a text and writer independent algorithm for the segmentation of sub-words in Arabic words has been presented. The concept is based around the global binarization of an image at various thresholding levels...
Data is growing at an unprecedented rate in commercial and scientific areas. Clustering algorithms for large data which require small memory consumption and scalability become increasingly important under this circumstance. In this paper, we propose a new clustering approach called stochastic gradient based fuzzy clustering(SGFC) which achieves the optimization based on stochastic approximation to...
A fuzzy c-regression model clustering algorithm based on Bias-Eliminated Least Squares method (BELS) is presented. This method is designed to develop an identification procedure for noisy nonlinear systems. The BELS method is used to identify consequent parameters and eliminate the bias. The proposed approach has been applied to benchmark modeling problem which proved a good performance.
One aim of basic oxygen furnace (BOF) steelmaking endpoint control is the temperature control. For the majority of the china's small or medium BOF, sublance can not be used as a result of restrictions of production conditions, so, researching the BOF endpoint control without sublance has a significant application value. For the data's characteristics of nonlinearity and high noises in the field, a...
This paper seeks an answer to the question: Can the fuzzy k-means (FKM), c-means (FCM), kernelized FCM (KFCM), and spatial constrained (SKFCM) work automatically without pre-define number of clusters. We present automatic fuzzy algorithms with considering some spatial constraints on the objective function. The algorithms incorporate spatial information into the membership function and the validity...
This paper proposes a new objective function for fuzzy c-regression model (FCRM) clustering algorithm. The main motivation for this work is to develop an identification procedure for nonlinear systems affected by measurement noise. The proposed methodology is based to adding a second regularization term in the objective function of FCRM clustering algorithm in order to take in account the data are...
In this paper, a modified fuzzy c-regression model (FCRM) clustering algorithm for identification of Takagi-Sugeno (T-S) fuzzy model is proposed. The FCRM clustering algorithm has considerable sensitive to noise. To overcome this problem, a modified FCRM clustering algorithm is presented. This latter is based to adding a second regularization term in the alternative optimization process of FCRM. This...
Understanding the genotype-phenotype association is a fundamental problem in genetics. A major open problem in mapping complex traits is identifying a set of interacting genetic variants (such as single nucleotide polymorphisms or SNPs) that influence disease susceptibility. Logic regression (LR) is a statistical approach that has been proposed to model interactions of SNPs. Several LR-based association...
This paper proposes a robust validity index for Fuzzy c-Means (FCM) algorithm. The Fuzzy c-Means algorithm has become of most widely used method in fuzzy clustering. After clustering, it is often necessary to evaluate its results. Such assessment techniques are called cluster validity. The disadvantage of FCM is that the number of clusters must be predetermined. Even if the number of clusters is given,...
In this paper, a modified Fuzzy C-means clustering algorithm is proposed for the segmentation of color images. The modified Fuzzy C-means clustering (FCM) algorithm includes both the local spatial information from neighboring pixels, and the spatial Euclidian distance to the cluster's center of gravity. This new method increases the accuracy of clustering, and improves the tolerance to noise. It also...
Word-length optimization provides opportunities for minimization of implementation cost metrics such as power, area and delay. The constraints on cost in case of implementation on miniature embedded systems platforms continues to grow more stringent. Implementation of complex systems such as wireless communication transceivers is known to greatly benefit from optimal word-lengths. However, WL optimization...
In this paper, we propose an approach exploiting the relationship between the position of the clustering centers of the observed data and the mixing parameters to realize blind separation of multiple sequences, drawn from finite alphabet set, from a single linear mixture. In theory, we prove that the system estimation by the algorithm is optimum in the sense of least square (LS) by mathematical induction...
This paper proposes a new validity index for the subtractive clustering (SC) algorithm. The subtractive clustering algorithm proposed by Chiu is an effective and simple method for identifying the cluster centers of sampling data based on the concept of a density function. In this paper, a modified SC algorithm for data clustering based on a cluster validity index is proposed to obtain the optimal...
Our goal is to establish how many different people are present in a set of N facial images, and determine the correspondence between people and images. Our approach is Bayesian: in the training phase, we learn a probabilistic generative model for face data. Individual identity is represented as a latent variable in this model, and is constrained to be identical when faces match. We use this model...
In recent years, localization in a variety of Wireless Sensor Networks (WSNs) is a compelling but elusive goal. Several algorithms that use different methodologies have been proposed to achieve this goal. The performances of these algorithms depend on several factors, such as the sensor node placement, anchor deployment or network topology. In this paper, we propose a robust localization algorithm...
In this paper, inspired by RANSAC and Hough Transform voting procedure, a modified RANSAC algorithm of detection of cylindrical reference target using LRF for positioning system is proposed. Utilizing the orientation-invariant property of the circle, cylindrical shaped bar is chosen as the reference target of the positioning system. The given radius of the cylinder and convex arc shaped contour observed...
This paper presents a robust interval type-2 possibilistic C-means (IT2PCM) clustering algorithm which is actually alternating cluster estimation, but membership functions are selected with interval type-2 fuzzy sets by the users. The cluster prototypes are calculated by type reduction combined with defuzzification; consequently they could be directly extracted to generate interval type-2 fuzzy rules...
A PieceWise AutoRegressive eXogenous (PWARX) model for the AMIRA DR300 DC motor is identified using clustering techniques available in the Hybrid Identification Toolbox (HIT). The choice of design parameters like magnitude of the noise variance and size of the local dataset are discussed. These parameters influence the quality and performance of the PWARX model. The performance of the PWARX model...
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