This study proposes a data-driven approach to leveraging large-scale COVID-19 datasets to enhance the predictive modeling of disease spread in the early stages. We systematically evaluate three machine learning models—ARIMA, Prophet, and LSTM—using a comprehensive framework that incorporates time-series analysis, multivariate data integration, and a Multi-Criteria Decision Making (MCDM) technique to assess model performance. The study focuses on key features such as daily confirmed cases, geographic variations, and temporal trends, while considering data constraints and adaptability across different scenarios. Our findings reveal that LSTM and ARIMA consistently outperform Prophet, with LSTM achieving the highest predictive accuracy in most cases, particularly when trained on 20-week datasets. ARIMA, however, demonstrates superior stability and reliability across varying time frames, making it a robust choice for short-term forecasting. A direct comparative analysis with existing approaches highlights the strengths and limitations of each model, emphasizing the importance of region-specific data characteristics and training periods. The proposed methodology not only identifies optimal predictive strategies but also establishes a foundation for automating predictive analysis, enabling timely and data-driven decision-making for disease control and prevention. This research is validated using data from New Zealand and its major trading partners—China, Australia, the United States, Japan, and Germany—demonstrating its applicability across diverse contexts. The results contribute to the development of adaptive forecasting frameworks that can empower public health authorities to respond proactively to emerging health threats.

Environmental sustainability is a global challenge in the face of increasing incidences of disasters affecting communities worldwide. This requires predicting net environmental effects accurately. While various approaches exist, we need more sophisticated prediction models that account for both environmental and social factors. This study presents a proof-of-concept machine learning model for predicting net environmental effects using synthetic data. We developed a multiple linear regression model incorporating nine key features: renewable energy usage, carbon emissions, air quality index, water usage, biodiversity impact, land use, public awareness, and environmental attitudes. We generated a synthetic dataset of 1000 samples using probability distributions and correlation structures derived from environmental literature and expert knowledge. Our model achieved an R-squared value of 0.67, demonstrating moderate predictive power. Feature importance analysis revealed renewable energy usage (coefficient = 0.71) and public awareness (coefficient = 0.44) as significant positive factors influencing environmental outcomes. Model validation included residual analysis and feature importance assessment, with results suggesting reasonable performance within linear regression constraints. Limitations of our study include reliance on synthetic data, assumption of linear relationships between variables, and limited environmental factors. Notwithstanding, our findings provide insights for environmental policymaking, particularly regarding renewable energy adoption and public awareness campaigns. Future work could focus on incorporating real-world data, exploring non-linear modeling approaches, and expanding the feature set to capture more complex environmental interactions. Our research contributes to data-driven environmental assessment by demonstrating the feasibility of combining both physical and social factors in predictive modeling.

This study provides a quantitative framework for wellbeing outcome prediction through intentional cognitive pattern alteration. We demonstrated 81.67% accurate prediction of wellbeing states, in a three-level classification (Low, Medium, High), using a Random Forest classifier with 16 features from psychological, physiological, and behavioural metrics. Our model singles out the gratitude cultivation (21.3%) and peace duration (23.7%) as the strongest predictors of positive well-being outcomes, which provides empirical support to traditional approaches of cognitive training with empirical evidence. Analysis of 1,000 synthetic cases shows that consistent practice of positive thought patterns over 3-6 months can strongly shift wellbeing states, with key behavioural markers showing progressive improvement which include increased joy moments, reduced anxiety episodes, and enhanced sleep quality. Our results establish that cognitive training outcomes can be quantitatively tracked and predicted with meaningful accuracy, hence providing a data-driven approach to mental health intervention design. Additionally, the research shows machine learning for mental health analysis to present a scalable method for wellbeing prediction. Integrating multiple data modalities, our model presents an integrative view of cognitive transformation that covers the gap between qualitative opinion and quantitative prediction. The contribution of this research is in presenting the viability of applying artificial intelligence (AI) models to facilitate enhanced mental health interventions through adaptive and personalized cognitive training programs. More generally, our results add to the emerging science of neuroplasticity-based cognitive training by delivering an evidence-based method for evaluating and predicting wellbeing improvement. The findings have implications that reach outside the research clinic, to clinical interventions, self-help programs, and mobile phone health applications, to offer a new mechanism for improving mental resilience and world life satisfaction through rigorous cognitive training.

