This paper features an analysis of causal relations between the daily VIX, S&P500 and the daily realised volatility (RV) of the S&P500 sampled at 5 min intervals, plus the application of an Artificial Neural Network (ANN) model to forecast the future daily value of the VIX. Causal relations are analysed using the recently developed concept of general correlation Zheng et al. and Vinod. The neural network analysis is performed using the Group Method of Data Handling (GMDH) approach. The results suggest that causality runs from lagged daily RV and lagged continuously compounded daily return on the S&P500 index to the VIX. Sample tests suggest that an ANN model can successfully predict the daily VIX using lagged daily RV and lagged daily S&P500 Index continuously compounded returns as inputs.

Stroke is a major public health issue with significant economic consequences. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Our study focuses on predicting stroke in a general context rather than specific subtypes. This clarification will not only ensure a clear understanding of our study’s scope but also enhance the overall transparency and impact of our findings. We construct an optimization model and describe an effective methodology and algorithms for machine learning classification, accommodating missing data and imbalances. Our models outperform previous efforts in stroke prediction, demonstrating higher sensitivity, specificity, accuracy, and precision. Data quality and preprocessing play a crucial role in developing reliable models. The proposed algorithm using SVMs achieves 98% accuracy and 97% recall score. In-depth data analysis and advanced machine learning techniques improve stroke prediction. This research highlights the value of data-oriented approaches, leading to enhanced accuracy and understanding of stroke risk factors. These methods can be applied to other medical domains, benefiting patient care and public health outcomes. By incorporating our findings, the efficiency and effectiveness of the public health system can be improved.

Diabetes causes an increase in the level of blood sugar, which leads to damage to various parts of the human body. Diabetes data are used not only for providing a deeper understanding of the treatment mechanisms but also for predicting the probability that one might become sick. This paper proposes a novel methodology to perform classification in the case of heavy class imbalance, as observed in the PIMA diabetes dataset. The proposed methodology uses two novel steps, namely resampling and random shuffling prior to defining the classification model. The methodology is tested with two versions of cross validation that are appropriate in cases of class imbalance—k-fold cross validation and stratified k-fold cross validation. Our findings suggest that when having imbalanced data, shuffling the data randomly prior to a train/test split can help improve estimation metrics. Our methodology can outperform existing machine learning algorithms and complex deep learning models. Applying our proposed methodology is a simple and fast way to predict labels with class imbalance. It does not require additional techniques to balance classes. It does not involve preselecting important variables, which saves time and makes the model easy for analysis. This makes it an effective methodology for initial and further modeling of data with class imbalance. Moreover, our methodologies show how to increase the effectiveness of the machine learning models based on the standard approaches and make them more reliable.

The integration of artificial intelligence (AI) into daily cognitive tasks has transformed traditional cognitive offloading—the delegation of mental processes to external tools—into a dynamic partnership with intelligent systems. This article examines the dual role of AI-driven cognitive offloading, exploring its potential to enhance efficiency and creativity while posing risks to memory consolidation, critical thinking, and intellectual autonomy. Grounded in extended mind theory (Clark & Chalmers, 1998) and empirical studies like the Google Effect (Sparrow et al., 2011), the analysis reveals how AI's generative capabilities (e.g., ChatGPT, Midjourney) shift offloading from passive storage to delegated thinking, where users adopt AI outputs with minimal scrutiny, fostering automation bias (Logg et al., 2019). In educational contexts, AI tools risk undermining deep learning by reducing retrieval practice and encouraging superficial engagement, as illustrated by a case study of Hatt University, where AI-assisted essays distorted grading systems and eroded student critical analysis. Similarly, in healthcare and finance, overreliance on AI recommendations may compromise professional judgment. To mitigate these risks, the article proposes strategies such as metacognitive training, explainable AI design (Sundar, 2020), and curriculum reforms prioritizing active engagement with AI outputs. Ethical and policy interventions are urged to address epistemic opacity, intellectual property, and the cultural redefinition of authorship. The study underscores the need for balanced AI integration—one that harnesses its benefits while safeguarding human cognitive autonomy.

This study examines the relationship between financial market segmentation and political risk. Financial economists have attributed market segmentation to factors such as foreign exchange risk, taxes, tariffs and capital controls whereas the influence of political risk has been largely ignored. It is discovered that markets are generally segmented on a regional basis. It is also found that there is a high correlation between political risk and capital market segmentation. However, some countries may appear to be integrated when not because their economies are affected by similar economic factors such as the price of commodities or level of economic development. These findings have profound implications for asset pricing. Multi-index models should be tested that incorporate a regional index, an economic development attribute, commodity factors and a political risk variable in order to price securities more effectively.

This paper focuses on the explanatory power of world influences, regional effects and political risk indices on the equity market returns for a selection of Asia Pacific countries. The results support the notion that emerging equity market returns are driven by both regional and global factors. Political risk indices provide little explanatory power over returns. It is discovered that ‘Regionalism’ is a strong influence upon the markets located within ASEAN, and is priced accordingly. Countries whose equity markets are considered to be more open exhibit greater covariability with the world market portfolio. Financial risk indices provide some explanatory power over volatility for roughly half of the sample. The results suggest that emerging market returns may be captured more effectively by multi‐index models that incorporate a regional factor and that political risk indices may help in explaining market volatility.