The aim of this paper is to find out the effects of the strategy of productive thinking upon the student’s achievement for the subject of research methodology in the College of Islamic Sciences. Achieving this objective, the researchers set the null hypotheses: (1) No difference is noticed to be statistically significant at the level of significance (0.05) among the student’s mean scores in the experimental group who were taught by the strategy of productive thinking, and the student’s mean scores in the control group who studied by the traditional method in the achievement test. (2) At level of sig. (0.05), there is no statistically significant difference in the mean of scores of the pre-tests and post ones in the achievement test of

HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims