Soft computing-based method for estimation of almond kernel mass from its shell features

Ashtiani, Seyed-Hassan Miraei; Rohani, Abbas; Aghkhani, Mohammad Hossein

doi:10.1016/j.scienta.2019.109071

Cited by 31 publications

(9 citation statements)

References 50 publications

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“…The SVM model as a nonparametric method was introduced by Vladimir Vapnik [68]. Generalisation ability is one of the advantages of the SVM model, which is why this model is also used for estimation of yield based on presented independent variables.…”

Section: Support Vector Machine (Svm) Modelmentioning

confidence: 99%

“…where C is the box constraint, ε is the epsilon margin, G(x i ,x j ) is the kernel function, L is the loss function, and α, α * is the nonnegative multiplier. [68]. Generalisation ability is one of the advantages of the SVM model, which is why this model is also used for estimation of yield based on presented independent variables.…”

Section: Support Vector Machine (Svm) Modelmentioning

confidence: 99%

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Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

et al. 2022

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Design and fabrication of a catalyst with the highest activity can be achieved by optimising the synthesis conditions. In this study, the sol-gel synthesis conditions of citric acid concentration, gelling temperature, complex time, and calcination temperature were studied for the preparation of a novel semi-hexagonal calcium/titania-zirconia nano-catalyst used in the esterification reaction. After synthesis of around 24 samples at various conditions, their activity was tested in the esterification reaction and the results were analysed by multi-layer perceptron (MLP) and support vector machine (SVM) models. Both models predicted the actual data with high coefficients of determination, and indicated that the calcination temperature has the most influence on the activity of the prepared semi-hexagonal calcium/titania-zirconia nano-catalyst for the esterification reaction. Moreover, the genetic algorithm (GA) was utilised for optimising the preparation conditions based on the SVM model, due to its higher generalisation capability for prediction. The prepared nano-catalysts under the optimum conditions of 1.42 acid ratio, gelling temperature of 72 °C, complex time of 2.65 h, and calcination temperature of 487 °C showed good crystalline structure and metal–metal and metal–oxygen cation bonding. Finally, the fabricated catalyst had a high surface area (276.5 m2/g) with 3.5 nm pore diameter and almost uniform particle size (80–110 nm) distribution, leading to a high conversion of 97.6% in the esterification reaction, with good catalytic stability up to five times.

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Section: Support Vector Machine (Svm) Modelmentioning

confidence: 99%

Section: Support Vector Machine (Svm) Modelmentioning

confidence: 99%

Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

et al. 2022

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“…Among various types of neural network models, the backpropagation neural network (BPNN) is widely used as a supervised classifier due to its ease of implementation and convergence as well as proper function approximation. A BPNN model usually consists of 3 layers where input and output neurons represent predictive and dependent variables, respectively, while hidden-layer neurons are tasked with information processing and transfer to the related neurons in the network (Miraei Ashtiani et al, 2020b). This model uses the gradient steepest descent method to adjust neuron weights and minimize output error (Kuo et al, 2020).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Computer-vision classification of corn seed varieties using deep convolutional neural network

Javanmardi

Ashtiani

Verbeek

et al. 2021

Journal of Stored Products Research

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Automated classification of seed varieties is of paramount importance for seed producers to maintain the purity of a variety and crop yield. Traditional approaches based on computer vision and simple feature extraction could not guarantee high accuracy classification. This paper presents a new approach using a deep convolutional neural network (CNN) as a generic feature extractor. The extracted features were classified with artificial neural network (ANN), cubic support vector machine (SVM), quadratic SVM, weighted k-nearest-neighbor (kNN), boosted tree, bagged tree, and linear discriminant analysis (LDA). Models trained with CNN-extracted features demonstrated better classification accuracy of corn seed varieties than models based on only simple features. The CNN-ANN classifier showed the best performance, classifying 2250 test instances in 26.8 s with classification accuracy 98.1%, precision 98.2%, recall 98.1%, and F1-score 98.1%. This study demonstrates that the CNN-ANN classifier is an efficient tool for the intelligent classification of different corn seed varieties.

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“…Also, random forest model predicted 91% and 84% accuracies for training and testing, respectively. Miraei Ashtiani et al (2020) estimated mass of almond kernels using from its shell features such as length, maximum width, and maximum thickness. For this purpose, a multi-linear regression model, multilayer perceptron neural network, radial basis function neural network, adaptive neuro-fuzzy inference system, and support vector machine algorithms were used.…”

Section: Academicpres Notulae Botanicae Horti Cluj-napoca Agrobotanicimentioning

confidence: 99%

Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

Koç

Gerdan

Eminoğlu

et al. 2020

Not Bot Horti Agrobo

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Classification of hazelnuts is one of the values adding processes that increase the marketability and profitability of its production. While traditional classification methods are used commonly, machine learning and deep learning can be implemented to enhance the hazelnut classification processes. This paper presents the results of a comparative study of machine learning frameworks to classify hazelnut (Corylus avellana L.) cultivars (‘Sivri’, ‘Kara’, ‘Tombul’) using DL4J and ensemble learning algorithms. For each cultivar, 50 samples were used for evaluations. Maximum length, width, compression strength, and weight of hazelnuts were measured using a caliper and a force transducer. Gradient boosting machine (Boosting), random forest (Bagging), and DL4J feedforward (Deep Learning) algorithms were applied in traditional machine learning algorithms. The data set was partitioned into a 10-fold-cross validation method. The classifier performance criteria of accuracy (%), error percentage (%), F-Measure, Cohen’s Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values are provided in the results section. The results showed classification accuracies of 94% for Gradient Boosting, 100% for Random Forest, and 94% for DL4J Feedforward algorithms.

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Soft computing-based method for estimation of almond kernel mass from its shell features

Cited by 31 publications

References 50 publications

Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

Computer-vision classification of corn seed varieties using deep convolutional neural network

Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

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