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The online teaching in colleges and universities during the COVID-19 outbreak is one of the challenges faced by faculty and students during this period especially for colleges and universities that meet the quality assurance standards and under the accreditation process. One of the main requirements of quality standards is to carry out a variety of opinion surveys at different stages among different levels of study, analyze, and then provide recommended solutions based on survey findings. Although many researchers have been carried out online teaching, there is no consensus on the impact of a sudden transition from face-to-face learning to online learning especially in community colleges in Saudi Arabia. The purpose of this paper is to present the outcomes of the study on students' experiences about online teaching during COVID-19 Outbreak. Smart PLS program is used for testing the model and to make sure that the variables are appropriate and the outcomes are valid.

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The rainfall-runoff process is one of the most complex hydrological phenomena. Estimating runoff in the basin is one of the main conditions for planning and optimal use of rainfall. Using machine learning models in various sciences to investigate phenomena for which statistical information is available is a helpful tool. This study investigates and compares the abilities of HEC-HMS and TOPMODEL as white box models and adaptive neural fuzzy inference system (ANFIS) and gene expression programming (GEP) as black box models in rainfall-runoff simulation using 5-year statistical data. Using the inputs of rainfall and temperature of the previous day and discharge in the steps of the previous 2 days reduced the prediction error of both models. Examining the role of different parameters in improving the accuracy of simulations showed that the temperature as an effective parameter in cold months reduces the amount of prediction error. A comparison of R2, RMSE, and MBE showed that black box models are more effective forecasting tools. Among the black box models, the ANFIS model with R2 = 0.82 has performed better than the GEP model with R2 = 0.76. For white box models, the HEC-HMS and TOPMODEL had R2 equal to 0.3 and 0.25, respectively.

The detection of human skin color has proven to be a useful and robust technique for detecting nude images, face detection, localization and tracking. This paper presents an Improved Chromatic Skin Color model to detect the human skin in JPEG images; the ICSC model detected the human skin with detection rates more than 90%. A threshold method and 2D Gaussian model will improve the accuracy of skin regions detected