Journal of Science Engineering Technology and Management Science

To improve kidney stone detection and classification using CT, the original inductive transfer-based ensemble deep learning system was improved. Combining the Xception model with pretrained networks improves deep feature extraction. This enables the model to identify kidney stones with intricate spatial patterns and finer textural variations. The YOLO family of object recognition models, which locate kidney issues in real time, may also be used by the technology to precisely detect stoneaffected regions in kidney CT scans. Flask offers a user-friendly front-end interface for model interaction, testing, and visualization. Clinical application gets simpler. Secure user authentication complies with medical data privacy laws and safeguards private patient information. The enhanced method is faster, more accurate, and more broadly applicable, according to experiments on many kidney stone datasets. This improved approach helps urologists make better medical decisions by lowering manual testing, errors, and early detection.

Keywords : Deep Neural Networks, Inductive Transfer Learning, YOLO, Ensemble Learning, Xception, Medical Imaging, Flask Framework, Secure Authentication, Kidney Stone Detection.

Author : M.RATNA KUMARI1 , D.KIRAN BABU2

Title : An Enhanced Kidney Stone Detection System Using Inductive Transfer-Based Ensemble Deep Neural Networks with Xception and YOLO Models

Volume/Issue : 2026;03(06)

Page No : 1123-1137

Accurately forecasting potential customers, reducing resource waste, and improving efficiency may all be necessary for telemarketing to be productive. To find clients, we use machine learning models such as Random Forest, XGBoost, AdaBoost, and Gradient Boosting. F1 score, recall, accuracy, and precision are used to assess these algorithms. To address class imbalance in telemarketing datasets, oversampling techniques like SMOTE, ADASYN, and Borderline SMOTE generate synthetic data for underrepresented classes. Model performance is improved by MinMax scaling data normalization and Mutual Information feature selection. Training and testing are conducted using telemarketing data from the Portuguese Bank Marketing Data Set. Modifying the XGBoost hyperparameters raises the model's accuracy to more than 98% for both the top 10 features and the complete set. To promote user involvement, the system has a real-time prediction interface built on Flask. Ensemble-based online machine learning, a successful telemarketing optimization technique, enables models to adjust to shifting customer preferences.

Keywords : Ensemble Learning; Online Machine Learning; Telemarketing; Class Imbalance; Synthetic Oversampling; SMOTE; ADASYN; Borderline SMOTE; Mutual Information; Feature Selection; Data Normalization; XGBoost; Random Forest; Gradient Boosting; Real-Time Prediction; Flask Interface; Bank Marketing Data

Author : SK. Himambasha1 , P. Sai Ram2

Title : Improved Ensemble-Based Online Machine Learning Approach for Telemarketing Success Prediction Using Hyperparameter-Tuned XGBoost

Volume/Issue : 2026;03(06)

Page No : 1138-1147