scholarly journals An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoT

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1664 ◽  
Author(s):  
Haiping Huang ◽  
Linkang Hu ◽  
Fu Xiao ◽  
Anming Du ◽  
Ning Ye ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Reference Electrode ◽  
Identity Authentication ◽  
Classification Model ◽  
Experimental Result ◽  
Ensemble Averaging ◽  
Security Risks ◽  
Adaptive Feature Selection ◽  
Authentication System ◽  
Audiovisual Presentation

With the continuous increment of security risks and the limitations of traditional modes, it is necessary to design a universal and trustworthy identity authentication system for intelligent Internet of Things (IoT) applications such as an intelligent entrance guard. The characteristics of EEG (electroencephalography) have gained the confidence of researchers due to its uniqueness, stability, and universality. However, the limited usability of the experimental paradigm and the unsatisfactory classification accuracy have so far prevented the identity authentication system based on EEG to become commonplace in IoT scenarios. To address these problems, an audiovisual presentation paradigm is proposed to record the EEG signals of subjects. In the pre-processing stage, the reference electrode, ensemble averaging, and independent component analysis methods are used to remove artifacts. In the feature extraction stage, adaptive feature selection and bagging ensemble learning algorithms establish the optimal classification model. The experimental result shows that our proposal achieves the best classification accuracy when compared with other paradigms and typical EEG-based authentication methods, and the test evaluation on a login scenario is designed to further demonstrate that the proposed system is feasible, effective, and reliable.

scholarly journals Ratiometric Impedance Sensing of Fingers for Robust Identity Authentication

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hyung Wook Noh ◽  
Chang-Geun Ahn ◽  
Hyoun-Joong Kong ◽  
Joo Yong Sim
Keyword(s):  
Error Rate ◽  
Classification Accuracy ◽  
Structural Information ◽  
Identity Authentication ◽  
Biometric Authentication ◽  
Impedance Sensing ◽  
Authentication System ◽  
The Difference ◽  
Spectral Patterns ◽  
Physiological Variations

Abstract We present a novel biometric authentication system enabled by ratiometric analysis of impedance of fingers. In comparison to the traditional biometrics that relies on acquired images of structural information of physiological characteristics, our biological impedance approach not only eliminates any practical means of making fake copies of the relevant physiological traits but also provides reliable features of biometrics using the ratiometric impedance of fingers. This study shows that the ratiometric features of the impedance of fingers in 10 different pairs using 5 electrodes at the fingertips can reduce the variation due to undesirable factors such as temperature and day-to-day physiological variations. By calculating the ratio of impedances, the difference between individual subjects was amplified and the spectral patterns were diversified. Overall, our ratiometric analysis of impedance improved the classification accuracy of 41 subjects and reduced the error rate of classification from 29.32% to 5.86% (by a factor of 5).

scholarly journals A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data

2016 ◽  
Vol 07 (01) ◽  
pp. 1-21 ◽  
Author(s):  
Nancy Jane ◽  
Kannan Arputharaj ◽  
Khanna Nehemiah
Keyword(s):  
Time Series ◽  
Rough Set ◽  
Classification Accuracy ◽  
Temporal Pattern ◽  
Clinical Decision Making ◽  
Classification Model ◽  
Experimental Result ◽  
Series Data ◽  
Forecasting Model ◽  
Neuro Fuzzy

SummaryClinical time-series data acquired from electronic health records (EHR) are liable to temporal complexities such as irregular observations, missing values and time constrained attributes that make the knowledge discovery process challenging.This paper presents a temporal rough set induced neuro-fuzzy (TRiNF) mining framework that handles these complexities and builds an effective clinical decision-making system. TRiNF provides two functionalities namely temporal data acquisition (TDA) and temporal classification.In TDA, a time-series forecasting model is constructed by adopting an improved double exponential smoothing method. The forecasting model is used in missing value imputation and temporal pattern extraction. The relevant attributes are selected using a temporal pattern based rough set approach. In temporal classification, a classification model is built with the selected attributes using a temporal pattern induced neuro-fuzzy classifier.For experimentation, this work uses two clinical time series dataset of hepatitis and thrombosis patients. The experimental result shows that with the proposed TRiNF framework, there is a significant reduction in the error rate, thereby obtaining the classification accuracy on an average of 92.59% for hepatitis and 91.69% for thrombosis dataset.The obtained classification results prove the efficiency of the proposed framework in terms of its improved classification accuracy.

scholarly journals Biometric identity Authentication System Using Hand Geometry Measurements

2021 ◽  
Vol 1804 (1) ◽  
pp. 012144
Author(s):  
Hesham Hashim Mohammed ◽  
Shatha A. Baker ◽  
Ahmed S. Nori
Keyword(s):  
Identity Authentication ◽  
Authentication System ◽  
Hand Geometry

