Optimizing Wireless Sensor Networks in E-Commerce with Message Passing Neural Networks

In today's e-commerce landscape, successful logistics management is essential for achieving success. In this post, we will go over how wireless sensor networks (WSNs) can help online stores streamline their logistics. Data collection and real-time monitoring made possible by wireless sensor networks (WSNs) increase supply chain visibility and control. The proposed approach includes both preprocessing and training the model. Part of the preprocessing phase is principal component analysis (PCA), which entails cleaning and transforming raw data into an appropriate format for analysis or model training. Throughout the training, CNN-BiGRU-BiLSTM was continuously utilized. With a 93.46% average accuracy, this innovative method surpasses the BiGRU and BiLSTM.

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Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/gccit63234.2024.10862228
Citations
1
Primary Topic
Energy Efficient Wireless Sensor Networks
Type
article
Field-Weighted Citation Impact
0.35
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article

Optimizing Wireless Sensor Networks in E-Commerce with Message Passing Neural Networks

M. Jeyaselvi, Amanjot Kaur Lamba, D. Suganthi, B. C. Saravanan et al.
1 citations
Energy Efficient Wireless Sensor Networks
0.35
article

Optimizing Wireless Sensor Networks in E-Commerce with Message Passing Neural Networks

M. Jeyaselvi, Amanjot Kaur Lamba, D. Suganthi, B. C. Saravanan, M Ganesha, M. Boobalan
article en
1 citations

Abstract

In today's e-commerce landscape, successful logistics management is essential for achieving success. In this post, we will go over how wireless sensor networks (WSNs) can help online stores streamline their logistics. Data collection and real-time monitoring made possible by wireless sensor networks (WSNs) increase supply chain visibility and control. The proposed approach includes both preprocessing and training the model. Part of the preprocessing phase is principal component analysis (PCA), which entails cleaning and transforming raw data into an appropriate format for analysis or model training. Throughout the training, CNN-BiGRU-BiLSTM was continuously utilized. With a 93.46% average accuracy, this innovative method surpasses the BiGRU and BiLSTM.

SRM Institute of Science and Technology (IN), Swami Vivekanand College of Pharmacy (IN), Sri Eshwar College of Engineering, Saveetha University (IN)
Openalex Percentile: Top 34%
Energy Efficient Wireless Sensor Networks
0.35
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Optimizing Wireless Sensor Networks in E-Commerce with Message Passing Neural Networks — M. Jeyaselvi, Amanjot Kaur Lamba, et al. · (2024) | TGRS Research Map | TGRS