Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

Daqrouq, Khaled; Sweidan, Husam I.; Balamesh, Ahmed; Ajour, Mohammed N.

doi:10.3390/e19060252

Cited by 31 publications

(24 citation statements)

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“…In our system, we implement PNN as the main classifier. PNN was used for pattern recognition applications as signature recognition [34] and speech recognition [35]. To use different classification methods using classification learner apps in the MATLAB software is implemented for comparison.…”

Section: Experiments 4: Classification Methodsmentioning

confidence: 99%

The Study of Recognition Methods Based on Wavelet Transform for Melanoma Detection

Almarei

Daqrouq

2020

JERR

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Skin cancer is one of the most cancers occurring in the world. Malignant melanoma is the most skin cancer type causing death around the world. Melanoma could be treated 100% if they are detected at earlier stages. In this paper, various melanoma detection systems were reviewed according to the year of publishing. All reviewed papers were based on feature extraction methods using wavelet transform (WT) in its two versions: Discrete wavelet transform (DWT), and wavelet packet transform (WPT) for melanoma recognition. Our methodology that was based on the WPT feature extraction and probabilistic neural network (PNN) was used for comparison. The ISIC database was used for differentiating between malignant (1110 images) and benign (1110 image) tumors. A (75% training /25% testing) verification system was applied. Many experiments were conducted using different parameters for each experiment. The support vector machine classifier (SVM) was the most common classifier combined with various types of wavelet features that have appeared in many kinds of literature during the last two decades, which achieved relatively the best accuracy ranged between [76% -98.29%]. In this paper, our combination method of the WPT and entropy was proposed and evaluated. Several experiments were conducted for testing. A Almarei and Daqrouq; JERR, 11(3): 46-61, 2020; Article no.JERR.55316 47 comparison manner was used for discussion of the investigation. The proposed method was an excellent detection method for melanoma regarding the complexity, where no preprocessing stage was conducted. Original Research Article

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Section: Experiments 4: Classification Methodsmentioning

confidence: 99%

The Study of Recognition Methods Based on Wavelet Transform for Melanoma Detection

Almarei

Daqrouq

2020

JERR

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“…The level of DWT was chosen empirically for the best performance. The determination of DWT of level 5 was adopted as a tradeoff between better performance and less dimensionality of the feature vector [24].…”

Section: The Methodsmentioning

confidence: 99%

The Use of LPC and Wavelet Transform for Influenza Disease Modeling

Daqrouq

Ajour

2018

Entropy

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Abstract:In this paper, we investigated the modeling of the pathological features of the influenza disease on the human speech. The presented work is novel research based on a real database and a new combination of previously used methods, discrete wavelet transform (DWT) and linear prediction coding (LPC). Three verification system experiments, Normal/Influenza, Smokers/Influenza, and Normal/Smokers, were studied. For testing the proposed pathological system, several classification scores were calculated for the recorded database, from which we can see that the proposed method achieved very high scores, particularly for the Normal with Influenza verification system. The performance of the proposed system was also compared with other published recognition systems. The experiments of these schemes show that the proposed method is superior.

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“…erefore, the entropy values of mrDMD modes can be used to distinguish different fault types. Here, we take approximate entropy (ApEn) [41] as a vibration signal fault pattern recognizer and setting the entropy values as the input vector of the back propagation neural network (BPNN) [42] to perform fault classification. In summary, a fault diagnosis scheme based on mr-tlsDMD and ApEn is presented for one-dimensional mechanical vibration signal' fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method for One-Dimensional Vibration Signal Based on Multiresolution tlsDMD and Approximate Entropy

Dang

et al. 2019

Shock and Vibration

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Dynamic mode decomposition (DMD) has certain advantages compared with the traditional fault signal diagnosis method. By exploiting the strength of DMD algorithm in signal processing, this paper proposes a joint fault diagnosis scheme to extract the spatial and temporal patterns and evaluate them for the complexity to diagnose the fault for one-dimensional mechanical signal. The multiscale method is adopted to decompose the reconstructed matrix of standard DMD modes into multiple scales with a given level parameter. Total least squares DMD algorithm is performed on each level to solve the noise sensitivity problem. Approximate entropy (ApEn) is performed on the grouped multiscale spatiotemporal modes that represent the dynamic characteristic information of the original signal. ApEn values are used as a fault recognizer to identify fault types. By applying the algorithm on three experimental mechanical vibration data, we verify the effectiveness of the proposed method. The result demonstrates that the proposed scheme can effectively recognize different fault forms as a fault diagnosis method.

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Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

Cited by 31 publications

References 50 publications

The Study of Recognition Methods Based on Wavelet Transform for Melanoma Detection

The Study of Recognition Methods Based on Wavelet Transform for Melanoma Detection

The Use of LPC and Wavelet Transform for Influenza Disease Modeling

A Fault Diagnosis Method for One-Dimensional Vibration Signal Based on Multiresolution tlsDMD and Approximate Entropy

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