Mosharaka for Research and Studies
Login/Sign Up
Main Menu
Papers Menu
Quick Links
Next Congress
International Congress on Engineering Technologies (EngiTek 2023)
19-21 June 2023, Irbid, Jordan

EnginApps 2023

MechaniTek 2023

ElectriTek 2023
EngiTek 2023 MainEngiTek 2023 CFPEngiTek 2023 SubmissionsEngiTek 2023 ProgramEngiTek 2023 Registration

All Forthcoming Congresses


All Forthcoming Conferences

Update on Tuesday, 1 February 2022: Paper 51.Cnf-17 reaches 462 views.
Papers Links
  • Browse
  • Subjects
  • Years
  • ElecEng
  • GC-ElecEng 2021
  • 63.Cnf-75
ElecEng Congresses with Published Papers
GC-ElecEng 2020, Valencia
GC-ElecEng 2021, Valencia
Papers Published at GC-ElecEng 2021
All 5 Papers
IDAuthors and TitlePages
43.Cnf-38 Mr. Aron Kondoro
Ms. Diana Rwegasira
Prof. Imed Dhaou
Prof. Hannu Tenhunen
Trends of using blockchain technology in the smart grid
63.Cnf-75 Mr. Andres Rojas
Prof. Gordana J. Dolecek
Evaluation of Supervised Machine Learning Classification Algorithms for Fingerprint Recognition
62.Cnf-106 Mr. Joan Carles Montero
Ms. Merce Feliu
Dr. Joan Bas
Optical Communications Through Low Orbit Satellites
63.Cnf-140 Dr. Mohammad Al-Mousa
Mr. Nael Sweerky
Dr. Ghassan Samara
Dr. Mohammed Alghanim
Ms. Abla Hussein
Mr. Braa Qadoumi
General Countermeasures of Anti-Forensics Categories
62.Cnf-143 Dr. Ghassan Samara
Mr. Mohammad Hussein
Dr. Khalid Alqawasmi
Alarm System at street junctions (ASSJ) to avoid accidents Using VANET system
63.Cnf-75 Paper View Page
Title Evaluation of Supervised Machine Learning Classification Algorithms for Fingerprint Recognition
Authors Mr. Andres Rojas, Instituto Nacional de Astrofísica, Puebla, Mexico
Prof. Gordana J. Dolecek, Instituto Nacional de Astrofísica, Puebla, Mexico
Abstract This paper presents the application of the Classification Learner MATLAB tool from the Statistics and Machine Learning Toolbox for the classification process in a fingerprint recognition system based on the set B from the public databases FVC2000, FVC2002, and FVC2004. The general results indicate that this system can achieve high accuracy values for several sub-databases using multiple supervised machine learning algorithms including decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, naive Bayes, and ensemble classifiers.
Track MLSP: Machine Learning for Signal Processing
Conference 2nd Mosharaka International Conference on Digital Signal Processing (MIC-Signals 2021)
Congress 2021 Global Congress on Electrical Engineering (GC-ElecEng 2021), 10-12 December 2021 (Remotely), Valencia, Spain
Pages 8-11
Topics Fingerprint Recognition
Supervised Learning
ISSN 2227-331X
BibTeX @inproceedings{75ElecEng2021,
title={Evaluation of Supervised Machine Learning Classification Algorithms for Fingerprint Recognition},
author={Andres Rojas, and Gordana J. Dolecek},
booktitle={2021 Global Congress on Electrical Engineering (GC-ElecEng 2021)},
organization={Mosharaka for Research and Studies} }
Paper Views 163 Paper Views Rank 243/525
Paper Downloads 82 Paper Downloads Rank 239/525
GC-ElecEng 2021 Visits: 75301||MIC-Signals 2021 Visits: 9004||MLSP Track Visits: 1141