Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Modeling the Chaotic Semantic States of Generative Artificial Intelligence (AI): A Quantum Mechanics Analogy Approach(Association for Computing Machinery, 2025-12-01) Liu T; McIntosh TR; Susnjak T; Watters P; Halgamuge MNGenerative artificial intelligence (AI) models have revolutionized intelligent systems by enabling machines to produce human-like content across diverse domains. However, their outputs often exhibit unpredictability due to complex and opaque internal semantic states, posing challenges for reliability in real-world applications. In this paper, we introduce the AI Uncertainty Principle, a novel theoretical framework inspired by quantum mechanics, to model and quantify the inherent unpredictability in generative AI outputs. By drawing parallels with the uncertainty principle and superposition, we formalize the trade-off between the precision of internal semantic states and output variability. Through comprehensive experiments involving state-of-the-art models and a variety of prompt designs, we analyze how factors such as specificity, complexity, tone, and style influence model behavior. Our results demonstrate that carefully engineered prompts can significantly enhance output predictability and consistency, while excessive complexity or irrelevant information can increase uncertainty. We also show that ensemble techniques, such as Sigma-weighted aggregation across models and prompt variations, effectively improve reliability. Our findings have profound implications for the development of intelligent systems, emphasizing the critical role of prompt engineering and theoretical modeling in creating AI technologies that perceive, reason, and act predictably in the real world.Item MM5: Multimodal image capture and dataset generation for RGB, depth, thermal, UV, and NIR(Elsevier B V, 2026-02-01) Brenner M; Reyes NH; Susnjak T; Barczak ALCExisting multimodal datasets often lack sufficient modality diversity, raw data preservation, and flexible annotation strategies, seldom addressing modality-specific cues across multiple spectral channels. Current annotations typically concentrate on pre-aligned images, neglecting unaligned data and overlooking crucial cross-modal alignment challenges. These constraints significantly impede advanced multimodal fusion research, especially when exploring modality-specific features or adaptable fusion methodologies. To address these limitations, we introduce MM5, a comprehensive dataset integrating RGB, depth, thermal (T), ultraviolet (UV), and near-infrared (NIR) modalities. Our capturing system utilises off-the-shelf components, incorporating stereo RGB-D imaging to provide additional depth and intensity (I) information, enhancing spatial perception and facilitating robust cross-modal learning. MM5 preserves depth and thermal measurements in raw, 16-bit formats, enabling researchers to explore advanced preprocessing and enhancement techniques. Additionally, we propose a novel label re-projection algorithm that generates ground-truth annotations directly for distorted thermal and UV modalities, supporting complex fusion strategies beyond strictly aligned data. Dataset scenes encompass varied lighting conditions (e.g. shadows, dim lighting, overexposure) and diverse objects, including real fruits, plastic replicas, and partially rotten produce, creating challenging scenarios for robust multimodal analysis. We evaluate the effects of multi-bit representations, adaptive gain control (AGC), and depth preprocessing on a transformer-based segmentation network. Our preprocessing improved mean IoU from 70.66% to 76.33% for depth data and from 72.67% to 79.08% for thermal encoding, using our novel preprocessing techniques, validating MM5’s efficacy in supporting comprehensive multimodal fusion research.Item The Lyme Disease Controversy: An AI-Driven Discourse Analysis of a Quarter Century of Academic Debate and Divides(2025-04-04) Susnjak T; Palffy C; Zimina T; Altynbekova N; Garg K; Gilbert LItem Towards Clinical Prediction with Transparency: An Explainable AI Approach to Survival Modelling in Residential Aged Care(2024-01-16) Susnjak T; Griffin EItem Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care.