Nowadays, due to the rapid increment of the pandemic COVID-19 which affects the teaching environment, a need appeared to use a new alternative education style, which helps to decrement the injuries and the continuation of the education. This study designs an application to measure the teachers' readiness to use e-learning. A questionnaire was designed to collect as much information as possible to measure the teachers' readiness to adopt e-learning in Iraqi schools after pandemic COVID-19 by analyzing the factors that affect the education process. The questionnaire consists of thirty-three questions in the Arabic language and includes three sections (background information, attitude toward e-learning, and computer skills). The collected responses are taken from Mosul's primary and secondary schools' teachers and the sample size is (261). A test of reliability was carried out on the study instrument, and the value of Cronbach's alpha was 0.913. The MATLAB R2014a was used to build an application in order to do the analyzing process and determine the readiness of e-learning.

Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem. Swarm Intelligence (SI), usually customer societies, communicate locally with their domain and with each other. Clients use very simple rules of behavior and the interactions between them lead to smart appearance, noticeable, individual behavior and optimized solution of problem and SI has been successfully applied in many fields, especially for malware ion tasks. SI also saves a considerable amount of time and enhances the precision of the malware recognition system. This paper introduces a malware recognition system for Hancitor malware using the Gray Wolf Optimization algorithm (GWO) and Artificial Bee Colony algorithm (ABC), which can effectively recognize Hancitor in networks.