Mohd Fairuz Shiratuddin

Hyper-Realistic Virtual Reality (Hyper-Real VR) environments have the potential to elicit profound emotional responses by leveraging high-fidelity visual elements. While previous research has extensively examined the role of Virtual Reality (VR) in evoking negative emotions, there is limited understanding of how Hyper-Real VR can be systematically designed to induce positive emotions, such as awe and calm. This paper introduces a conceptual framework that examines the impact of four key visual factors, i.e. geometry, material surfaces, lighting, and colour, and their sub-factors in shaping emotional experiences. Geometry, including scale and proportion, influences spatial perception and depth, which are crucial for inducing awe. Material surfaces, such as reflections and textures, enhance realism and presence, reinforcing emotional engagement. Lighting, particularly global illumination and shadows, modulates mood and spatial perception, creating immersive experiences that promote awe and calm. Colour, through physically based rendering (PBR), values, and tones, shapes emotional responses by enhancing realism and aesthetic harmony. This framework integrates theories from presence research, perceptual psychology, and environmental design to establish a structured approach for designing emotionally engaging Hyper-Real VR environments. By mapping visual factors to emotional outcomes, it provides a foundation for optimising VR experiences to elicit awe and calm. The proposed framework has implications for fields such as digital therapy, mental well-being, and immersive entertainment. Future research will validate this model through empirical studies, further refining the role of hyper-realistic visual elements in emotional engagement within VR.

Vaccine hesitancy is still a significant barrier to achieving widespread immunity in many communities. In this paper, we evaluated a serious game focusing on vaccination against COVID-19. This study investigates the potential of virtual reality (VR) as an innovative educational tool to address this issue. Focusing on the serious game “Spike Force”, which simulates the mechanisms of the mRNA COVID-19 vaccine, this research evaluates the game’s effectiveness in enhancing participants’ understanding, altering attitudes, and influencing behaviours related to vaccination. Participants engaged with “Spike Force,” and their knowledge, attitudes, and behaviours were assessed through pre- and post-gameplay questionnaires. The findings show that immersive VR experiences can significantly improve vaccine literacy, increase confidence in vaccine-related discussions, and promote positive behavioural changes toward vaccination. These results suggest that VR could play an effective advocacy role for public health education, particularly in combating vaccine hesitancy.

There are several ways to search for songs in an online music library. A few types of visual variables to represent music information such as colour, position, shape, size, and visual texture have been explored in Music Information Retrieval (MIR). However, from a comprehensive literature review, there is no research focusing explicitly on the use of visual texture for browsing music. In this research, we define visual texture as an image of texture designed using the drawing application. In this paper, a framework for visualising music mood using visual texture is proposed. This proposed framework can be used by designers or software developers to select suitable visual elements when designing a clear and understandable visual texture to represent specific music moods in the music application. This research offers a new way of browsing digital music collection and assisting the music listener community to discover new song especially in mood category. To validate the framework, usability testing was conducted. This paper presents the process of developing and validating the proposed framework.

The performance of classification algorithms with imbalanced streaming data depends upon efficient re-balancing strategy for learning tasks. The difficulty becomes more elevated with multi-class highly imbalanced streaming data. In this paper, we investigate the multi-class imbalance problem in data streams and develop an adaptive framework to cope with imbalanced data scenarios. The proposed One-Vs-All Adaptive Window re-Balancing with Retain Knowledge (OVA-AWBReK) classification framework will combine OVA binarization with Automated Re-balancing Strategy (ARS) using Racing Algorithm (RA). We conducted experiments on highly imbalanced datasets to demonstrate the use of the proposed OVA-AWBReK framework. The results show that OVA-AWBReK framework can enhance the classification performance of the multi-class highly imbalanced data.

In the real world of credit card fraud detection, due to a minority of fraud related transactions, has created a class imbalance problem. With the increase of transactions at massive scale, the imbalanced data is immense and has created a challenging issue on how well Machine Learning (ML) techniques can scale up to efficiently learn to detect fraud from the massive incoming data and to respond faster with high prediction accuracy and reduced misclassification costs. This paper is based on experiments that compared several popular ML techniques and investigated their suitability as a “scalable algorithm” when working with highly imbalanced massive or “Big” datasets. The experiments were conducted on two highly imbalanced datasets using Random Forest, Balanced Bagging Ensemble, and Gaussian Naïve Bayes. We observed that many detection algorithms performed well with medium-sized dataset but struggled to maintain similar predictions when it is massive.

