Large-scale offline signature recognition via deep neural networks and feature embedding

Çalık, Nurullah; Kurban, Onur Can; Yılmaz, Ali Rıza; Yıldırım, Tülay; Durak–Ata, Lütfiye

doi:10.1016/j.neucom.2019.03.027

Cited by 41 publications

(21 citation statements)

References 30 publications

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“…Although some training examples produce a positive input to a ReLU of a neuron, causing the learning process to happen in that neuron, we still find the importance of local normalization scheme in generalization aids. The batch normalization that determines the mean and variance for the input feature x is determined as in (Calik et al, 2019) by which the mean of the expected value of x is determined. Moreover, the variance is the expected value the determined as the square of each enrolled features x subtracted from the mean of the whole features .…”

Section: Computer Sciencementioning

confidence: 99%

Peer Review #2 of "COVID-19: a new deep learning computer-aided model for classification (v0.1)"

2021

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Chest X-ray (CXR) image is one of the most feasible diagnosis modalities to early detect the infection of COVID-19 viruses, which is classified as a pandemic, according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this paper, a model is proposed for analyzing and evaluating grayscale Chest X-Ray images called Chest X-Ray COVID Network (CXRVN) based three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers; mini-batch gradient descent and Adam optimizer, are applied, and the model has almost the same performance. Besides, CXRVN accepts CXR image in grayscale that is a perfect image representation for CXR and consumes less memory storage and processing time.Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet, and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model is evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score, and

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Section: Computer Sciencementioning

confidence: 99%

Peer Review #2 of "COVID-19: a new deep learning computer-aided model for classification (v0.1)"

2021

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“…With the recent development in computer systems, the application of deep neural networks becomes more common in the modeling of complex and enormous sized problems such as large‐scale off‐line signature recognition, large‐scale sentiment classification, metaphase finding, temporal modeling approaches for large‐scale YouTube‐8m video, segmentation of precursor lesions in cervical cancer, and signature recognition application . Convolutional neural network (CNN) is a class of deep neural networks, inspired by the biological process of animals visual cortex.…”

Section: Ai‐based Regression Algorithmsmentioning

confidence: 99%

Application of artificial intelligence algorithms on modeling of reflection phase characteristics of a nonuniform reflectarray element

Mahoutı

2019

Int J Numerical Modelling

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Reflectarray antennas (RAs) have the ability to combine the advantages of both traditional parabolic reflector and phased array antennas without the need for feed network designs. Microstrip reflectarrays (MRAs) have the advantages of being small size, light weighted, easy to prototyped, high gain, low side-lobe level, and a predetermined radiation pattern. These can be achieved by precise calculation of reflection phase at each RA unit independently with a phase compensation proportional to the distance from the feed. The challenging problem is to have a fast and high accurate unit element to be used in multidimension, multiobjective design optimization. Herein, artificial intelligence algorithms (AIAs) have been used for prediction of reflection phase characterization of an X band MRA unit element with respect to the geometrical design parameters. Firstly, a nonuniform unit RA has been designed in 3D electromagnetic (EM) simulation tool for creating the training validation data sets. Then, the data sets are given to the different types of AIA regression models such as multilayer perceptron, symbolic regression, and convolutional neural network. From the results of the validation data set, it can be concluded that the proposed models have sufficient accuracy that can be used in a computationally efficient design optimization process of a large-scale RA design.

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“…Deep Convolution neural network named AlexNet [8]. Convolution neural network has three layers as shown in Figure 5.…”

Section: B Feature Extraction Features Are Extracted Frommentioning

confidence: 99%

“…Euclidean distance classifier is used for verification purpose. An architecture based on CNN called Large Scale Signature Network is proposed in[8] for signature verification. Batch normalization is used to improve the performance.…”

mentioning

confidence: 99%

Offline Signature Recognition u sing Pretrained Convolution Neural Network Model

Kumari¹,

Rana²

2019

IJEAT

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Offline Signature recognition plays an important role in Forensic issues. In this paper, we explore Signature Identification and Verification using features extracted from pretrained Convolution Neural Network model (Alex Net). All the experiments are performed on signatures from three dataset (SigComp2011) (Dutch, Chinese), SigWiComp2013 (Japanese) and SigWIcomp2015 (Italian). The result shows that features extracted from pretrained Deep Convolution neural network and SVM as classifier show better results than that of Decision Tree. The accuracy of more than 96% for Japanese, Italian, Dutch and Chinese Signatures is obtained with Deep Convolution neural network and SVM as classifier.

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Large-scale offline signature recognition via deep neural networks and feature embedding

Cited by 41 publications

References 30 publications

Peer Review #2 of "COVID-19: a new deep learning computer-aided model for classification (v0.1)"

Peer Review #2 of "COVID-19: a new deep learning computer-aided model for classification (v0.1)"

Application of artificial intelligence algorithms on modeling of reflection phase characteristics of a nonuniform reflectarray element

Offline Signature Recognition u sing Pretrained Convolution Neural Network Model

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