Deep learning models have been used in several domains, however, adjusting is still required to be applied in sensitive areas such as medical imaging. As the use of technology in the medical domain is needed because of the time limit, the level of accuracy assures trustworthiness. Because of privacy concerns, machine learning applications in the medical field are unable to use medical data. For example, the lack of brain MRI images makes it difficult to classify brain tumors using image-based classification. The solution to this challenge was achieved through the application of Generative Adversarial Network (GAN)-based augmentation techniques. Deep Convolutional GAN (DCGAN) and Vanilla GAN are two examples of GAN architectures used for image generation. In this paper, a framework, denoted as BrainGAN, for generating and classifying brain MRI images using GAN architectures and deep learning models was proposed. Consequently, this study proposed an automatic way to check that generated images are satisfactory. It uses three models: CNN, MobileNetV2, and ResNet152V2. Training the deep transfer models with images made by Vanilla GAN and DCGAN, and then evaluating their performance on a test set composed of real brain MRI images. From the results of the experiment, it was found that the ResNet152V2 model outperformed the other two models. The ResNet152V2 achieved 99.09% accuracy, 99.12% precision, 99.08% recall, 99.51% area under the curve (AUC), and 0.196 loss based on the brain MRI images generated by DCGAN architecture.

<p class="0abstract">Recently, the utilization of IT in the Higher Education institutions has expanded, thus e-Learning must to turn out to be completely embedded into e-Learning and showing practice rather than the traditional approaches to e-Learning. However, this development inside e-Learning frameworks isn't yet completely realized, and the most significant difficulties to this objective are holding students inside instruction and attracting them to take part in Higher Education. One of the methodologies that have been taken to address these difficulties is to utilize the Web services approach to encourage working and participation between Higher Education institutions. Taking account that XML documents are the most commonly applied and successful type of Web services. Being PHP is the best choice for web developers, especially for Web services. This paper will present the proposed approach which aims to expand the current architecture of Web services to meet the technical requirements of the e-Learning framework. It will give an outline of certain strategies that help designers to create SOAP with PHP language. It will portray how to expand PHP Sakai and Moodle to help Web services by utilizing the PHP SOAP method. Besides, it will delineate that this utilization of Web services is a direct advancement of Web programming models, and it will exhibit how PHP can be utilized as a fast and easy development tool for creating them. The spotlight of this paper is to research the expansion of Web services to support PHP Sakai and Moodle by utilizing the PHP SOAP strategy.</p>

Traffic safety aims to change the attitude of citizens towards careless traffic on the roads, making this the first step towards changing behavior. Also, teach the rules of safe pedestrian behavior and minimize the risks of road accidents. So many regulations have been set to avoid road accidents and traffic jams, which is the study scope of this paper using IT technology. With the expanding interests in Computer vision use cases such as vehicles self-driving, face recognition, intelligent transportation frameworks and so on individuals are hoping to assemble custom AI models to recognize and distinguish specific objects. Object detection is part of a computer's vision where objects that can be observed externally and are found in videos can be identified and tracked by computers. Therefore, object tracking is an important part of video analysis. There are many proposed methods such as Tracking, Learning, Detection, Mean shift and MIL. In this paper, the computer vision state in object detecting domain along with its challenges are discussed, also we address some requirements and techniques to overcome these challenges. Finally, TensorFlow technology is presented as a recommended solution to support Lane’s violation.

Nowadays, technology has an important role of our life, including smart devices, social media due to the importance of security and web for interaction so it has become targeted it by cybercriminal. The growing threat of cybersecurity has prompted the kingdom to pay more attention to its national cybersecurity strategy as the state embarks on a Vision 2030 plan, which aims to diversify the economy and create new jobs. Therefore, Web Applications are always having security threats, which considered as a big problem. Several steps introduced successful analysis of vulnerabilities in web applications. There are no efficient and easy to use tools for the security assessment of such applications. This paves the way for hackers to easily attack. In this paper, we recommend an efficient method to assess the vulnerability using Python, which can used to conduct Vulnerability Assessment on web applications. This work will be useful for organizations and programmers to keep their information and applications more secure and viable for usage in sensitive environments.