Background Mobile health (mHealth) apps play an important role in delivering education, providing advice on treatment, and monitoring patients’ health. Good usability of mHealth apps is essential to achieve the objectives of mHealth apps efficiently. To date, there are questionnaires available to assess the general system usability but not explicitly tailored to precisely assess the usability of mHealth apps. Hence, the mHealth App Usability Questionnaire (MAUQ) was developed with 4 versions according to the type of app (interactive or standalone) and according to the target user (patient or provider). Standalone MAUQ for patients comprises 3 subscales, which are ease of use, interface and satisfaction, and usefulness. Objective This study aimed to translate and validate the English version of MAUQ (standalone for patients) into a Malay version of MAUQ (M-MAUQ) for mHealth app research and usage in future in Malaysia. Methods Forward and backward translation and harmonization of M-MAUQ were conducted by Malay native speakers who also spoke English as their second language. The process began with a forward translation by 2 independent translators followed by harmonization to produce an initial translated version of M-MAUQ. Next, the forward translation was continued by another 2 translators who had never seen the original MAUQ. Lastly, harmonization was conducted among the committee members to resolve any ambiguity and inconsistency in the words and sentences of the items derived with the prefinal adapted questionnaire. Subsequently, content and face validations were performed with 10 experts and 10 target users, respectively. Modified kappa statistic was used to determine the interrater agreement among the raters. The reliability of the M-MAUQ was assessed by 51 healthy young adult mobile phone users. Participants needed to install the MyFitnessPal app and use it for 2 days for familiarization before completing the designated task and answer the M-MAUQ. The MyFitnessPal app was selected because it is one among the most popular installed mHealth apps globally available for iPhone and Android users and represents a standalone mHealth app. Results The content validity index for the relevancy and clarity of M-MAUQ were determined to be 0.983 and 0.944, respectively, which indicated good relevancy and clarity. The face validity index for understandability was 0.961, which indicated that users understood the M-MAUQ. The kappa statistic for every item in M-MAUQ indicated excellent agreement between the raters (κ ranging from 0.76 to 1.09). The Cronbach α for 18 items was .946, which also indicated good reliability in assessing the usability of the mHealth app. Conclusions The M-MAUQ fulfilled the validation criteria as it revealed good reliability and validity similar to the original version. M-MAUQ can be used to assess the usability of mHealth apps in Malay in the future.

The coronavirus disease 2019 has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet) based on the confirmed cases and weather variables such as temperature, humidity, wind and precipitation are developed. This study aims to identify the best classification model to classify COVID-19 by using significant weather features chosen by Principle Component Analysis (PCA) feature selection method. The DS4C COVID-19 dataset is used to train and validate each machine learning model. Several data preprocessing tasks such as data cleaning and feature selection have been conducted on the raw dataset to ensure the quality of the training data. The performance of these machine learning algorithms is further rectified based on the selected features set by PCA. Each classifier is then optimized using different tuning parameters to achieve optimum values before comparing the output of the three classifiers against each other. The observational results have shown that the optimized CNN classifier with seven weather variables selected by PCA achieved the highest performance among all the techniques. The experimental results obtained show that the weather variables are more relevant in predicting the confirmed cases as compared to the other variables. Thus, from this result, it is evident that temperature, humidity, wind and precipitation are important features for predicting COVID-19 confirmed cases.

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The exponential growth of todays technologies has resulted in the growth of high-throughput data with respect to both dimensionality and sample size. Therefore, efficient and effective supervision of these data becomes increasing challenging and machine learning techniques were developed with regards to knowledge discovery and recognizing patterns from these data. This paper presents machine learning tool for preprocessing tasks and a comparative study of different classification techniques in which a machine learning tasks have been employed in an experimental set up using a data set archived from the UCI Machine Learning Repository website. The objective of this paper is to analyse the impact of refined feature selection on different classification algorithms to improve the prediction of classification accuracy for room occupancy. Subsets of the original features constructed by filter or information gain and wrapper techniques are compared in terms of the classification performance achieved with selected machine learning algorithms. Three feature selection algorithms are tested, specifically the Information Gain Attribute Evaluation (IGAE), Correlation Attribute Evaluation (CAE) and Wrapper Subset Evaluation (WSE) algorithms. Following a refined feature selection stage, three machine learning algorithms are then compared, consisting the Multi-Layer Perceptron (MLP), Logistic Model Trees (LMT) and Instance Based k (IBk). Based on the feature analysis, the WSE was found to be optimal in identifying relevant features. The application of feature selection is certainly intended to obtain a higher accuracy performance. The experimental results also demonstrate the effectiveness of Instance Based k compared to other ML classifiers in providing the highest performance rate of room occupancy prediction.

The aim of this paper is to assess students' perceptions of their competency and interests in Science, Technology, Engineering and Mathematics (STEM) throughout Malaysia. These perceptions are obtained during and after they were engaged in using a STEM module and building a robotic prototype that was in line with the STEM teachers' specification, and was conducted at the National Science Centre, Malaysia. This activity was undertaken because the target ratio for the number of students enrolling in STEM programs is not met. The developed STEM module is based on four stages of the learning cycle in Kolb's experiential learning theory. The stages are Concrete Experience, Reflective Observation, Abstract Conceptualization, and Active Experimentation. These stages have five key educational activities which are watching videos, reading modules, assembling robotic components, drag and drop using blockly software and lastly playing a robotic game. The key element of the activities is the utilization of a robotic prototype as the main component in increasing the students' interest in STEM via games. This module was evaluated in both qualitative and quantitative case studies of students to inform teachers' perceptions of the developed modules and robotic prototypes. Data were collected through two training events at a science exhibition at the National Science Centre and taken from two distinct groups, namely primary and secondary school students in range 11 to 15 year old. The evaluation comprised of five areas, which were interaction, engagement, challenge, competency, and interest. The results show that developed module and robotic prototype based n teacher's perception received positive response from the respondents. It can efficiently raise students' interest in STEM that meets the Malaysia Education Blueprint 2013-2025.