scholarly journals Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5695
Author(s):  
Maciej Stanuch ◽  
Marek Wodzinski ◽  
Andrzej Skalski
Keyword(s):  
Neural Network ◽  
True Positive Rate ◽  
Equal Error Rate ◽  
Identity Verification ◽  
Positive Rate ◽  
Acceptance Set ◽  
Infrared Wavelengths ◽  
Palm Vein ◽  
Contact Free ◽  
Biometric Features

Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to enhance the security of these devices by adding supplementary modality while keeping the user experience at the same level. Palm vein systems are based on infrared wavelengths used for capturing images of users’ veins. It is both convenient for the user, and it is one of the most secure biometric solutions. The proposed system uses IR and UV wavelengths; the images are then processed by a deep convolutional neural network for extraction of biometric features and authentication of users. We tested the system in a verification scenario that consisted of checking if the images collected from the user contained the same biometric features as those in the database. The True Positive Rate (TPR) achieved by the system when the information from the two modalities were combined was 99.5% by the threshold of acceptance set to the Equal Error Rate (EER).

scholarly journals A Learning-based Neural Network Model for the Detection and Classification of SQL Injection Attacks

2017 ◽  
Vol 7 (2) ◽  
pp. 16-41 ◽  
Author(s):  
Naghmeh Moradpoor Sheykhkanloo
Keyword(s):  
Neural Network ◽  
Web Application ◽  
Query Language ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Injection Technique ◽  
Injection Attacks ◽  
Proposed Model ◽  
Positive Rate ◽  
Sql Injection Attacks

Structured Query Language injection (SQLi) attack is a code injection technique where hackers inject SQL commands into a database via a vulnerable web application. Injected SQL commands can modify the back-end SQL database and thus compromise the security of a web application. In the previous publications, the author has proposed a Neural Network (NN)-based model for detections and classifications of the SQLi attacks. The proposed model was built from three elements: 1) a Uniform Resource Locator (URL) generator, 2) a URL classifier, and 3) a NN model. The proposed model was successful to: 1) detect each generated URL as either a benign URL or a malicious, and 2) identify the type of SQLi attack for each malicious URL. The published results proved the effectiveness of the proposal. In this paper, the author re-evaluates the performance of the proposal through two scenarios using controversial data sets. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed model in terms of accuracy, true-positive rate as well as false-positive rate.

WLI Fuzzy Clustering and Adaptive Lion Neural Network (ALNN) for Cloud Intrusion Detection

2019 ◽  
Vol 11 (1) ◽  
pp. 1-17
Author(s):  
Pinki Sharma ◽  
Jyotsna Sengupta ◽  
P. K. Suri
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Clustered Data ◽  
False Positive Rate ◽  
Cloud Service ◽  
True Positive Rate ◽  
Aggregated Data ◽  
Marquardt Algorithm ◽  
Positive Rate ◽  
And Performance

Cloud computing is the internet-based technique where the users utilize the online resources for computing services. The attacks or intrusion into the cloud service is the major issue in the cloud environment since it degrades performance. In this article, we propose an adaptive lion-based neural network (ALNN) to detect the intrusion behaviour. Initially, the cloud network has generated the clusters using a WLI fuzzy clustering mechanism. This mechanism obtains the different numbers of clusters in which the data objects are grouped together. Then, the clustered data is fed into the newly designed adaptive lion-based neural network. The proposed method is developed by the combination of Levenberg-Marquardt algorithm of neural network and adaptive lion algorithm where female lions are used to update the weight adaptively using lion optimization algorithm. Then, the proposed method is used to detect the malicious activity through training process. Thus, the different clustered data is given to the proposed ALNN model. Once the data is trained, then it needs to be aggregated. Subsequently, the aggregated data is fed into the proposed ALNN method where the intrusion behaviour is detected. Finally, the simulation results of the proposed method and performance is analysed through accuracy, false positive rate, and true positive rate. Thus, the proposed ALNN algorithm attains 96.46% accuracy which ensures better detection performance.

