A conceptual model for e-learning supporting tools design based on cue model and Kansei engineering

The Covid-19 pandemic has triggered changes in learning due to the practice of social distancing to curb the spread of the virus. E-learning platforms have become the main platform for learning throughout the pandemic. However, e-learning does have challenges when it comes to ensuring student’s optimum participation throughout the learning experience that require extensive research about techniques and methods for an optimum e-learning experience. This includes various e-learning supporting tools that provides easy communication and immediate assistance to enhance user experience. The supporting tools or software usability and functionality design determined as imperative in enhancing the e-learning user experience. Thus, this research proposes a conceptual model for designing the e-learning supporting tools based on the CUE Model, integrated with Kansei Engineering for optimum user experience that can serve as a guideline for the e-learning supporting tools designer. The outcome of this research will create new research fields that incorporate multiple domains, including the e-learning domain, software and supporting tools design, emotions and user experience

Aggressive movement detection using optical flow features base on digital & thermal camera

Detection and tracking of people in digital images has been subject to extensive research in the past decades.Following the growing availability of thermal cameras and the distinctive thermal signature of humans, research effort has been focusing on developing people detection and tracking methodologies applicable to this sensing modality.Thermal imaging technology can be used to detect aggressive levels in humans based on the radiated heat from their face and body. Previous research proposed an approach to figure out human aggressive features using Horn-Schunck optical flow algorithm in order to find the flow vector for all video frames using digital camera only. However, still not strong enough to confirm and verify the existence of an aggressive movement. Then, we propose another approach using thermal videos to detect aggressive features in human aggressive movement.Video frames are collected using thermal camera and then extracted into thermal images. This research also guides and discovers the patterns of body distracted movement.Result below will show the comparison between both cameras digital and thermal camera

Slangs And Short Forms Of Malay Twitter Sentiment Analysis Using Supervised Machine Learning

The current society relies upon social media on an everyday basis, which contributes to finding which of the following supervised machine learning algorithms used in sentiment analysis have higher accuracy in detecting Malay internet slang and short forms which can be offensive to a person. This paper is to determine which of the algorithms chosen in supervised machine learning with higher accuracy in detecting internet slang and short forms. To analyze the results of the supervised machine learning classifiers, we have chosen two types of datasets, one is political topic-based, and another same set but is mixed with 50 tweets per targeted keyword. The datasets are then manually labelled positive and negative, before separating the 275 tweets into training and testing sets. Naïve Bayes and Random Forest classifiers are then analyzed and evaluated from their performances. Our experiment results show that Random Forest is a better classifier compared to Naïve Bayes