In Intelligent Manufacturing, Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors, ensuring more informed decision-making and adaptive system management. It also promotes decision making and provides scientific analysis to enhance the efficiency of the operation, cost reduction, maximizing the process of production and so on. Various methods are employed to enhance productivity, yet achieving sustainable manufacturing remains a complex challenge that requires careful consideration. This study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid Weighted Support Vector-based Lévy flight (HWS-LF) algorithm. The objective of the HWS-LF method is to improve the environmental, economic, and social aspects of manufacturing processes. In this approach, Support Vector Machines (SVM) are used to classify data points by identifying the optimal hyperplane to separate different classes, thereby supporting predictive maintenance and quality control in manufacturing. Random Forest is applied to boost efficiency, resource allocation, and production optimization. A Weighted Average Ensemble technique is employed to combine predictions from multiple models, assigning different weights to ensure an accurate system for evaluating manufacturing performance. Additionally, Lévy flight Optimization is incorporated to enhance the performance of the HWS-LF method further. The method’s effectiveness is assessed using various evaluation metrics, including accuracy, precision, recall, F1-score, and specificity. Results show that the proposed HWS-LF method outperforms other state-of-the-art techniques, demonstrating superior productivity and system performance.

Breast cancer is a type of cancer responsible for higher mortality rates among women. The cruelty of breast cancer always requires a promising approach for its earlier detection. In light of this, the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors. In addition, the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram. Accordingly, the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost (ESA-XGBNet) for binary classification of mammograms. For this, the work is trained, tested, and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM, INbreast, and MIAS databases. Maximum classification accuracy of 97.585% (CBIS-DDSM), 98.255% (INbreast), and 98.91% (MIAS) is obtained using the proposed ESA-XGBNet architecture as compared with the existing models. Furthermore, the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.