A New Enhanced Template Protection Algorithm on Iris Recognition

Over the past few years, biometric systems have become prominent in terms of verification of the user identity due to increased demand of security in the networked society. Iris recognition system is a novel technology for the verification of user which is considered as the most secure, reliable and stable technique. It is generally accepted in the areas with high security. Though, security is major concern in this field, a significant number of approaches have been proposed to secure iris biometrics, But still, there is a scope to improve these techniques. Thus, in this work, a novel model is proposed which employs a bitmask compression technique to secure the template obtained for iris by compressing its actual size. In addition; SVM is used for the classification process. Mean Square Error, Bit Error Rate, PSNR, and GAR are different parameters which are used for measuring the effectiveness of the proposed model. The simulation results are carried out in MATLAB software and the comparative results validated the efficacy of the novel model with respect to security, efficacy and accuracy.

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Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2018.3.3044 ◽

2018 ◽

Vol 13 (3) ◽

pp. 408-428 ◽

Cited By ~ 4

Author(s):

Phu Vo Ngoc

Keyword(s):

Latent Semantic Analysis ◽

Semantic Analysis ◽

Sentiment Classification ◽

Training Data ◽

The Novel ◽

Data Set ◽

Proposed Model ◽

Testing Data ◽

Novel Model ◽

Better Than

We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.

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Masked Sentence Model Based on BERT for Move Recognition in Medical Scientific Abstracts

Journal of Data and Information Science ◽

10.2478/jdis-2019-0020 ◽

2019 ◽

Vol 4 (4) ◽

pp. 42-55

Author(s):

Gaihong Yu ◽

Zhixiong Zhang ◽

Huan Liu ◽

Liangping Ding

Keyword(s):

Contextual Information ◽

English Literature ◽

Language Model ◽

Classification Model ◽

The Novel ◽

Model Based ◽

Contextual Features ◽

Proposed Model ◽

Novel Model ◽

Fine Tune

Abstract Purpose Move recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units. To improve the performance of move recognition in scientific abstracts, a novel model of move recognition is proposed that outperforms the BERT-based method. Design/methodology/approach Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences. In this paper, inspired by the BERT masked language model (MLM), we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition. Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps. Then, we compare our model with HSLN-RNN, BERT-based and SciBERT using the same dataset. Findings Compared with the BERT-based and SciBERT models, the F1 score of our model outperforms them by 4.96% and 4.34%, respectively, which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-the-art results of HSLN-RNN at present. Research limitations The sequential features of move labels are not considered, which might be one of the reasons why HSLN-RNN has better performance. Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed, which is a typical biomedical database, to fine-tune our model. Practical implications The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences. Originality/value T he study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way. The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.

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A Novel of New 7D Hyperchaotic System with Self-Excited Attractors and Its Hybrid Synchronization

Computational Intelligence and Neuroscience ◽

10.1155/2021/3081345 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Ahmed S. Al-Obeidi ◽

Saad Fawzi Al-Azzawi ◽

Abdulsattar Abdullah Hamad ◽

M. Lellis Thivagar ◽

Zelalem Meraf ◽

...

Keyword(s):

Lyapunov Stability Theory ◽

Control Technique ◽

Hyperchaotic System ◽

The Novel ◽

Nonlinear Feedback ◽

New Class ◽

Proposed Model ◽

Quadratic Nonlinearities ◽

Hybrid Synchronization ◽

Novel Model

In this study, a novel 7D hyperchaotic model is constructed from the 6D Lorenz model via the nonlinear feedback control technique. The proposed model has an only unstable origin point. Thus, it is categorized as a model with self-excited attractors. And it has seven equations which include 19 terms, four of which are quadratic nonlinearities. Various important features of the novel model are analyzed, including equilibria points, stability, and Lyapunov exponents. The numerical simulation shows that the new class exhibits dynamical behaviors such as chaotic and hyperchaotic. This paper also presents the hybrid synchronization for a novel model via Lyapunov stability theory.

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Malicious traffic detection combined deep neural network with hierarchical attention mechanism

Scientific Reports ◽

10.1038/s41598-021-91805-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xiaoyang Liu ◽

Jiamiao Liu

Keyword(s):

Data Flow ◽

Detection Efficiency ◽

False Positive Rate ◽

Attention Mechanism ◽

The Novel ◽

Malicious Attack ◽

Detection Model ◽

Proposed Model ◽

Traffic Detection ◽

Novel Model

AbstractGiven the gradual intensification of the current network security situation, malicious attack traffic is flooding the entire network environment, and the current malicious traffic detection model is insufficient in detection efficiency and detection performance. This paper proposes a data processing method that divides the flow data into data flow segments so that the model can improve the throughput per unit time to meet its detection efficiency. For this kind of data, a malicious traffic detection model with a hierarchical attention mechanism is also proposed and named HAGRU (Hierarchical Attention Gated Recurrent Unit). By fusing the feature information of the three hierarchies, the detection ability of the model is improved. An attention mechanism is introduced to focus on malicious flows in the data flow segment, which can reasonably utilize limited computing resources. Finally, compare the proposed model with the current state of the method on the datasets. The experimental results show that: the novel model performs well in different evaluation indicators (detection rate, false-positive rate, F-score), and it can improve the performance of category recognition with fewer samples when the data is unbalanced. At the same time, the training of the novel model on larger datasets will enhance the generalization ability and reduce the false alarm rate. The proposed model not only improves the performance of malicious traffic detection but also provides a new research method for improving the efficiency of model detection.

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IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

Journal of Advanced Sciences and Engineering Technologies ◽

10.32441/jaset.v1i2.119 ◽

2018 ◽

Vol 1 (2) ◽

pp. 34-44

Author(s):

Faris E Mohammed ◽

Dr. Eman M ALdaidamony ◽

Prof. A. M Raid

Keyword(s):

Iris Recognition ◽

Recognition Performance ◽

Recognition System ◽

Individual Identification ◽

Work Place ◽

Identification Process ◽

Finger Vein ◽

Noise Point ◽

Vein Recognition ◽

A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems. © 2018 JASET, International Scholars and Researchers Association

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