A Multimodal Biometrics for Aadhaar Based Secured E-Voting System

False polling is still a significant issue in elections in the latest moments. In this job, an effort is made to fix this issue using current Aadhaar card database and electoral biometrics. This scheme authenticates electors by combining multimodal biometrics such as picture, eye, and palm printing, after which registration is verified by verifying the age that enables only qualified applicants to register. The time needed for authentication is decreased by using the corresponding Aadhaar amount and multimodal biometrics. This is authentication will be achieved by inspecting whether the registered Aadhaar amount and multimodal biometrics match or not without linking it with the entire biometric database to boost authentication pace. The polling of ballots will be performed automatically so that space is decreased and results can be announced in less moment. The improvisations strive to increase the system's safety, efficiency, efficiency, scalability. This suggested a safe internet e-voting system (EVS) that utilizes as its backend UIDAI or Aadhaar database. In this job, the entry pictures are originally preprocessed and the removal of features is achieved using Improved Gabor filters. Enhanced Support Vector Machine Algorithm (eSVM) is used to match and classify features development of this scheme will render the polling method more comfortable and can, therefore, contribute to enhanced turn-out. Using this multimodal biometric system for voting purposes, election rigging was easily avoided.

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Feature selection for support vector machine-based face-iris multimodal biometric system

Expert Systems with Applications ◽

10.1016/j.eswa.2011.02.155 ◽

2011 ◽

Vol 38 (9) ◽

pp. 11105-11111 ◽

Cited By ~ 42

Author(s):

Heng Fui Liau ◽

Dino Isa

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Support Vector ◽

Biometric System ◽

Multimodal Biometric System ◽

Selection For

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A Robust Multimodal Biometric System VIA Multiple Svms

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1042.09811s19 ◽

2019 ◽

Vol 8 (11S) ◽

pp. 203-209

Keyword(s):

Support Vector ◽

Biometric Authentication ◽

Biometric System ◽

Multimodal Biometrics ◽

Score Level Fusion ◽

Multimodal Biometric System ◽

Average Accuracy ◽

Vector Machines ◽

Final Score ◽

Matching Score

Generally single Support Vector Machine (SVM) is employed in existing multimodal biometric authentication techniques, and it assumes that whole set of the classifiers is available. But sometimes it is not possible due to some circumstances e.g. injury, some medical treatment etc. This paper includes a robust multimodal biometric authentication system that integrates FKP (Finger-Knuckle Print), face and fingerprint at matching score level fusion using multiple parallel Support Vector Machines (SVMs). Multiple SVMs are applied to overcome the problem of missing biometric modality. Every possible combination of three modalities (FKP, face and fingerprint) are taken into consideration and all combinations have a corresponding SVM to fuse the matching scores and produce the final score set for decision making. Proposed system is more flexible and robust as compared to existing multimodal biometric system with single SVM. The average accuracy of proposed system is estimated on a publicly available dataset with the use of MUBI tool(Multimodal Biometrics Integration tool) and MATLAB 2017b.

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

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.

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