An Expert System for Classification of Potato Tubers using Image Processing and Artificial Neural Networks

Ebrahimi, Ebrahim; Mollazade, Kaveh; Arefi, Arman

doi:10.1515/1556-3758.2656

Cited by 15 publications

(14 citation statements)

References 23 publications

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“…To convert the RGB numbers obtained to L * a * b *, relations (5–8) were used (Ebrahimi, Mollazade, & Arefi, 2012; Erdem, Ozluoymak, & Kizildag, 2018). Finally, the total color change (Δ E ) was calculated using Equation (9) (Abbaspour‐Gilandeh et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Influence of ultrasound pre‐treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer

Jahanbakhshi

Yeganeh

Momeny

2020

J. Food Process. Preserv.

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Drying is one of the ways to reduce postharvest waste and processing in agricultural products. Drying with hot air is one of the most popular drying methods in the food industry. The purpose of this study is to investigate the effect of ultrasound and temperature on the quality and thermodynamic properties in the process of drying nectarine slices in a hot air dryer. The drying process was performed at four levels of ultrasonic pre-treatment of 0 min (control sample), 10, 20, and 40 min and three temperature levels of 50, 60, and 75°C. Experiments were performed on 5 mm thick How to cite this article: Jahanbakhshi A, Yeganeh R, Momeny M. Influence of ultrasound pre-treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer.

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Section: Methodsmentioning

confidence: 99%

Influence of ultrasound pre‐treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer

Jahanbakhshi

Yeganeh

Momeny

2020

J. Food Process. Preserv.

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“…This conception is effortlessly deployed, instead of the tiresome and empiric choices of the neural architecture, learnt only through the largest laboratory data [32]. This requires at least 50 samples for each class, unlike our model which necessitates the shortest time and a minimum dimension reduction possible in order not only to reduce the learning latency, hardware resources, power consumption but also to automatize the optimal MLP design [33]. Precisely, the average time and data dimension reduction needed to train our model is around 150 times faster than the whole classic training, since there are three classes.…”

Section: Resultsmentioning

confidence: 99%

An Electronic Nose Natural Neural Learning Model in Real Work Environment

Ouhmad

Makkaoui²,

Beni‐Hssane

et al. 2019

IEEE Access

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The natural identification of an odor, smelled once, highlights a new idea on the artificial intelligence for electronic noses to mimic as close as possible the biological olfactory system. For the first time, a natural on-line training with only one sample, to extract both eigen-weights and eigen-bias, for each odor is built herein to elaborate a natural identifier neural model in a real work environment. The proposed model efficiently reduces the maximum extent of traditional neural models complexities, namely generic work-laboratory, dimensional data learning (typically from 60 to 80%), model adaptability complication, time-consuming, heavy experiment materials and chemical products. The outcomes show that the invented model identifies correctly 100% the three hazardous contaminants of the test data (all data minus only three samples for learning) processed from gas sensors weakened in the harshest conditions. Therewith, this natural identifier model methodology is elaborated easily enough to be efficient in different environments conditions to accurately control the air quality safety in any local area. INDEX TERMS Artificial neural network (ANN), natural identifier neural learning (NINL), eigen-weights and bias, electronic nose (e-nose), sensors, highly toxic gases, real environment, air quality safety.

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“…The software called Przybyl Image Detector System ('PIDsystem'), supporting the process of isolating classification features of fruit and vegetable products, was used in the research (Ebrahimi et al 2012;Przybył et al 2018).…”

Section: Methodsmentioning

confidence: 99%

Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

2020

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The aim of the study was to develop a neural model enabling classification of fruit spray dried powders, on the basis of graphic data acquired from a bitmap received in the process of spray drying. The neural model was developed with multi-layer perceptron topology. Input variables were expressed in 46 image descriptors based on RGB, YCbCr, HSV (B) and HSL models. Sensitivity analysis of input variables and principal component analysis determined the significance level of each attribute. The optimal model with the lowest error value root mean square, at the level of 0.04 contained 46 neurons in the input layer, 11 neurons in the hidden layer, 10 neurons in the output layer. The results allowed to show that dyeing force (color features) had influence on effective differentiation of the research material consisting of spray-dried powders of rhubarb juice with various dried juice content levels: 30, 40 and 50% as well as high (“H”) and low (“L”) level of saccharification a chosen carrier (potato maltodextrin).

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An Expert System for Classification of Potato Tubers using Image Processing and Artificial Neural Networks

Cited by 15 publications

References 23 publications

Influence of ultrasound pre‐treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer

Influence of ultrasound pre‐treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer

An Electronic Nose Natural Neural Learning Model in Real Work Environment

Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

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