Neural Network and Extreme Gradient Boosting in Near Infrared Spectroscopy
Author:
Affiliation:
1. Universiti Tun Hussein Onn Malaysia,Faculty of Electrical and Electronic Engineering,Parit Raja,Malaysia
Funder
Ministry of Higher Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9795928/9795932/09796490.pdf?arnumber=9796490
Reference18 articles.
1. Predicting the nutrition deficiency of fresh pear leaves with a miniature near-infrared spectrometer in the laboratory
2. Accurate prediction of glucose concentration and identification of major contributing features from hardly distinguishable near-infrared spectroscopy
3. Rapid and Uninvasive Characterization of Bananas by Hyperspectral Imaging with Extreme Gradient Boosting (XGBoost)
4. Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia
5. Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content
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1. Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes;Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy;2024-10
2. Developing an Optimal Brain Computer Interface Model using Functional Near Infrared Spectroscopy: A Review;Engineering Journal;2023-10
3. ISRToken: Learning similarities tokens for precise infrared spectrum recognition model via transformer;Infrared Physics & Technology;2023-09
4. Effects of Pre-Processing and Principal Components for Artificial Neural Network in Non-Destructive Internal Quality Prediction of Mango across Different Harvest Periods;2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE);2023-08-25
5. Machine Learning Methods in Near Infrared Spectroscopy for Predicting Sensory Traits in Sweetpotatoes;2023
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