Fall detection framework for smart home

This paper will describe the concept of fall detection framework for smart home environment which will focus on elderly people. We also discuss and compared general fall detection system and fall detection framework that been implemented. The study of this paper will also help to get understanding about indoor fall detection techniques, advantages, drawbacks and the challenges to enhance near in the future

Prediction model for potential school dropout using data mining – a proposed flowchart

In line with the first aspiration in Malaysia Education Blueprint, which is to give access to children for education to let the child realise their full potential, the ministry is focusing on tackling the issue of students drop out in primary and secondary schools. In particular, this study is focusing on building a prediction model to identify a potential school dropout using data mining approach. The pilot sample of this study is taken from a data source from primary and secondary schools located in one of the states. WEKA, an open-source tool for data mining is used to evaluate the attributes predicting potential school dropout. From the analysis, the outcome will be determined using the highest frequency from the classification techniques in the prediction model. The paper aims to present a methodology flow which will be used to identify the main factors of potential school dropouts from many attributes that will be tested using data mining techniques

SSGARL: hybrid evolutionary computation and reinforcement learning for flexible ligand docking

This paper presents and investigates the performance of a hybrid algorithm of steady-state genetic algorithm and reinforcement learning (SSGARL) in the problem of protein-ligand docking. The performance was measured in terms of the lowest found docking energy, the number of energy evaluation and the time taken to complete a docking task. Ten ligands of varying flexibility were chosen to bind with thermolysin to compare the performance of SSGARL and Iterated Local Search global optimizer’s algorithm of AutoDock Vina. The results reveal that SSGARL finds the lowest docking energy, requires lesser number of energy evaluation and is faster in docking the highly flexible ligands

Implementation of learning analytics in primary and secondary school: a systematic literature review

Increased adoption of educational technologies, the emergence of digital classroom concepts, and the interest in big data innovations have led to a growing awareness of the potential implementation of learning analytics to support learning development in educational institutions. However, most studies have focused on the implementation of learning analytics in higher education. As a result, research evidence and studies on actual and ongoing implementation in pre-higher education are still scarce. Therefore, there is a need to understand better the implementation of learning analytics at primary and secondary from the current learning analytics literature. This systematic literature review (SLR) aimed to identify learning analytics research that focuses on implementing learning analytics at pre-higher education levels, including in pre-school, primary, or secondary school. This SLR was carried out based on the SALSA framework to determine the protocol, search, appraisal, synthesis, analysis, and reporting approaches. The findings of the SLR support the arguments that the implementation of learning analytics in school is still in its infancy, where the implementation has been observed mostly in developed countries. Most of the Implementation is aimed at descriptive analytics, focusing on the purpose of monitoring, analysis, and feedback. The literature review has shown a lack of research on the implementation of learning analytics at the primary and secondary education levels

Multiobjective deep reinforcement learning for recommendation systems

Most existing recommendation systems (RSs) are primarily concerned about the accuracy of rating prediction and only recommending popular items. However, other non-accuracy metrics such as novelty and diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering and combined with evolutionary algorithms to handle bi-objective optimization. Besides cold-start problem from collaborative filtering, it also vulnerable to highly sparse environment, while the evolutionary algorithm suffers from premature convergence and curse of dimensionality. These limitations have prompted this work to propose deep reinforcement learning (DRL) approaches for MO optimization in RSs. Several works in DRL are available but none has addressed MO RS problems. In this study, the performances of proposed DRL approaches that based on Deep Q-Network in MO recommendation problem were investigated. The approaches were evaluated with movie recommendation dataset by using three conflicting metrics, namely precision, novelty, and diversity. The results demonstrated that deep reinforcement learning approaches has superiority performance in MO optimization, and its capability of recommending precise item along with achieving high novelty and diversity against the benchmark that using probabilistic based multi-objective approach based on evolutionary algorithm (PMOEA). Although PMOEA algorithm secured higher average value in precision, it has lower values of novelty and diversity than the proposed DRL approaches. The DRL approaches surpassed the benchmark results in average of maximum novelty and the average of mean diversity metrics, the optimization between accuracy and non-accuracy metrics is inevitable. In addition, the experiments revealed that incorporation of user latent features enhanced the recommendation quality

Body mass index awareness using game-based learning in Malaysia: game design and initial user experiences

Mobile-based gaming applications can motivate and facilitate in educating adolescents in health awareness and further can prevent obesity. The purpose of this paper was to observe and assess the initial acceptance of a prototype mobile game application in promoting a healthy Body Mass Index among adolescents. Based on the observations, the developed game was found to be interesting and has positive feedback. Positive user experiences were being expressed on the game's style and aesthetics, operability, learnability, autonomous learning, enjoyment, and social interaction offered. All of these added the excitements of the players and most of them were looking forward to continuing playing in the future. However, some challenges were reported, related to the player's confidence, challenges offered and winning opportunity. The positive findings and concerns discovered during the testing session are reported in this paper. Good health promotion via game-based learning using mobiles demonstrates the potential to offer enjoyment and feasible for adolescents although in our case, further usability testing with larger-scale of participants and a longer session is neede

Systematic Review of Enjoyment Element in Health-Related Game-Based Learning

Educational games are often used as teaching and learning tools, with studies showing that game-based learning is widely accepted among children and teenagers. The experience of enjoyment typically associated with playing games provides for a deeper learning experience and allows the individual to connect various concepts, skills, and knowledge, as well as sparking creativity. This paper builds upon previous studies of enjoyment in health-based gaming and aims to articulate a definition of enjoyment in gaming. Drawing on Miles’ taxonomy, the review further set out to identify and bridge gaps in our theoretical understanding of enjoyment. Three theories were found to be particularly relevant for explaining the concept of enjoyment in relation to health-based gaming: self-determination theory, flow theory, and uses and gratification theory

Comparison of individual and collaborative game-based learning using tablet in improving students’ knowledge in primary classroom environment

In the 21st century, mobile games have become a growing interest among children, including primary school students. This can be a great opportunity for instructors to utilize mobile games approaches and students' interest in games for education teaching and learning purposes, specifically in the primary classroom environment. However, there is no study yet considering the effect between individual and collaborative game-based learning (GBL) on students' knowledge. Experimental research was conducted in a public primary school to address the issue. The present study successfully determined the effectiveness of individual and collaborative GBL using tablets in improving students' knowledge in a primary classroom environment. Three groups of Standard Five students were given a pre-test before the intervention, followed by each group were taught using different approaches of conventional learning, individual and collaborative GBL, respectively. Subsequently, the students were given a post-test after the intervention. The test of homogeneity results indicated that the model was normally distributed and could be used for the intervention. The results of analysis of covariance (ANCOVA) on students' post-test scores show the collaborative GBL had the highest mean, which was 9.324 at Day 4. Additionally, the collaborative group also had the highest mean of trials and success rate during intervention among the three groups as the days increased, which were 3.8 and 0.6, respectively. The collaborative GBL was the most effective among three different approaches. The students had a group discussion to share and organize ideas and critical thinking between their group members on what they have learned from previous intervention days into the current intervention session. The major findings of the present study revealed the potential of collaborative GBL using the tablet in the primary classroom environment in improving students' understanding, knowledge, problem-solving, communications and critical thinking skills