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Adaptive latent fingerprint segmentation using feature selection and random decision forest classification
2017
Information Fusion
Preserved Fulltext
Using these selected features, a trained Random Decision Forest based algorithm classifies the local patches as background or foreground. ...
selection technique using modified RELIEF formulation for analyzing the influence of multiple category features on latent fingerprint segmentation, and (iii) a novel SIVV based metric to measure the effect ...
Keith Morris (West Virginia University) for his insightful and support on latent fingerprint. ...
doi:10.1016/j.inffus.2016.05.002
fatcat:7av3fvetpfbodkxnarul3lbt7y
The Power of Ensemble Models in Fingerprint Classification: A case study
2021
figshare.com
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The comparison was executed using the stratified cross-validation process to set the confidence interval for the evaluation of performance measured by success rate, using Random Forest, XGBoost and Decision ...
Student one-tailed paired t-test showed that Random Forest and XGBoost do not have statistical differences with significance of 95%, however, their performance is superior than the one of the simple Decision ...
Classifiers Random Forest, XGBoost and Decision Tree were used in this paper.
Random Forest Random Forest is a kind of ensemble model. ...
doi:10.6084/m9.figshare.14791368.v1
fatcat:53h6vrpj2vao3oajqbb4hnw4mi
Segmentation of Latent Fingerprint using Neural Network
2019
International Journal of Engineering and Advanced Technology
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Fingerprint region then divides into y*y blocks and extracts the features of each block and uses them as an input of NN to classify the blocks into fingerprint and non-fingerprint blocks. ...
This method automatically segments the fingerprints and non-fingerprints patterns without human intervention. The morphological method is used for segmentation of the fingerprint region. ...
For segmentation, it requires accurate confidence measures. Vatsa et al. [2] , purposed Adaptive latent fingerprint segmentation using feature selection and random decision forest classification. ...
doi:10.35940/ijeat.a9820.109119
fatcat:ad62ez5fsvcfznxtqwkvfwygla
Special issue on bio-medical signal processing for smarter mobile healthcare using big data analytics
2019
Journal of Ambient Intelligence and Humanized Computing
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Acknowledgements We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this special issue ...
three machine learning algorithms: support vector machine, random forest, and k-nearest neighbors. ...
"Bio-Medical and Latent Fingerprint Enhancement and Matching Using Advanced Scalable Soft Computing Models," authors develop a model for enhancement of latent fingerprint and matching algorithm, which ...
doi:10.1007/s12652-019-01425-9
fatcat:fnhogeep3zby3cgxnq2z5jogye
Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authentication
[article]
2020
arXiv
pre-print
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We demonstrate the NFE retrofit to a classic artificial neural network for a simple scenario of fingerprint-based user authentication. ...
Powered by new advances in sensor development and artificial intelligence, the decreasing cost of computation, and the pervasiveness of handheld computation devices, biometric user authentication (and ...
Unfortunately, both SVMs and ANNs (as well as the other frequently-used classifiers, like k-nearest neighbors (KNN), decision trees and random forests, etc.) rely on learned structures that have to be ...
arXiv:2003.08433v1
fatcat:ntqrft3ig5bztiitwthja5odye
DeepTeeth: A Teeth-photo Based Human Authentication System for Mobile and Hand-held Devices
[article]
2021
arXiv
pre-print
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Region of interest (RoI) is then extracted using the markers and the obtained sample is enhanced using contrast limited adaptive histogram equalization (CLAHE) for better visual clarity. ...
The system can be used in many ways including device unlocking and secure authentication. ...
From a large batch of 2500 Triplets, 150 hard and 200 random triplets were selected to train Siamese Network Experimental Setting. We normalize and randomize the dataset for creating the input batch. ...
arXiv:2107.13217v1
fatcat:wvdp2pw6tzfcnksxkkyqfprcu4
Table of contents
2020
2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)
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Using Bat Algorithm 186-190
P038
1570673453
Improving the Efficiency of Automated Latent
Fingerprint Identification Using Stack of Convolutional
Auto-encoder
191-196
P039
1570673875
IoT and ...