A hybrid optimization-based deep belief neural network for the classification of vegetation area in multi-spectral satellite image

2021 ◽  
Vol 24 (4) ◽  
pp. 363-379
Author(s):  
Anil B. Gavade ◽  
Vijay S. Rajpurohit
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Land Cover ◽  
False Positive Rate ◽  
Satellite Image ◽  
Harmony Search ◽  
Classification Problem ◽  
True Positive Rate ◽  
Positive Rate

Over the last few decades, multiple advances have been done for the classification of vegetation area through land cover, and land use. However, classification problem is one of the most complicated and contradicting problems that has received considerable attention. Therefore, to tackle this problem, this paper proposes a new Firefly-Harmony search based Deep Belief Neural Network method (FHS-DBN) for the classification of land cover, and land use. The segmentation process is done using Bayesian Fuzzy Clustering,and the feature matrix is developed. The feature matrix is given to the proposed FHS-DBN method that distinguishes the land coverfrom the land use in the multispectral satellite images, for analyzing the vegetation area. The proposed FHS-DBN method is designedby training the DBN using the FHS algorithm, which is developed by the combination of Firefly Algorithm (FA) and Harmony Search (HS) algorithm. The performance of the FHS-DBN model is evaluated using three metrics, such as Accuracy, True Positive Rate (TPR), and False Positive Rate (FPR). From the experimental analysis, it is concludedthat the proposed FHS-DBN model achieves ahigh classification accuracy of 0.9381, 0.9488, 0.9497, and 0.9477 usingIndian Pine, Salinas scene, Pavia Centre and university, and Pavia University scene dataset.

scholarly journals A Learning-based Neural Network Model for the Detection and Classification of SQL Injection Attacks

2020 ◽  
pp. 450-475
Author(s):  
Naghmeh Moradpoor Sheykhkanloo
Keyword(s):  
Neural Network ◽  
Web Application ◽  
Query Language ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Injection Technique ◽  
Data Sets ◽  
Injection Attacks ◽  
Proposed Model ◽  
Positive Rate

Structured Query Language injection (SQLi) attack is a code injection technique where hackers inject SQL commands into a database via a vulnerable web application. Injected SQL commands can modify the back-end SQL database and thus compromise the security of a web application. In the previous publications, the author has proposed a Neural Network (NN)-based model for detections and classifications of the SQLi attacks. The proposed model was built from three elements: 1) a Uniform Resource Locator (URL) generator, 2) a URL classifier, and 3) a NN model. The proposed model was successful to: 1) detect each generated URL as either a benign URL or a malicious, and 2) identify the type of SQLi attack for each malicious URL. The published results proved the effectiveness of the proposal. In this paper, the author re-evaluates the performance of the proposal through two scenarios using controversial data sets. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed model in terms of accuracy, true-positive rate as well as false-positive rate.

scholarly journals A Novel Filtered Segmentation-Based Bayesian Deep Neural Network Framework on Large Diabetic Retinopathy Databases

2020 ◽  
Vol 34 (6) ◽  
pp. 683-692
Author(s):  
Shaik Akbar ◽  
Divya Midhunchakkaravarthy
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Neural Network ◽  
Feature Space ◽  
True Positive Rate ◽  
Vital Role ◽  
Learning Framework ◽  
Segmentation Approach ◽  
Positive Rate ◽  
Conventional Models

Image thresholding-based segmentation models play a vital role in the detection of Diabetic retinopathy (DR) on large databases. Most of the conventional segmentation-based classification models are independent of over segmented regions and outliers. Also, these models have less true positive rate and high error rate on different DR feature sets. In order to overcome these problems, a novel filtered based segmentation framework is designed and implemented on the large DR feature space. In this work, a novel image filtering approach, optimal image segmentation approach and hybrid Bayesian deep learning framework are developed on the large DR image databases. Experimental results proved that the proposed filtered segmentation-based Bayesian deep neural network has better accuracy and runtime than the conventional models on different DR variation databases.

Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs

2021 ◽  
Author(s):  
Cansu Görürgöz ◽  
Kaan Orhan ◽  
Ibrahim Sevki Bayrakdar ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
True Positive Rate ◽  
Turnaround Time ◽  
Classification Model ◽  
Data Sets ◽  
Data Set ◽  
Region Detection ◽  
Positive Rate

Objectives: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images. Methods: The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training. The algorithm consisted of: (1) the Jaw classification model, (2) Region detection models, and (3) the Final algorithm using all models. Finally, an analysis of the latest model has been integrated alongside the others. The sensitivity, precision, true-positive rate, and false-positive/negative rate were computed to analyze the performance of the algorithm using a confusion matrix. Results: An artificial intelligence algorithm (CranioCatch, Eskisehir-Turkey) was designed based on R-CNN inception architecture to automatically detect and number the teeth on periapical images. Of 864 teeth in 156 periapical radiographs, 668 were correctly numbered in the test data set. The F1 score, precision, and sensitivity were 0.8720, 0.7812, and 0.9867, respectively. Conclusion: The study demonstrated the potential accuracy and efficiency of the CNN algorithm for detecting and numbering teeth. The deep learning-based methods can help clinicians reduce workloads, improve dental records, and reduce turnaround time for urgent cases. This architecture might also contribute to forensic science.

A Biometric-Secured Neighborhood Vector Relational Coefficient Framework for Social Network Communication

2018 ◽  
Vol 27 (02) ◽  
pp. 1850004
Author(s):  
Jayanthi Sivasubramaniyam ◽  
C. Chandrasekar
Keyword(s):  
Social Network ◽  
Access Control ◽  
Social Communication ◽  
System Analysis ◽  
True Positive Rate ◽  
Communication Framework ◽  
The Social ◽  
Positive Rate ◽  
Network Authentication ◽  
Biometric Features

Multimodal Biometric has been widely researched to improve social network user communication. Appropriate modeling of authentication and access control is highly required to perform precise system analysis in a social network framework. In this work, a Multimodal Biometric Secured (MBSec) Social Network User Communication framework to provide user authentic social communication in a safe environment is investigated. The multimodal user biometric features (i.e. face and fingerprint) with neighborhood feature pixel dominancy stored in a spatial vector are gene encoded to generate biometric identity keys. The authenticity of user is validated by decoding gene encoded biometric features with biometric identity keys. User relational coefficient is calculated for users, using prior knowledge instances shared between different social users. The social network user’s relational coefficient is sequenced in a matrix to identify the occurrence of authentic user relational social communication. Experiments were conducted using face and fingerprint images collected from BioSecure dataset. Our results demonstrate that the proposed social network user communication framework is able to significantly improve the true positive rate of authenticated users, compared to conventional FR approaches that only make use of a single model. Further, we demonstrate that our MBSec framework has a low social network authentication time and access control time.

Neural Network Detection and Segmentation of Mental Foramen in Panoramic Imaging

2020 ◽  
Vol 44 (3) ◽  
pp. 168-173
Author(s):  
Lazar Kats ◽  
Marilena Vered ◽  
Sigalit Blumer ◽  
Eytan Kats
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Biomedical Application ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Mental Foramen ◽  
Dice Similarity Coefficient ◽  
Tel Aviv ◽  
Positive Rate ◽  
Panoramic Images

Objective: To apply the technique of deep learning on a small dataset of panoramic images for the detection and segmentation of the mental foramen (MF). Study design: In this study we used in-house dataset created within the School of Dental Medicine, Tel Aviv University. The dataset contained randomly chosen and anonymized 112 digital panoramic X-ray images and corresponding segmentations of MF. In order to solve the task of segmentation of the MF we used a single fully convolution neural network, that was based on U-net as well as a cascade architecture. 70% of the data were randomly chosen for training, 15% for validation and accuracy was tested on 15%. The model was trained using NVIDIA GeForce GTX 1080 GPU. The SPSS software, version 17.0 (Chicago, IL, USA) was used for the statistical analysis. The study was approved by the ethical committee of Tel Aviv University. Results: The best results of the dice similarity coefficient ( DSC), precision, recall, MF-wise true positive rate (MFTPR) and MF-wise false positive rate (MFFPR) in single networks were 49.51%, 71.13%, 68.24%, 87.81% and 14.08%, respectively. The cascade of networks has shown better results than simple networks in recall and MFTPR, which were 88.83%, 93.75%, respectively, while DSC and precision achieved the lowest values, 31.77% and 23.92%, respectively. Conclusions: Currently, the U-net, one of the most used neural network architectures for biomedical application, was effectively used in this study. Methods based on deep learning are extremely important for automatic detection and segmentation in radiology and require further development.

