In this work we present a technique to extract the heart contours from noisy echocardiograph images. Our technique is based on improving the image before applying contours detection to reduce heavy noise and get better image quality. To perform that, we combine many pre-processing techniques (filtering, morphological operations, and contrast adjustment) to avoid unclear edges and enhance low contrast of echocardiograph images, after implementing these techniques we can get legible detection for heart boundaries and valves movement by traditional edge detection methods.

Profiles of indignation and indiscretion in pre-Islamic poetry

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

The Influence of Some Vitamins and Biochemical Parameters on Iraqi Females’ Patients with Malignant Breast Cancer"

In Iraq, breast cancer incidence exceeds any other type of cancers and the etiology not understood well.Epstein Barr virus is a gamma herpesviruses and one of carcinogenic viruses that may implicated tobreast carcinogenesis. The nuclear antigen-1 (EBNA-1) protein is the sole EBV antigen that presentedin all tumors related to EBV and plays pivotal roles in carcinogenesis of the virus. Examination appliedby immunohistochemistry (IHC) to detect and demonstrate the correlation between (EBNA-1) and tumorsuppressor protein (P53) expression. The study includes paraffin-embedded tissue blocks of ninety 90malignant breast tissues and thirty 30 normal breast autopsies. EBNA-1 was significantly expressed in 40/90(44.4%) of malignant tissues wh

Background: the difference in expression of type IV collagen in borderline tumors and ovarian carcinomas has been studied, but the association with adhesion molecules like CD44 have not gain enough interest. Objectives: The purpose of this study is to assess the expression of CD44v6 and type IV collagen status in borderline tumors and invasive ovarian carcinomas and the correlation between them to define the role of these molecules in tumor invasion and metastasis. Type of the study: A cross sectional study Methods: The study included a total of (101) formalin-fixed paraffin-embedded ovarian tissue blocks; of which (19) cases were borderline tumors and (82) cases were overt ovarian carcinomas. Sections from each block were immunohistoche