Muhammad Zahid Iqbal

As professionals increasingly encounter artificial intelligence (AI) in their workplaces, questions around adoption barriers, ethical concerns, and job security have grown in prominence. This paper reports an exploratory cross-sectional descriptive survey of self-reported perceptions among a convenience sample of 324 UK-based industry professionals. The study does not identify determinants, predictors, or causes of AI adoption. The survey examined eight binary items covering daily personal AI use, perceived strategic importance, cost as a barrier, ethical concern, perceived income change, subjective job-displacement anxiety, perceived work-performance change, and preference for conversational AI tools over traditional search engines. All eight items were completed by all 324 eligible respondents. Using Wald 95% confidence intervals, 47.2% (95% CI 41.8–52.7) reported using AI in their daily jobs, while 66.4% (95% CI 61.2–71.5) perceived AI as important for remaining competitive. Cost was identified as a barrier by 76.5% (95% CI 71.9–81.2), and 65.7% (95% CI 60.6–70.9) reported concern about ethical implications. These two figures are separate aggregate proportions and are not treated as an individual-level behavioural gap. Only 37.0% (95% CI 31.8–42.3) reported an income increase associated with AI at work, whereas 71.9% (95% CI 67.0–76.8) reported improved work performance. These two items were separate self-report questions; no respondent-level association was tested, and neither item measured objective productivity or pay. Job-automation concern was reported by 48.5% (95% CI 43.0–53.9). The same share as the performance item, 71.9% (95% CI 67.0–76.8) preferred ChatGPT-like tools over traditional search engines. The sample was recruited through professional networks, email, social media, and organisational mailing lists and is likely to over-represent professionals already interested in digital tools. Findings are therefore presented as descriptive perceptions from this respondent pool and are not generalised to UK professionals as a whole.

Postnatal depression (PND) is a critical mental health issue that affects approximately one in seven women and has a profound impact on infant bonding and family dynamics. Despite its prevalence, PND often goes undiagnosed as a result of ignorance and stigma. This study uses machine learning techniques to predict PND, offering a novel approach to early detection and intervention. Employing multilayer perceptron (MLP), decision tree, XGBoost, support vector machine (SVM), and logistic regression and KNeighbors, this research aimed to analyze various demographic, medical, and psychosocial factors to identify patterns and predictors of PND. The data was subjected to rigorous model evaluation, where the MLPClassifier emerged as the superior model with an AUC of 0.99, indicating near-perfect predictive capability. The study also explored the model’s practical application by integrating it into a web interface using Streamlit, facilitating real-time prediction and analysis of PND risk, thereby making strides toward enhancing maternal mental health care. The findings of this study not only augment the existing body of literature on PND prediction but also highlight the role of ML as an indispensable tool in advancing healthcare technology. By achieving a high level of accuracy in the prediction of PND, the MLPClassifier model has the potential to significantly improve clinical outcomes for mothers and their families.

The growing worldwide prevalence of Diabetes Mellitus highlights the urgent need for effective early detection methods to enable prompt intervention. This study develops a machine learning-based decision-support prototype for predicting diabetes risk using health metrics from the DiaHealth dataset, a recently published Bangladeshi open-source dataset for Type 2 diabetes prediction. Five supervised learning algorithms were evaluated: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF). Models were assessed across three stages: before feature scaling, after standardisation, and following hyperparameter optimisation via GridSearchCV, using accuracy, precision, recall, and F1-score as evaluation metrics. LR and SVM showed marked improvements after standardisation, consistent with their sensitivity to feature magnitude, whilst tree-based approaches such as DT and RF remained largely unchanged. KNN displayed minimal sensitivity to scaling, which is discussed in relation to the feature distributions of the dataset. Following hyperparameter tuning, RF achieved the highest accuracy of 95%, outperforming all other models. RF predictions were interpreted using Local Interpretable Model-agnostic Explanations (LIME) to promote transparency in model decision-making. The best-performing model was subsequently deployed as an interactive web-based prototype application using Streamlit, providing real-time prediction outputs. These findings demonstrate how preprocessing choices and hyperparameter tuning can differentially affect algorithm performance and illustrate the potential of combining explainable AI with practical deployment for diabetes risk assessment in a research context.

Property viewing and real estate decision-making are heavily dependent on spatial understanding and trust, which are difficult to establish through conventional digital media such as videos, photographs and floor plans. The rise of immersive technologies like Virtual Reality presents a new opportunity to transform how people explore and evaluate properties. This research presents a VR-based property tour system guided by an AI-driven MetaHuman agent capable of natural conversation, contextual awareness and socially aware movement. Developed in Unity and deployed on the Meta Quest 2 with the Convai conversational AI framework, users can explore a high-fidelity virtual apartment while receiving personalised, adaptive guidance from the agent in real time. Initial observations from a preliminary user evaluation with ten participants suggest positive improvements across presence, trust, authenticity, engagement and decision confidence, highlighting the clear potential of AI-driven MetaHuman agent for immersive property exploration and brand engagement in real estate contexts.

The combination of Artificial Intelligence (AI) and Virtual Reality (VR) is transforming how brands connect with audiences by enabling more interactive and personalised experiences that encourage active engagement rather than passive content consumption. The adoption of AI-driven Metahuman agents is accelerating across digital marketing, with leading brands deploying photo-realistic AI agents as interactive brand ambassadors. Advances in conversational AI, real-time natural language processing, and Metahuman rendering technologies have made AI-guided immersive experiences increasingly achievable. However, limited research has explored the integration of AI-powered Metahuman agents within immersive VR property tours, particularly regarding user experience, presence, agent realism, and brand engagement outcomes. This paper presents a novel prototype integrating an AI-driven Metahuman agent into an immersive property tour environment and reports a user evaluation involving 39 participants from diverse age groups and regions. A structured questionnaire covering five constructs: usability, immersion and presence, agent quality, brand engagement, and overall experience was administered alongside a NASA Task Load Index (NASA-TLX) workload assessment and qualitative feedback. Results showed that all constructs were rated significantly above the scale midpoint with large effect sizes. Interaction quality emerged as the strongest predictor of brand engagement and overall satisfaction. NASA-TLX findings indicated low cognitive and physical workload, while qualitative feedback identified real-time AI conversation as the most valued feature. The findings highlight the potential of AI-driven virtual agents in VR environments as effective brand influencers for property engagement.