The PM10 concentration is subject to significant changes brought on by both gaseous and meteorological variables. The aim of this research was to explore the performance of a hybrid model combining the support vector machine (SVM) and the boosted regression trees (BRT) technique in predicting the PM10 concentration for 3 consecutive days. The BRT model was trained by utilizing maximum daily data in the cities of Alor Setar, Klang, and Kuching from the years 2002 to 2017. The SVM–BRT model can optimize the number of predictors and predict PM10 concentration; it was shown to be capable of predicting air pollution based on the models’ performance with NAE (0.15–0.33), RMSE (10.46–32.60), R2 (0.33–0.70), IA (0.59–0.91), and PA (0.50–0.84). This was accomplished while saving training time by reducing the feature size given in the data representation and preventing learning from noise (overfitting) to improve accuracy. This knowledge establishes the foundation for the development of efficient methods to prevent and/or minimize the health effects of PM10 exposure on one’s health.

Examination Management Systems (EMS) is a comprehensive system developed using JAVA programming language for data processing and Microsoft Access for reporting purposes. This system has been used and implemented at several campuses including Universiti Teknologi MARA Cawangan Pulau Pinang, Permatang Pauh Campus. The core objective of this system is to improve the efficiency and reducing the operational risks. A survey has been conducted to examine the user acceptance level of UiTM Cawangan Pulau Pinang since implemented in 2014. Questionnaires have been distributed to chief invigilators at every session of examination for 10 semesters and results of analysis are reported and concluded.

Learning a program is important for all students, not only students from the field of computer science but all fields. Programming languages are different from human communication languages as they have different structural forms. This makes it difficult for beginners especially for non-computer science students to understand the structure of programming languages. Therefore, to learn and understand the programming language more effectively, this article focuses on the important structure in learning a program from the initial stage to the advanced level suitable for non-computer science students. The objective of this article is to suggest important elements that can be assessed on these students which are to measure their understanding as they learn programming languages. The questions proposed to measure students' understanding were based on Bloom's Taxonomy, which covers six levels of understanding. It is hoped that this assessment proposal can act as a guideline for educators in fully focusing on important matters during the teaching and learning process.

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