Ensemble learning becomes a backbone in disease diagnosis using several classifiers to ensure improved prediction accuracy and also model reliability. However, conventional ensemble techniques often suffer some critical challenges, like poor diversity among base models, less efficient convergence, and sometimes high computational costs. That is why addressing these matters is essential to make further strides in ensemble-based diagnostic frameworks. This study introduces the Adaptive Ensemble Optimization with Hybrid Learning (AE HL) as an Novel Bagging Approach with Teaching-Learning-Based Optimization (BA-TLBO). The AE-HL framework encompasses a new fitness function that uses a new diversity metric with the Hamming distance to optimize both accuracy and classifier diversity effectively. To counteract inefficiencies in convergence, AE-HL uses adaptive optimization strategy that learns to balance exploration and exploitation during the learning phase. A multi-phase An optimization technique is employed, that limits the amount of computation by successively refining the best promising configurations; dynamic bag size adaptations improve the trade-off between variance and bias and, hence generalization over different datasets. Furthermore, the approach is integrated with a lightweight Explainable AI (XAI) module in order to support interpretability without an increase in complexity. The method is tested on several benchmark datasets for disease diagnosis where it is shown that AE-HL outperformed best among several ensemble optimization techniques. In summary, the proposed method obtained the highest accuracy with explainability and diversity in comparison with advanced metrics and statistical analysis. These results confirm the robustness, efficiency, and transparency of the AE-HL as a solution for enhancing systems for disease diagnosis

Counterfeit detection in banknotes remains a significant challenge, given the advanced techniques employed by counterfeits. Many existing solutions are either in accessible to the general public or lack the robustness required for reliable authentication. To overcome these limitations, this study proposes a web-based system for bank note verification, integrating machine learning and image processing. The system allows users to upload images of banknotes through a user-friendly interface designed with responsive web technologies, while backend operations are managed using Django. Image preprocessing methods, including Gaussian blurring, normalization, and Sobel edge detection, are applied to enhance visual quality and extract essential statistical features such as entropy, variance, skewness, and kurtosis. These features serve as inputs to a logistic regression model that classifies banknotes as authentic or counterfeit. Experimental results reveal that the proposed system achieves high accuracy on a balanced dataset. Additionally, comparative analysis with other machine learning classifiers shows that the system out performs existing state-of-the-art models, offering are liable solution for practical use