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This article introduces an innovative method for optimizing smart manufacturing system (SMS) by combining digital twin technology (DTT) with teaching–learning-based optimization (TBLO). It creates a simulated model of the physical manufacturing environment, enabling real-time monitoring, simulation and analysis. By leveraging the TLBO algorithm, the system enhances the decision-making process for complex manufacturing tasks, facilitating continuous improvement and adaptation to dynamic production demands. The proposed framework aims to minimize production costs, reduce downtime and improve overall efficiency by optimizing key parameters such as resource allocation, production scheduling and machine performance. Experimental results demonstrate that the DT-TLBO approach can reduce production costs by up to 20%, decrease downtime by 30% and improve overall system efficiency by 25%. This innovative combination of advanced technologies offers a promising solution for modern manufacturing challenges, paving the way for smarter, more responsive production environments.

Liver cancer ranks as the third most common cause of cancer-related death rate, resulting in an estimated 830,180 deaths globally. Recent, 2023 estimation of the American Cancer Society predicted around 29,380 deaths due to this disease in America alone. However, with early detection and proper drug management the survival rate can be greatly extended. Significantly, the noteworthy observation pertains to the absence of consistent drug responses among patients with identical cancer types at the same stage. Hence the need of the hour is the precision medicine that predict patient specific drug response based on molecular data genome expression. In this regard, we propose drug response system that uses deep learning framework that predicts drug response in new cell lines or patient with the aim of accurate prediction and suggesting précised medicine to that particular cancer patient. The prediction of the framework is on the lines of recommended system’s projection of drugs and cell lines into latent pharmacogenomic space. During our experimentation, we utilized the Genomics of Drug Sensitivity in Cancer dataset and observed that Cancer Drug Response and Patient Specific Recommender System (CDRP-PRS) consistently delivers robust predictions and effective response, even when applied to previously unseen patient-derived cell line datasets. CDRP-PRS inferences in the pharmacogenomic space are helpful to understand the drug mechanism to identify the cellular subtypes and their categorized associations. Further it is aimed is to predict the accuracy of drug responses for patients and suggest precise medications tailored to their specific cancer.

Detection of heart diseases (HD) at an early stage diminishes the mortality rate. However, handling huge data is cumbersome for physicians. Hence, there is a need for a tool regarding the automation and processing of large data to help in making precise decisions. The proposed methodology aims to predict cases of HD or cardiovascular diseases (CD) well in advance based on the features of the patient. In the proposed methodology, the Cleveland Dataset of HD collected from University of California, Irvine (UCI) repository is evaluated. The various phases in the proposed methodology include, insight about the data, analyzing the data, feature engineering and finally building the model. The K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), classifier and convolutional neural network (CNN) are used to predict the HD and are evaluated on the dataset. The metrics considered are classification accuracy (CA), recall (r) and precision (p). The resulted CA, ‘r’ and ‘p’ for KNN-based approach are 66.7%, 91.7% and 88.8%, respectively. The SVM-based approach with ‘linear’ kernel achieved CA, ‘r’ and ‘p’ are 74.2%, 85.0% and 74.0%, respectively. The ANN-based approach resulted in 70.08%, 77.0% and 84.2% of CA, ‘p’ and ‘r’, respectively. Finally, the CNN-based prediction achieved CA, ‘p’ and ‘r’ are 83.61%, 76.0% and 97.0%, respectively. The experimental study concludes that the CNN-based prediction model outperformed the KNN, SVM and ANN-based prediction approaches regarding the prediction accuracy. An optimization algorithm can be incorporated into the model in future.