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.

Gamification plays a pivotal role in enhancing user engagement in the Metaverse, particularly among Generation Z users who value autonomy, immersion, and identity expression. However, current research lacks a cohesive framework tailored to designing gamified social experiences in immersive virtual environments. This study presents a framework-oriented systematic literature review, guided by PRISMA 2020 and SPIDER, to investigate how gamification is applied in the Metaverse and how it aligns with the behavioral needs of Gen Z. From 792 screened studies, seventeen high-quality papers were synthesized to identify core gamification mechanics, including avatars, XR affordances, and identity-driven engagement strategies. Building on these insights, we propose the Affordance-Driven Gamification Framework (ADGF), a conceptual model for designing socially immersive experiences, along with a five-step design process to support its real-world application. Our contributions include a critical synthesis of existing strategies, Gen Z-specific design considerations, and a dual-framework approach to guide researchers and practitioners in developing emotionally engaging and socially dynamic Metaverse experiences.

Objectives
The objective of this research was to investigate if the use of virtual reality, increasingly utilised within intensive care medicine due to its demonstrated benefits in improving pain and anxiety, has been reported to result in seizures.

Review method used
A rapid systematic review and synthesis of qualitative and quantitative data was performed.

Data sources
Five databases (PubMed, Scopus, EMBASE, PsycInfo, and CINAHL) were systematically searched. An additional gray literature search was also conducted. Articles were restricted to those published on or after January 1st, 2014.

Review methods
The number of participants, virtual reality sessions, and length of sessions was undertaken. Subgroup analysis was undertaken for both adult and paediatric patient populations. An additional subgroup analysis was undertaken on articles which did not exclude individuals with a history of epilepsy. A tailored risk-of-bias assessment was conducted.
Results
Of the 563 articles identified through database and gray literature searching, 27 articles met inclusion criteria. A total of 886 patients have been reported within the literature with a combined 1843 virtual reality sessions, totalling more than 614.64 h of virtual reality. No seizures have been reported within intensive care patients receiving virtual reality interventions.

Conclusions
Historically, individuals with a history of epilepsy and photosensitivity have been commonly excluded from interventions and clinical trials involving virtual reality. The results of this systematic review demonstrate that the risk of virtual reality is minimal when utilised appropriately. A history of photosensitivity or epilepsy should not constitute an absolute contraindication for the use of virtual reality. Instead, clinicians should utilise clinical judgement when evaluating a patient's risk and ensure that appropriate visual experiences are utilised which do not unnecessarily strobe the patient.

The main contribution of this paper is to introduce a framework for integrating Machine Learning (ML), Human, and Virtual Reality (VR) into one platform to promote a collaborative visualisation environment that can assist in better analysis and improve the human-machine teaming capability. This platform was demonstrated using a case study in ana-lysing road traffic conditions. The ‘Ab-normal Machine Learning Road Traffic Detection in VR (AbnMLRTD-VR)’ prototype system was developed to assist the human analyst. The proposed system has two main integrative components: a data-driven ML model and a 3D real-time visualisation in a VR environment. An unsupervised ML model was built using real traffic data. The AbnMLRTD-VR system highlights the outliers in the road sections in actual road contexts of a road traffic network. This gives the human analyst a 3D real-time immersive visualisation in a VR environment to evaluate road conditions. The AbnMLRTD-VR system demonstrated that it could help minimise the need for human pre-labelling of the data. It enables the visualisation of the road traffic conditions more meaningfully and to understand the context of the road traffic conditions of road sections at any given time.

To improve users’ tendency towards shopping in Virtual Reality (VR), en-hancing the User Experience (UX) of the VR shopping environments is of primary importance. Product viewability, reachability, and personalisation are some of the primary UX factors in a shopping environment. This paper proposes and discusses three factors for a Personalised Adaptive Aisle (PAA) in a VR shopping environment to improve the shopping experience. They are 1) Shelf placement for viewability and reachability, 2) User view-point in VR, and 3) Personalised Product placement.

Deep learning (DL) has achieved superior classification in many applications due to its capability of extracting features from the data. However, the success of DL comes with the tradeoff of possible overfitting. The bias towards the data it has seen during the training process leads to poor generalisation. One way of solving this issue is by having enough training data so that the classifier is invariant to many data patterns. In the literature, data augmentation has been used as a type of regularisation method to reduce the chance for the model to overfit. However, most of the relevant works focus on image, sound or text data. There is not much work on numerical data augmentation, although many real-world problems deal with numerical data. In this paper, we propose using a technique based on Fuzzy C-Means clustering and fuzzy membership grades. Fuzzy-related techniques are used to address the variance problem by generating new data items based on fuzzy numbers and each data item's belongings to different fuzzy clusters. This data augmentation technique is used to improve the generalisation of a Deep Neural Network that is suitable for numerical data. By combining the proposed fuzzy data augmentation technique with the Dropout regularisation technique, we manage to balance the classification model's bias-variance tradeoff. Our proposed technique is evaluated using four popular data sets and is shown to provide better regularisation and higher classification accuracy compared with popular regularisation approaches.

Engagement with upper limb rehabilitation post-stroke can improve rehabilitation outcomes. Virtual Reality can be used to make rehabilitation more engaging. In this paper, we propose a multiple case study to determine: (1) whether game design principles (identified in an earlier study as being likely to engage) actually do engage, in practice, a sample of stroke survivors with a Desktop Virtual Reality-based Serious Game designed for upper limb rehabilitation; and (2) what game design factors support the existence of these principles in the game. In this study, we considered 15 principles: awareness , feedback , interactivity , flow , challenge , attention , interest , involvement , psychological absorption , motivation , effort , clear instructions , usability , purpose , and a first-person view . Four stroke survivors used, for a period of 12 weeks, a Virtual Reality-based upper limb rehabilitation system called the Neuromender Rehabilitation System. The stroke survivors were then asked how well each of the 15 principles was supported by the Neuromender Rehabilitation System and how much they felt each principle supported their engagement with the system. All the 15 tested principles had good or reasonable support from the participants as being engaging. Use of feedback was emphasised as an important design factor for supporting the design principles, but there was otherwise little agreement in important design factors among the participants. This indicates that more personalised experiences may be necessary for optimised engagement. The insight gained can be used to inform the design of a larger scale statistical study into what engages stroke survivors with Desktop Virtual Reality-based upper limb rehabilitation.

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.

Class imbalance is a serious issue in classification as a traditional classifier is generally biased towards the majority class. The accuracy of the classifier could be further impacted in cases where additionally to the class imbalance, there are overlapped data instances. Further, data sparsity has shown to be a possible issue that may lead to non- invariance and poor generalisation. Data augmentation is a technique that can handle the generalisation issue and improve the regularisation of the Deep Neural Network (DNN). A method to handle both class overlap and class imbalance while also incorporating regularisation is proposed in this paper. In our work, the imbalanced dataset is balanced using SMOTETomek, and then the non-categorical attributes are fuzzified. The purpose of fuzzifying the attributes is to handle the overlapping in the data and provide some form of data augmentation that can be used as a regularisation technique. Therefore, in this paper, the invariance is achieved as the augmented data are generated based on the fuzzy concept. The balanced augmented dataset is then trained using a DNN classifier. The datasets used in the experiments were selected from UCI and KEEL data repositories. The experiments show that the proposed Fuzzy data augmentation for handling overlapped and imbalanced data can address the overlapped and imbalanced data issues, and provide regularisation using data augmentation for numerical data to improve the performance of a DNN classifier.