Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item The veridical Near-Death Experience Scale: construction and a first validation with human and artificial raters(Frontiers Media S A, 2025-10-16) Greyson B; Long J; Holden JM; Jourdan J-P; King RA; Mays S; Mays R; Rivas T; Tassell-Matamua N; van Lommel P; Woollacott M; Tressoldi P; Panda RIntroduction: In this study, we describe the construction of the veridical Near-Death Experience Scale (vNDE Scale), a structured instrument for evaluating the evidential strength of perceptions reported during near-death experiences (NDEs), and its first validation by human and artificial raters. Methods: The construction was implemented using a typical Delphi Method. The first draft of the scale was evaluated by 13 experts in NDE, who were asked to suggest revisions and comments within a month for the first round and 20 days for the second round. Results: A general consensus was achieved on the second round on eight criteria related to the timing of the investigation, the medical and physical conditions, the level of third-person verification, and the number, type, and quality of perceptions reported by the near-death experiencer, to be rated on a four-level Likert scale. The validation phase consisted of the application of the vNDE Scale to 17 cases of potentially veridical NDEs by 11 independent human raters and three artificial raters based on Large-Language Models. In 14 of the17 cases (82.3%), the overall agreement between human and artificial judges was over 75%, considering the two close levels of evidence strength, i.e., moderate plus strong, low plus very low, or vice-versa. Discussion: The vNDE Scale is a practical tool for evaluating the evidential strength of perceptions reported by near-death experiencers.Item When AI Meets Livestreaming: Exploring the Impact of Virtual Anchor on Tourist Travel Intention(MDPI (Basel, Switzerland), 2025-09-03) Zhu Z; Hall CM; Tao L; Qin Z; Li Y; Khan J; Belk R; Zuo NThe development of Artificial intelligence (AI) technology has brought new ideas and opportunities to destination marketing. However, existing studies lack sufficient empirical research to explore the impact of AI anchors on tourists’ travel intentions. To fill this research gap, this study explores the influence of perceived anthropomorphism and perceived playfulness on tourists’ telepresence, inspiration, and travel intention in AI virtual anchor-based travel livestreaming. Through the analysis of 291 valid data sets, it was found that in AI virtual anchor-based travel livestreaming, perceived anthropomorphism positively affects telepresence but does not affect tourists’ inspiration. Playfulness positively affects tourists’ telepresence and inspiration in AI virtual anchor-based travel livestreaming. This study also found that neither perceived anthropomorphism nor perceived playfulness directly affects tourists’ travel intention, but both can be achieved through the mediating effect of telepresence. The findings provide empirical evidence of the value for tourism researchers and destinations in adopting AI technology for livestreaming.Item The Application of Artificial Intelligence and Big Data in the Food Industry(MDPI (Basel, Switzerland), 2023-12-18) Ding H; Tian J; Yu W; Wilson DI; Young BR; Cui X; Xin X; Wang Z; Li W; Yılmaz MTOver the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.Item AI-based discovery of habitats from museum collections(Cell Press, 2024-04-02) Jones CB; Stock K; Perkins SEMuseum collection records are a source of historic data for species occurrence, but little attention is paid to the associated descriptions of habitat at the sample locations. We propose that artificial intelligence methods have potential to use these descriptions for reconstructing past habitat, to address ecological and evolutionary questions.Item Potential Predictors of Psychologically Based Stock Price Movements(MDPI (Basel, Switzerland), 2024-08) East R; Wright M; Stengos TInvestment in stocks is increasingly dependent on artificial intelligence (AI), but the psychological and social factors that affect stock prices may not be fully covered by the measures currently used in AI training. Here, we search for additional measures that may improve AI predictions. We start by reviewing stock price movements that appear to be affected by social and psychological factors, drawing on stock market behaviour during the COVID-19 pandemic. A review of processes that are likely to produce such stock market movements follows: the disposition effect, momentum, and the response to information. These processes are then explained by regression to the mean, negativity bias, the availability mechanism, and information diffusion. Taking account of these processes and drawing on the consumer behaviour literature, we identify three factors which may not be covered by current AI training data that could affect stock prices: publicity in relation to capitalization, stock-holding penetration in relation to capitalization, and changes in the penetration of stock holding.Item The evolution of bacterial genome assemblies - where do we need to go next?