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All Journal JOIV : International Journal on Informatics Visualization
Wahid, Noorhaniza
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Computer Science & IT

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Virtual Campus Tour Application through Markerless Augmented Reality Approach Liang, Ang Wei; Wahid, Noorhaniza; Gusman, Taufik
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.743

Abstract

Augmented Reality (AR) technology has been widely used on campus tours by universities all around the world. However, the students that stay very far away do not have a chance to visit around the campus. Also, the information that is available on the official website is static, resulting in the visitors feeling less engaged with the information. Hence, the virtual campus tour application using the markerless AR technology, namely AR-UTHM Tour is proposed to be developed on the Android mobile-based platform to visualize the buildings and facilities that are available in the university, specifically Universiti Tun Hussein Onn Malaysia (UTHM). This approach allows the users to visualize the 3D models by pointing the camera at any flat surface. Then, the feature point will be generated to generate a virtual plane. The information about the facilities was obtained from the UTHM official website and the 3D models of the buildings were referred to the floor plan and the actual images. The user acceptance test has been conducted on 30 students of UTHM using Technology Acceptance Model (TAM). The result shows that more than 50% of the respondents have successfully executed the AR session without any error. Overall results show that the users are satisfied with the AR-UTHM Tour application. In conclusion, this application is suitable to be used as a medium to introduce and promote UTHM virtually. Future improvements in terms of detailing the aesthetic of the 3D model will be taken into consideration.
Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-based Feature Selection: A comparative study Yab, Li Yu; Wahid, Noorhaniza; A Hamid, Rahayu
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1509

Abstract

Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance of feature selection and classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. The proposed mWOA and mGWO were given a new inversed control parameter which was expected to enable more search area for the search agents in the early phase of the algorithms and resulted in a faster convergence speed. The objective of this comparative study is to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. The proposed methods were implemented in MATLAB where 12 datasets with different dimensionality from the UCI repository were used. kNN was chosen as the classifier to evaluate the classification accuracy of the selected features. Based on the experimental results, mGWO did not show significant improvements in feature reduction and maintained similar accuracy as the original GWO. On the contrary, mWOA outperformed the original WOA in terms of the two criteria mentioned even on high-dimensional datasets. Evaluating the execution time of the proposed methods, utilizing different classifiers, and hybridizing proposed methods with other metaheuristic algorithms to solve feature selection problems would be future works worth exploring.
Co-Authors A Hamid, Rahayu Gusman, Taufik Liang, Ang Wei Yab, Li Yu