HERBAL MEDICINES QUESTIONNAIRE AND EVALUATION OF ATTITUDE, PERCEPTIONS AND SELF-USE AMONG HEALTH CARE PROFESSIONALS IN RAK, UAE: PILOT STUDY

Objective: The aim of this study was to assess the perceptions of different healthcare professionals towards HM. Methods: The 16-item questionnaire on the belief of health care professionals in herbal medicine was designed by the interdisciplinary task force. Eligible participants were health care providers who were English-and Arabic-literate. The response rate was 78% of participants (781 of 1000) were respondents. In total, 553 out of 781 (71%) participants indicated that they had previously used herbal medicines. The remaining 228 participants did not believe in herbal medicine due to lack of scientific evidence, ineffectiveness and other reasons. Results: The findings of this study indicate that health care professionals including pharmacists believe they have a responsibility to provide information on HM to their patients. However, the current consensus among the respondents is that current HM-focused knowledge is inadequate for such an application. Conclusion: Health care professionals believe in using HM for their needs and have a responsibility to provide information on HM to their patients

Phishing websites detection by using optimized stacking ensemble model

Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent

Orthodontic treatment needs in the western region of Saudi Arabia: a research report

BACKGROUND: Evaluation of self perceived and actual need for orthodontic treatment helps in planning orthodontic services and estimating the required resources and man power. In the present study, the perceptive need as evaluated by patients and the actual need to orthodontic treatment, as assessed by orthodontists, were evaluated at two types of dental practices in the city of Jeddah using the Index of Orthodontic Treatment Need (IOTN). METHODS: A consecutive sample of 743 adults seeking orthodontic treatment at two different types of dental practices in Jeddah; King Abdulaziz University, Faculty of Dentistry (KAAU) (Free treatment) and two private dental polyclinics (PDP) (Paid treatment), was examined for orthodontic treatment need using the dental health component (DHC) of the IOTN. The self-perceived need for orthodontic treatment was also determined using the aesthetic component (AC) of the IOTN. The IOTN score and the incidence of each variable were calculated statistically. AC and DHC categories were compared using the Chi-Square and a correlation between them was assessed using Spearman's correlation test. AC and DHC were also compared between the two types of dental practices using the Chi-Square. RESULTS: The results revealed that among the 743 patients studied, 60.6% expressed no or slight need for treatment, 23.3% expressed moderate to borderline need and only16.1% thought they needed orthodontic treatment. Comparing these estimates to professional judgments, only 15.2% conformed to little or no need for treatment, 13.2% were assessed as in borderline need and 71.6% were assessed as in need for treatment (p < 0.001). Spearman's correlation test proved no correlation (r = -.045) between the two components. Comparing the AC and the DHC between the KAAU group and PDP group showed significant differences between the two groups (p < 0.001). CONCLUSION: Patient's perception to orthodontic treatment does not always correlate with professional assessment. The IOTN is a valid screening tool that should be used in orthodontic clinics for better services especially, in health centers that provide free treatment