Real-Time Color Image Classification Based On Deep Learning Network

Abed, Mohammed Hamzah; Al-Rammahi, Atheer Hadi Issa; Radif, Mustafa

doi:10.35741/issn.0258-2724.54.5.23

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…The softmax layer produces the distribution over the 1000 class labels. Relu is applied after each convolutional and fully connected layer [29,30]. Table 2 illustrates the accuracy of vein palm image classification based on AlexNet.…”

Section: Results and Experimentalmentioning

confidence: 99%

Palm Vein Identification Based on Hybrid Feature Selection Model

Alfoudi¹

2021

IJIES

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Palm Vein Identification (PVI) is a modern biometric security technique used for enhancing security and authentication systems. The key characteristics of palm vein patterns include its uniqueness to each individual, its unforgettability, non-intrusiveness and its ability for disallowing unauthorized persons. However, the extracted features from the palm vein patterns are huge with high redundancy. In this paper, we propose a combined model of two-Dimensional Discrete Wavelet Transform, Principal Component Analysis (PCA), and Particle Swarm Optimization (PSO) (2D-DWTPP) that feeds wrapper model with an optimal subset of features to enhance the prediction accuracy of -palm vein patterns. The 2D-DWT extract features from palm vein images, using the PCA to reduce the redundancy in palm vein features. The system has been trained to select high recognition features based on the wrapper model. The proposed system uses four classifiers as an objective function to determine PVI which include Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT) and Naïve Bayes (NB). The empirical results proved that the proposed model has the best results with SVM. Moreover, our proposed 2D-DWTPP model has been evaluated and the results show remarkable efficiency in comparison with AlexNet and other classifiers without feature selection. Experimentally, the proposed model has better accuracy as reflected by 98.65% whereas AlexNet has 63.5% accuracy and the classifier without feature selection process has 78.79% accuracy.

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Section: Results and Experimentalmentioning

confidence: 99%

Palm Vein Identification Based on Hybrid Feature Selection Model

Alfoudi¹

2021

IJIES

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“…The accuracy improvement of Convolutional Neural Networks (CNN) is still an interesting study for scholars. CNN is superior performance in computer vision, especially in image recognition [1]- [3]. Furthermore, CNN benefits from high computation [4], a dominant deep learning technique [5], and rich hyperparameter [6], [7], [8] as an advantage of CNN.…”

Section: Introductionmentioning

confidence: 99%

Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition

Munsarif

Noersasongko²,

Andono³

et al. 2023

Int. J. Adv. Intell. Informatics

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Convolutional Neural Networks (CNNs) perform well compared to other deep learning models in image recognition, especially in handwritten alphabetic numeral datasets. CNN's challenging task is to find an architecture with the right hyperparameters. Usually, this activity is done by trial and error. A genetic algorithm (GA) has been widely used for automatic hyperparameter optimization. However, the original GA with fixed chromosome length allows for suboptimal solution results because CNN has a variable number of hyperparameters depending on the depth of the model. Previous work proposed variable chromosome lengths to overcome the drawbacks of native GA. This paper proposes a variable length GA by adding global hyperparameters, namely optimizer and learning speed, to systematically and automatically tune CNN hyperparameters to improve performance. We optimize seven hyperparameters, such as the learning rate. Optimizer, kernel, filter, activation function, number of layers and pooling. The experimental results show that a population of 25 produces the best fitness value and average fitness. In addition, the comparison results show that the proposed model is superior to the basic model based on accuracy. The experimental results show that the proposed model is about 99.18% higher than the baseline model.

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“…The experimental results show that the application of a deep learning co product artificial neural network model in image processing can be improved more effectively by using the appropriate algorithm. [11] proposed an attractive model for a real-time image classification architecture based on deep learning with fully connected layers to extract precise features. [12] conducted a study on the use of machine learning in recognizing, detecting, classifying images in various fields.…”

Section: Introductionmentioning

confidence: 99%

Classification of Rice Growth Stage on UAV Image Based on Convolutional Neural Network Method

Sunarya

Treman

Nugraha

2023

j. nas. pendidik. teknik. inform.

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Currently, the majority of the agricultural sector in Indonesia is carried out by small communities. Half of the Indonesian people (approximately 10 million people) work in the agricultural sector and utilize agricultural land. Some of the tools used by farmers are still using traditional tools, but some are already using modern farming tools. In general, agricultural tools are divided into 3 categories, namely agricultural tools used before the seeds are planted, agricultural tools used when caring for seedlings that are growing and developing, and agricultural tools used when harvesting. One of the technologies used in agriculture is the use of drones or Unmanned Aerial Vehicles (UAV) in the process of sowing fertilizers and seeds and spraying pesticides. The current use of UAVs supports agriculture with manual operation and based on GPS waypoint positioning. In the process, the visual aspects that can be obtained from the UAV have not been considered, so the treatment carried out on agricultural land is the same. The problem of similarity in treatment can lead to similar treatment on heterogeneous agricultural land. Agricultural land should be treated according to the conditions of the land. Because the condition of the land will affect the growth of the planted vegetation. Another problem found in agricultural land is the different rice growth in each paddy field. Rice growth can be seen by farmers through visual aspects but farmers cannot directly see the visual condition of rice growth as a whole because of the large area of land. Utilization of UAV by taking high-resolution aerial imagery can provide visuals of the overall condition of rice from various angles of image capture. The general objective of this research is to classify rice growth on high resolution UAV images based on the Convolutional Neural Network (CNN). The data used in this study were acquired using a multirotor UAV in the same rice field area. The data consists of 500 images consisting of 5 groups. Group 1-2 is the vegetative phase, group 3 is the generative phase and group 4-5 is the ripening phase. CNN is used to conduct training with variations of epochs are 100, 250 and 500. The best accuracy results are obtained in the training epoch 500 with 96% of Accuration

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Real-Time Color Image Classification Based On Deep Learning Network

Cited by 6 publications

References 17 publications

Palm Vein Identification Based on Hybrid Feature Selection Model

Palm Vein Identification Based on Hybrid Feature Selection Model

Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition

Classification of Rice Growth Stage on UAV Image Based on Convolutional Neural Network Method

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