scholarly journals Digital Signature of Document using Human Palm

2019 ◽  
Vol 9 (1) ◽  
pp. 4293-4298
Keyword(s):  
Digital Signature ◽  
Histogram Equalization ◽  
Acceptance Rate ◽  
Equal Error Rate ◽  
False Acceptance Rate ◽  
Image Preprocessing ◽  
Time Period ◽  
Proposed Model ◽  
Cubic Spline Function ◽  
Two Sides

A digital signature is a checksum which depends on the time period during which it was produced. Human palm biometric is one of the fastest, accurate, reliable and secure biometric techniques for identification and verification because it provides automatic authentication of an individual based on unique features in palm structure. In this paper, an efficient digital signature model for the document is proposed by using human palm. Human palm can give unique features which can be used in generating a secure digital signature. Therefore, this model consists of two sides: the embedding side and extracting side. The embedding side includes (1) image preprocessing stage:(color to grayscale and histogram equalization). (2) feature extraction stage:(GLCM (Haralick) algorithm). (3) Generating digital signature stage:(Elliptic Curve and Cubic Spline function with MD5 algorithm). While the extracting side contains extracting signature stage and matching stage. The accuracy of the generated digital signature by the proposed model is 100%, however false acceptance rate (FAR) is 0%, false reject rate (FRR) is 0%, and equal error rate (ERR) is 0%.

scholarly journals Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eun-Gyu Ha ◽  
Kug Jin Jeon ◽  
Young Hyun Kim ◽  
Jae-Young Kim ◽  
Sang-Sun Han
Keyword(s):  
Artificial Intelligence ◽  
Model Performance ◽  
Histogram Equalization ◽  
Permanent Dentition ◽  
Clinical Use ◽  
Image Preprocessing ◽  
Test Dataset ◽  
Panoramic Radiographs ◽  
Proposed Model ◽  
External Test

AbstractThis study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.

Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

2020 ◽  
Vol 11 (1) ◽  
pp. 37-57
Author(s):  
Law Kumar Singh ◽  
Munish Khanna ◽  
Hitendra Garg
Keyword(s):  
Rejection Rate ◽  
Acceptance Rate ◽  
Physiological Traits ◽  
Equal Error Rate ◽  
Multimodal Biometrics ◽  
False Acceptance Rate ◽  
Face Features ◽  
Palm Print ◽  
Hand Geometry ◽  
Biometric Features

Multimodal biometrics refers to the exploiting combination of two or more biometric modalities in an identification of a system. Fingerprint, face, retina, iris, hand geometry, DNA, and palm print are physiological traits while voice, signature, keystrokes, gait are behavioural traits used for identification by a system. Single biometric features like faces, fingerprints, irises, retinas, etc., deteriorate or change with time, environment, user mode, physiological defects, and circumstance therefore integrating multi features of biometric traits increase robustness of the system. The proposed multimodal biometrics system presents recognition based on face detection and fingerprint physiological traits. This proposed system increases the efficiency, accuracy and decreases execution time of the system as compared to the existing systems. The performance of proposed method is reported in terms of parameters such as False Rejection Rate (FRR), False Acceptance Rate (FAR) and Equal Error Rate (EER) and accuracy is reported at 95.389%.

scholarly journals Fingerprint verification using computational geometry

DYNA ◽  
2016 ◽  
Vol 83 (195) ◽  
pp. 128-137 ◽  
Author(s):  
Manuel Ramírez Flores ◽  
Gualberto Aguilar Torres ◽  
Gina Gallegos García ◽  
Miguel Ángel García Licona
Keyword(s):  
Delaunay Triangulation ◽  
Error Rate ◽  
Connected Graph ◽  
Local Stability ◽  
Acceptance Rate ◽  
Equal Error Rate ◽  
False Acceptance Rate ◽  
Fingerprint Verification ◽  
Minimum Angle ◽  
False Acceptance

This paper presents a robust minutiae based method for fingerprint verification. The proposed method uses Delaunay Triangulation to represent minutiae as nodes of a connected graph composed of triangles. The minimum angle over all triangulations is maximized, which gives local stability to the constructed structures against rotation and translation variations. Geometric thresholds and minutiae data were used to characterize the triangulations created from input and template fingerprint images. The effectiveness of the proposed method is confirmed through calculations of false acceptance rate (FAR), false rejected rate (FRR) and equal error rate (EER) over FVC2002 databases compared to the results of other approaches.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

scholarly journals Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1568
Author(s):  
Junmo Kim ◽  
Geunbo Yang ◽  
Juhyeong Kim ◽  
Seungmin Lee ◽  
Ko Keun Kim ◽  
...  
Keyword(s):  
Incremental Learning ◽  
Physical State ◽  
Acceptance Rate ◽  
Support Vector ◽  
Ecg Signal ◽  
Biometric Authentication ◽  
False Acceptance Rate ◽  
Ecg Signals ◽  
Using Data ◽  
False Acceptance

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

Multimodal score level fusion for recognition using face and palmprint

2020 ◽  
pp. 002072092092966
Author(s):  
Milind E Rane ◽  
Umesh S Bhadade
Keyword(s):  
Recognition System ◽  
Acceptance Rate ◽  
Face Database ◽  
Biometric Recognition ◽  
False Acceptance Rate ◽  
Score Level Fusion ◽  
Score Fusion ◽  
Matching Score ◽  
Level Fusion ◽  
Fusion Approach

The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.

