The effect of cognitive trips via the Internet (web quest) accompanying practical lessons in learning some basic handball skills for female students

This research dealt with shedding light on the nature of material misrepresentations, in addition to knowing the extent to which the quality of accounting information systems contributes to reducing material misrepresentations On the theoretical side, a number of sources were relied upon in dealing with the research problem and presentation of the topic, while in the practical side, it was relied on the questionnaire form, where the research sample was (accountants and auditors), where 50 forms were distributed and 50 were received, and the data was analyzed and hypotheses tested through the program Statistical spss to show the relationship between the variables. The research reached a number of conclusions, the most important of which is t

One of the challenging issues encountered during drilling operations is the lost circulation. Numerous issues might arise because of losses, such as wasting of time and higher drilling cost. Several types of lost circulation materials have been developed and are being used to limit mud losses and avoid associated issues. Each solution has benefits and drawbacks.

In this study, a core flooding test was performed to study the effectiveness of polyacrylamide (PAM) granular gel on the reduction of the circulation lost. One common type of fracture characteristic is fractures with tips, commonly known as partially open fracture (POF). However, PAM gel therapy in POFs received little attention in prior research. Models of partly open fra

     In the last few years, the literature conferred a great interest in studying the feasibility of using memristive devices for computing. Memristive devices are important in structure, dynamics, as well as functionalities of artificial neural networks (ANNs) because of their resemblance to biological learning in synapses and neurons regarding switching characteristics of their resistance. Memristive architecture consists of a number of metastable switches (MSSs). Although the literature covered a variety of memristive applications for general purpose computations, the effect of low or high conductance of each MSS was unclear. This paper focuses on finding a potential criterion to calculate the conductance of each MMS rather t

Issam al-Din al-Asfrani's footnote
On the interpretation of the oval
Imam
Issam al-Din Ibrahim Arbashah al-Asfrani
(Th 159 e)
  Surah Al-Baqarah (verse 55-911)

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

Investigating the strength and the relationship between the Self-organized learning strategies and self-competence among talented students was the aim of this study. To do this, the researcher employed the correlation descriptive approach, whereby a sample of (120) male and female student were selected from various Iraqi cities for the academic year 2015-2016.  the researcher setup two scales based on the previous studies: one to measure  the Self-organized learning strategies which consist of (47) item and the other to measure the self-competence that composed of (50) item. Both of these scales were applied on the targeted sample to collect the required data

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

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