WITHDRAWN: Role of convolutional neural networks for any real time image classification, recognition and analysis

Kumar, K. Kranthi; Kumar, Mahesh; Samsonu, Ch.; Krishna, K. Vamshi

doi:10.1016/j.matpr.2021.02.186

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…It moves progressively from left to right for each row until reaching the picture extremity. A convolution calculation is performed for each position to obtain an activation map that indicates the location of the features in the picture: As the card value increases, the more similar the image part scanned to the selected element becomes [19,44]. The following equation shows the convolution product performed:…”

Section: Convolution Layermentioning

confidence: 99%

Enhanced motor fault detection system based on a dual-signature image classification method using CNN

2023

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This paper proposes a new Motor Image Classification (MIC) approach based on a multi-signal conversion technique using Convolutional Neural Network (CNN). In this regard, two one-dimensional (1D) signals are combined and converted into a (2D) color image with motor information pixels. Initially, the vibration signal is converted into the frequency domain. Each point of this signal is firstly assigned a color according to its amplitude and then placed successively on a specific column to obtain a pixilated image. An outline is added to the image representing the internal motor temperature. Therefore, the vibratory and thermal situation of the engine is clearly represented in a Dual-Signature Image (DSI). Our system proves the efficiency of the color compared to grayscale images. It ensures fast and effective prevention, which results in a long service lifetime and maximum motor availability. The diagnostic success rate of our system is 99.93%.

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Section: Convolution Layermentioning

confidence: 99%

Enhanced motor fault detection system based on a dual-signature image classification method using CNN

2023

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“…Each forward pass through the network results in a certain parameterized loss function. Each of the gradients created for each of the weights is multiplied by a certain learning rate, to move our weights in whatever direction its gradient is pointing (11,19) . Dropping out: It is to be noted that dropout is applied at the final stage during the training phase.…”

Section: Flattening Layermentioning

confidence: 99%

“…To stable the network during the training and validation phase of the model, we need to normalize the input and adjust the scale since it is used to speed up the training process and allows every layer of the network to learn by itself. But the batch size depends on the amount of dataset if the amount of images is high it needs a large value of batch size and the reverse is true (19) .…”

Section: Flattening Layermentioning

confidence: 99%

Flower Disease Diagnosis ModelUsing Deep Convolutional Neural Network

Getnet,

Ashebir,

Gardie

2023

IJST

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Objective: Design a convolutional neural network model for flower disease detection that can recognize and categorize flower diseases according to their disease types. Method: A 10-way SoftMax classification system is used to group the majority of diseases that affect flowers into distinct disease classes (normal, powdery mildew, rose aphid, botrytis blight, downey mildew, red spider mites, Japanese beetles, rose resettle, gray mold, and black spot). A total of 4200 images-70% for training, 15% for validation, and 15% for testing-were used to train and validate the model's performance. From the Tana Flora flower cultivation center in Bahir Dar Amhara, Ethiopia, we obtained a dataset. The proposed model was trained by generating additional images using image augmentation techniques to overcome over fitting problem. Findings: Our flower disease identification model produced cutting-edge results with a test accuracy of 94.67%. When used on the same dataset, state-of-the-art models like Alex Net's test accuracy was 85.6%, Google Net's test accuracy was 90.98, and VGG19's test accuracy was 89.3%. Our flower disease detection model has been shown to be faster to train and has a smaller model size thanks to the median filter that we used to improve the quality of the photos and a novel segmentation method that fit our dataset. Novelty: We proposed a new CNN architecture and new segmentation algorithm to identify flower disease. Through our experiments, we have shown median filtering, and ways of segmentation improve the performance of our model.

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“…Deep learning has improved pattern recognition technology in the field of speech and vision. In addition, a convolutional neural network (CNN) has made a breakthrough in image recognition [ 12 , 13 ]. It provides a new idea for the identification of coal gangue [ 14 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

Coal gangue recognition based on spectral imaging combined with XGBoost

Zhou

Lai

2023

PLoS ONE

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The identification of coal gangue is of great significance for its intelligent separation. To overcome the interference of visible light, we propose coal gangue recognition based on multispectral imaging and Extreme Gradient Boosting (XGBoost). The data acquisition system is built in the laboratory, and 280 groups of spectral data of coal and coal gangue are collected respectively through the imager. The spectral intensities of all channels of each group of spectral data are averaged, and then the dimensionality is reduced by principal component analysis. XGBoost is used to identify coal and coal gangue based on the reduced dimension spectral data. The results show that PCA combined with XGBoost has the relatively best classification performance, and its recognition accuracy of coal and coal gangue is 98.33%. In this paper, the ensemble-learning algorithm XGBoost is combined with spectral imaging technology to realize the rapid and accurate identification of coal and coal gangue, which is of great significance to the intelligent separation of coal gangue and the intelligent construction of coal mines.

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WITHDRAWN: Role of convolutional neural networks for any real time image classification, recognition and analysis

Cited by 10 publications

References 10 publications

Enhanced motor fault detection system based on a dual-signature image classification method using CNN

Enhanced motor fault detection system based on a dual-signature image classification method using CNN

Flower Disease Diagnosis ModelUsing Deep Convolutional Neural Network

Coal gangue recognition based on spectral imaging combined with XGBoost

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