Offline Signature Recognition and Verification System using Efficient Fuzzy Kohonen Clustering Network (EFKCN) Algorithm

Suryani, Dewi; Irwansyah, Edy; Chindra, Ricki

doi:10.1016/j.procs.2017.10.025

Cited by 16 publications

(13 citation statements)

References 5 publications

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“…The objects (variables) for the factor analysis were scores of the fi rst to fourth year mechanic engineering students of the Bauman MSTU, the Gubkin University, and the MADI when answering the interviewing questions (No. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. SAP Statistica 10 was used for the analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Out of the cluster analysis varieties, Kohonen neural networks were used to make to improve the clustering accuracy based on a combination of linear and nonlinear relationships. Clustering is based on a minimization criterion of Euclidean distances between the objects of one cluster [12]: where d ij is the distance between the i-th and j-th objects (4)…”

Section: Methodsmentioning

confidence: 99%

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Development of reflective competence in students in technical universities of Russia

Панова¹,

Плугина²,

Kaminsky³

et al. 2018

J Appl Eng Science

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One of the main problems of the modern higher education system in the Russian Federation (RF) is underrepresentation of knowledge in the educational process as a factor that determines not only the content of thinking but also the personality content. Hence, it updates the issue of providing pedagogical conditions for the development of students' ability to refl ect, which causes quality changes in the self-awareness structure. Through expansion, its cognitive components ensure positive developments in self-formation of future specialists and their self-adjustment. Based on a literary review, the article analyzes and substantiates the refl ection concept content. By means of interviewing, the level of the refl ective competence in students in the process of solving problems was analyzed through the example of mechanic engineering students of Russian universities. Based on multidimensional factor analysis, structural (constructive and situational-destructive) factors of students' refl ection in the educational process were identifi ed. Student clusters were formed and substantiated by the growth rates of refl ective competence factors. The optimal development level of the situational-destructive factor in student refl ection in the process of solving problems was determined. Education technologies for developing refl ective competence in students were elaborated. Parameters of pedagogical refl ection development in the educational process were substantiated. A pedagogical experiment was conducted to assess the effectiveness of the proposed pedagogical conditions for the formation and development of refl ective competence in students in the Russian universities under study. The research of the problem of refl ection development in students in the higher education system of the Russian Federation makes a certain contribution to pedagogical science and teaching practice and opens up new opportunities for the student educational process.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Development of reflective competence in students in technical universities of Russia

Панова¹,

Плугина²,

Kaminsky³

et al. 2018

J Appl Eng Science

View full text Add to dashboard Cite

show abstract

“…There has been lot of work present in the literature [2, 6, 8, 13, 16-19, 23-26, 28, 32, 35, 38-41, 44-46]. The signature verification methods has been divided in three categories based on the classifier used, i.e., distance-based [19,38,41,42], SVM-based [8,39,46] and neural networkbased [2,4,25,36].…”

Section: Related Workmentioning

confidence: 99%

“…The authors have presented a clustering-based network for signature verification in [42]. They utilized the regular and center moment-based features of the signature images.…”

Section: Related Workmentioning

confidence: 99%

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Signature verification using geometrical features and artificial neural network classifier

Jain

Singh

2020

Neural Comput & Applic

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Signature verification has been one of the major researched areas in the field of computer vision. Many financial and legal organizations use signature verification as an access control and authentication. Signature images are not rich in texture; however, they have much vital geometrical information. Through this work, we have proposed a signature verification methodology that is simple yet effective. The technique presented in this paper harnesses the geometrical features of a signature image like center, isolated points, connected components, etc., and with the power of Artificial Neural Network (ANN) classifier, classifies the signature image based on their geometrical features. Publicly available dataset MCYT, BHSig260 (contains the image of two regional languages Bengali and Hindi) has been used in this paper to test the effectiveness of the proposed method. We have received a lower Equal Error Rate (EER) on MCYT 100 dataset and higher accuracy on the BHSig260 dataset. INTRODUCTIONBiometric plays a vital role in the authentication of an individual in many financial institutions, and signatures are the most widely used modality for this purpose.Biometrics is used to identify or verify an individual digitally. The security applications that have been used in many financial and educational institutions using biometrics technology for decades [17]. Biometrics are classified into two categories: physiological and behavioral [13]. Physiological biometrics includes Face, Iris, Fingerprint, etc. and behavioral biometrics has signature, gait, etc.. Authentication of the

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A Novel Technique for Fake Signature Detection Using Two-Tiered Transfer Learning

Kuriakose

Agarwal

Dixit

et al. 2021

Proceedings of International Conference on Computational Intelligence

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Offline Signature Recognition and Verification System using Efficient Fuzzy Kohonen Clustering Network (EFKCN) Algorithm

Cited by 16 publications

References 5 publications

Development of reflective competence in students in technical universities of Russia

Development of reflective competence in students in technical universities of Russia

Signature verification using geometrical features and artificial neural network classifier

A Novel Technique for Fake Signature Detection Using Two-Tiered Transfer Learning

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