The aim of the current research is to identify the self-control of kindergarten teachers as well as to identify the significance of the differences according to a variable (years of service, academic achievement, specialization). Its final paragraphs consist of (35) paragraphs, and its psychometric properties were verified and the tool was applied to a sample of (400) teachers chosen randomly from kindergarten teachers affiliated to the General Directorates of Education in Baghdad, Rusafa, and Karkh for the academic year 2019-2020

In light of the objectives of the current research, the following results were reached

1. The current research sample is from kindergarten teachers with self-contro
2. There are diffe

The current research aims to verify the relationship and impact of internal control activities as an independent variable in its dimensions (efficiency, training, independence) in making administrative decisions as a responsive variable and its dimensions (identifying and diagnosing the problem, searching for alternatives, evaluating alternatives, choosing the appropriate alternative, implementing the decision, following up Decision and evaluation) in the Kirkuk Water Directorate, as well as to identify the levels of their practices, and then try to come up with a number of recommendations contribute to the strengthening of internal control activities and the process of making administrative decisions of the researched Directorat

In this article, a continuous terminal sliding mode control algorithm is proposed for servo motor systems. A novel full-order terminal sliding mode surface is proposed based on the bilimit homogeneous property, such that the sliding motion is finite-time stable independent of the system’s initial condition. A new continuous terminal sliding mode control algorithm is proposed to guarantee that the system states reach the sliding surface in finitetime. Not only the robustness is guaranteed by the proposed controller but also the continuity makes the control algorithm more suitable for the servo mechanical systems. Finally, a numerical example is presented to depict the advantages of the proposed control algorithm. An application in the rota

Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 %  1.66 %. This