We examine 10 hypothetical patients suffering from some of the symptoms of COVID 19 (modified) using topological concepts on topological spaces created from equality and similarity interactions and our information system. This is determined by the degree of accuracy obtained by weighing the value of the lower and upper figures. In practice, this approach has become clearer.

The objective of this study was to assess the impact of the COVID-19 pandemic on healthcare providers (HCPs) at personal and professional levels.

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt

The problem of job burnout has become one of the main problems for researchers in social welfare organizations (social protection bodies) - one of the formations of the Ministry of Labor and Social Affairs. Its negative effects increased in light of the COVID-19 pandemic, and in light of the Corona pandemic, the pressures and burdens of workers varied, which resulted in high rates of anxiety, tension, and intellectual and physical exhaustion, and then negatively affected their efficiency in performing work at the individual and organizational level, especially after the increasing tasks of these Bodies in carrying out their role in achieving the general goals and objectives as beingThe general goals are that they are responsible for providi

Recurrent respiratory tract infections are responsible for about 85% of all diseases in childhood, and are associated with significant morbidity and mortality. The aim of this study is to evaluate the main causes underlying recurrent respiratory tract infections in 176 pediatric patients aged 2 month to 4 year and weight from 4 to11 kg referred to the child center hospital and Al-sader hospital prospective study.All parents were given information sheet which then analyzed and the percentage of incidence of causes were recorded, we found that higher % related to many causes; mostly related to the parent like poor family education, mother carelessness, incomplete vaccination, other related to empirical diagnosis, and short course of t

In recent decades, the identification of faces with and without masks from visual data, such as video and still images, has become a captivating research subject. This is primarily due to the global spread of the Corona pandemic, which has altered the appearance of the world and necessitated the use of masks as a vital measure for epidemic prevention. Intellectual development based on artificial intelligence and computers plays a decisive role in the issue of epidemic safety, as the topic of facial recognition and identifying individuals who wear masks or not was most prominent in the introduction and in-depth education. This research proposes the creation of an advanced system capable of accurately identifying faces, both with and