Performance Evaluation of Data-Driven Engineering Models Under Rapid Technological Transition
Keywords:
Data-driven engineering, technological transition, performance evaluation, concept drift, adaptive modelling, system dynamics, predictive modelsAbstract
The rapid pace of technological innovation significantly impacts the effectiveness and adaptability of data-driven engineering models. This paper investigates the performance stability, generalization capability, and robustness of these models in environments characterized by frequent technological shifts. By integrating historical data and emergent design paradigms, the study assesses model accuracy, resilience to concept drift, and responsiveness to real-time system dynamics. The analysis involves comparative performance evaluation, identifying key metrics that determine a model's sustainability across evolving technological contexts. The results reveal that adaptability, model retraining frequency, and hybrid algorithmic approaches play critical roles in ensuring consistent performance under transition stressors.
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Copyright (c) 2020 Aisyah Syafiq, Adriana Zulaikha (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
