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• W4382289013 abstract "In order to enhance customer satisfaction, logistics management is a component of the supply chain process that organizes, carries out, and regulates the effective flow and storage of goods, services, and related information from the point of source to the site of consumption. Due to a number of factors, including liberalization, weaker state interference of transportation, alterations in customer behavior, technological innovations, business increasing power, and globalization of trading, determining the precise supply chain pricing remains a difficult problem. With this impetus, this paper seeks to predict the logistic pricing by proposing a “Quad Mount Fabricated Deep Fully Connected Neural Network (QMF-DFCNN)” that uses the supply chain pricing dataset retrieved from the KAGGLE machine-learning repository. The supply chain pricing dataset contains 32 features with 10,037 logistic details, which had been processed with incomplete values. The Quad Mount Fabricated Deep Fully Connected Neural Network initiates by performing exploratory data analysis and the data division is done at 80:20. The coding is implemented with Python through an Nvidia V100 GPU workstation with 30 training iterations subjected to a batch size of 64. The dataset has been subjected to all the regressors to examine the performance of logistic pricing prediction. Execution results portray that the Gradient boost regressor is with high RSquared Value. The dataset was exposed to a gradient boost regressor to extract the six important features. The gradient boost feature extracted dataset applied to a Deep Fully Connected Neural Network had designed with single input having six features, an output layer with pricing attribute and four dense layers with 10 nodes each. Experimental results show that the proposed Quad Mount Fabricated Deep Fully Connected Neural Network shows a minimum Mean Squared Error of 19.2135 and a maximum RSquared error of 0.984 when analyzed with all other regression models." @default.
• W4382289013 created "2023-06-28" @default.
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• W4382289013 date "2023-01-01" @default.
• W4382289013 modified "2023-10-01" @default.
• W4382289013 title "Quad Mount Fabricated Deep Fully Connected Neural Network Based Logistic Pricing Prediction" @default.
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• W4382289013 doi "https://doi.org/10.1007/978-981-99-1203-2_43" @default.
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