E-learning is regarded as a mandatory teaching and learning approach in higher education worldwide. Despite its importance and popularity, several issues on its use and effectiveness still remain. Universities are facing problems oflow e-learning usage among students and even academic staffs. This study investigate students' acceptance of e-learning in university using modified TAM model consists of six constructs namely instructor characteristics, computer self-efficacy, course design, perceived usefulness, perceived ease of use and intention to use. Results shown that computer self-efficacyhas significantly effects ease of use, while perceived ease of use significantly affectsintention to use e-learning.

Studies of educational games (EG) are rapidly growing in recent years due to its promising potential for education. New with many games produced from industry or games are still low due to issues from both student matching with syllabus as well as factors that contribute to student acceptance of EG in orde study proposed and validated games acceptance framework collected with 180 university students Learning Expectancy, Effort Expectancy, Attitud Games designers can leverage the issues concern with students

Particle Swarm Optimization (PSO) is a popular algorithm used extensively in continuous optimization. One of its well-known drawbacks is its propensity for premature convergence. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GA) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use adaptive parameterization when applying the GA operators. In this work, adaptively parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that an adaptive approach with position factor is more effective for the proposed PSO hybrids. Compared to single PSO with adaptive inertia weight, all the PSO hybrids with adaptive probability have shown satisfactory performance in generating near-optimal solutions for all tested functions.

Particle Swarm Optimization (PSO) is a well known technique for solving various kinds of combinatorial optimization problems including scheduling, resource allocation and vehicle routing. However, basic PSO suffers from premature convergence problem. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GAs) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use dynamic parameterization when applying the GA operators. In this work, dynamic parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that all the PSO hybrids with dynamic probability have shown satisfactory performance in finding the best distance of the Vehicle Routing Problem With Time Windows.

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In the era of Industrial 4.0, many urgent issues in the industries can be effectively solved with artificial intelligence techniques, including machine learning. Designing an effective machine learning model for prediction and classification problems is an ongoing endeavor. Besides that, time and expertise are important factors that are needed to tailor the model to a specific issue, such as the green building housing issue. Green building is known as a potential approach to increase the efficiency of the building. To the best of our knowledge, there is still no implementation of machine learning model on GB valuation factors for building price prediction compared to conventional building development. This paper provides a report of an empirical study that model building price prediction based on green building and other common determinants. The experiments used five common machine learning algorithms namely Linear Regression, Decision Tree, Random Forest, Ridge and Lasso tested on a set of real building datasets that covered Kuala Lumpur District, Malaysia. The result showed that the Random Forest algorithm outperforms the other four algorithms on the tested dataset and the green building determinant has contributed some promising effects to the model.

A recommender system aims to provide users with personalized online product or service recommendations to handle the online information overload problem that keep rapidly increasing. The main problems in order to resolve the problems, one of the current trust aware mechanism that includes rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the performances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the different views have an effect on the accuracy and rating coverage of the tw Keywords: recommender system; collaborative filtering; trust aware; distrust. A recommender system aims to provide users with personalized online product or service e online information overload problem that keep rapidly increasing. The main problems in the CF recommender system are sparsity and cold start. In order to resolve the problems, one of the current researches has been directed to the CF with echanism that includes trust as additional information in order to predict the rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the erformances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the different views have an effect on the accuracy and rating coverage of the two algorithms.recommender system; collaborative filtering; trust aware; distrust. A recommender system aims to provide users with personalized online product or service e online information overload problem that keep rapidly CF recommender system are sparsity and cold start. In been directed to the CF with a trust as additional information in order to predict the rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the erformances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the o algorithms.

-External auditor is one of the governance mechanisms in mitigating corporate managerial misconduct and thereby enhance the credibility of accounting information. Thus, the main objective of this study is to develop machine learning prediction model on auditor choice of the firm which signal the quality of auditing and financial reporting processes.This paper presents the fundamental knowledge on the design and implementation of machine learning model based on four selected algorithms tested on the real dataset of 2,262 firm-year observations of companies listed on Malaysian stock exchange from 2000 to 2007. The performance of each machine learning algorithm on the auditor choice dataset has been observed based on three groups of features selection namely firm characteristics, governance and ownership. The findings indicated that the machine learning models present better accuracy performance with ownership features selection mainly with the Naïve Bayes algorithm. Keywords-Auditor Choice, Machine Learning, Prediction