scholarly journals An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

2022 ◽  
Vol 9 (1) ◽  
pp. 99-109
Author(s):  
Jindal et al. ◽  
Keyword(s):  
Decision Tree ◽  
Integrated Approach ◽  
Signature Verification ◽  
Automated Verification ◽  
Support Vector ◽  
Learning Support ◽  
Static Characteristics ◽  
Multiple Classifiers ◽  
Verification System ◽  
Multi Classification

A signature is a handwritten representation that is commonly used to validate and recognize the writer individually. An automated verification system is mandatory to verify the identity. The signature essentially displays a variety of dynamics and the static characteristics differ with time and place. Many scientists have already found different algorithms to boost the signature verification system function extraction point. The paper is aimed at multiplying two different ways to solve the problem in digital, manual, or some other means of verifying signatures. The various characteristics of the signature were found through the most adequately implemented methods of machine learning (support vector and decision tree). In addition, the characteristics were listed after measuring the effects. An experiment was performed in various language databases. More precision was obtained from the feature.

SVM-DSmT COMBINATION FOR THE SIMULTANEOUS VERIFICATION OF OFF-LINE AND ON-LINE HANDWRITTEN SIGNATURES

2012 ◽  
Vol 11 (03) ◽  
pp. 1250018 ◽  
Author(s):  
NASSIM ABBAS ◽  
YOUCEF CHIBANI
Keyword(s):  
Function Theory ◽  
Belief Function ◽  
Signature Verification ◽  
Support Vector ◽  
Verification System ◽  
Belief Function Theory ◽  
On Line ◽  
Handwritten Signature ◽  
Belief Models ◽  
False Acceptance

A combination handwritten signature verification system is proposed for managing conflicts provided from each individual off-line and on-line support vector machine (SVM), respectively. Basically, the system is divided into three parts: (i) Off-line verification system, (ii) on-line verification system and (iii) combination module using belief function theory. The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Combination is performed using belief models such as Dempster-Shafer (DS) rule and proportional conflict redistribution (PCR) rule followed by the likelihood ratio-based decision making. Experiments are conducted on the well-known NISDCC signature collection using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework using Dezert-Smarandache (DSm) theory yields the best verification accuracy even when individual off-line and on-line classifications provide conflicting results.

scholarly journals Offline Signature Verification System Using SVM Classifier with Image Pre-processing Steps and SURF Algorithm

2021 ◽  
Vol 2107 (1) ◽  
pp. 012069
Author(s):  
Li Wen Goon ◽  
Swee Kheng Eng
Keyword(s):  
Feature Extraction ◽  
Noise Removal ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Speeded Up Robust Features ◽  
Verification System ◽  
Offline Signature Verification ◽  
Average Accuracy ◽  
Processing Steps

Abstract A signature is a mark or name that represents the identity of the people and the Signature Verification System (SVS) is used to validate the identity of people. The signature verification system is mostly used for bank cheques, vouchers, intelligence agencies and others. There are two types of SVS which are online and offline signature verification systems. The paper deals with an offline signature verification system. The proposed system consists of four main stages, (i) image acquisition, (ii) image pre-processing, (iii) feature extraction and (iv) classification. The image pre-processing steps involved binarization, noise removal using Gaussian filter and image resizing and thinning. In the feature extraction stage, Bag-of-Features with the Speeded Up Robust Features (SURF) extractor was utilized. In the third stage, the Support Vector Machine (SVM) classifier is used. Lastly, the confusion matrix and the verification rate were used to evaluate the performance of the classifier. In this paper, we implement and compare the performance of the signature verification system without entering the user ID and the signature verification system entering the user ID. For the ratio of 75% and 25% of the training and testing, respectively, the average accuracy for the signature verification system without entering the user ID is 71.36%, whereas the average accuracy for the signature verification system entering the user ID is 79.55%.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

The Influence of Template Ageing on a Dynamic Signature Verification System

2014 ◽  
Vol 24 ◽  
pp. 47-52
Author(s):  
Joanna Putz-Leszczynska
Keyword(s):  
Potential Risk ◽  
Signature Verification ◽  
Verification System ◽  
Verification Systems ◽  
Dynamic Signature

This paper addresses template ageing in automatic signature verification systems. Handwritten signatures are a behavioral biometric sensitive to the passage of time. The experiments in this paper utilized a database that contains signature realizations captured in three sessions. The last session was captured seven years after the first one. The results presented in this paper show a potential risk of using an automatic handwriting verification system without including template ageing Purchase Article for $10 

scholarly journals A New Integrated Approach for Municipal Landfill Siting Based on Urban Physical Growth Prediction: A Case Study Mashhad Metropolis in Iran

2021 ◽  
Vol 13 (5) ◽  
pp. 949
Author(s):  
Salman Qureshi ◽  
Saman Nadizadeh Shorabeh ◽  
Najmeh Neysani Samany ◽  
Foad Minaei ◽  
Mehdi Homaee ◽  
...  
Keyword(s):  
Urban Growth ◽  
Integrated Approach ◽  
Physical Growth ◽  
Support Vector ◽  
Markov Chain Method ◽  
Physical Expansion ◽  
Hierarchy Process ◽  
The City

Due to irregular and uncontrolled expansion of cities in developing countries, currently operational landfill sites cannot be used in the long-term, as people will be living in proximity to these sites and be exposed to unhygienic circumstances. Hence, this study aims at proposing an integrated approach for determining suitable locations for landfills while considering their physical expansion. The proposed approach utilizes the fuzzy analytical hierarchy process (FAHP) to weigh the sets of identified landfill location criteria. Furthermore, the weighted linear combination (WLC) approach was applied for the elicitation of the proper primary locations. Finally, the support vector machine (SVM) and cellular automation-based Markov chain method were used to predict urban growth. To demonstrate the applicability of the developed approach, it was applied to a case study, namely the city of Mashhad in Iran, where suitable sites for landfills were identified considering the urban growth in different geographical directions for this city by 2048. The proposed approach could be of use for policymakers, urban planners, and other decision-makers to minimize uncertainty arising from long-term resource allocation.

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