This study presents a novel machine learning approach to assess human well-being through the analysis of brain wave activities. We developed a Random Forest classifier to categorize brain wave patterns into three states of well-being: good, normal, and bad. Using synthetic data simulating electroencephalography (EEG) readings, our model achieved an overall accuracy of 96.17%. The feature importance analysis revealed that alpha waves (34%) and beta waves (29%) were the most significant predictors of well-being states, which aligns with existing neuroscientific literature linking alpha activity to relaxation and beta activity to cognitive engagement. The confusion matrix demonstrated the model's particular strength in distinguishing between optimal and suboptimal well-being states, with no misclassifications between these extremes. ROC curve analysis further confirmed excellent discriminative ability across all three classes, with AUC values ranging from 0.984 to 0.999. The study demonstrates the potential of machine learning in interpreting complex neurophysiological data for personalised health monitoring, potentially enabling real-time assessment and intervention strategies. While promising, the use of synthetic data necessitates further validation with real-world EEG recordings. This research contributes to the growing field of computational neuroscience and its applications in mental health and well-being assessment, potentially paving the way for more objective and personalised mental health interventions. Future directions include incorporating temporal dynamics, accounting for individual variability, and integrating multiple data sources for a more holistic approach to well-being assessment.

In this paper, we present a Random Forest classifier machine learning model for predicting short-range weather in in tropical regions like Malaysia. Our model uses environmental factors such as temperature, humidity, wind speed, and cloud cover to predict weather conditions like clear skies, rain, and thunderstorms. Tropical weather, influenced by high humidity, fluctuating temperatures, and frequent rainfall, present unique challenges for forecasting accurately. To address these challenges, we trained a Random Forest classifier on a synthetic (simulated) dataset comprising 1,500 samples, each representing a specific weather scenario. Our model achieved an accuracy of 98.66% in predicting short-term weather conditions, identifying cloud cover, precipitation intensity, and humidity as the most influential factors. Our model’s high accuracy demonstrates its potential for predicting short-range weather conditions in tropical regions. Potential applications of the model spans sectors like agriculture, energy, tourism, disaster management, and public health. In agriculture, the model can be used to optimize irrigation schedules and crop management. In the energy sector, it can be used to optimize energy production and distribution. In disaster management, it can alert residents of impending bad weather, so they are more prepared. In the health sector, it can provide timely weather alerts and assist those who are more prone to arthritis and migraine attacks. We can enhance the model by using real-world data and regional customization.

Insider threats pose a significant and growing risk to organizational cybersecurity, with recent studies indicating a 47% increase in insider incidents from 2018 to 2022. This paper presents a comparative analysis of unsupervised and supervised machine learning approaches for detecting potential insider threats through network traffic anomaly identification. We develop and evaluate an Isolation Forest (unsupervised) and a Random Forest (supervised) model, training them on a simulated dataset representing six months of network logs from a mid-sized company. Our study introduces a unique feature set combining traditional network metrics with temporal and behavioral indicators, enhancing the models' detection capabilities. Results show that the Random Forest classifier outperforms the Isolation Forest, with F1-scores of 0.6425 and 0.4624, respectively. However, the unsupervised approach shows promise in scenarios lacking labeled data. Key findings reveal that increased connection frequency and data transfer volume are critical indicators of potential threats, with temporal patterns also playing a significant role. This study provides valuable insights into the strengths and limitations of each approach, offering practical implications for real-world digital forensics investigations. We contribute to the field by proposing a hybrid approach that leverages the strengths of both methods, potentially improving the accuracy and adaptability of insider threat detection systems. These findings pave the way for more robust, context-aware cybersecurity measures in the digital age.