Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder with complex diagnostic challenges due to symptom heterogeneity and limited clinical criteria. This study presents an AI-driven diagnostic framework integrating tabular deep learning, causal inference, and interpretability techniques to enhance PCOS diagnosis. To the best of our knowledge, this is the first study to jointly leverage state-of-the-art tabular models—TabNet, FT-Transformer, and TabPFN—alongside heterogeneous ensemble methods in the context of PCOS diagnosis. Our approach achieves competitive performance, with the Bayesian Neural Network (BNN) attaining 93.83
The adoption of photovoltaic solar panels (PVSP) in rural areas remains uneven despite sustained policy support, indicating the need for predictive frameworks that can reliably inform decentralized energy planning in geographically complex regions. This study develops an interpretable and robust machine learning framework for predicting rural household willingness to adopt PVSP. This paper proposes an explainable, gray level co-occurrence matrix (GLCM)-optimized nested stacking ensemble model applied to survey data from rural households in Jammu and Kashmir. Texture-based GLCM descriptors are used to optimize socio-economic variables by capturing latent inter-feature dependencies, while a multi-level nested stacking technique integrates heterogeneous base learners to enhance generalization and prevent information leakage. The concept of model-agnostic explainability was used by employing the aggregation of global local interpretable model-agnostic explanations (LIME) interpretation of adoption factors among multiple households. From the experimental results, it is clear that good generalization has been achieved with an accuracy of 91.38
Ensuring that examination questions are pedagogically sound and aligned with the curriculum remains a key challenge in automated assessment. Most existing approaches focus on answer correctness or surface-level textual similarity, overlooking whether questions genuinely reflect syllabus content, require appropriate reasoning, or maintain structural coherence. This study presents SyllabiQA-RSNet, a syllabus-driven framework for automatically evaluating exam question quality. The model combines DeBERTa—large contextual embeddings with graph attention-based syllabus reasoning, capturing dependencies across syllabus sections and multi-step logical connections between questions and course content. Robustness scoring is formulated as a multi-task problem, with the model simultaneously predicting answers, generating reasoning chains, and producing continuous robustness scores. Evaluation on the SyllabusQA benchmark, comprising 5078 question–answer pairs from 63 university syllabi across STEM and non-STEM fields, demonstrates that SyllabiQA-RSNet substantially outperforms baseline models. It achieved a Pearson correlation of 0.79 and Spearman correlation of 0.76 with reference robustness scores, compared to 0.64 for DeBERTa-only and 0.58 for RoBERTa, while reducing RMSE to 0.082. The framework also enhanced answer prediction, particularly for reasoning-intensive questions, improving Exact Match scores by up to 7.2 points and producing higher-quality reasoning chains (+ 8.3 BLEU-4, + 8.8 ROUGE-L). Ablation studies confirmed the necessity of both syllabus graph reasoning and explicit robustness supervision, with their removal causing up to an 11-point drop in robustness correlation. These results indicate that explicitly modeling syllabus structure is critical for reliable, interpretable assessment of question quality, offering insights beyond simple answer correctness and enabling automated evaluation that aligns closely with pedagogical objectives.
Developers and marketers of artificial intelligence (AI) powered innovations have pushed technology to the forefront, well ahead of user experience and ethical implications. This exploratory study used qualitative semi-structured interviews (N = 21) and then quantitative analysis (N = 629) to predict user motivators and detractors to AI adaptation. Fifty-eight percent of respondents in this study did not agree that AI will benefit our society. There was a significant difference those identified as highly feminine and the highly masculine. Those politically conservative were less likely to support AI initiatives than those politically liberal. Independent variables were factored into latent themes “Ethical Outcomes”, Social Media Risk vs. Reward”, and “Governmental Constraint.” Ethical Outcomes was the key significant predictor of “trust AI with my personal and business activities.” The effect of Social Media Risk was marginal but significant. Logistic regression with the dependent variable “overall, artificial intelligence will benefit our society” once again captured Ethical Outcomes. The concern for social media implications was 19
Cardiovascular diseases (CVDs) are the leading cause of global mortality, necessitating efficient and accessible diagnostic tools for early detection. While deep learning models have demonstrated high accuracy in arrhythmia classification, their deployment in resource-limited clinical settings remains challenging due to computational constraints. This study addresses this gap by developing and validating lightweight deep learning models specifically optimized for edge device deployment, enabling real-time ECG analysis in remote and under-resourced healthcare environments. Utilizing the MIT-BIH Arrhythmia Dataset, which includes 48 half-hour ECG recordings from 47 individuals, we trained and evaluated 2 models: a standard Convolutional Neural Network (CNN) and a computationally efficient Depth-Wise Separable Convolutional Neural Network (DWCNN). Preprocessing involved segmenting ECG recordings into individual heartbeats and addressing class imbalance through downsampling, resulting in a balanced dataset of 21,186 images across 6 arrhythmia types. The DWCNN achieved competitive diagnostic performance (precision: 0.97, recall: 0.97) while utilizing 75
