What is he best known for?
The fields of study he is best known for:
- Agriculture
- Statistics
- Ecology
Abdul Mounem Mouazen mostly deals with Partial least squares regression, Water content, Soil water, Mean squared error and Analytical chemistry.
Abdul Mounem Mouazen combines subjects such as Calibration, Soil test and Residual with his study of Partial least squares regression.
His work is dedicated to discovering how Water content, Subsoiler are connected with Compaction and other disciplines.
His work in Soil water addresses subjects such as Geotechnical engineering, which are connected to disciplines such as Bulk density.
His Mean squared error study deals with Mineralogy intersecting with Standard deviation.
His Analytical chemistry study combines topics in areas such as Total organic carbon and Calibration.
His most cited work include:
- Visible and near infrared spectroscopy in soil science (666 citations)
- Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy (289 citations)
- On-line measurement of some selected soil properties using a VIS–NIR sensor (250 citations)
What are the main themes of his work throughout his whole career to date?
Abdul Mounem Mouazen mainly investigates Partial least squares regression, Soil test, Soil science, Water content and Analytical chemistry.
His Partial least squares regression research incorporates themes from Mean squared error, Soil carbon, Coefficient of determination and Residual.
His Soil test study introduces a deeper knowledge of Soil water.
The Soil water study combines topics in areas such as Gas chromatography and Cross-validation.
His Water content research is multidisciplinary, relying on both Gravimetric analysis, Soil texture, Subsoiler, Mineralogy and Moisture.
His work on Visible and near infrared spectroscopy as part of general Analytical chemistry research is frequently linked to Line, bridging the gap between disciplines.
He most often published in these fields:
- Partial least squares regression (32.67%)
- Soil test (31.19%)
- Soil science (27.72%)
What were the highlights of his more recent work (between 2018-2021)?
- Partial least squares regression (32.67%)
- Soil test (31.19%)
- Soil science (27.72%)
In recent papers he was focusing on the following fields of study:
His main research concerns Partial least squares regression, Soil test, Soil science, Calibration and Coefficient of determination.
Partial least squares regression connects with themes related to Principal component analysis in his study.
Abdul Mounem Mouazen undertakes interdisciplinary study in the fields of Soil test and Diffuse reflectance infrared fourier transform through his works.
His research on Soil science often connects related areas such as Water content.
His studies in Calibration integrate themes in fields like Mean squared error, Remote sensing and Accuracy and precision.
His work deals with themes such as Soil water, Least squares, Residual and Spectral bands, which intersect with Coefficient of determination.
Between 2018 and 2021, his most popular works were:
- On-line vis-NIR spectroscopy prediction of soil organic carbon using machine learning (33 citations)
- Can spectral analyses improve measurement of key soil fertility parameters with X-ray fluorescence spectrometry? (19 citations)
- Prediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopy (16 citations)
In his most recent research, the most cited papers focused on:
- Agriculture
- Statistics
- Ecology
His scientific interests lie mostly in Soil test, Calibration, Coefficient of determination, Partial least squares regression and Mean squared error.
Soil test is a subfield of Soil science that he explores.
His research related to Soil fertility and Soil water might be considered part of Soil science.
His work in Calibration is not limited to one particular discipline; it also encompasses Precision agriculture.
As part of the same scientific family, Abdul Mounem Mouazen usually focuses on Coefficient of determination, concentrating on Residual and intersecting with Differential evolution, Support vector machine, Statistics, Sugar and Sugar beet.
His Mean squared error research focuses on Water content and how it connects with Cross-validation.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
