Online ISSN: 2515-8260

Keywords : SVD

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.

Secure Hybrid Watermarking Technique In Medical Imaging

G. Nagaraju; P. Pardhasaradhi; V. S. Ghali; G.R.K Prasad

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 5, Pages 160-176

Medical imaging organizations currently face a significant problem with stealing of medical data. Watermarking is a powerful tool that can be used to authorize the hidden information in cover image. The unique features associated with watermarking are authorization and copyright protection. Because of its unique characteristics, watermarking associated with medical data hiding gives more robustness in medical imaging. In spite of this adding security to watermarking in medical imaging, gives more protection for medical information. This paper explored two main fields that, the encryption of medical images using DNA Encoding and Spatiotemporal Chaos Algorithm and the embedding of medical images in cover image using hybrid transformation of NSCT, RDWT and SVD. The goal of this study was to develop a methodology to improve the robustness, imperceptibility and security for medical information without implementing a physical model, thus saving time, money and reducing the risks associated with hacking partners. For this paper, I used patient health document as one watermark and his medical image as another watermark. The theoretical model has demonstrated that it is possible to use this type of technique and apply it to a complex digital image transmission. The correlation observed before and after encryption and embedding procedures. Experimental results show how robustness and imperceptibility and security of medical images are improved.

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.