بحوث سكوبس — علي حسين شامان

COVID-19 gains from the research and technology component's establishment of information science, artificial intelligence, and computer understanding. The article aims to discuss the numerous facets of today's modern technology utilized to combat COVID-19 emergencies on various scales, such as medicinal picture handling, illness tracking, expected outcomes, computational science, and medications. Techniques: A complex search of the knowledge base associated with existing COVID-19 innovation is conducted. Furthermore, a concise survey of the excluded data is conducted, analyzing the various aspects of current developments for dealing with the COVID-19 pandemic. The below are the outcomes: We have a window of musings on the audit of the tech propellers used to mitigate and mask the significant impact of the upheaval. Even though several investigations into current innovation in COVID-19 have surfaced, there are still required implementations and contributions of innovation in this war. Consequently, a thorough presentation of the available data is given, and several modern technology implementations for combating the pandemic of COVID-19. Continuous advancements of advanced technologies have aided in improving the public's lives, and there is a strong belief that proven study plans utilizing AI would be of great benefit in assisting people in combating this infection. © 2021

Cloud services are the cutting edge technology, however the growing demand for the internet of things has certain limitations which are high latency expectation and high cost of cloud resources, and this is caused by long-distance between application and cloud. Fog computing is a distributed extension of the cloud, which provide storage and computation at the network level. It consists of an internet of things (IoT) application, a fog control node, and a fog access node. This research works towards minimizing the cloud cost in scheduling. For this purpose, a cost-effective task and user scheduling algorithm are performed. The first task scheduling model is composed based on composers' roles after that task scheduling algorithm is performed to handle the various task at the fog access node in an optimized manner. Finally, the reallocation mechanism reduces the time and service delay. For the analysis purpose extensive simulation is carried out and performance statistics were compared with other existing algorithms. It was observed that the proposed algorithm provides highly cost-optimized user and task scheduling with better performance statistics and reduces the delay in the task by providing optimization in the concurrent task at the fog node. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Todays, networks security of has become the important problem in each distributed system. A lot of attacks are becoming less able to detect with software of antivirus and firewall. For improving the security, intrusion detection systems (IDSs) are utilized for detecting the anomalies in traffic of network. Network anomaly detection issue is determining, if incoming traffic of network is anomalous/ legitimate. The automated system of detection schemed for identifying the incoming anomalous patterns of traffic usually apply widely utilized techniques of machine learning. In the article, we have utilized the Information Gain- based algorithm. The algorithm chooses the features optimal number from dataset of NSL-KDD. Additionally, we have integrated selection of feature with the technique of machine learning namely as Support Vector Machine (SVM) by utilizing the algorithm of artificial bee colony as well as Optimization-Cuckoo Search Algorithm for optimizing SVM hyper parameters for dataset effective classification. Proposed method performance has been assessed on the modern intrusion dataset as NSLKDD. Experimental results show that the proposed method outperforms also achieves high accuracy in comparison to the other modern techniques in NSLKDD. © 2021 Zibeline International Publishing Sdn. Bhd.. All rights reserved.