A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

Current Thesis has aimed to identify : The Psychological barriers for university students , Differences in psychological barriers depending on the variable sex (Males – Females) , Adjustment to College life for university students, Differences in Adjustment to College at university life depending on the variable sex (Males – Females), and finally, The correlation between psychological barriers and Adjustment to College life. The researcher has prepared a sample consisted of (100) male and female students who were randomly selected from university students, The researcher has adopted a measure of (2002) to measure the psychological barriers, also the researcher adjustment scale with university life.
The results showed that universi

This study was conducted to determine the relationship between test anxiety and cognitive representation among university students. To this end, 152 student (male, female) were chosen randomly from scientific and social departments to fill out the questionnaires of test anxiety and cognitive representation. The researcher utilized Independent Samples T-Test, Pearson product-moment correlation coefficient, Cronbach's alpha and T-Test in his study. The result revealed that there were negative and a weak correlation between test anxiety and cognitive representation among university students.

This research for university students dialectic unemployment and work, focusing on
serious discussion, accurate and provide opinions between specialists and those interested in
the university youth issues and identify them in the light of the dialectical relationship
between unemployment and work for the purpose interpreted accurate and access to reality
without coverage and linked to the movement of the changing society now and the situation
that is happening in Iraq, where the loss of the safety and the weakness of the determinants of
economic well-being and the decline of public and social health and the unemployment rate
security in the heart of - programs - and blocs parties that Tgtill public opinion in any

The aim of the study is to diagnose the real level of technology usage in teaching and learning EFL at university from teachers and students’ viewpoints, and see if it is possible to achieve something of the researchers’ dream - accessing top universities. Two questionnaires have been used to measure the range of technology usage in Colleges of Education for Women, Baghdad and Iraqi Universities, and College of Basic Education. The results have shown that the reality of using technology is still away from the dream. The results have been ascribed to two reasons: The first is the little knowledge of using technology in teaching, and the second is that technology is not included in the curriculum.

Smart thinking requires a continuous flexible systeroatic  teaching in order that the lecturer can reach at easily, The Successful individuals in smart thin king are the most knowledgably with  it, where the cognitive (intuitive- systematic) style has common bases with another cognitive styles in many traits, and these two concepts are the core of theorization of the rost important cognitive styles. The present study aims to measure the Smart thinking among university lecturers according to sex variable and recognize the statistically differences significance in the level of cognitive (intuitive- systematic) style among the university lecturers according to sex variable and recognize the correlation between smart thinki

             The study aims budget in grades use of smart phones to individuals (sample) according variable sex (males and females) and used researcher descriptive analytical method consisted sample of (300) students have chosen the way stratified random, and the study variables (academic achievement of students, sex and the use of Smart phones) resolution was adopted as a tool for data collection. The most important results of the study that females are more commonly used for smart phones, as well as the existence of a positive relationship between the inverse statistically significant use of smart phones and the rate of school for students and the use of smart phones h