Filters








524 Hits in 8.2 sec

Adaptive latent fingerprint segmentation using feature selection and random decision forest classification

Anush Sankaran, Aayush Jain, Tarun Vashisth, Mayank Vatsa, Richa Singh
2017 Information Fusion  
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

Raphael Mendes, Rosalvo Neto
2021 figshare.com  
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  
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

Gwanggil Jeon, Awais Ahmad, Salvatore Cuomo, Wei Wu
2019 Journal of Ambient Intelligence and Humanized Computing  
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]

Abhishek Jana, Md Kamruzzaman Sarker, Monireh Ebrahimi, Pascal Hitzler, George T Amariucai
2020 arXiv   pre-print
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]

Geetika Arora, Rohit K Bharadwaj, Kamlesh Tiwari
2021 arXiv   pre-print
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)  
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)  
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

Xiangeng Wang, Xiaolei Zhu, Mingzhi Ye, Yanjing Wang, Cheng-Dong Li, Yi Xiong, Dong-Qing Wei
2019 Frontiers in Bioengineering and Biotechnology  
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

Georgina Cosma, David Brown, Matthew Archer, Masood Khan, A. Graham Pockley
2017 Expert systems with applications  
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

K. Dr. Krishna Prasad
2018 Zenodo  
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]

Sebastian Raschka, Benjamin Kaufman
2020 arXiv   pre-print
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]

Issar Arab, Khaled Barakat
2021 arXiv   pre-print
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

Kirti A. Patil, Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
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]

Gabriela Csurka
2017 arXiv   pre-print
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
« Previous Showing results 1 — 15 out of 524 results