Acoustic source separation is considered as one of the challenging issues in the signal enhancement field and it becomes even more problematic when some acoustic sources are mixed in the presence of noise. In this paper, the weighted gaussian Mean Shift Algorithm method for dual-channel systems is generalized to multichannel systems. The source separation mechanism is blind and it has the ability to extract several sources from various mixtures. The proposed method is compared with a number of existing Algorithms for acoustic source separation under noisy conditions recorded by multiple hidrophones. The results demonstrate the effectiveness of the proposed method.

Raw point clouds usually include noise and outliers. Also, the point clouds generated by photogrammetry methods are noisier than the point clouds that are derived from active methods such as laser scanners, hence many challenges for reconstructing and meshing surface using these three-dimensional data would be possible. Also, maintaining sharp features is essential during the process of noise removal. Many techniques have been developed to remove noise from the point cloud, but only a few of them are suitable for maintaining Sharp features during the noise removal process. This paper tries to provide a new statistical method with the ability to maintain sharp features, to remove noise. In the proposed method, first, the point cloud is clustered using the Mean-Shift clustering Algorithm. As the clustering accuracy depends on the kernel size, the optimal size of the window is achieved through the hill climbing optimization. Then, in each cluster, the distance between each point and the Mean of the other points of that cluster is calculated,next, appropriate thresholds are used to detect and remove noise from point cloud by applying them on the number of members of each cluster and computed distances. So the sharp features, such as the edges, are preserved. The experimental results obtained from the implementation of the proposed method on the three sets of 3D data, provided by the laser scanner, illustrate that this method, compared with the other methods presented in the literature review, increases the accuracy about 4% in noise removing and 5. 19 percent in maintaining sharp features.

The rapid development of communication technologies and information and online computers and their usage in processes of the industrial production have facilitated simultaneous monitoring of multiple variables (characteristics) in a process. In this work, we applied boosted decision tree
        and Monte Carlo simulation to propose an efficient method for detecting incontrol and out-of-control states in multivariate control processes. In this work, four classifiers (methods)-  ,      ,     ,   – are used for detecting the process control states. Then, with converting detection results these four classifiers, the boosted decision tree is made and provides the ultimate result as the incontrol or the out-of-control states. To show how the proposed model works and the superiority of this method over  ,     ,     , and  methods, we run it on a standardized trivariate normal process. To compare and evaluate the performance of classifiers, we used ARL functions and the evaluation measures including Accuracy (ACC), Sensitivity (TPR), Specificity (SPC), and Precision (PPV). The findings not only showed the superiority of the proposed method over the tradition Chi-square but also confirmed former results on the efficiency of decision tree for rapid detecting of Mean Shifts in multivariate processes in which data are gathered automatically.

In human physiology, cholesterol plays an imperative part in membrane cells which regulates the function of G-protein-coupled receptors (GPCR) family. Cholesterol is an individual type of lipid structure and about 90 percent of cellular cholesterol is present at plasma membrane region. Cholesterol Recognition/interaction Amino acid Consensus (CRAC) sequence, generally referred as the CRAC (L/V)-X1− 5-(Y)-X1− 5-(K/R) and the new cholesterol-binding domain is similar to the CRAC sequence, but exhibits the inverse orientation along the polypeptide chain i. e. CARC (K/R)-X1− 5-(Y/F)-X1− 5-(L/V). GPCR is treated as a biggest super family in human physiology and probably more than 900 protein genes included in this family. Among all membrane proteins GPCR is responsible for novel drug discovery in all pharmaceuticals industry. In earlier researches the researchers did not find the required number of valid motifs in terms of helices and motif types so they were lacking clinical relevance. The research gap here is that they were not able to predict the motifs effectively which are belonging to multiple motif types. To find out better motif sequences from human GPCR, we explored a hybrid computational model consisting of hybridization of Rough Set with Mean-Shift Algorithm. In this paper we made comparison among our resulted output with other techniques such as fuzzy C-Means (FCM), FCM with spectral clustering and we concluded that our proposed method targeted well on CRAC region in comparison to CARC region which have higher biological relevance in medicine industry and drug discovery.

One of the issues in medical science, which has attracted the attention of many researchers, isliver segmentation from computer tomography images. Because the first step in the process of diagnosis of liver illnesses and its tumors is, having an appropriate image of the segmented liver in these images. The purpose of this paper is to provide an automated Algorithm for liver segmentation in the CT images. Previous research has shown that the use of texture feature results in more favorable results in liver segmentation. The proposed Algorithm of this paper is based on texture analysis to liver segmentation using the Kirsch edge detector, Mean Shift, and k-Means clustering. Results of the implementation of the proposed Algorithm on 400 images of Milad hospital in Tehran containing liver and its lateral organs, showed the average of Dice criterion of 96%. Also, in the performance of the proposed Algorithm on the sliver07 database, the average of Dice criterion is equal to 96.86%. Therefore, the proposed Algorithm can be used as the first step in the process of diagnosis of liverillnesses and its tumors.

The most important problem for investors, at the beginning stages of their works, is the way of assigning their investment to one or more different investment alternatives in such a way that with the least possible risk the maximum return become obtainable. In the economic literature this is known as the problem of portfolio selection. This article tries to introduce an efficient way for supporting decision maker in the selection of appropriate portfolio for investment purposes. The portfolio is based upon the Mean-variance-skewness with the return of portfolio is considered to be fuzzy to match with the world reality more. This article proposes a hybrid intelligent Algorithm for finding an optimal or new optimal solution of the problem. Here, authors use Genetic Algorithm to find the right portfolio with the help of neural network and fuzzy computer simulation knowledge. Due to the fact that trained neural network was used the computation time has reduced tremendously in comparison with the straight use of the fuzzy simulation. Authors have used two example problems to demonstrate the efficiency of the proposed Algorithm in comparison with other hybrid Algorithms from the literature.