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<p>In recent times, cloud manufacturing has been employed with blockchain technologies. Many researchers have analyzed the third-party problems between customers and service providers. The cloud manufacturing providers handle the service provided for the customers. Hence, the third-party problem occurs in service-related participants. So, third-party involvement has to be evaded for better client satisfaction. Also, proper supervisions are needed to provide efficient services by the cloud manufacturers. Managing more clouds is a complex task. The security of the data might be at risk if the cloud manufacturing frameworks are not properly managed. Therefore, we developed a deep learning-based customer satisfaction model with Blockchain technology to securely analyze customer reviews for increasing sales growth in companies. Initially, data related to customer reviews is attained from standard sources. The gathered data are given to the prediction stage. Here, we use the cloud manufacturing platform for customer satisfaction. The sentiment analysis is performed by Adaptive Transformer Network (ATN) to predict customer reviews. Here, the parameter present in the ATN is optimized by Hybrid Sewing Training with Osprey Optimization Algorithm (HSTOOA). The developed HSTOOA optimization is used for choosing the cloud manufacturing provider based on customer satisfaction. The performance of the designed customer review prediction method is improved by parameter optimization.</p>

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Integrated deep learning and blockchain-based framework for cloud manufacturing with improved customer satisfaction

  • 期刊论文

  • 发表日期：

    2025年3月15日

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  • N. Ramshankar,
  • J. Anvar Shathik,
  • K. Raju,
  • S. Murugesan

摘要

In recent times, cloud manufacturing has been employed with blockchain technologies. Many researchers have analyzed the third-party problems between customers and service providers. The cloud manufacturing providers handle the service provided for the customers. Hence, the third-party problem occurs in service-related participants. So, third-party involvement has to be evaded for better client satisfaction. Also, proper supervisions are needed to provide efficient services by the cloud manufacturers. Managing more clouds is a complex task. The security of the data might be at risk if the cloud manufacturing frameworks are not properly managed. Therefore, we developed a deep learning-based customer satisfaction model with Blockchain technology to securely analyze customer reviews for increasing sales growth in companies. Initially, data related to customer reviews is attained from standard sources. The gathered data are given to the prediction stage. Here, we use the cloud manufacturing platform for customer satisfaction. The sentiment analysis is performed by Adaptive Transformer Network (ATN) to predict customer reviews. Here, the parameter present in the ATN is optimized by Hybrid Sewing Training with Osprey Optimization Algorithm (HSTOOA). The developed HSTOOA optimization is used for choosing the cloud manufacturing provider based on customer satisfaction. The performance of the designed customer review prediction method is improved by parameter optimization.