The current research aims to reveal the strength of education and the direction of the relationship between the formal thinking and learning methods of Kindergarten department students. To achieve this objective, the researcher developed a scale of formal thinking according to the theory of (Inhelder & Piaget 1958) consisting of (25) items in the form of declarative phrases derived from the analysis of formal thinking skills based on a professional situation that students are expected to interact with in a professional way. The research sample consisted of (100) female students selected randomly who were divided into four groups based on the academic stages, the results revealed that The level of formal thinking of the main sample is moderate. The sample was distributed among learning methods in different percentages and in the following descending order (convergent 30%, adaptive 27%, divergent 24%, absorptive 19%). There is a significant difference in terms of the academic levels in favor of the fourth stage. There is a weak negative correlation between the two variables. The research came out with a set of recommendations, including holding training workshops for teachers about the importance and detecting students’ preferred learning methods
Deposits with the Industrial Bank, together with loans received from others, are non-financial financial resources, which are sources of supply to the Industrial Bank of funds that allow the Bank to expand the granting of loans. The increases in this resource indicates that the bank is practicing comprehensive banking, which is consistent with the nature of the transformation of the banking system towards multiple businesses. Therefore, the research comes to highlight the causality of the trend between total deposits and total loans. And if the causality is found, is it one-way or two-way? How long is the impact?
Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre
... Show MoreBotnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet
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Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci
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Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated
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This study aimed to measure digital stress among students at the University of Baghdad and to identify differences in digital stress based on gender (male–female), academic level (first–fourth year), and field of study (scientific–humanities). The study sample consisted of 200 students from the University of Baghdad. The "Digital Stress" scale, developed by the researcher specifically for this study, was applied, and the descriptive-analytical method was adopted. After administering the scale and analyzing the results using appropriate statistical methods, the findings indicated that university students experience a high level of digital stress, while no statistically significant differences were found in digital stress accord
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