Scholarlink

In this study, the influence of temperature on the moisture content of couscous grains was predicted using a model developed with a machine and deep learning-based approach, particularly focusing on artificial neural networks. Experimental data were collected by subjecting couscous grains to temperatures of 50, 60, and 70 °C using the Electronic Moisture Analyzer MA 1104. Various model architectures were tested by modifying activation functions and the number of neurons in hidden layers. These configurations were rigorously evaluated using mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2). A satisfactory agreement between the predictions with the experimental data was observed, achieving an R2 of 0.99988 and an MSE of 0.00374.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine and Deep Learning for Prediction of Moisture Content Under Different Temperatures During Drying

  • 章节

  • 发表日期：

    2025年

查看全文
  • Fouad Ait Hmazi,
  • Taoufik Hachimi,
  • Hajar Rejdali,
  • Hamza Bagar,
  • Abderrahim Jaafar,
  • Nassima Naboulsi,
  • Yahya Riyad,
  • Hicham Doghmi,
  • Abdellah Madani,
  • Ibrahim Mrani

摘要

In this study, the influence of temperature on the moisture content of couscous grains was predicted using a model developed with a machine and deep learning-based approach, particularly focusing on artificial neural networks. Experimental data were collected by subjecting couscous grains to temperatures of 50, 60, and 70 °C using the Electronic Moisture Analyzer MA 1104. Various model architectures were tested by modifying activation functions and the number of neurons in hidden layers. These configurations were rigorously evaluated using mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2). A satisfactory agreement between the predictions with the experimental data was observed, achieving an R2 of 0.99988 and an MSE of 0.00374.