Tobacco Leaf Grading Based on Deep Convolutional Neural Networks and Machine Vision

2021 ◽  
Vol 65 (1) ◽  
pp. 11-22
Author(s):  
Mengyao Lu ◽  
Shuwen Jiang ◽  
Cong Wang ◽  
Dong Chen ◽  
Tian’en Chen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Classification Model ◽  
List Type ◽  
Tobacco Leaves ◽  
Tobacco Leaf ◽  
Grading Model

HighlightsA classification model for the front and back sides of tobacco leaves was developed for application in industry.A tobacco leaf grading method that combines a CNN with double-branch integration was proposed.The A-ResNet network was proposed and compared with other classic CNN networks.The grading accuracy of eight different grades was 91.30% and the testing time was 82.180 ms, showing a relatively high classification accuracy and efficiency.Abstract. Flue-cured tobacco leaf grading is a key step in the production and processing of Chinese-style cigarette raw materials, directly affecting cigarette blend and quality stability. At present, manual grading of tobacco leaves is dominant in China, resulting in unsatisfactory grading quality and consuming considerable material and financial resources. In this study, for fast, accurate, and non-destructive tobacco leaf grading, 2,791 flue-cured tobacco leaves of eight different grades in south Anhui Province, China, were chosen as the study sample, and a tobacco leaf grading method that combines convolutional neural networks and double-branch integration was proposed. First, a classification model for the front and back sides of tobacco leaves was trained by transfer learning. Second, two processing methods (equal-scaled resizing and cropping) were used to obtain global images and local patches from the front sides of tobacco leaves. A global image-based tobacco leaf grading model was then developed using the proposed A-ResNet-65 network, and a local patch-based tobacco leaf grading model was developed using the ResNet-34 network. These two networks were compared with classic deep learning networks, such as VGGNet, GoogLeNet-V3, and ResNet. Finally, the grading results of the two grading models were integrated to realize tobacco leaf grading. The tobacco leaf classification accuracy of the final model, for eight different grades, was 91.30%, and grading of a single tobacco leaf required 82.180 ms. The proposed method achieved a relatively high grading accuracy and efficiency. It provides a method for industrial implementation of the tobacco leaf grading and offers a new approach for the quality grading of other agricultural products. Keywords: Convolutional neural network, Deep learning, Image classification, Transfer learning, Tobacco leaf grading

scholarly journals RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL

2020 ◽  
pp. 1-2
Author(s):  
Zhang- sensen
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Brain Network ◽  
Classification Model ◽  
Comparative Experiment ◽  
Machine Learning Methods ◽  
Healthy Elderly ◽  
Healthy Elderly People ◽  
The Brain

mild cognitive impairment (MCI) is a condition between healthy elderly people and alzheimer's disease (AD). At present, brain network analysis based on machine learning methods can help diagnose MCI. In this paper, the brain network is divided into several subnets based on the shortest path,and the feature vectors of each subnet are extracted and classified. In order to make full use of subnet information, this paper adopts integrated classification model for classification.Each base classification model can predict the classification of a subnet,and the classification results of all subnets are calculated as the classification results of brain network.In order to verify the effectiveness of this method,a brain network of 66 people was constructed and a comparative experiment was carried out.The experimental results show that the classification accuracy of the integrated classification model proposed in this paper is 19% higher than that of SVM,which effectively improves the classification accuracy

scholarly journals Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2929 ◽  
Author(s):  
Yuanyuan Wang ◽  
Chao Wang ◽  
Hong Zhang
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Classification Accuracy ◽  
Experimental Results ◽  
Fine Tuning ◽  
Classification Model ◽  
Great Success ◽  
Sar Images ◽  
Convolutional Networks ◽  
Ship Classification

With the capability to automatically learn discriminative features, deep learning has experienced great success in natural images but has rarely been explored for ship classification in high-resolution SAR images due to the training bottleneck caused by the small datasets. In this paper, convolutional neural networks (CNNs) are applied to ship classification by using SAR images with the small datasets. First, ship chips are constructed from high-resolution SAR images and split into training and validation datasets. Second, a ship classification model is constructed based on very deep convolutional networks (VGG). Then, VGG is pretrained via ImageNet, and fine tuning is utilized to train our model. Six scenes of COSMO-SkyMed images are used to evaluate our proposed model with regard to the classification accuracy. The experimental results reveal that (1) our proposed ship classification model trained by fine tuning achieves more than 95% average classification accuracy, even with 5-cross validation; (2) compared with other models, the ship classification model based on VGG16 achieves at least 2% higher accuracies for classification. These experimental results reveal the effectiveness of our proposed method.

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