scholarly journals Classification of Electrocardiogram of Congenital Heart Disease Patients by Neural Network Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yongjie Yuan ◽  
Yongjun Zhang ◽  
Junyuan Wang ◽  
Ping Fang
Keyword(s):  
Neural Network ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart ◽  
True Positive Rate ◽  
True Positive ◽  
Accuracy Rate ◽  
Network Algorithms ◽  
Positive Rate

The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted between August 2016 and August 2019 were selected for analysis to compare the classification effect and accuracy rate of IMNN, MNN, CNN ECG, and RNN ECG algorithms. It was found that the RNN ECG algorithm had higher classification sensitivity and true positive rate in terms of normal or bundle (NB) branch block beat, supraventricular abnormal (SA) rhythm, abnormal ventricular (AV) beat, and fusion beat (FB) than the CNN ECG algorithm ( P < 0.05 ), and the classification sensitivity and true positive rate of IMNN algorithm in the four aspects were significantly higher than those of MNN algorithm ( P < 0.05 ). The classification accuracy of CNN ECG algorithm and RNN ECG algorithm was above 98%, while that of MNN algorithm and IMNN algorithm was better than that of CNN ECG algorithm and RNN ECG algorithm, and the accuracy rate can reach 98.5% or more. Moreover, the accuracy rate of the IMNN algorithm can reach more than 98%. In conclusion, IMNN not only has a good classification ability in the simulated environment but also performs well in the actual environment, which is worthy of clinical promotion.

scholarly journals Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs

2021 ◽  
pp. 1-21
Author(s):  
Asma Naseer ◽  
Maria Tamoor ◽  
Arifah Azhar
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
False Positive Rate ◽  
False Negative ◽  
True Positive Rate ◽  
X Rays ◽  
Infected People ◽  
Computer Aided ◽  
High True Positive Rate ◽  
Positive Rate

Background: Coronavirus Disease 2019 (COVID-19) is contagious, producing respiratory tract infection, caused by a newly discovered coronavirus. Its death toll is too high, and early diagnosis is the main problem nowadays. Infected people show a variety of symptoms such as fatigue, fever, tastelessness, dry cough, etc. Some other symptoms may also be manifested by radiographic visual identification. Therefore, Chest X-Rays (CXR) play a key role in the diagnosis of COVID-19. Methods: In this study, we use Chest X-Rays images to develop a computer-aided diagnosis (CAD) of the disease. These images are used to train two deep networks, the Convolution Neural Network (CNN), and the Long Short-Term Memory Network (LSTM) which is an artificial Recurrent Neural Network (RNN). The proposed study involves three phases. First, the CNN model is trained on raw CXR images. Next, it is trained on pre-processed CXR images and finally enhanced CXR images are used for deep network CNN training. Geometric transformations, color transformations, image enhancement, and noise injection techniques are used for augmentation. From augmentation, we get 3,220 augmented CXRs as training datasets. In the final phase, CNN is used to extract the features of CXR imagery that are fed to the LSTM model. The performance of the four trained models is evaluated by the evaluation techniques of different models, including accuracy, specificity, sensitivity, false-positive rate, and receiver operating characteristic (ROC) curve. Results: We compare our results with other benchmark CNN models. Our proposed CNN-LSTM model gives superior accuracy (99.02%) than the other state-of-the-art models. Our method to get improved input, helped the CNN model to produce a very high true positive rate (TPR 1) and no false-negative result whereas false negative was a major problem while using Raw CXR images. Conclusions: We conclude after performing different experiments that some image pre-processing and augmentation, remarkably improves the results of CNN-based models. It will help a better early detection of the disease that will eventually reduce the mortality rate of COVID.

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