Modern intrusion detection systems (MIDS) face persistent challenges in coping with the rapid evolution of cyber threats, high-volume network traffic, and imbalanced datasets. Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively. This study introduces an advanced, explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets, which reflects real-world network behavior through a blend of normal and diverse attack classes. The methodology begins with sophisticated data preprocessing, incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions, ensuring standardized and model-ready inputs. Critical dimensionality reduction is achieved via the Harris Hawks Optimization (HHO) algorithm—a nature-inspired metaheuristic modeled on hawks’ hunting strategies. HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance. Following feature selection, the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types. The stacked architecture is then employed, combining the strengths of XGBoost, SVM, and RF as base learners. This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers. The model was evaluated using standard classification metrics: precision, recall, F1-score, and overall accuracy. The best overall performance was recorded with an accuracy of 99.44% for UNSW-NB15, demonstrating the model’s effectiveness. After balancing, the model demonstrated a clear improvement in detecting the attacks. We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter. Also, the proposed model is computationaly efficient. To support transparency and trust in decision-making, explainable AI (XAI) techniques are incorporated that provides both global and local insight into feature contributions, and offers intuitive visualizations for individual predictions. This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.

Motor imbalance is a critical failure mode in rotating machinery, potentially causing severe equipment damage if undetected. Traditional vibration-based diagnostic methods rely on direct sensor contact, leading to installation challenges and measurement artifacts that can compromise accuracy. This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar. A dataset of 1802 experimental trials was sourced, covering four imbalance levels (0, 10, 20, 30 g) across varying motor speeds (500–1500 rpm) and load torques (0–3 Nm). Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals, preserving both amplitude and phase information for analysis. A multi-domain feature extraction methodology captured imbalance signatures in time, frequency, and complex signal domains. From 65 initial features, statistical analysis using Kruskal–Wallis tests identified significant descriptors, and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions, achieving 69% dimensionality reduction without loss of performance. Six machine learning algorithms, Random Forest, Extra Trees Classifier, Extreme Gradient Boosting, Categorical Boosting, Support Vector Machine with radial basis function kernel, and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation. The Extra Trees Classifier achieved the best performance with 98.52% test accuracy, 98% cross-validation accuracy, and minimal variance, maintaining per-class precision and recall above 97%. Its superior performance is attributed to its randomized split selection and full bootstrapping strategy, which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset. The model’s average inference time of 70 ms enables near real-time deployment. Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations, providing a robust, scalable, and noninvasive solution for industrial motor condition monitoring, particularly in hazardous or space-constrained environments.

Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone; however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.