(Elsevier B.V., 2025-02-18) Susnjak T; Griffin EBACKGROUND AND OBJECTIVE: Scalable, flexible and highly interpretable tools for predicting mortality in residential aged care facilities for the purpose of informing and optimizing palliative care decisions, do not exist. This study is the first and most comprehensive work applying machine learning to address this need while seeking to offer a transformative approach to integrating AI into palliative care decision-making. The objective is to predict survival in elderly individuals six months post-admission to residential aged care facilities with patient-level interpretability for transparency and support for clinical decision-making for palliative care options. METHODS: Data from 11,944 residents across 40 facilities, with a novel combination of 18 features was used to develop predictive models, comparing standard approaches like Cox Proportional Hazards, Ridge and Lasso Regression with machine learning algorithms, Gradient Boosting (GB) and Random Survival Forest. Model calibration was performed together with ROC and a suite of evaluation metrics to analyze results. Explainable AI (XAI) tools were used to demonstrate both the cohort-level and patient-level model interpretability to enable transparency in the clinical usage of the models. TRIPOD reporting guidelines were followed, with model parameters and code provided publicly. RESULTS: GB was the top performer with a Dynamic AUROC of 0.746 and a Concordance Index of 0.716 for six-month survival prediction. Explainable AI tools provided insights into key features such as comorbidities, cognitive impairment, and nutritional status, revealing their impact on survival outcomes and interactions that inform clinical decision-making. The calibrated model showed near-optimal performance with adjustable clinically relevant thresholds. The integration of XAI tools proved effective in enhancing the transparency and trustworthiness of predictions, offering actionable insights that support informed and ethically responsible end-of-life (EoL) care decisions in aged care settings. CONCLUSION: This study successfully applied machine learning to create viable survival models for aged care residents, demonstrating their usability for clinical settings via a suite of interpretable tools. The findings support the introduction into clinical trials of machine learning with explainable AI tools in geriatric medicine for mortality prediction to enhance the quality of EoL care and informed discussions regarding palliative care.Item Multimodal Deep Learning for Android Malware Classification(MDPI (Basel, Switzerland), 2025-02-28) Arrowsmith J; Susnjak T; Jang-Jaccard J; Buccafurri FThis study investigates the integration of diverse data modalities within deep learning ensembles for Android malware classification. Android applications can be represented as binary images and function call graphs, each offering complementary perspectives on the executable. We synthesise these modalities by combining predictions from convolutional and graph neural networks with a multilayer perceptron. Empirical results demonstrate that multimodal models outperform their unimodal counterparts while remaining highly efficient. For instance, integrating a plain CNN with 83.1% accuracy and a GCN with 80.6% accuracy boosts overall accuracy to 88.3%. DenseNet-GIN achieves 90.6% accuracy, with no further improvement obtained by expanding this ensemble to four models. Based on our findings, we advocate for the flexible development of modalities to capture distinct aspects of applications and for the design of algorithms that effectively integrate this information.Item A review of climate change impact assessment and methodologies for urban sewer networks(Elsevier B V, 2025-06) Karimi AM; Jelodar MB; Susnjak T; Sutrisna MUnderstanding how climate change affects urban sewer networks is essential for the sustainable management of these infrastructures. This research uses a systematic literature review (PRISMA) to critically review methodologies to assess the effects of climate change on these systems. A scientometric analysis traced the evolution of research patterns, while content analysis identified three primary research clusters: Climate Modelling, Flow Modelling, and Risk and Vulnerability Assessment. These clusters, although rooted in distinct disciplines, form an interconnected framework, where outputs of climate models inform flow models, and overflow data from flow models contribute to risk assessments, which are gaining increasing attention in recent studies. To enhance risk assessments, methods like Gumbel Copula, Monte Carlo simulations, and fuzzy logic help quantify uncertainties. By integrating these uncertainties with a Bayesian Network, which can incorporate expert opinion, failure probabilities are modelled based on variable interactions, improving prediction. The study also emphasises the importance of factors, such as urbanisation, asset deterioration, and adaptation programs in order to improve predictive accuracy. Additionally, the findings reveal the need to consider cascading effects from landslides and climate hazards in future risk assessments. This research provides a reference for methodology selection, promoting innovative and sustainable urban sewer management.Item Transfer learning on transformers for building energy consumption forecasting—A comparative study(Elsevier B V, 2025-06-01) Spencer R; Ranathunga S; Boulic M; van Heerden AH; Susnjak TEnergy consumption in buildings is steadily increasing, leading to higher carbon