داء السكري من النوع 2 (T2DM) هو مرض معقد يصيب العديد من الأعضاء إلى جانب البنكرياس مثل الكلى والكبد والدماغ والعين. بسبب ارتفاع السكر في الدم لفترات طويلة وعدم السيطرة على مرض السكري مع وجود عوامل خطر أخرى ، يمكن أن تحدث مضاعفات مرض السكري. تشمل مضاعفات مرض السكري مضاعفات الأوعية الدموية الدقيقة والأوعية الدموية الكبيرة التي تستهدف الكلى. تهدف الدراسة الحالية إلى التحقق من مستوى الانجوتينسن المحول للانزيم  A

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

Recently new concepts such as free data or Volunteered Geographic Information (VGI) emerged on Web 2.0 technologies. OpenStreetMap (OSM) is one of the most representative projects of this trend. Geospatial data from different source often has variable accuracy levels due to different data collection methods; therefore the most concerning problem with (OSM) is its unknown quality. This study aims to develop a specific tool which can analyze and assess the possibility matching of OSM road features with reference dataset using Matlab programming language. This tool applied on two different study areas in Iraq (Baghdad and Karbala), in order to verify if the OSM data has the same quality in both study areas. This program, in general, consists

Abstract

The study aims to build a training program based on the Connectivism Theory to develop e-learning competencies for Islamic education teachers in the Governorate of Dhofar, as well as to identify its effectiveness. The study sample consisted of (30) Islamic education teachers to implement the training program, they were randomly selected. The study used the descriptive approach to determine the electronic competencies and build the training program, and the quasi-experimental approach to determine the effectiveness of the program. The study tools were the cognitive achievement test and the observation card, which were applied before and after. The study found that the effectiveness of the training program

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

Various speech enhancement Algorithms (SEA) have been developed in the last few decades. Each algorithm has its advantages and disadvantages because the speech signal is affected by environmental situations. Distortion of speech results in the loss of important features that make this signal challenging to understand. SEA aims to improve the intelligibility and quality of speech that different types of noise have degraded. In most applications, quality improvement is highly desirable as it can reduce listener fatigue, especially when the listener is exposed to high noise levels for extended periods (e.g., manufacturing). SEA reduces or suppresses the background noise to some degree, sometimes called noise suppression alg

Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy

The research aims to show the role or extent of the impact of financing in its various forms on the municipal performance before and after the financial deficit through relying on the analytical research methodology of the research community represented by the Directorate General of Municipalities and the Directorate of Maysan municipalities as a sample of research (13) municipal institutions for a period of (8) years, Considering the completion of the final accounts of these years, which provides the necessary data for the study, in addition to the variation in the quality and amounts of grants allocated to municipal institutions during these years, which gives a clearer and more comprehensive picture of the reality of allocatio