Assessment of performance evaluation of High- Technology industry using DEA, AHP, and machine learning techniques
Subject Areas : Journal of New Applied and Computational Findings in Mechanical Systems
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Keywords: Technology, Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), Machine Learning, Artificial Neural Networks, Assessment,
Abstract :
his study evaluates the efficiency and productivity of 10 active units in the field of high technology using Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), and machine learning techniques. Input criteria include research investment, number of specialized human resources, and operational costs, while output criteria consist of the number of published articles, registered patents, and commercialized products. AHP was used to calculate the relative weights of the criteria, followed by the application of the CCR and BCC models to assess the relative efficiency of the units. To enhance prediction accuracy, machine learning algorithms, including Artificial Neural Networks (ANN) and Decision Trees, were employed. The results showed that six units in the CCR model and eight units in the BCC model were identified as efficient. The ANN model, with 95% accuracy, predicted unit efficiency more precisely. The analysis revealed that Total revenue and number of published articles had the greatest impact on efficiency. This research presents a combined approach for evaluating and improving performance in high technology, offering valuable tools for managers and policymakers to optimize resource allocation and make strategic decisions.
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