Rheumatic and autoimmune diseases represent a significant diagnostic challenge due to overlapping clinical presentations and the complexity inherent in their pathogenesis. In this work, we present a framework for explainable machine learning to improve the precision and interpretability of diagnoses for these illnesses. The dataset used in this study, which was collected from Iraq, consists of$\mathbf{1 2, 0 8 5}$samples for six primary diseases and normal conditions. The five machine learning algorithms are: Decision Tree, Random Forest, XGBoost, AdaBoost, and Light Gradient Boosting Machine, with parameters tuned for evaluation. Explainable global AI approaches were employed at the global level to enhance the visibility and interpretability of the model, allowing a better understanding of how significant each feature was used and contributed to the diagnostic results. For example, the Random Forest model achieved an accuracy of 83.867% and an AUC of 89.641%, which outperformed many of these approaches.

Fake news has become a major issue owing to its quick growth on the internet, difficulty in identifying it from true news, and people's reliance on social media platforms as primary sources. It has negative effects at several levels, including individual, communal, political, and economical. Detecting Arabic fake news requires significant effort owing to limited datasets and studies in the sector. In this study, Employ Perturbation Adversarial attacks was applied as a regularization technique for fake news classification. Adversarial examples are generated by perturbing the model's word embedding matrix. The AraBERTv2 model is utilized for preprocessing operations. Five different machine learning models were trained on clean data and tested using both clean and adversarial examples to evaluate the generalization capabilities of the classification models. To address the scarcity of Arabic datasets, A translated English fake news was utilized dataset Experimental results indicate that the LightGBM and AdaBoost algorithms exhibited the best performance (90%) compared to other classifiers.

the rapid spread of fake news online, coupled with the difficulty of distinguishing it from real news, has become a serious issue, especially with social media being a primary news source for many people. The fake news can have damaging effects on individuals, communities, and political and economic systems. Detecting Arabic fake news presents additional challenges due to the limited availability of relevant datasets and research. In this study, Perturbation Adversarial attacks was applied as a regularization technique for fake news detection, generating adversarial examples by modifying the model’s word embedding matrix. The AraBERTv2 model is used for preprocessing, and the lack of Arabic data was overcome by utilizing a translated English fake news dataset. A Recurrent Neural Network (RNN) model was trained on clean data, testing it with both clean and adversarial examples to evaluate its generalization capability. The results demonstrate that the RNN model performs effectively, achieving strong accuracy in Arabic fake news detection.

The explosion of medical literature over the past decade has resulted in efficient and accurate techniques for text categorization to handle huge amount of data. This work combines ensemble learning methods with coupled multi-word embedding techniques to improve cancer text classification. The intricate semantic links present in medical tests are frequently outside the scope of traditional word embedding models, resulting in not ideal categorization results. To address this problem, we employ e continuous bag-of-words and Skip-gram approaches that yield more complete word representations capturing multiple linguistic nuances. Subsequently, such embeddings are passed through LGBM, CatBoost, and NGBoost ensemble learning classifiers to enhance classification accuracy. With 99.868% accuracy rate, LGBM and CatBoost were the most successful ensemble approaches examined. These approaches provide solid foundation for future work on the use of ensemble methods with complex word representations and for advancing the field of medical text classification.

The complexity of overlapping symptoms and interactions among multiple diseases makes it very difficult to accurately diagnose comorbidities. This article presents an innovative comorbidity diagnosis improvement approach that integrates conflicting group learning and numerous diverse machine learning algorithms such as Random Forest, Gradient Boosting, AdaBoost, Bagging and Extra Trees. The ensemble model is advantageous over individual models because it improves diagnostic accuracy while improving adversarial robustness. Effective adversarial training methods can also be used to strengthen the model against interference which may interfere with the diagnosis. Benchmark comorbidity datasets explore the effectiveness of the proposed method that is not solely more efficient in terms of accuracy compared to other methods, but similarly more efficient in its aptitude to endure adversarial instances. The current study is supposed to be merged into the investigative pipeline to allow for the development of vigorous diagnosis schemes. Of the algorithms tested in the study, the Extra Trees algorithm achieved 91.845% which was the highest performance obtained among the other algorithms.

Similar to most spoken languages worldwide, Arabic language is facing a significant problem with the rapid spread of fake information, which requires effective methods to detect and combat it. This research introduces a novel approach to detecting fake news in Arabic using Ensemble Techniques. The process involves gathering a comprehensive collection of Arabic news articles and carefully labeling them as either fake or authentic. To ensure the quality of the data, various text preprocessing techniques such as cleaning, tokenization, and stemming are applied. The detection accuracy is then enhanced by leveraging Ensemble Techniques. Multiple base classifiers, each with its own strengths, are employed to thoroughly analyze the text data for deceptive patterns. By utilizing this ensemble approach, the diversity of classifiers is harnessed to improve the reliability of detecting fake news. In this case, a robust ensemble learning framework that combines three states of the art algorithms are utilized for the detection of fake news. Two different methods for extracting features, Term Frequency and Term Frequency-Inverse Document Frequency are used to improve the accuracy of identifying fake news. The results show significant improvements in fake news detection accuracy, which provide valuable insights and methodologies for combating misinformation in the Arabic digital sphere, which is crucial for maintaining information integrity and promoting informed decision-making.