Online ISSN: 2515-8260

Keywords : room occupancy detection


Design And Development Of An Augmented Reality Application To Learn Mandarin

Zaidatol Haslinda ABDULLAH SANI; Seng HUIYI; Teoh Shun HONG; Dinna N. MOHD NIZAM; Aslina BAHARUM

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 3814-3826

This paper presents the design and development of an augmented reality (AR) app to enhance learning Mandarin among university students using a user-centered design life cycle (UCDL). A survey was conducted to investigate the difficulty of learning Mandarin and the thoughts of using technology to assist the students in learning the language. Forty-five students participated in the survey. The results show that participants have difficulty learning to speak, write, read, or listen in Mandarin, with writing was found to be the most difficult (M = 3.49, SD = .94). The majority of the participants (n = 39, 87%) reported having never seen or used an AR education app. However, most (n = 36, 80%) also said that they are interested in using an AR app to learn Mandarin. A low-fidelity prototype of an AR app to assist students in learning Mandarin was designed. An expert usability evaluation was conducted with three experts. Thirty-three usability problems were found, and further changes to the low-fi were designed. A usability evaluation of the low-fi with a group of students will be conducted followed by the app’s development. A final round of usability testing of the final app will also be conducted.

Implementation Of Statistical Learning Model For Room Occupancy Detection

Raja Fazliza Raja Suleiman; Muhammad Iqbal Nebil

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 3737-3746

This paper presents several room occupancy detection methods using statistical learning model. Occupancy detection system is mainly used for energy saving in green buildings such as offices and residential apartments. The system will automatically switched-off the lighting, heating or ventilation appliances when the room is empty. The proposed work uses temperature and humidity sensor to detect human presence. All the input values from this sensor are transmitted to an IoT platform called Blynk (for data monitoring), through the medium of an open-source microcontroller board NodeMCU. The collected data is analyzed using two different approaches which are supervised learning model and unsupervised learning model. Results show that for supervised learning, SVM performs slightly better than Decision Tree. While for unsupervised subspace learning, Minimax yields better probability of detection than SVD in worst case criterion.