AI-Based Enhancing Supply Chain Management Using PSO with K-Nearest Neighbour Approach in Biotech
DOI:
https://doi.org/10.32628/CSEIT2511675Keywords:
Artificial intelligence, Bio-tech, supply chain management, outlier detection, PC2ST, PSO-KNNAbstract
Recent developments in biotechnology rely heavily on new technologies such as Artificial Intelligence (AI). Over the past few years, AI has played a significant role in driving developments in the biotechnology industry. Effective Supply Chain Management (SCM) to build long-term partnerships requires rigorous standards and decision-making processes that have a significant impact on overall results. To resolve this problem, we propose the Particle Swarm Optimization-K-Nearest Neighbors (PSO-KNN) algorithm in biotech to improve AI-based supply chain management. Furthermore, the Z-score Normalized Outlier Detection (ZSNOD) model is utilized to pre-process the data by eliminating missing or random values. After that, the optimal feature can be selected using the Pearson Covariance-Chi-Square Test (PC2ST) model during the feature selection process.Finally, AI-based supply chain management in biotechnology enhances efficiency by utilizing the PSO-KNN algorithm to classify data and minimize errors. Moreover, the proposed method can enhance supply chain management in biotech by increasing test accuracy to 96.28% through performance evaluation, including precision, recall, time complexity, error rate, and overall accuracy.
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