Foreground-background image segmentation has been an important research problem. It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences. It provides candidate objects for further attentional selection, e. g., in video surveillance. In this paper, we introduce an automatic and efficient Foreground-background segmentation. The proposed method starts with the detection of visually salient image regions with a saliency map that uses Fourier transform and a Gaussian filter. Then, each point in the maps classifies as salient or non-salient using a binary threshold. Next, a hole filling operator is applied for filling holes in the achieved image, and the area-opening method is used for removing small objects from the image. For better separation of the foreground and background, dilation and erosion operators are also used. Erosion and dilation operators are applied for shrinking and expanding the achieved region. Afterward, the foreground and background samples are achieved. Because the number of these data is large, K-means clustering is used as a sampling technique to restrict computational efforts in the region of interest. K cluster centers for each region are set for training of Support vector Machine (SVM). SVM, as a powerful binary classifier, is used to segment the interest area from the background. The proposed method is applied on a benchmark dataset consisting of 1000 images and experimental results demonstrate the supremacy of the proposed method to some other foreground-background segmentation methods in terms of ER, VI, GCE, and PRI.

In exploratory projects, the identification of geochemical anomalies in different areas may become complicated under the influence of geological processes. To solve these ambiguities, different methods should be used for a correct understanding of the available information. In this research, by expressing the concept of hierarchical clustering to identify elements related to mineralization, singularity, and how to draw singularity maps in the form of multifractal models and Support vector machine method, the anomalous areas where there is a possibility of mineralization are seprated from the context regions. At first, two elements, gold and copper, were identified as elements related to mineralization in the created clusters using the hierarchical clustering method and Ward's method. To calculate the singularity index of these two elements, the method based on the window and the power relation of grade area was used at each point. Finally, by separating the singularity index values into two parts, training and testing, and with the help of the SVM method, the process of classification and estimation of singularity index values was done to identify anomalous areas for unknown areas. A case study has been carried out on the data of the porphyry copper deposit rich in Dali gold with an area of 900×800 meters located in the Urmia-Dokhtar magmatic belt. The data is related to surface soil samples in the target area. The results of this method are consistent with the previous studies conducted in the region. The results of the hybrid method used in this research show good agreement with previous studies. As a result, the use of these introduced hybrid methods can be a suitable guide for the production of geochemical maps in unknown areas.

In [14] the authors have studied robust semi-mean absolute deviation portfolio optimization model when assets expected returns involve uncertainty. They applied a data driven approach via Support vector clustering to construct the uncertainty set using Support vector clustering. In this paper, we show that their robust formulation is not the worst case counterpart of the original model. Then we give the true robust model of the underlying problems in the best an worst cases. Experiments are conducted to show the optimal objective value of the robust model in [14] belongs to the interval generated by our best and worst case models.

Monetary and financial institutions play an essential role in the economic development of any country. Financial systems can make an economy more productive by concentrating scarce resources and funds for massive investments. Examining the bank's financial performance and creating a suitable clustering system based on indicators that affect the financial performance of banks is of great importance for bank supervisors, bank depositors and shareholders, and banking sector policymakers. In the current research, the clustering of the member banks of Iran's capital market is done based on credit risk and indicators affecting financial performance using data from the period of 2009 to 2021 related to eleven selected banks of Iran's capital market and the Support vector regression (SVR) model. Two measures of return on assets (ROA) and return on equity (ROE) have been implemented and analyzed as indicators of the bank's financial performance. For this purpose, the coefficients of the banks were first extracted using the Support vector regression model and then clustered with these coefficients using the average linkage method. The results indicate that the clustering of banks using both financial performance measures is similar. Based on this, in the clustering with three clusters, Saderat, Mellat, Parsian, Postbank, Pasargad, Sina, Saman, Ekhztannovin and Karabhan banks are placed in one cluster and trade and capital banks are placed in other clusters. In the clustering with four clusters, Saderat, Mellat, Parsian, Pasargad, Sina, Saman, Ekhztannovin and Karabhan banks are in one cluster and Postbank, Trade and Capital banks are in other clusters. In the clustering with five clusters according to the data of the examined banks, Saderat, Mellat, Parsian, Sina, Saman and Ekhtaznovin banks are in one cluster (category), Pasargad and Karabehan banks are in the second cluster, and Postbank, Capital and Tejarat banks are in other clusters.

Text clustering and classification are two main tasks of text mining. Feature selection plays a key role in the quality of the clustering and classification results. Although word-based features such as Term Frequency-Inverse Document Frequency (TF-IDF) vectors have been widely used in different applications, their shortcomings in capturing semantic concepts of text have motivated researches to use semantic models for document vector representations. The Latent Dirichlet Allocation (LDA) topic modeling and doc2vec neural document embedding are two well-known techniques for this purpose. In this work, we first studied the conceptual difference between the two models and showed that they had different behaviors and capture semantic features of texts from different perspectives. We then proposed a hybrid approach for document vector representation to benefit from the advantages of both models. The experimental results on 20newsgroup showed the superiority of the proposed model compared to each one of the baselines on both text clustering and classification tasks. We achieved a 2. 6% improvement in F-measure for text clustering and a 2. 1% improvement in F-measure in text classification compared to the best baseline model.

Basic life Support (BLS) following by Advanced cardiac life Support (ACLS) is intended to rescue the patients with acute circulatory or respiratory failure or both. The most important determinant of short and long-term neurologically intact survival is the interval from the onset of the cardiac or respiratory onset to restoration of effective spontaneous functions of these vital activities.It is commonly accepted that every physician, regardless of specialty, should be able to  perform CPR. It must be also emphasized that CPR, almost invariably, necessitates a rapid interventional follow-care with ACLS procedure.Without well-performed basic life Support, advanced cardiac life Support is of no remark-: able benefit, BLS and ACLS are processes that must be performed step by step and with respect to the patient's condition.