Design
Thinking and Agile Principles
031-035
P008
1570677761
Android Malware Detection Using Chi-Square Feature
Selection and Ensemble Learning Method
036-041
P009
1570678607 An Empirical Study ...
doi:10.1109/pdgc50313.2020.9315770
fatcat:idng5upuj5fwfndjx76pk47tqa
Contents
2018
2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)
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143-147
2.13
1570416555
Vehicle Make and Model Recognition using Random Forest
Classification For Intelligent Transportation Systems
148-154
2.14
1570416579
ByteWise: A Case Study in Neural ...
Image Processing and Multimedia Technology
9.1
1570413623
A Classification and Clustering Method for Tracking Multiple
Objects
537-544
9.2
1570416398 Audio Processing with Channel Filtering using ...
doi:10.1109/ccwc.2018.8301783
fatcat:qmdq273aojbarcn2gszmz6yyx4
STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity
2019
Frontiers in Bioengineering and Biotechnology
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Compared with other powerful multi-label methods, ML-kNN, MTSVM, and RAkELd, our multi-label classification model of NLPS-RF (random forest-based NLSP) has proven to be a feasible and effective model, ...
ATP-binding cassette to solute carrier families using both structural fingerprints and chemical ontologies information of substrates. ...
In those models, the physicochemical, topological descriptors of ligand molecules, MACCS and variants of Morgan fingerprints were used as input features. ...
doi:10.3389/fbioe.2019.00306
pmid:31781551
pmcid:PMC6851049
fatcat:evzqvxqoh5fipcvvf7tqmzn3iy
A survey on computational intelligence approaches for predictive modeling in prostate cancer
2017
Expert systems with applications
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hybrids of these, as well as Bayesian based approaches, and Markov models. ...
cancer predictive models, and the suitability of these approaches are discussed. ...
Random Forests Random Forests (Breiman, 2001) are an Ensemble learning method that creates a number of decision trees using a random selection of attributes. ...
doi:10.1016/j.eswa.2016.11.006
fatcat:ii6gbq6qcbai5kxvcy4l7kkg54
A Text Book Of Research Papers On Fingerprint Recognition & Hash Code Techniques
2018
Zenodo
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This book also contains applications of Multifactor authentication model using Fingerprint Hash code, OTP and Password and compares this new model with existing similar systems. ...
This book contains research articles related to Fingerprint image enhancement, recognition and Hash code generation methods. ...
Latent fingerprint segmentation with adaptive total variation model. ...
doi:10.5281/zenodo.1409464
fatcat:243353fegrfxji7jvxzmqg5sge
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition
[article]
2020
arXiv
pre-print
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architectures and feature representations of molecular data. ...
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. ...
Acknowledgements Support for this work was provided by the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison with funding from the Wisconsin Alumni ...
arXiv:2001.06545v3
fatcat:e5f4v3fnyvdwtliftwia6rwyc4
ToxTree: descriptor-based machine learning models for both hERG and Nav1.5 cardiotoxicity liability predictions
[article]
2021
arXiv
pre-print
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ToxTree-hERG Classifier, a pipeline of Random Forest models, was trained on a large curated dataset of 8380 unique molecular compounds. ...
The machine learning models were trained for both regression, predicting the potency value of a drug, and multiclass classification at three different potency cut-offs (i.e. 1μM, 10μM, and 30μM), where ...
Acknowledgment The authors would like to acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant. ...
arXiv:2112.13467v1
fatcat:5rymyaegjjfa3ms5lowtn7vntu
Features and Methods of Human Age Estimation: Opportunities and Challenges in Medical Image Processing
2021
Turkish Journal of Computer and Mathematics Education
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Each of these methods has their merits and demerits. The popular manual and semi-automated age estimation methods are prone to human observation error and need sophisticated equipments. ...
Age estimation of living species is an open and interesting problem due to its medico-legal importance and humans are no exception to this. ...
[76] evaluated MRI based skeletal maturation using landmark localisation algorithm and Regression Random Forest (RRF) decision tree algorithm. ...
doi:10.17762/turcomat.v12i1s.1770
fatcat:psjcdatiizfr5c6ydwvybuzppq
Domain Adaptation for Visual Applications: A Comprehensive Survey
[article]
2017
arXiv
pre-print
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The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications. ...
Fourth, we overview the methods that go beyond image categorization, such as object detection or image segmentation, video analyses or learning visual attributes. ...
[262] uses random decision forests to transfer relevant features between domains. ...
arXiv:1702.05374v2
fatcat:5va4oz4evjfhxgxddflpbb6pxi
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