Technology based learning analysis of CBCS model at KKU - Case study of College of Computer Science King Khalid University, Saudi Arabia

The higher education frame works are attempted to develop the human capital of the country thinking of the next generation technology. The Kingdom of Saudi Arabia is prioritized the higher education sector is one among the segment in current scenario. The establishment of new universities and the business schools are evidence that the Kingdom of Saudi Arabia is providing higher education with the Islamic culture and values to build up the upcoming young generation. While providing the higher education model, mostly focused on the four sectors namely Islamic culture and values, basic science for life enhancement, higher education for technology and prepare the potential young youth for employment to buildup the human resource of the country. As part of the national mission and the vision of the country, the educations sectors are distributed the higher education frame work as Choice Based Credit System (CBCS) curriculum into these above stated four sectors. The study is made to analysis the Higher Education frame work of College of Computer Science curriculum, King Khalid University, Abha, Kingdom of Saudi Arabia. It evaluates the learnerâ??s suitability potential to meet the enhancement of higher education in the same major subject and Employability in the specified filed with Islamic culture and values

Technology based learning analysis of CBCS model at KKU - Case study of College of Computer Science King Khalid University, Saudi Arabia

The higher education frame works are attempted to develop the human capital of the country thinking of the next generation technology. The Kingdom of Saudi Arabia is prioritized the higher education sector is one among the segment in current scenario. The establishment of new universities and the business schools are evidence that the Kingdom of Saudi Arabia is providing higher education with the Islamic culture and values to build up the upcoming young generation. While providing the higher education model, mostly focused on the four sectors namely Islamic culture and values, basic science for life enhancement, higher education for technology and prepare the potential young youth for employment to buildup the human resource of the country. As part of the national mission and the vision of the country, the educations sectors are distributed the higher education frame work as Choice Based Credit System (CBCS) curriculum into these above stated four sectors. The study is made to analysis the Higher Education frame work of College of Computer Science curriculum, King Khalid University, Abha, Kingdom of Saudi Arabia. It evaluates the learner’s suitability potential to meet the enhancement of higher education in the same major subject and Employability in the specified filed with Islamic culture and values

Rock brittleness prediction through two optimization algorithms namely particle swarm optimization and imperialism competitive algorithm

Brittleness index (BI) is a significant rock parameter when dealing with projects performed in rocks. The main goal of this research work is to propose the novel practical models to predict the BI through particle swarm optimization (PSO) and imperialism competitive algorithm (ICA). For this aim, two forms of equations, i.e., linear and power are considered and the weights of these equations are optimized by PSO and ICA. In the other words, four predictive models, namely ICA linear, ICA power, PSO linear, and PSO power models are developed to predict BI in this study. In the modeling of the predictive models, 79 datasets are used, so that Schmidt hammer rebound number, wave velocity, density, and Point Load Index (Is50) are selected as the independent (input) parameters and the BI values are considered as the dependent (output) parameter. Then, the performances of the proposed predicting models are checked using two error indices, namely coefficient correlation (R2) and root mean squared error (RMSE). The results showed that the PSO power model has superior fitting specification for the prediction of the BI compared to the other prediction models and is quite practical for use. As a result, linear and power models of PSO received higher performance prediction compared to ICA. PSO power (with R2 train = 0.937, R2 test = 0.959, RMSE train = 0.377 and RMES test = 0.289) showed the most powerful technique to predict BI of the granite samples

Stimating and optimizing safety factors of retaining wall through neural network and bee colony techniques

An important task of geotechnical engineering is a suitable design of safety factor (SF) of retaining wall under both static and dynamic conditions. This paper presents the advantages of both prediction and optimization of retaining wall SF through artificial neural network (ANN) and artificial bee colony (ABC), respectively. These techniques were selected because of their capability in predicting and optimizing science and engineering problems. To gain purpose of this research, a comprehensive database consisted of 2880 datasets of wall height, wall width, wall mass, soil mass and internal angle of friction as input parameters and SF of retaining wall as output was prepared. In fact, SF is considered as a function of the mentioned parameters. At the first step of modeling, several ANN models were constructed and the best one among them was selected. The coefficient of determination (R2) value of 0.998 for both training and testing datasets was obtained for the best ANN model which indicates an excellent accuracy level in predicting SF values. In the next step of modeling, the results of selected ANN model were used as an input for the optimization technique of ABC. In general, 11 models of ABC optimization with different strategies were built. As a result, by decreasing wall height value from 10 m to 8 m and 5.628 m and using almost constant values for the other input parameters, SF values were obtained as 2.142 and 5.628, respectively. Results of (8.003, 0.794, 0.667, 1800 and 2800) and (5.628, 0.763, 0.660, 1735 and 2679) were obtained for wall height, wall width, internal friction angle, soil mass and wall mass of the best models with 2.142 and 5.628 SF values, respectively