This study tackles body shaming on Reddit using a novel dataset of 8,067 comments from June to November 2024, encompassing external and self-directed harmful discourse. We assess traditional Machine Learning (ML), Deep Learning (DL), and transformer-based Large Language Models (LLMs) for detection, employing accuracy, F1-score, and Area Under the Curve (AUC). Fine-tuned Psycho-Robustly Optimized BERT Pretraining Approach (Psycho-RoBERTa), pre-trained on psychological texts, excels (accuracy: 0.98, F1-score: 0.994, AUC: 0.990), surpassing models like Extreme Gradient Boosting (XG-Boost) (accuracy: 0.972) and Convolutional Neural Network (CNN) (accuracy: 0.979) due to its contextual sensitivity. Local Interpretable Model-agnostic Explanations (LIME) enhance transparency by identifying influential terms like “fat” and “ugly.” A term co-occurrence network graph uncovers semantic links, such as “shame” and “depression,” revealing discourse patterns. Targeting Reddit’s anonymity-driven subreddits, the dataset fills a platform-specific gap. Integrating LLMs, LIME, and graph analysis, we develop scalable tools for real-time moderation to foster inclusive online spaces. Limitations include Reddit-specific data and potential misses of implicit shaming. Future research should explore multi-platform datasets and few-shot learning. These findings advance Natural Language Processing (NLP) for cyberbullying detection, promoting safer social media environments.

Accurate and early prediction of diabetes is crucial for initiating prompt treatment and minimizing the risk of long-term health issues. This study introduces a comprehensive machine learning model aimed at improving diabetes prediction by leveraging two clinical datasets: the PIMA Indians Diabetes Dataset and the Early-Stage Diabetes Dataset. The pipeline tackles common challenges in medical data, such as missing values, class imbalance, and feature relevance, through a series of advanced preprocessing steps, including class-specific imputation, engineered feature construction, and SMOTETomek resampling. To identify the most informative predictors, a hybrid feature selection strategy is employed, integrating recursive elimination, Random Forest-based importance, and gradient boosting. Model training uses Random Forest and Gradient Boosting classifiers, which are fine-tuned and combined through weighted ensemble averaging to boost predictive performance. The resulting model achieves 93.33% accuracy on the PIMA dataset and 98.44% accuracy on the Early-Stage dataset, outperforming previously reported approaches. To enhance transparency and clinical applicability, both local (LIME) and global (SHAP) explainability methods are applied, highlighting clinically relevant features. Furthermore, probability calibration is performed to ensure that predicted risk scores align with true outcome frequencies, increasing trust in the model’s use for clinical decision support. Overall, the proposed model offers a robust, interpretable, and clinically reliable solution for early-stage diabetes prediction.

Fraud in financial data is a significant concern for both businesses and individuals. Credit card transactions involve numerous features, some of which may lack relevance for classifiers and could lead to overfitting. A pivotal step in the fraud detection process is feature selection, which profoundly impacts model accuracy and execution time. In this paper, we introduce an ensemble-based, explainable feature selection framework founded on SHAP and LIME algorithms, called "X-SHAoLIM". We applied our framework to diverse combinations of the best models from previous studies, conducting both quantitative and qualitative comparisons with other feature selection methods. The quantitative evaluation of the "X-SHAoLIM" framework across various model combinations revealed consistent accuracy improvements on average, including increases in Precision (+5.6), Recall (+1.5), F1-Score (+3.5), and AUC-PR (+6.75). Beyond enhanced accuracy, our proposed framework, leveraging explainable algorithms like SHAP and LIME, provides a deeper understanding of features' importance in model predictions, delivering effective explanations to system users.