Abstract
Objective(s): To evaluate housekeeping services staff work environment and their health status, as well as to determine the impact of the work environment upon their health status.
Methodology: A descriptive design is employed throughout the present study to evaluate housekeeping services staff work environment and their health status, as well as to determine the impact of the work environment upon their health status from November 3rd 2017 to June 30th 2018. A purposive “nonprobability” sample of (101) housekeeping staff is selected for the present study. An instrument is constructed for the purpose of the study and it is consists of (2) parts: (I) Evaluation of work environment, and (II) Evaluation of housekeeping staff's health status. A pilot study is conducted for the determination of the instrument’s validity and reliability. The content validity of the instrument is determined by panel of (35) experts. So, the instrument is considered adequately valid measure after performing the modifications that based on the experts’ responses. The internal consistency reliability is determined through split-half technique and the computation of Cronbach alpha correlation coefficient of (r=0.87) which is indicated that the instrument is adequately reliable measure. Data are collected through the use of the study instrument and the structured interview technique as means of data collection. Data are analyzed through descriptive statistical analysis approach of frequency, percentage, mean, total score and range and inferential statistical data analysis approach of linear regression.
Results: The study indicates that the work environment is moderately risky (92.1%) and
the housekeeping staff health status is ranging between fair (42.6%) to good (42.6%) for most of them. The biological, chemical and physical aspects of work environment have great impact upon the housekeeping services staff health status.
Recommendations: The study recommends that work environment can be monitored to maintain occupational health and safety; periodic medical examination has to be initiated and presented to the housekeeping services staff and further research can be conducted on different settings and large sample size.
Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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