A Novel Strategy for Improving the Counter Propagation Artificial Neural Networks in Classification Tasks

Belattar, Sara; Abdoun, Otman; Haimoudi, El Khatir

doi:10.24138/jcomss-2021-0121

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…The development of computer vision algorithms through the use of neural networks attracts great attention of scientists. In works [7,8], the use of an artificial neural network of counter-propagation (CP-ANN), which has such advantages as the ability to learn and classify, is considered. CP-ANN neural networks still have some limitations in pattern recognition tasks when they encounter ambiguity during the learning process, leading to incorrect classification of the self-organizing Kohonen map (K-SOM).…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Recognition of aerial photography objects based on data sets with different aggregation of classes

Prystavka

Dukhnovska

Kovtun

et al. 2023

EEJET

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The object of this work is the recognition algorithms of aerial photography objects, namely, the analysis of recognition accuracy based on data sets with different aggregation classes. To solve this problem, an information system for object recognition based on aerial photography data has been developed. An architecture based on neural network architectures of the ConvNets group with structural modifications was chosen and used to create the information system. The use of a convolutional neural network of the ConvNets group in the architecture of the information system for the recognition of objects of aerial photography gives high accuracy rates when training the information system and validating its results. But the authors did not find any studies on the learning of the neural network of the ConvNets group. Therefore, it was decided to conduct an analysis in which case the ConvNets network will provide validation results with higher accuracy when the training takes place on datasets with or without class aggregation. The authors performed an analysis of the accuracy of recognition of aerial photography objects based on data sets with different aggregation classes. The dataset used for neural network training consisted of 3-channel labeled images of 64x64 pixels size. Based on the analysis, the optimal number of epochs for training is selected, which makes it possible to recognize aerial photography objects with greater accuracy and speed. It was concluded that greater accuracy in image classification is achieved for sampling without crossing data from different classes (without aggregation of classes). The result of the work is recommended for use in the automation of dataset filling and information filtering of visual images

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Recognition of aerial photography objects based on data sets with different aggregation of classes

Prystavka

Dukhnovska

Kovtun

et al. 2023

EEJET

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“…Artificial intelligence (AI) has been widely applied in various areas, and it has emerged as a critical tool for the advancement of the world [15]. In the agriculture field, AI helps the farmers to use the intelligence systems and technologies thanks to their intelligence features compared to traditional techniques [16]- [18].…”

Section: Convolutional Neural Network Cnnmentioning

confidence: 99%

Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis

Belattar

Abdoun

Haimoudi

2022

IJEECS

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Agriculture is an important sector for developing countries and farmers. Recently, numerous techniques for increasing agricultural productivity have been utilized. However, different issues are still encountered by farmers including various plant diseases. Plant diseases diagnoses are challenging research, and they should be analyzed and treated by detecting the diseased plant leaves. For that reason, in this paper, we develop our proposed architecture using convolutional neural networks (OP-CNN) as a computer-aided to detect and diagnose plant diseases. The proposed architecture can assist farmers in increasing both the quantity and quality of their agricultural productivity. Besides this, the OP-CNN helps to reduce disease prevalence through early detection. The performance of our proposed model is compared with other convolutional neural networks (CNN) architectures in order to validate its capability. The strawberry dataset was employed to train and test the models since the strawberry is one of the main crops in the Larache Province (Morocco). The experimental tests demonstrate that our proposed OP-CNN reaches the highest values versus DenseNet121, VGG19, and ResNet50 with 100%, 99%, 97%, and 63% respectively for classification accuracy, 100%, 100%, 98% and, 79% respectively for precision, 100%, 99%, 97%, and 63% respectively for recall, and 100%, 99%, 97%, and 58% respectively for "F" _1Score.

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Analysis and Comparison Between Artificial Neural Network Models in Image Recognition

Belattar

Abdoun

Haimoudi

2023

Lecture Notes in Mechanical Engineering

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A Novel Strategy for Improving the Counter Propagation Artificial Neural Networks in Classification Tasks

Cited by 4 publications

References 35 publications

Recognition of aerial photography objects based on data sets with different aggregation of classes

Recognition of aerial photography objects based on data sets with different aggregation of classes

Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis

Analysis and Comparison Between Artificial Neural Network Models in Image Recognition

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