The increasing complexity and frequency of cyberattacks have made accurate and reliable intrusion detection systems (IDSs) essential for modern network security. In this study, an explainable triple-hybrid deep learning framework is proposed for binary intrusion detection using the CICIDS2017 dataset. The proposed architecture integrates three complementary branches, namely a Transformer encoder, a bidirectional long short-term memory (BiLSTM) network, and a multilayer perceptron (MLP), to capture global feature interactions, sequential dependencies, and nonlinear discriminative patterns from network traffic data. To enhance adaptive representation learning, the framework employs a branch-gating mechanism and a fusion-gating module before final classification. The model was evaluated in a Benign-versus-Attack setting using 5-fold stratified cross-validation and assessed through accuracy, precision, recall, F1-score, ROC-AUC, confusion matrices, and explainability analysis. Experimental results showed strong and stable performance across folds, with a mean validation accuracy of 97.18%, a best-fold accuracy of 97.43%, and a mean ROC-AUC of 0.9975. LIME-based explanations further improved transparency, confirming the framework as an effective and interpretable solution for binary intrusion detection.

In this paper, a hybrid multi-layer machine learning model is proposed for early prediction of PD using voice data. The suggested technique combines a number of base learners namely Random Forest, XGBoost, LightGBM, and CatBoost through ensemble methods like Voting Classifier, and Stacking Classifier. A thorough feature engineering, from correlation analysis to feature selection and polynomial feature extraction, was applied to improve the model’s quality. Experiments results show that the hybrid model obtained accuracy of 91.30% with high precision and recall especially for Class-1(Parkinson’s disease). Although very strong in its entirety the limited ability to discriminate healthy subjects (Class 0) (recall around 0.75). Among the selected models, Voting Classifier and CatBoost were robust and presented generalization well for all performance measures. The contribution of this work is two-folds: it demonstrates that ensemble models can be used to enhance diagnostic accuracy, and it addresses three main issues inherent in Parkinson's disease detection. Optimization of the performance of the model in discriminating healthy from Parkinson’s disease individuals is highly valuable, with an emphasis on the generalization and clinical usage.