A computer vision as a tool for automated quality control in smart manufacturing

2025-11-05T13:24:01+01:00

Computer vision (CV) has emerged as one of the most significant enablers of intelligent factoring system quality control, automated in the context of the AI revolution in the industrial setting today. In this research, we discuss how CV-based architecture can be applied to achieve real-time, adaptive, and scalable quality assurance. This is new research because it is an amalgamation – the evaluation of different mathematical models and artificial intelligence (AI). Deep learning, transfer learning, Bayesian networks, and edge computing are among the solutions, as are fog-cloud partnerships and their direct impact on manufacturing output, productivity, and decision-making efficiency. The article provides comparative data on the performance of other CV frameworks in different industrial conditions by critically examining the new case studies. The practical implications are recommendations for adopting vision-driven systems to improve product consistency, increase human-machine interaction, and reduce operational downtime. In addition, the paper identifies shortcomings in computational resources, system compatibility, and information security that should be addressed in the next generation of smart factories.

Estimating survival rates using artificial intelligence combined with the Aalen–Johansen estimator in multi-state models

2025-08-16T15:41:22+02:00

Accurate survival prediction is essential for clinical decision-making, health economics, and treatment planning. Traditional methods like the Kaplan-Meier and Cox models are widely used but have limitations when applied to complex multi-state processes or individualized predictions. The Aalen–Johansen estimator, a non-parametric approach suited for multi-state Markov models, improves population-level inference but lacks the ability to incorporate covariates or capture nonlinear relationships. In this study, we propose a hybrid framework that combines the Aalen–Johansen estimator with artificial intelligence (AI) techniques, specifically gradient boosting machines (GBM) and long short-term memory (LSTM) networks. By transforming transition probabilities into subject-level pseudo-observations, AI models can learn personalized survival functions based on individual covariates. We validate our approach on both simulated and real-world clinical datasets. The hybrid model outperforms traditional estimators in predictive accuracy, as measured by calibration and discrimination metrics such as Brier score and area under the curve (AUC). This AI–Aalen–Johansen framework enhances risk stratification and clinical decision-making by providing more accurate, scalable, and interpretable survival predictions. Our results support its potential as a valuable tool in modern healthcare analytics, contributing to the advancement of precision medicine.

Sustainable Engineering and Innovation

A computer vision as a tool for automated quality control in smart manufacturing

Estimating survival rates using artificial intelligence combined with the Aalen–Johansen estimator in multi-state models