Visual design plays an important role in grabbing web users’ attention in an online environment. Previous research has demonstrated that different types of visual design causes different impact towards the end-users. This paper observes the impact of persuasive visual towards users’ first impression, attitudes, and behaviours. It extends existing web visual design by empirically examining the critical roles of the principles of social influence in the form of visual persuasion in motivating users to have a favourable impression of a particular website. Survey data was collected in an experimental study that was conducted online. Structural model assessment is carried out using confirmatory factor analysis (CFA) in conjunction with PLS-SEM analyses. The general analysis of model fit indicates that the two models proposed in this paper surpassed the cut off values for model acceptance with most of the model fit criteria reflects outstanding explanatory power. The result of the analysis indicates that visual persuasion has a big impact in influencing users’ attitudes on the web; strong enough to affect their behavioural intention.

Dyslexia is a hidden learning disability that causes difficulties in reading and writing despite average intelligence. Electroencephalogram (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. This paper examines pros and cons of existing EEG-based analysis and classification frameworks for dyslexia and recommends optimizations through the findings to assist future research.

This paper reviews the types of mobile Augmented Reality (mAR) interface being utilized in various applications such as education, advertisement, production, tourism and other applications. The objective of this paper is to examine the limitations on the types of mAR when they are used in higher education in terms of interaction between learning, teaching and instructional design. Based on the review, it can be concluded that there is an insufficient mAR interface being used in viewing the augmented images for classroom learning. For example, there are only two interfaces found that is being applied in the current mAR education applications. A comparative review presented in this paper suggests the appropriate mAR interface that can be implemented in education that could possibly enhance the learning outcomes.

Content-Based Image Retrieval (CBIR) system has become a focus of research in the area of image processing and machine vision. General CBIR system automatically index and retrieve images with visual features such as colour, texture and shape. However, current research found that there is a significant gap between visual features and semantic features used by humans to describe images. In order to bridge the semantic gap, some researchers have proposed methods for managing and decreasing image features, and extract useful features from a feature vector. This paper presents an image retrieval system utilising fuzzy rough set based on mutual information decreasing method and the Support Vector Machine (SVM) classifier. The system has training and testing phases. In order to reduce the semantic gap, the propose retrieval system used relevance feedback to improve the retrieval performance. This paper also compared the proposed method with other traditional retrieval systems that use PCA, kernel PCA, Isomap and MVU for their feature reduction method. Experiments are carried out using a standard Corel dataset to test the accuracy and robustness of the proposed system. The experiment results show the propose method can retrieve images more efficiently than the traditional methods. The use of fuzzy rough set based on mutual information decreasing method, SVM and relevance feedback ensures that the propose image retrieval system produces results which are highly relevant to the content of an image query.

Content-Based Image Retrieval (CBIR) system has become a focus of research in the area of image processing and machine vision. General CBIR system automatically index and retrieve images with visual features such as colour, texture and shape. However, current research found that there is a significant gap between visual features and semantic features used by humans to describe images. In order to bridge the semantic gap, some researchers have proposed methods for managing and decreasing image features, and extract useful features from a feature vector. This paper presents an image retrieval system utilising fuzzy rough set based on mutual information decreasing method and the Support Vector Machine (SVM) classifier. The system has training and testing phases. In order to reduce the semantic gap, the propose retrieval system used relevance feedback to improve the retrieval performance. This paper also compared the proposed method with other traditional retrieval systems that use PCA, kernel PCA, Isomap and MVU for their feature reduction method. Experiments are carried out using a standard Corel dataset to test the accuracy and robustness of the proposed system. The experiment results show the propose method can retrieve images more efficiently than the traditional methods. The use of fuzzy rough set based on mutual information decreasing method, SVM and relevance feedback ensures that the propose image retrieval system produces results which are highly relevant to the content of an image query.