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.

With the advancement of artificial intelligence and the proliferation of autonomous interactions among intelligent systems, inter-AI contracts have emerged as a novel domain within cyber law. The primary research question of this study is how existing legal frameworks can govern contracts between AI systems and what challenges arise regarding liability, legal validity, and enforcement of these contracts. The significance of this topic lies in the fact that AI-AI interactions may create legal obligations without direct human intervention, rendering traditional laws insufficient to address emerging needs. The objective of this article is to provide a legal analysis of inter-AI contracts and to examine the capacity of emerging cyber law principles to address this phenomenon. The research method is descriptive–analytical, based on documentary study, including the review of existing laws, international instruments, and hypothetical inter-AI contract examples. The results indicate that while traditional concepts of contracts and legal responsibility offer preliminary adaptability to AI-AI interactions, legal gaps and the lack of clarity in defining machine intent and obligations necessitate the development of novel frameworks and specific cyber law regulations. The innovation of this study lies in presenting a combined analysis of contract law and emerging cyber law concepts for inter-AI interactions and proposing legal criteria for validating and ensuring the enforceability of such contracts. The findings can guide the formulation of comprehensive regulations and legal safeguards for autonomous intelligent interactions.

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.

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.

The development of artificial intelligence has gained momentum in recent years in many fields, most of which have been trying to improve organizational functions. However, there are gaps in how organizations should use artificial intelligence to improve organizational productivity. Regarding the application of artificial intelligence and the conditions of internal organizations, this research is a conceptual research model that identifies the effects that artificial intelligence (AI) can have in improving organizational productivity. This research was conducted with the aim of investigating the impact of artificial intelligence in improving organizational productivity in 1402. The statistical population of the research included all the selected employees of the affiliated companies of the Ministry of Energy in Tehran, whose total number was 330, out of which 175 people were considered as the sample size using the Morgan table and simple random sampling method. The method of data collection was based on the standard questionnaires of artificial intelligence of Micallef et al. (2023) and the productivity of Achio (1994). After the distribution and collection of questionnaires, information review and hypothesis testing was done using the structural equation modeling method and with the help of Smart PLS 2 software in two parts of the measurement model and the structural part. In the first part, the technical characteristics of the questionnaire including reliability, convergent validity and divergent validity specific to PLS were investigated. In the second part, the significant coefficients of the software were used to check the research hypotheses. Finally, the findings of the research confirmed the impact of artificial intelligence and its functions, including infrastructure, the ability to expand work and preventive positions in the studied society.