Automated detection of software vulnerabilities is critical for enhancing security, yet existing methods often struggle with the complexity and diversity of modern codebases. In this paper, we propose a novel ensemble stacking approach that synergizes multiple pre-trained large language models (LLMs)—CodeBERT, GraphCodeBERT, and UniXcoder—to improve vulnerability detection in source code. Our method uniquely combines the semantic understanding of CodeBERT, the structural code representations of GraphCodeBERT, and the cross-modal capabilities of UniXcoder. By fine-tuning these models on the Draper VDISC dataset and integrating their predictions using meta-classifiers such as Logistic Regression, Support Vector Machines (SVM), Random Forest, and XGBoost, we effectively capture complex code patterns that individual models may miss. The meta-classifiers aggregate the strengths of each model, enhancing overall predictive performance. Our ensemble demonstrates significant performance gains over existing methods, with notable improvements in accuracy, precision, recall, F1-score, and AUC-score. This advancement addresses the challenge of detecting subtle and complex vulnerabilities in diverse programming contexts. The results suggest that our ensemble stacking approach offers a more robust and comprehensive solution for automated vulnerability detection, potentially influencing future AI-driven security practices.

Neuropeptides (NPs) are fragile proteins that serve as essential signaling molecules in the neurological system, playing a key role in modulating various physiological processes. Identifying particular neuropeptide sequences relevant to specific disorders would be beneficial for accelerating the development of diagnostic tools. The study proposed another approach to detecting NPs with multi-layer perception (MLP) and a bagging classifier-based meta-learning method called NeuroBooster. This investigation initially focused on five feature extractions based on composition, such as AAC, PAAC, physicochemical properties, QSO, and transfer-learning, such as Bert, and F2V strategies. Subsequently, we used the XGB feature selection method in the Bert and F2V methods to obtain the most 100D crucial features. The predicted probabilistic outcomes of NPs from the 8 preliminary models merged and derived a two-stage dataset with 40 dimensions of features and transmitted them into three classic models and two meta-models, through rigorous criteria for evaluation. Compared with the existing predictor, our proposed model NeuroBooster achieved a higher accuracy of 91.91% in the independent test method. Consequently, we discovered important features in these five models underscoring that physicochemical properties are potential targets for identification, thereby revealing new avenues for therapies.

Liver diseases (LD) encompass a variety of disorders associated with the liver, including infectious hepatitis, obesity, cirrhosis, and malignancy, which constitute significant health issues in the world. Due to the minimal symptoms, comprehending the disease becomes extremely difficult until its severe stages; earlier detection is advantageous for appropriate action, which could save lives. This study developed a StackLD framework based on the stacking-ensemble-based machine learning approach. In the preliminary phase, we collected the Indian Liver Patient Dataset (ILPD) dataset, which contains 11 features, and the dataset highlighted a significant discrepancy. To overcome this, we used the SMOTE to rebalance the dataset, facilitating the development of robust machine learning models. We applied 7 different models such as XGB, LGBM, DT, KNN, RF, KNN, and stacking approaches with several evaluation metrics on independent test methods The analysis presented that the stacking technique executed superior in accuracy, sensitivity, specificity, and area under the curve, with values of 0.8622, 0.8933, 0.8369, and 0.9275, respectively. These outcomes indicate that our approach effectively differentiates between positive and negative classes. This study illustrates that the Alkphos, Sgot, and Sgpt elements have a significant role in determining liver disease (LD) from various features. As a result, a web server was built using these attributes, demonstrating that our model accurately predicts liver disorders at an early stage which is useful insight into the medical field with the potential to improve diagnostic procedures and patient outcomes.