Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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Lokesha Weerasinghe
Maintaining the attendance database of thousands of students has become a tedious task in the universities in Sri Lanka. This paper comprises of 3 phases: signature extraction, signature recognition, and signature verification to automate the process. We applied necessary image processing techniques, and extracted useful features from each signature. Support Vector Machine (SVM), multiclass Support Vector Machine and Kolmogorov Smirnov test is used to signature classification, recognition, and verification respectively. The described method in this report represents an effective and accurate approach to automatic signature recognition and verification. It is capable of matching, classifying, and verifying the test signatures with the database of 83.33%, 100%, and 100% accuracy respectively.
Lokesha Weerasinghe. 2019. \u201cAn Efficient Automated Attendance Entering System by Eliminating Counterfeit Signatures using Kolmogorov Smirnov Test\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 19 (GJCST Volume 19 Issue G2): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 102
Country: Sri Lanka
Subject: Global Journal of Computer Science and Technology - G: Interdisciplinary
Authors: Lokesha Weerasinghe, B.H. Sudantha (PhD/Dr. count: 0)
View Count (all-time): 258
Total Views (Real + Logic): 5219
Total Downloads (simulated): 1382
Publish Date: 2019 05, Mon
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Neural Networks and Rules-based Systems used to Find Rational and
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Maintaining the attendance database of thousands of students has become a tedious task in the universities in Sri Lanka. This paper comprises of 3 phases: signature extraction, signature recognition, and signature verification to automate the process. We applied necessary image processing techniques, and extracted useful features from each signature. Support Vector Machine (SVM), multiclass Support Vector Machine and Kolmogorov Smirnov test is used to signature classification, recognition, and verification respectively. The described method in this report represents an effective and accurate approach to automatic signature recognition and verification. It is capable of matching, classifying, and verifying the test signatures with the database of 83.33%, 100%, and 100% accuracy respectively.
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