An accurate signature verification system based on proposed HSC approach and ANN architecture

Kadhm, Mustafa Salam; Mohammed, Mamoun Jassim; Ayad, Hayder

doi:10.11591/ijeecs.v21.i1.pp215-223

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Our model overcomes the pre-trained model. [36] 98.49% -Jampour and Naserasadi (2019) [37] 98.76% -Li Liu et al (2021) [38] -6.74 Maruyama et al (2021) [39] -0.82 Alsuhimat and Mohamad [40] 87.7% 11.40 Kadhm et al [41] 99.7% -Proposed Method 100% 0.0…”

Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =mentioning

confidence: 99%

Signature verification based on proposed fast hyper deep neural network

Hashim,

Mohsin,

Alkhayyat

2024

IJ-AI

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<span>Many industries have made widespread use of the handwittern signature verification system, including banking, education, legal proceedings, and criminal investigation, in which verification and identification are absolutely necessary. In this research, we have developed an accurate offline signature verification model that can be used in a writer-independent scenario. First, the handwitten signature images went through four preprocessing stages in order to be suitable for finding the unique features. Then, three different types of features namely principal component analysis (PCA) as appearance-based features, gray-level co-occurrence matrix (GLCM) as texture-features, and fast Fourier transform (FFT) as frequency-features are extracted from signature images in order to build a hybrid feature vector for each image. Finally, to classify signature features, we have designed a proposed fast hyper deep neural network (FHDNN) architecture. Two different datasets are used to evaluate our model these are SigComp2011, and CEDAR datasets. The results collected demonstrate that the suggested model can operate with accuracy equal to 100%, outperforming several of its predecessors. In the terms of (precision, recall, and F-score) it gives a very good results for both datasets and exceeds (1.00, 0.487, and 0.655 respectively) on Sigcomp2011 dataset and (1.00, 0.507, and 0.672 respectively) on CEDAR dataset.</span>

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Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =mentioning

confidence: 99%

Signature verification based on proposed fast hyper deep neural network

Hashim,

Mohsin,

Alkhayyat

2024

IJ-AI

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“…While ML models have been around for quite a long time, they have acquired energy with the ascent of AI. DL models are presently utilized in a large portion of intelligent AI applications [33], [34].…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Data Processing Method for Artificial Neural Network ANN Based Micro grid Protection Model

Baidya

Nandi

2023

EAE

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Effective fault detection and isolation technologies are very necessary for uninterrupted power supply and for making a flexible protection scheme. Almost all protection schemes in the power system are based on data exchange among protection units through a strong communication structure. Thus, it is important to deal with a large amount of data. Artificial Intelligence (AI) is one of the key factors in this regard. AI has several sections and Artificial Neural Network (ANN) is one of them. It is suggested to implement the ANN-based models while working with big data. The existing protection models are facing difficulties while trying to deal with big data. Thus ANN-based approaches have come into the front line in advanced power system networks. The performance of the ANN model is depending on the training of the data set. Hence in this work, we are focusing on preparing the data to provide input in the ANN model. The principal component analysis (PCA) method is applied here for reduced the dimension of a large number of data sets. The new data set is used to run the k-means clustering algorithm. It is shown that the clustering is more accurate with the processed data set by PCA. Therefore, the prepared data set is used to run the ANN model that has a smaller size with higher information and minimum computational time. This study shows the data preparation part to train the ANN model.

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“…ANN's are machine learning models which can be used for classification purposes. An ANNs model contains multiple units (layers) for information processing The layers are typically named as input layer, hidden layer, and output layer [7], [20]. ANNs collect the knowledge through detecting the patterns and relationships in data and learning or training through experience.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Deep convolutional neural network model for bad code smells detection based on oversampling method

Khleel

Nehéz²

2022

IJEECS

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Code <span>smells refers to any symptoms or anomalies in the source code that shows violation of design principles or implementation. Early detection of bad code smells improves software quality. Nowadays several artificial neural network (ANN) models have been used for different topics in software engineering: software defect prediction, software vulnerability detection, and code clone detection. It is not necessary to know the source of the data when using ANN models but require large training sets. Data imbalance is the main challenge of artificial intelligence techniques in detecting the code smells. To overcome these challenges, the objective of this study is to presents deep convolutional neural network (D-CNN) model with synthetic minority over-sampling technique (SMOTE) to detect bad code smells based on a set of Java projects. We considered four code-smell datasets which are God class, data class, feature envy and long method and the results were compared based on different performance measures. Experimental results show that the proposed model with oversampling techniques can provide better performance for code smells detection and prediction results can be further improved when the model is trained with more datasets. Moreover, more epochs and hidden layers help increase the accuracy of the model.</span>

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An accurate signature verification system based on proposed HSC approach and ANN architecture

Cited by 6 publications

References 15 publications

Signature verification based on proposed fast hyper deep neural network

Signature verification based on proposed fast hyper deep neural network

Data Processing Method for Artificial Neural Network ANN Based Micro grid Protection Model

Deep convolutional neural network model for bad code smells detection based on oversampling method

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