emissions. Predicting energy consumption is a key factor in addressing climate change. There has been a significant shift from traditional statistical models to advanced deep learning (DL) techniques for predicting energy use in buildings. However, data scarcity in newly constructed or poorly instrumented buildings limits the effectiveness of standard DL approaches. In this study, we investigate the application of six data-centric Transfer Learning (TL) strategies on three Transformer architectures—vanilla Transformer, Informer, and PatchTST—to enhance building energy consumption forecasting. Transformers, a relatively new DL framework, have demonstrated significant promise in various domains; yet, prior TL research has often focused on either a single data-centric strategy or older models such as Recurrent Neural Networks. Using 16 diverse datasets from the Building Data Genome Project 2, we conduct an extensive empirical analysis under varying feature spaces (e.g., recorded ambient weather) and building characteristics (e.g., dataset volume). Our experiments show that combining multiple source datasets under a zero-shot setup reduces the Mean Absolute Error (MAE) of the vanilla Transformer model by an average of 15.9 % for 24 h forecasts, compared to single-source baselines. Further fine-tuning these multi-source models with target-domain data yields an additional 3–5 % improvement. Notably, PatchTST outperforms the vanilla Transformer and Informer models. Overall, our results underscore the potential of combining Transformer architectures with TL techniques to enhance building energy consumption forecasting accuracy. However, careful selection of the TL strategy and attention to feature space compatibility are needed to maximize forecasting gains.Item From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape(MDPI (Basel, Switzerland), 2025-02-01) McIntosh TR; Susnjak T; Liu T; Watters P; Xu D; Liu D; Halgamuge MN; Mladenov VThis comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the recent technological breakthroughs and the gathering advancements toward possible Artificial General Intelligence (AGI). It critically examined the current state and future trajectory of generative AI, exploring how innovations in developing actionable and multimodal AI agents with the ability scale their “thinking” in solving complex reasoning tasks are reshaping research priorities and applications across various domains, while the survey also offers an impact analysis on the generative AI research taxonomy. This work has assessed the computational challenges, scalability, and real-world implications of these technologies while highlighting their potential in driving significant progress in fields like healthcare, finance, and education. Our study also addressed the emerging academic challenges posed by the proliferation of both AI-themed and AI-generated preprints, examining their impact on the peer-review process and scholarly communication. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare, and outlined a strategy for future AI research that focuses on a balanced and conscientious use of generative AI as its capabilities continue to scale.Item Ransomware Reloaded: Re-examining Its Trend, Research and Mitigation in the Era of Data Exfiltration(Association for Computing Machinery New York, NY, United States, 2024-10-07) McIntosh T; Susnjak T; Liu T; Xu D; Watters P; Liu D; Hao Y; Ng A; Halgamuge M; Atienza D; Milano MRansomware has grown to be a dominant cybersecurity threat by exfiltrating, encrypting, or destroying valuable user data and causing numerous disruptions to victims. The severity of the ransomware endemic has generated research interest from both the academia and the industry. However, many studies held stereotypical assumptions about ransomware, used unverified, outdated, and limited self-collected ransomware samples, and did not consider government strategies, industry guidelines, or cyber intelligence. We observed that ransomware no longer exists simply as an executable file or limits to encrypting files (data loss); data exfiltration (data breach) is the new norm, espionage is an emerging theme, and the industry is shifting focus from technical advancements to cyber governance and resilience. We created a ransomware innovation adoption curve, critically evaluated 212 academic studies published during 2020 and 2023, and cross-verified them against various government strategies, industry reports, and cyber intelligence on ransomware. We concluded that many studies were becoming irrelevant to the contemporary ransomware reality and called for the redirection of ransomware research to align with the continuous ransomware evolution in the industry. We proposed to address data exfiltration as priority over data encryption, to consider ransomware in a business-practical manner, and recommended research collaboration with the industry.