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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Utomo, Yesun
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Overview of Text Based Personality Prediction Using Deep Learning Kelvin, Kelvin; Utomo, Yesun
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 2 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i2.11550

Abstract

Text-Based Personality Prediction (TBPP) has garnered increasing attention in recent years, particularly within the frameworks of the Myers-Briggs Type Indicator and the Big Five Personality Model. This study presents a comprehensive systematic review of TBPP methodologies, focusing specifically on research published since 2017. Leveraging Google Scholar, a meticulous selection process was employed to identify and analyze papers meeting relevance criteria. The selected studies were analyzed for research design, data collection methods, preprocessing techniques, and modeling approaches. Notably, the study identifies prevalent Natural Language Processing methods utilized in TBPP, such as Recurrent Neural Networks, Convolutional Neural Networks, Long Short-Term Memory networks, ensemble methods, and pre-trained models like BERT. Results indicate that combining knowledge graphs with Bi-LSTM models achieved the highest accuracy for Big Five traits at 71.5%, while a BERT-CNN-RNN ensemble reached 85% accuracy for MBTI. The synthesized findings offer valuable insights into the current landscape of TBPP, with the aim of informing both researchers and practitioners. Furthermore, the study provides recommendations for future research directions, emphasizing the importance of refining methodologies and addressing challenges to foster continued innovation in personality prediction within the TBPP domain.
Low-Resolution Face Recognition: A Review of Methods and Data Utomo, Yesun; Kelvin
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.14082

Abstract

This paper provides a review of previous studies in Low-Resolution Face Recognition (LRFR), specifically focusing on cross-resolution Face Recognition (FR) methods. While state-of-the-art deep learning FR systems achieve high accuracy on high-resolution (HR) images, they are generally unsuitable for low-resolution (LR) images frequently encountered in applications like surveillance systems, where faces often have low pixel counts due to capture conditions. Cross-resolution FR, which compares an HR image with an LR image, presents a significant challenge due to the distinct visual properties of images at different resolutions. The paper discusses two primary approaches to address this problem: super-resolution (SR), which is a transformative method that aims to construct HR images from LR ones, and unified feature space (UFS), a non-transformative method that maps facial features from varying resolutions into a shared feature space. This work summarizes both SR and UFS methods. Based on the review, the paper concludes that non-transformative (UFS) methods are more suitable for future directions. This recommendation is driven by their lower computational power requirements, proven effectiveness in real-world implementations such as mobile devices and drones, and alignment with current technological trends. The paper also emphasizes the need for further research using real or natural LR face images to identify degradation patterns and compare results between real and artificially generated LR images.
Co-Authors Kelvin Kelvin Kelvin