The effect of using facebook markup language (fbml) for designing an e-learning model in higher education

This study examines the use of Facebook Markup Language (FBML) to design an e-learning model to facilitate teaching and learning in an academic setting. The qualitative research study presents a case study on how, Facebook is used to support collaborative activities in higher education. We used FBML to design an e-learning model called processes for e-learning resources in the Specialist Learning Resources Diploma (SLRD) program. Two groups drawn from the SLRD program were used; First were the participants in the treatment group and second in the control group. Statistical analysis in the form of a t-test was used to compare the dependent variables between the two groups. The findings show a difference in the mean score between the pre-test and the post-test for the treatment group (achievement, the skill, trends). Our findings suggest that the use of FBML can support collaborative knowledge creation and improved the academic achievement of participatns. The findings are expected to provide insights into promoting the use of Facebook in a learning management system (LMS).Comment: Mohammed Amasha, Salem Alkhalaf, "The Effect of using Facebook Markup Language (FBML) for Designing an E-Learning Model in Higher Education". International Journal of Research in Computer Science, 4 (5): pp. 1-9, January 201

A Model of an E-Learning Web Site for Teaching and Evaluating Online

This research is endeavoring to design an e-learning web site on the internet having the course name as "Object Oriented Programming" (OOP) for the students of level four at Computer Science Department (CSD). This course is to be taught online (through web) and then a programme is to be designed to evaluate students performance electronically while introducing a comparison between online teaching , e-evaluation and traditional methods of evaluation. The research seeks to lay out a futuristic perception that how the future online teaching and e-electronic evaluation should be the matter which highlights the importance of this research

Improvement of Control System Performance by Modification of Time Delay

This paper presents a mathematical approach for improving the performance of a control system by modifying the time delay at certain operating conditions. This approach converts a continuous time loop into a discrete time loop. The formula derived is applied successfully to an applicable control system. The results show that the proposed approach efficiently improves the control system performance. The relation between the sampling time and the time delay is obtained. Two different operating conditions are examined to assess the proposed approach in improving the performance of the control system.Comment:

Third-Order Approximate Solution of Chemical Reaction-Diffusion Brusselator System Using Optimal Homotopy Asymptotic Method

The objective of this paper is to investigate the effectiveness and performance of optimal homotopy asymptotic method in solving a system of nonlinear partial differential equations. Since mathematical modeling of certain chemical reaction-diffusion experiments leads to Brusselator equations, it is worth demanding a new technique to solve such a system. We construct a new efficient recurrent relation to solve nonlinear Brusselator system of equations. It is observed that the method is easy to implement and quite valuable for handling nonlinear system of partial differential equations and yielding excellent results at minimum computational cost. Analytical solutions of Brusselator system are presented to demonstrate the viability and practical usefulness of the method. The results reveal that the method is explicit, effective, and easy to use

An Algorithm: Optimal Homotopy Asymptotic Method for Solutions of Systems of Second-Order Boundary Value Problems

Optimal homotopy asymptotic method (OHAM) is proposed to solve linear and nonlinear systems of second-order boundary value problems. OHAM yields exact solutions in just single iteration depending upon the choice of selecting some part of or complete forcing function. Otherwise, it delivers numerical solutions in excellent agreement with exact solutions. Moreover, this procedure does not entail any discretization, linearization, or small perturbations and therefore reduces the computations a lot. Some examples are presented to establish the strength and applicability of this method. The results reveal that the method is very effective, straightforward, and simple to handle systems of boundary value problems

A Comparative Study on Market Index Prediction: Long Short- Term Memory (LSTM) vs. Decision Tree Model

The main objective of this article is to develop a linear exponential function risks in Saudi banks (LINEXLF) to estimate the shape parameter, reliability, and hazard rate functions of the Pareto distribution based on Type II Censored Data. By weighting LINEX loss function to produce a modified loss function called weighted linear exponential (WLINEXLF) loss function. We then use WLINEXLF to derive the shape parameter, reliability, and hazard rate functions of the Pareto distribution. Furthermore, to examine the performance of the proposed method WLINEXLF we conduct a Monte Carlo simulation. The comparison is between the proposed method and other methods including maximum likelihood estimation (MLE) and Bayesian estimation under the squared error loss function. The results of the simulation show that the proposed method WLINEXLF in this article has the best performance in estimating shape parameter, reliability, and hazard rate functions, according to the smallest values of mean squared error (MSE). This result means that the proposed method can be applied in real data in banking industrial sectors. This paper aims to use the modified loss function to estimate the shape parameter, reliability (), and hazard rate functions h() in Saudi banks of the Pareto distribution based on Type II Censored Data

Detection of DDoS Attacks using Enhanced FS with BRSA- based Deep Learning Model in IoT Environment

Network assaults and floods, are rising due to the increasing number of IoT devices, posing security and dependability concerns. These attacks cause a denial of service (DoS) and network interruption for IoT devices. Researchers have established multiple methods to track down assaults on weak IoT gadgets. This study provides a deep learning and swarm metaheuristic technique for detecting DDoS assaults in an Internet of Things (IoT) setting. The group search firefly method, a revolutionary improvement on the classic firefly algorithm, is used as a feature selection tool to zero in on the best candidates. In addition, the hyperparameters of the DarkNet are selected and optimized with the help of a suggested technique called the Boosted Reptile Search technique (BRSA) for effective botnet detection. The operatives of the red fox algorithm (RFO) and the triangular mutation operator (TMO) were used to effect this change. The TMO was utilized to progress the misuse phase of the RSA, whereas the RFO was used to improve the exploration phase. The suggested model is verified using the N-BaIoT dataset. Various cutting-edge methods were employed to evaluate and contrast the projected models efficacy. The outcome proves that the recommended approach is superior to alternatives in identifying multiclass botnet assaults