Recently, the market of E-Learning is soaring and is known as a new paradigm in modern education. E-Learning acts as a medium, consisting of several types of computers and electronic media that are communication tools as well as the Internet, which provides training and informative access on certain subjects. By using E-Learning students can attend online classes anywhere, regardless of time and place. Generally, E-Learning is more geared towards self-training and is ideal for individuals who work full-time but desire to further their studies. Thus, this study has been developed and integrated with factors leading towards the effectiveness of E-Learning as a tool in Teaching and Learning (T&L) approach. A quantitative approach was applied by using a self-administered distribution of questionnaire targeted at higher education students. Therefore, the outcomes of this research will help to provide insightful information to the current education system in Malaysia, particularly in crafting strategies to enhance the learning education for the country in general.

Macroeconomic indicators are the key to success in the development of any country and are very much important for the overall economy of any country in the world. In the past, researchers used the traditional methods of regression for estimating macroeconomic variables. However, the advent of efficient machine learning (ML) methods has led to the improvement of intelligent mechanisms for solving time series forecasting problems of various economies around the globe. This study focuses on forecasting the data of the inflation rate and the exchange rate of Pakistan from January 1989 to December 2020. In this study, we used different ML algorithms like k-nearest neighbor (KNN), polynomial regression, artificial neural networks (ANNs), and support vector machine (SVM). The data set was split into two sets: the training set consisted of data from January 1989 to December 2018 for the training of machine algorithms, and the remaining data from January 2019 to December 2020 were used as a test set for ML testing. To find the accuracy of the algorithms used in the study, we used root mean square error (RMSE) and mean absolute error (MAE). The experimental results showed that ANNs archives the least RMSE and MAE compared to all the other algorithms used in the study. While using the ML method for analyzing and forecasting inflation rates based on error prediction, the test set showed that the polynomial regression (degree 1) and ANN methods outperformed SVM and KNN. However, on the other hand, forecasting the exchange rate, SVM RBF outperformed KNN, polynomial regression, and ANNs.

Tourism research identified that the risk element has a significant impact on tourist intention to travel and tourist destination choice criteria. The connection between domestic tourists perceived risk and revisit intention has not been widely explored mainly in Malaysia context, but the existing studies suggest a close connection among them.This study aims to examine the affect tourist perceived risk toward their revisit intention to visit tourist destination in Malaysia. A total of 384 sample valid responses were obtained.The sampling method for domestic tourist was convenience sampling method. Data collection instrument was a questionnaire which adapting the self-administered questionnaire distributionThe Partial Least Square Structural Equation Model (SEM-PLS) was used to analyze the information. A Partial Least Square findings verified model fitness in the population studied. Similarly, the results of the path analysis found that the intention of visiting tourist destination in Malaysia was affected by physical and financial risk.