(OAE Publishing Inc., 2022-04-12) Altermann E; Tegetmeyer HE; Chanyi RM; Ventura MGenome sequencing has fundamentally changed our ability to decipher and understand the genetic blueprint of life and how it changes over time in response to environmental and evolutionary pressures. The pace of sequencing is still increasing in response to advances in technologies, paving the way from sequenced genes to genomes to metagenomes to metagenome-assembled genomes (MAGs). Our ability to interrogate increasingly complex microbial communities through metagenomes and MAGs is opening up a tantalizing future where we may be able to delve deeper into the mechanisms and genetic responses emerging over time. In the near future, we will be able to detect MAG assembly variations within strains originating from diverging sub-populations, and one of the emerging challenges will be to capture these variations in a biologically relevant way. Here, we present a brief overview of sequencing technologies and the current state of metagenome assemblies to suggest the need to develop new data formats that can capture the genetic variations within strains and communities, which previously remained invisible due to sequencing technology limitations.Item Careering’ – toward radicalism in radical times: Links to human security and sustainable livelihoods(SAGE Publications, 2024-08-13) Hopner V; Carr SCIn this Age of the Anthropocene, the world of work is being radically disrupted by mass precarity, rising wage and income inequality, habitat destruction, and the rise of artificial intelligence. Facing such insecurity, people, we show, are careering toward radical ways of making a living. They range from radical professionals to social media influencing and environmental activism. Human security is fundamentally enhanced by sustainable livelihoods, and we explore ways not only to de-radicalise, but also to accept and embrace radical careering, if and whenever it serves the purpose of making people's livelihoods more sustainable for society, economies, and ecosystems. The article concludes by introducing an Index of Sustainable Livelihoods (SL-I). Success to the successful. The Sustainable Livelihoods Index (SL-I) is designed to be a ‘visible hand’ for end-users, including career counsellors, students, and workers undergoing career transitions, by Corporate Responsibility Officers, and by government ministries supporting just workforce transitions into sustainable livelihoods.Item Patients perceptions of Artificial Intelligence in diabetic eye screening(Wolters Kluwer Health, Inc. on behalf of the Asia-Pacific Academy of Ophthalmology, 2022-05-01) Yap A; Wilkinson B; Chen E; Han L; Veghefi E; Galloway C; Squirrell DPurpose: Artificial intelligence (AI) technology is poised to revolutionize modern delivery of health care services. We set to evaluate the patient perspective of AI use in diabetic retinal screening. Design: Survey. Methods: Four hundred thirty-eight patients undergoing diabetic retinal screening across New Zealand participated in a survey about their opinion of AI technology in retinal screening. The survey consisted of 13 questions covering topics of awareness, trust, and receptivity toward AI systems. Results: The mean age was 59 years. The majority of participants identified as New Zealand European (50%), followed by Asian (31%), Pacific Islander (10%), and Maori (5%). Whilst 73% of participants were aware of AI, only 58% have heard of it being implemented in health care. Overall, 78% of respondents were comfortable with AI use in their care, with 53% saying they would trust an AI-assisted screening program as much as a health professional. Despite having a higher awareness of AI, younger participants had lower trust in AI systems. A higher proportion of Maori and Pacific participants indicated a preference toward human-led screening. The main perceived benefits of AI included faster diagnostic speeds and greater accuracy. Conclusions: There is low awareness of clinical AI applications among our participants. Despite this, most are receptive toward the implementation of AI in diabetic eye screening. Overall, there was a strong preference toward continual involvement of clinicians in the screening process. There are key recommendations to enhance the receptivity of the public toward incorporation of AI into retinal screening programs.Item Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems.(MDPI (Basel, Switzerland), 2021-12-22) Liu T; Sabrina F; Jang-Jaccard J; Xu W; Wei YA smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.Item Deep Q-Learning Based Reinforcement Learning Approach for Network Intrusion Detection(MDPI (Basel, Switzerland), 2022-03-11) Alavizadeh H; Alavizadeh H; Jang-Jaccard J; Quaresma P; Nogueira V; Saias JThe rise of the new generation of cyber threats demands more sophisticated and intelligent cyber defense solutions equipped with autonomous agents capable of learning to make decisions without the knowledge of human experts. Several reinforcement learning methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a new generation of the network intrusion detection method, which combines a Q-learning based reinforcement learning with a deep feed forward neural network method for network intrusion detection. Our proposed Deep Q-Learning (DQL) model provides an ongoing auto-learning capability for a network environment that can detect different types of network intrusions using an automated trial-error approach and continuously enhance its detection capabilities. We provide the details of fine-tuning different hyperparameters involved in the DQL model for more effective self-learning. According to our extensive experimental results based on the NSL-KDD dataset, we confirm that the lower discount factor, which is set as 0.001 under 250 episodes of training, yields the best performance results. Our experimental results also show that our proposed DQL is highly effective in detecting different intrusion classes and outperforms other similar machine learning approaches.