One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

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

Deep Learning Techniques For Skull Stripping of Brain MR Images

       This research aims to identify the impact of the reciprocal reading strategy on developing the innovative reading skills of fifth-grade secondary school students/ scientific branch. To achieve the goal of the research, an achievement test to measure the academic achievement of fifth-grade secondary school students in  Arabic language grammar was prepared,  a sample of (60) female students from the secondary school of Al- Bawaba Al-Sharkey for Girls in Baghdad were chosen for the academic year (2019-2020). For the purpose of applying the experiment, the test was applied on a pilot sample consisting of (30) students from the same research community. The researcher herself had taught the three

The educational sector is one of the important sectors in the world, and it is considered one of the means of community development. In addition, it is one of the means of making the country’s renaissance and devel-opment because it represents the factory of thinking minds that make change. There is no doubt that this sector is the same as any other sector. The deficit in the studied scientific planning has been prolonged, which led to its deterioration, and the problems of education remain diverse and inherited from previous time periods, where the hierarchical cluster analysis was used on postgraduate students in universities in Iraq, except for Kurdistan region, and the number of universities that were included in the study was

This research aims at identifying the Iraqi EFL students' academic buoyancy in reading and listening skills and then finding out the difference between the two skills; Furthermore, finding out the five predictors differences of each of the two scales. Two academic buoyancy scales used to collect data. After achieving the scales' psychometric characteristics, they are submitted to the study sample. The sample of (100) female students selected according to the stratified random method from the second class at the Department of English\ College of Education for Women\ University of Baghdad. The results show that the students have academic reading buoyancy and have not academic listening buoyancy; the difference between the two abilities shows