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This paper seeks to explore the challenges faced by excellent teachers in preparing for authentic assessment in their English as a Second Language (ESL) classrooms. This qualitative case study involving six excellent teachers was based on purposive sampling. Data sources in this study used classroom observation and a series of semi-structured interviews with the excellent teachers. Thematic analysis was used to identify emerging themes from the codes gathered from the interviews and observations. Data showed that excellent teachers faced multiple challenges prior to preparing for authentic assessment. They have also been using variations of authentic assessment to help children in their learning besides accurate documentation and extensive reading from around the globe to equip themselves with the current knowledge. Since there is no clear guideline for teachers who practice authentic assessment in their classes, this study provided some insights on the preparations and the use of authentic assessment as part of their teaching and learning process.

The term "personality" can be defined as the mixture of features and qualities that built an individual's distinctive characters, including thinking, feeling and behaviour. Nowadays, it is hard to select the right employees due to the vast pool of candidates. Traditionally, a company will arrange interview sessions with prospective candidates to know their personalities. However, this procedure sometimes demands extra time because the total number of interviewers is lesser than the total number of job seekers. Since technology has evolved rapidly, personality computing has become a popular research field that provides personalisation to users. Currently, researchers have utilised social media data for auto-predicting personality. However, it is complex to mine the social media data as they are noisy, come in various formats and lengths. This paper proposes a machine learning technique using Random Forest classifier to automatically predict people's personality based on Myers-Briggs Type Indicator® (MBTI). Researchers compared the performance of the proposed method in this study with other popular machine learning algorithms. Experimental evaluation demonstrates that Random Forest classifier performs better than the different three machine learning algorithms in terms of accuracy, thus capable in assisting employers in identifying personality types for selecting suitable candidates.

Electricity-saving strategies are an essential solution to overcoming increasing global CO2 emission and electricity consumption problems; therefore, the determinant factors of electricity consumption in households need to be assessed. Most previous studies were conducted in developed countries of subtropical regions that had different household characteristic factors from those in developing countries of tropical regions. A field survey was conducted on electricity consumption for Malaysian households to investigate the factors affecting electricity consumption that focused on technology perspective (building and appliance characteristics) and socio-economic perspective (socio-demographics and occupant behaviour). To analyse the determinant factors of electricity consumption, direct and indirect questionnaire surveys were conducted from November 2017 to January 2018 among 214 university students. Direct questionnaire surveys were performed in order to obtain general information that is easily answered by respondents. On the other hand, some questions such as electricity consumption and detailed information of appliances must be confirmed by the respondents’ parents or other household members through an indirect questionnaire survey. The results from multiple linear regression analyses of the survey responses showed that appliance characteristic factors were the main variables influencing electricity consumption and house characteristics were the least significant. Specifically, air conditioners, fluorescent lamps, and flat-screen TVs emerged as appliances with the most significant effect on electricity consumption. Occupant behaviour factors had a more significant influence than socio-demographic factors. The findings in this study can be used by policymakers to develop electricity-saving strategies in Malaysia.

Breast cancer is a complex and heterogeneous disease due to its diverse morphological features, as well as different clinical outcome. As a result, breast cancer patients may response to different therapeutic options. Currently, difficulties in recognizing the breast cancer types lead to inefficient treatments. Generally, there are two types of breast cancer, known as malignant and benign. Therefore it is necessary to devise a clinically meaningful classification of the disease that can accurately classify breast cancer tissues into relevant classes. This study aims to classify breast cancer lesions which have been obtained from fine needle aspiration (FNA) procedure using random forest. Random forest is a classifier built based on the combination of decision trees and has been identified to perform well in comparison to other machine learning techniques. This method has been tested on approximately 700 dat� which consists of 458 instances from benign cases and 241 instances belong to malignant cases. The performance of proposed method is measured based on sensitivity, specificity and accuracy. The experimental resultsshow that, random forest achieved sensitivity of 75%, specificity of 70% and accuracy about 72%. Thus, it can be concluded that random forest can accurately classify breast cancer types given a small number of features and it works as a promising tool to differentiate malignant from benign tumor at early stage.

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The aim of this study was to conduct short-term measurements on household electricity demand under hot weather conditions in a residential area in Kuala Lumpur. The measurements included total and air conditioner (AC) electricity consumption of 10 households in an apartment building as well as outdoor air temperatures, which were collected from March to May 2016. Results indicated that the average AC electricity consumption contributed to a major portion of total household electricity consumption, which ranged from 19.4 to 52.3% during the measurement period. Additionally, 1-minute interval time series data indicated household energy consumption more accurately than 30- or 60-minute interval.