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Multiclass moisture classification in woodchips using IIoT Wi-Fi and machine learning techniques

  • Published:2021-11 Issue: Volume:154 Page:107445
  • ISSN:0098-1354
  • Container-title:Computers & Chemical Engineering
  • language:en
  • Short-container-title:Computers & Chemical Engineering

Author:

Suthar Kerul,He Q. Peter

Publisher

Elsevier BV

Subject

Computer Science Applications,General Chemical Engineering

Reference57 articles.

1. Neural Networks and Deep Learning - A Textbook, Machine Learning;Aggarwal,2018

2. Comparison of different diversity techniques in MIMO antennas;Ahamed,2017

3. Estimation of moisture in wood chips by near infrared spectroscopy;Amaral;Maderas Cienc. y Tecnol.,2020

4. Standard test methods for moisture content of wood ASTM D4442;Annu. B. ASTM Stand.,2016

5. Random search for hyper-parameter optimization;Bergstra;J. Mach. Learn. Res.,2012

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1. MoistNet: Machine vision-based deep learning models for wood chip moisture content measurement;Expert Systems with Applications;2025-01

2. WiFi Sensing Based Textile Wetness Monitoring;ICC 2024 - IEEE International Conference on Communications;2024-06-09

3. A comprehensive review on wood chip moisture content assessment and prediction;Renewable and Sustainable Energy Reviews;2024-01

4. A hybrid CNN+LSTM-based intrusion detection system for industrial IoT networks;Engineering Science and Technology, an International Journal;2023-02

5. Water desorption monitoring of cellulose pulps by NIR spectroscopy;Industrial Crops and Products;2023-02
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