Real-time online fingerprint image classification using adaptive hybrid techniques

<p class="Abstract">This paper presents three different hybrid classification techniques applied for the first time in real-time online fingerprint classification. Classification of online real time fingerprints is a complex task as it involves adaptation and tuning of classifier parameters for better classification accuracy. To accomplish the optimal adaptation of parameters of functional link artificial neural network (FLANN) for real-time online fingerprint classification, proven and established optimizers, such as Biogeography based optimizer (BBO), Genetic algorithm (GA), and Particle swarm optimizer (PSO) are intelligently infused with it to form hybrid classifiers. The global features of the real-time fingerprints are extracted using a Gabor filter-bank and then passed into adaptive hybrid classifiers for the desired classification as per the Henry system. Three hybrid classifiers, the optimized weight adapted Biogeography based optimized functional link artificial neural network (BBO-FLANN), Genetic algorithm based functional link artificial neural network (GA-FLANN) and Particle swarm optimized functional link artificial neural network (PSO-FLANN), are explored for real-time online fingerprint classification, where the PSO-FLANN technique is showing superior performance as compared to GA-FLANN and BBO-FLANN techniques. The best accuracy observed by the application of PSO-FLANN, is 98% for real-time online fingerprint classification.</p>

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A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear

Future Generation Computer Systems ◽

10.1016/j.future.2018.07.022 ◽

2018 ◽

Vol 89 ◽

pp. 539-547 ◽

Cited By ~ 32

Author(s):

Mazin Abed Mohammed ◽

Mohd Khanapi Abd Ghani ◽

N. Arunkumar ◽

Raed Ibraheem Hamed ◽

Mohamad Khir Abdullah ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Nasopharyngeal Carcinoma ◽

Object Detection ◽

Real Time ◽

Computer Aided ◽

Haar Feature ◽

Artificial Neural

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Forecasting CSI 300 Index Using a Hybrid Functional Link Artificial Neural Network and Particle Swarm Optimization with Improved Wavelet Mutation

2017 International Conference on Computer Network, Electronic and Automation (ICCNEA) ◽

10.1109/iccnea.2017.55 ◽

2017 ◽

Cited By ~ 2

Author(s):

Tian Lu ◽

Zhongyan Li

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Hybrid Functional ◽

Functional Link ◽

Swarm Optimization ◽

Artificial Neural

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Nonlinear System Identification Using Clonal Particle Swarm Optimization-based Functional Link Artificial Neural Network

Computational Vision and Robotics - Advances in Intelligent Systems and Computing ◽

10.1007/978-81-322-2196-8_11 ◽

2015 ◽

pp. 89-96 ◽

Cited By ~ 2

Author(s):

Kumar Gaurav ◽

Sudhansu Kumar Mishra

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Particle Swarm Optimization ◽

System Identification ◽

Nonlinear System ◽

Nonlinear System Identification ◽

Particle Swarm ◽

Functional Link ◽

Swarm Optimization ◽

Artificial Neural

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COMPARISON BETWEEN PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM IN ARTIFICIAL NEURAL NETWORK FOR LIFE PREDICTION OF NC TOOLS

Journal of Advanced Manufacturing Systems ◽

10.1142/s0219686708001073 ◽

2008 ◽

Vol 07 (01) ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

SHILONG WANG ◽

FEI ZHENG ◽

LING XU

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Particle Swarm Optimization ◽

Convergence Rate ◽

Tool Life ◽

Life Prediction ◽

Particle Swarm ◽

Swarm Optimization ◽

Artificial Neural

Accurate life prediction of NC (Numeric Control) tools is very essential in an advanced manufacturing system. In this paper, tool life prediction in a drilling process was researched. An Artificial Neural Network (ANN) has been established for prediction, with drill diameter, cutting speed and feed rate as input parameters and tool life as an output parameter. To improve the performance of the network, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were applied independently to train the network instead of standard Backward Propagation (BP) algorithm, which has drawbacks of low convergence rate and weak generalization capacity. And the two methods were compared in terms of algorithm complexity, convergence rate and prediction accuracy, with reference to standard BP method.

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Remote Sensing to Minimize Energy Consumption of Six-axis Robot Arm Using Particle Swarm Optimization and Artificial Neural Network to Control Changes in Real Time

Sensors and Materials ◽

10.18494/sam.2020.2428 ◽

2020 ◽

Vol 32 (2) ◽

pp. 499

Author(s):

Somyot Kaitwanidvilai ◽

Veerasak Chanarungruengkij ◽

Poom Konghuayrob

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Particle Swarm Optimization ◽

Energy Consumption ◽

Real Time ◽

Particle Swarm ◽

Robot Arm ◽

Swarm Optimization ◽

Artificial Neural

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Real time selective harmonic minimization for multilevel inverters using genetic algorithm and artificial neural network angle generation

Proceedings of The 7th International Power Electronics and Motion Control Conference ◽

10.1109/ipemc.2012.6258973 ◽

2012 ◽

Cited By ~ 7

Author(s):

Faete J. T. Filho ◽

Leon M. Tolbert ◽

Burak Ozpineci

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Real Time ◽

Multilevel Inverters ◽

Artificial Neural

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Real-Time Identification of Fuzzy PID-Controlled Maglev System using TLBO-Based Functional Link Artificial Neural Network

Arabian Journal for Science and Engineering ◽

10.1007/s13369-020-05292-x ◽

2021 ◽

Vol 46 (4) ◽

pp. 4103-4118

Author(s):

Amit Kumar Sahoo ◽

Sudhansu Kumar Mishra ◽

Babita Majhi ◽

Ganapati Panda ◽

Suresh Chandra Satapathy

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Real Time ◽

Fuzzy Pid ◽

Functional Link ◽

Maglev System ◽

Artificial Neural ◽

Real Time Identification

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Modeling and optimizing an electrochemical oxidation process using artificial neural network, genetic algorithm and particle swarm optimization

Journal of the Serbian Chemical Society ◽

10.2298/jsc170721101l ◽

2018 ◽

Vol 83 (3) ◽

pp. 379-390

Author(s):

Banghai Liu ◽

Chunji Jin ◽

Jiteng Wan ◽

Pengfang Li ◽

Huanxi Yan

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Particle Swarm Optimization ◽

Electrochemical Oxidation ◽

Back Propagation ◽

Particle Swarm ◽

Swarm Optimization ◽

Operation Conditions ◽

Artificial Neural

This study proposes a novel hybrid of artificial neural network (ANN), genetic algorithm (GA), and particle swarm optimization (PSO) to model and optimize the relevant parameters of an electrochemical oxidation (EO) Acid Black 2 process. The back propagation neural network (BPNN) was used as a modelling tool. To avoid over-fitting, GA was applied to improve the generalized capability of BPNN by optimizing the weights. In addition, an optimization model was developed to assess the performance of the EO process, where total organic carbon (TOC) removal, mineralization current efficiency (MCE), and the energy consumption per unit of TOC (ECTOC) were considered. The operation conditions of EO were further optimized via PSO. The validation results indicted the proposed method to be a promising method to estimate the efficiency and to optimize the parameters of the EO process.

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