One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first method used the minimum distance, and the second method used the clustering algorithm called DBSCAN. Both methods were tested with and without reclustering using the self-organizing map (SOM). The result from comparing the images after segmenting them and comparing the time taken to implement the segmentation process shows the effectiveness of these methods when used with SOM.
Smoking and central obesity have both been linked to periodontitis, but their combined relationship with periodontal disease may be influenced by demographic and behavioral factors. This cross-sectional study analyzed records of 420 adult dental patients attending the College of Dentistry at the University of Baghdad. Data included demographic characteristics, smoking status, periodontal clinical findings, body mass index (BMI), and waist-to-height ratio (WHtR). Periodontitis was defined according to the 2018 classification framework, and logistic regression models were used to examine the associations of smoking and obesity-related indicators with periodontitis. The overall prevalence of periodontitis was 36.4%. Participants with p
... Show MoreIn this review of literature, the light will be concentrated on the role of stem cells as an approach in periodontal regeneration.
This paper deals with finite element modeling of the ultimate load behavior of double skin composite (DSC) slabs. In a DSC slab, shear connectors in the form of nut bolt technique studs are used to transfer shear between the outer skin made of steel plates and the concrete core. The current study is based on finite element analysis using ANSYS Version 11 APDL release computer program. Experimental programmes were carried out by the others, two simply supported DSC beams were tested until failure under a concentrated load applied at the center. These test specimens were analyzed by the finite element method and the analyses have shown that these slabs displayed a high degree of flexural characteristics, ultimate strength,
... Show MoreAlice Walker's novel The Color Purple1983, whose events take place in the rural Georgia and addresses the most important issues in the early 20thcentury; like male dominant society and women submission to men, weaves a mosaic picture of male-female relationships. Black men like Alphonso, Albert and Harpo are portrayed as oppressors, cruel and they exercise power and violence over their wives and daughters. Through the negative portrayal of black male characters, Walker is accused to be a men hating writer because she ''Views oppression as an essentially masculine activity which springs from the male's aggressive need to dominate. In the novel, man is the premium mobile, the one by whom and t
... Show MoreClassifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ
... Show MoreThe intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is
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