Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Amkor, Ali
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
An evaluation of machine learning algorithms coupled to an electronic olfactory system: a study of the mint case Amkor, Ali; Maaider, Kamal; El Barbri, Noureddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4335-4344

Abstract

The aim of this investigatation is to compare the utility of machine learning algorithms in distinguishing between untreated and processed mint beside in predicting the spray day of the insecticide. Within seven days, mint treated samples with the malathion insecticide are collected, and their aromas are Studied using a laboratory-manufactured sensor array system based on commercial metallic semiconductor (MOS) gas sensors. To distinguish the mint type, some results of machine learning algorithms were compared to know the decision trees (DT), Naive Bayes, support vector machines (SVM), and ensemble classifier. Furthermore, to predict the treatment day support vector machines regression (SVMR) and partial least squares regression (PLSR) were compared. Regarding the best results, in the discrimination case, a success rate of 92.9% was achieved by the ensemble classifier while in the prediction case, a correlation coefficient of R=0.82 was reached by the SVMR. Good results are achieved if the right gas sensor array system is designed and realized coupled with a good choice of the appropriate machine learning algorithms.
An electronic nose for insecticides detection in food: the case of alpha-cypermethrin in Swiss chard Amkor, Ali; Aboulkacem, Asmae; El Barbri, Noureddine
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5956

Abstract

Due to the disturbingly high quantities of pesticides found in recent years, it has become essential to analyze the food composition intended for consumption. In this study and from a similar angle, we are interested in the detection of alpha-cypermethrin insecticide residues in edible Swiss chard. To this end, we suggest an electronic nose that was constructed using metal oxide gas sensors. Following data collection and pre-processing, two machine learning algorithms—principal component analysis (PCA) and support vector machine (SVM)—were used to analyze the sensor matrix data. The PCA method initially showed that the first three principal components (PCs) may account for more than 96.5% of the sample variation with a clear distinction between known groups corresponding to treated and untreated samples. The identification of untreated Swiss chard from treated one was then accomplished using the SVM method with five folds cross-validation, with a success rate of 92.3%. These results show that our suggestion, which is quick, easy, and affordable, can be utilized as an effective substitute for current methods for identifying Swiss chard that has been treated with the hazardous alpha-cypermethrin.
Co-Authors Aboulkacem, Asmae El barbri, Noureddine Maaider, Kamal