The brain's magnetic resonance imaging (MRI) is tasked with finding the pixels or voxels that establish where the brain is in a medical image The Convolutional Neural Network (CNN) can process curved baselines that frequently occur in scanned documents. Next, the lines are separated into characters. In the Convolutional Neural Network (CNN) can process curved baselines that frequently occur in scanned documents case of fonts with a fixed MRI width, the gaps are analyzed and split. Otherwise, a limited region above the baseline is analyzed, separated, and classified. The words with the lowest recognition score are split into further characters x until the result improves. If this does not improve the recognition s

The research seeks to identify the proposed scenarios to predict and ward off monetary credit risks that the bank is exposed to in the future, using the banking stress tests model, and showing their impact on capital adequacy and profitability ratio,To achieve this purpose, Sumer Commercial Bank was taken as a case study, and mathematical equations were used to extract the results. Low percentage of profits and returns, strictness in the process of granting credit and financing operations in order to reduce credit risks.

The research aims to identify the extent to which Iraqi private banks practice profit management motivated by reducing the taxable base by increasing the provision for loan losses by relying on the LLP it model, which consists of a main independent variable (net profit before tax) and independent sub-variables (bank size, total debts to total equity, loans granted to total obligations) under the name of the variables governing the banking business. (Colmgrove-Smirnov) was used to test the normal distribution of data for all banks during the period 2017-2020, and then find the correlation between the main independent variable sub and the dependent variable by means of the correlation coefficient person, and then using the multiple

In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

For many years, reading rate as word correct per minute (WCPM) has been investigated by many researchers as an indicator of learners’ level of oral reading speed, accuracy, and comprehension. The aim of the study is to predict the levels of WCPM using three machine learning algorithms which are Ensemble Classifier (EC), Decision Tree (DT), and K- Nearest Neighbor (KNN). The data of this study were collected from 100 Kurdish EFL students in the 2^nd-year, English language department, at the University of Duhok in 2021. The outcomes showed that the ensemble classifier (EC) obtained the highest accuracy of testing results with a value of 94%. Also, EC recorded the highest precision, recall, and F1 scores with values of 0.92 for

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of

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The research deals with the statement of the importance of unanimous in Islamic Sharia in terms of being the third source of Islamic legislation after the Holy Qur’an and the Sunnah of the Prophet, and the extent of interest of the scholars of interpretation in it, and those who took great interest in mentioning what was agreed upon in every site that necessitated mentioning its issues within, or mentioning an event that has encountered an unanimous, and in various sciences, to extract and extrapolate Sharia rules when these rules are lost in the texts, because it is based on them and derived from them. Among these distinguished scholars is Muhammad bin Jarir al-Tabari. Besides, the research discusses in

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D