Using Machine Learning Techniques to Improve the Performance of Numerical Weather Prediction Models

DOI

10.2991/978-94-6463-858-5_277How to use a DOI?

Keywords

Web Server; Web Database; Service Provider; Remote User; Data Processing; User Queries; Dataset Storage; Weather Prediction Accuracy; Result,s; Prediction Type; Ratio Train & Test Data; Bar Chart; Visualization; Profile Management; Register & Login Data; Retrieval Data Storage

Abstract

The security of industrial supply Chains (ISCs) has progressed with the incorporation of industrial internet of things (IIoT) and Blockchain (BC) technology, presenting sturdy defense in opposition to cyber attacks and ensuring operational resilience. This work examines lightweight machine learning algorithms for real-time cyber-attack detection using the WUSTL-IIOT-2021 dataset to enhance ISC safety. Feature choice techniques, which include Mutual facts (MI) and further timber (ET), have been utilized to figure the most pertinent features, thereby diminishing computational complexity while retaining efficiency. This study offers a evaluation technique for assessing machine learning models, emphasizing their efficacy in figuring out cyber-attacks in a blockchain-enabled data security Context. The consequences indicate that the voting Classifier attained premiere overall performance, achieving a flawless accuracy of one hundred% with MI-decided on features and ninety nine% accuracy with ET-selected capabilities, highlighting its talent in particular and dependable chance detection. Those findings underscore the importance of customized function selection and streamlined algorithms in improving cybersecurity for IIoT and blockchain-enabled records safety systems, facilitating efficient and scalable real-time applications.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - O. Sampath
AU  - Yaramala Venkata Dharani
PY  - 2025
DA  - 2025/11/04
TI  - Using Machine Learning Techniques to Improve the Performance of Numerical Weather Prediction Models
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3324
EP  - 3333
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_277
DO  - 10.2991/978-94-6463-858-5_277
ID  - Sampath2025
ER  -