Esan, Dorcas Oladayo
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Journal : Journal of Information Systems and Informatics

Advanced 3D Artistic Image Generation with VAE-SDFCycleGAN Esan, Dorcas Oladayo; Owolawi, Pius Adewale; Tu, Chunling
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.900

Abstract

Generation of a 3-dimensional (3D)-based artistic image from a 2-dimensional (2D) image using a generative adversarial network (GAN) framework is challenging. Most existing artistic GAN-based frameworks lack robust algorithms lack suitable 3D data representations that can fit into GAN to produce high-quality 3D artistic images. To produce 3D artistic images from 2D image that considerably improves scalability and visual quality, this research integrates innovative variational autoencoder signed distance function, cycle generative adversarial network (VAE-SDFCycleGAN). The proposed method feeds a single 2D image into the network to produce a mesh-based 3D shape. The network encodes a 2D image of the 3D object into latent representations, and implicit surface representations of 3D images corresponding to those of 2D images are subsequently generated. VAE extracts feature from the two-dimensional input image and reconstructs a voxel-type grid using a signed distance function. Cycle GAN produces improved and high-quality 3D artistic images from 2D images. The publicly available COCO dataset was used to evaluate the proposed advanced 3D-VAE-SDFCycleGAN. The model produced a peak signal noise ratio (PSNR) of 31.35, mean square error (MSE) of 65.32, and structural similarity index measure (SSIM) of 0.772 which indicates the improved quality of the generated images. The results are compared with other traditional GAN methods and the results obtained show that the proposed method outperforms the others in terms of quantitative and qualitative evaluation metrics.
Impact Assessment of Digital Learning Tools in South African Higher Education Esan, Dorcas Oladayo; Masombuka, Themba
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.999

Abstract

Technological advancements have significantly reshaped the operational landscape of tertiary institutions, enhancing both student and academic efficiency processes. In South Africa, many students in higher learning institutions scrambled to use technology for teaching and learning due to load shedding, poor internet connectivity, lack of technological skills, lack of technology training by the tertiary institutions, etc. This study employs the UTAUT to understand better how technological innovations impact South African higher institutions. The UTAUT model includes components such as effort expectancy, self-awareness, social influence, facilitating conditions, and voluntary use to fully understand the factors influencing technology development and adoption. Three hundred and ten (N=310) students from underprivileged tertiary institutions in the Eastern Cape participated in this study. The study used a quantitative research methodology based on a 5-point Likert scale to gauge the respondents' intention to use technology for teaching and learning. Regression analysis and NOVA statistical tools were used to analyse the acquired data. The findings revealed that most participating students believe that technological advancements had a positive impact on their ability to teach and learn. The research findings imply that faculty should implement training programs on digital tools, improve IT infrastructure, provision of free internet bundles, and develop policies that support the adoption of e-learning technologies.