Background: Traumatic ulcerative granuloma with stromal eosinophilia is an impressive benign chronic ulcerative lesion of the oral mucosa with vague etiopathogenesis. It was supposed to represent an oral counterpart of primary cutaneous CD30+ lymphoproliferative disorder. Histopathologically, it is characterized by mixed inflammatory infiltrate predominated by histiocytes, lymphocytes and eosinophils along with presence of scattered large atypical mononuclear cells. It has worrisome clinical presentation. It may heal spontaneously, but in most occasions it persists and never heal unless removed surgically (incisional or excisional biopsy). A rare subset may show worrisome immunohistochemical features. Follow up is highly recommended. Mat

Background: Psychological stress is considered the major etiological factor precipitating myofacial pain and temporomandibular disorders.It is known that stress induce various adaptational responses of physiologic systems. The process includes increase in the activity of the hypothalamic-pituitary-adrenal axis which promotes cortisol secretion. Salivary cortisol has been used as a measure of free circulating cortisol levels.The use of salivary biomarkers has gained increased popularity since collecting samples is non-invasive and painless. The aim of thisstudy was to evaluate the level of cortisol in saliva among sample of university students having myofacial pain, during the final exam period and whether this finding could have a significa

ECG is an important tool for the primary diagnosis of heart diseases, which shows the electrophysiology of the heart. In our method, a single maternal abdominal ECG signal is taken as an input signal and the maternal P-QRS-T complexes of original signal is averaged and repeated and taken as a reference signal. LMS and RLS adaptive filters algorithms are applied. The results showed that the fetal ECGs have been successfully detected. The accuracy of Daisy database was up to 84% of LMS and 88% of RLS while PhysioNet was up to 98% and 96% for LMS and RLS respectively.

A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

In digital images, protecting sensitive visual information against unauthorized access is considered a critical issue; robust encryption methods are the best solution to preserve such information. This paper introduces a model designed to enhance the performance of the Tiny Encryption Algorithm (TEA) in encrypting images. Two approaches have been suggested for the image cipher process as a preprocessing step before applying the Tiny Encryption Algorithm (TEA). The step mentioned earlier aims to de-correlate and weaken adjacent pixel values as a preparation process before the encryption process. The first approach suggests an Affine transformation for image encryption at two layers, utilizing two different key sets for each layer. Th

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.