Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.

This study attempts to find out the impact of the human behavioral factors (HBFs) including emotion, factors of deals with processes within and between groups as well as with the impact of these processes on individuals’ attitudes and moods, personality, beliefs and values, perception and motivation on the knowledge management system–cycles (KMS-Cs) which comprises sharing; it considers findings from social psychology and discusses their applicability in knowledge management (KM) research and practice; social psychological concepts that strongly influence knowledge processes in organizations are first introduced. It is creating, storing and transferring of academic staff while analyzing the certificates on the acquired behaviors and knowledge which were involved in each of the communications, decision-making, creating new ideas, providing new knowledge, idea diversity, progressing, enhancing and improving the organization, using up-to-date technology and proactivity between the independent and dependent variables. In order to test the study hypotheses, data of 219 respondents working at the University of Sulaimani were collected. The results of the study revealed the academic staff psychology effect on KMS-Cs with a substantial relationship between the HBFs and cycles of KM during academic and administrative work. Also, it surged their academic staff efficiency through a conceptual model called KM behavior (KMB); knowledge management systems (KMSs) are applications of the organization's communication and information systems (CISs) designed to support the various KM processes. They are generally not technologically distinct from the CISs but rely on databases, such as those designed to put organizational participants in contact with recognized experts in a variety of topic areas (Yakan, 2008; Al Hayani, 2020). Information technology (IT) used in KM is known as KMS. In general, KMSs are computer systems that enable organizations to manage knowledge that is efficient and cost-effective. KMS is a class of information systems applied to the management of organizational knowledge. KMS is a system that increases organizational performance by enabling employees to make better decisions when applying their knowledge as part of their daily business activities. Research hypotheses Ho: HBFs and KMS-Cs are not correlated. H1: HBFs have no impact on KMS-Cs. H2: certificates have no effect on HBFs and KMS-Cs. Data collection and sample demographics: in this study, the relevant information for assessing the HBFs and their impact on the KMS-Cs was gathered through a questionnaire survey. The HBF was measured using the following items: emotions, attitudes and moods, personality, beliefs and values, perception and motivation. The knowledge management cycle (KMC) was measured using the following items: knowledge sharing, knowledge creation, knowledge storing and knowledge transfer. The total number of employees at the University of Sulaimani, Sulaimaniya, at the time of data collection (May, 2019) was 117. Since the information available on the number of academic staff at the University of Sulaimani is according to the departments, this study employed a proportionate stratified random sampling method to select the number of academic staff from colleges and departments at the University of Sulaimani. The total number of academic staff at the University of Sulaimaniis is 1,740. Therefore, the appropriate sample size for this study is at least 5% of the population (i.e. 90 respondents) (Langham, 1999). The questionnaire was administered personally through Google Form where questionnaires were collected from the respondents. Examination of the response rate shows that the response rate for this study is excellent. The research instrument consists of two main sections. The first section incorporates a nominal scale to identify respondents' demographic information. The second section uses the five-point Likert-type scale from fully disagree (1) to fully agree (5). All of the measurement items went through backward translation (translated from English into Korean and back into English) to ensure consistency and to resolve discrepancies between the two versions of the instrument (Mullen, 1995; Aldiabat et al., 2018). The participants were almost equal in terms of gender, 59 were males and 58 were females. The certificate for each one of the PhD, MSc and BSc was 39 participants. The number of participants whose age was between 23 and 32 years was 26, between 33 and 42 years was 50, between 43 and 52 years was 29, between 53 and 62 years was 10 and above 62 years was 2. Validity and reliability: in addition to the steps mentioned earlier to assess the validity and reliability of the study tools, a further test was executed. The reliability to measure many inner variables in regularity, Cronbach’s alpha is generally utilized in order to evaluate it and the value should exceed 0.70 for each variable (Alharbi, and Drew, 2014) (Table 1). Cronbach's alpha regards to the test of reliability of a skill for each of the HBF and KMC. The study is considered the organizations relationship between HBFs and KMS-Cs and the influence of the factors on the cycles. So, the new ideas emerge to create knowledge about product development among employees. The group experience works as an essential element (Grimsdottir and Edvardsson, 2018). Knowledge resides in human minds and, as a result, employee behavior and explanatory skills are the key drivers of KM (Prieto and Revilla, 2005). First, knowledge creation, sharing and storing is increased when the organization has motivated the employees. Second, knowledge is shared rapidly when the employees have owned a strong personality, new idea, impression and perception. Third, both the beliefs and values lead to creating new knowledge when the employees obtained it inside the organization. Then, the emotion factors illustrated the weak relation with knowledge sharing, knowledge creation, knowledge storing and knowledge transfer. Knowledge is considered as a great factor in achieving organizational goals (Hammami and Alkhaldi, 2017). Therefore, this study has explained that knowledge is an essential element for employees and organizations. Furthermore, it progresses the skills and capabilities during the job. Nevertheless, this knowledge is impacted through human behaviors because the behavior evolves crucial factors that help the academic staff to create, share, store and transfer the knowledge through motivation, perception, personality, attitudes, moods, beliefs and values. Knowledge sharing is a culture of social interaction involving the exchange of knowledge, experiences and skills of employees across the organization (Zugang et al., 2018). Organizations need to pay particular attention to the method of communication used where knowledge becomes useless if employees are not encouraged to study and use it in their daily activities (Boatca et al., 2018). Knowledge sharing can be achieved by taking into account technical standards (KMS), social standards (environment) and personality (motivation) (Özlen, 2017).