scholarly journals A Digital Signature Model Using XAdES Standard as a Rest Service

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 289
Author(s):  
Renato Carauta Ribeiro ◽  
Murilo Góes de Almeida ◽  
Edna Dias Canedo
Keyword(s):  
Public Administration ◽  
Digital Signature ◽  
Current System ◽  
Current Model ◽  
Ministry Of Education ◽  
Java Language ◽  
Digital Format ◽  
Rest Service ◽  
Proposed Model ◽  
Guidelines And Standards

The digital signature of documents and degrees is a topic widely discussed in the Federal Public Administration. Several laws and ordinances were created to standardize the issuance, validation and legal validity of digitally signed documents in national territory, such as the ordinances created by the Ministry of Education (MEC) to regulate the issuance of degrees in digital format. These ordinances created guidelines and standards that must be adopted by Federal Universities for the signing of in digital format. The main objective of this work is to study these ordinances, the main technologies and digital signature standards used in the literature to create a digital signature system model for University of Brasília-UnB, which complies with the MEC and ICP-Brazil standards. Moreover, the model must be developed with the main standards and technologies in the market, cohesive to the current UnB architecture, easy to maintain and update to new standards that may emerge, and also be a fully open source project. An architectural model and a prototype in Java language were developed using XAdES4j library as a microservice intermediated by the bus used in UnB. The prototype developed was compared with the current digital signature system named C3Web. The comparative tests and results between the two solutions showed that the current system used in UnB does not perform the signature in accordance with the standard proposed by the MEC, in addition to being a private system using proprietary technologies for the execution of digital signatures. The tests performed with the proposed model demonstrated that it performs the digital signature in accordance with the XAdES-T standard, regulations of the MEC and ICP-Brazil. In addition, the solution presented a performance comparable to the current system used by UnB with a little more effective security than the current system used. The current model developed in this work can be a basis for the creation of future subscription systems for Brazilian Universities.

Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis

2020 ◽  
Vol 33 (02) ◽  
pp. 431-445
Author(s):  
Azarnoosh Kafi ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Confidence Interval ◽  
Data Envelopment ◽  
Data Set ◽  
Interval Model ◽  
Time Period ◽  
Proposed Model ◽  
The Future ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forecasting algorithm along with 95% confidence interval to generate interval data set for the next time period. What’s more, the manager’s opinion inserts and plays its role in the proposed forecasting model. Equipped with forecasted data set and with respect to data set from previous periods, the efficiency for the future period can be forecasted. This is done by proposing a proposed model and solving it by the confidence interval method. The proposed method is then implemented on the data of an automotive industry and, it is compared with the Monte Carlo simulation methods and the interval model. Using the results, it is shown that the proposed method works better to forecast the efficiency confidence interval. Finally, the efficiency and confidence interval of 95% is calculated for the upcoming period using the proposed model.

A NOTE ON EPQ MODEL FOR SEASONAL PERISHABLE PRODUCTS WITH STOCK DEPENDENT DEMAND

2008 ◽  
Vol 25 (03) ◽  
pp. 301-315 ◽  
Author(s):  
S. PANDA ◽  
S. SAHA ◽  
M. BASU
Keyword(s):  
Perishable Products ◽  
Programming Technique ◽  
Economic Production Quantity ◽  
Economic Production ◽  
Time Period ◽  
Capacity Level ◽  
Proposed Model ◽  
Production Inventory ◽  
Epq Model ◽  
Production Quantity

A single item, single cycle economic production quantity model for perishable products is proposed where the demand is two-component and stock dependent. The production inventory scenario of products like cake, bread, fast foods, fishes, garments, cosmetics etc in the festival season is considered. The profit function is formulated under the assumption that the time period of the festival seasons is fixed and the display capacity of the produced item is limited. In the formulation process, to introduce more flexibility, a goal programming technique is incorporated to achieve the producer's desired profit and stock of as much inventory as possible below the display capacity level. A numerical example is presented to illustrate the proposed model. A sensitivity analysis of the model is also carried out.

scholarly journals Asphaltene Formation Damage Stimulation by Ultrasound: An Analytical Approach Using Bundle of Tubes Modeling

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Arash Rabbani ◽  
Mohammad Hossein Ghazanfari ◽  
Mahmood Amani
Keyword(s):  
Present Model ◽  
Formation Damage ◽  
Model Parameters ◽  
Surface Deposition ◽  
Novel Approach ◽  
Time Period ◽  
Proposed Model ◽  
Stimulation Efficiency ◽  
Permeability Impairment

This study presents a novel approach for bundle of tubes modeling of permeability impairment due to asphaltene-induced formation damage attenuated by ultrasound which has been rarely attended in the available literature. Model uses the changes of asphaltene particle size distribution (APSD) as a function of time due to ultrasound radiation, while considering surface deposition and pore throat plugging mechanisms. The proposed model predicts the experimental data of permeability reduction during coinjection of solvent and asphaltenic oil into core with reasonable agreement. Viscosity variation due to sonication of crude oil is used to determine the fluid mobility applied in the model. The results of modeling indicate that the fluid samples exposed to ultrasound may cause much less asphaltene-induced damage inside the porous medium. Sensitivity analysis of the model parameters showed that there is an optimum time period during which the best stimulation efficiency is observed. The results of this work can be helpful to better understand the role of ultrasound prohibition in dynamic behavior of asphaltene deposition in porous media. Furthermore, the present model could be potentially utilized for modeling of other time-dependent particle induced damages.

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