Applications of Recurrent Neural Network for Biometric Authentication &amp; Anomaly Detection

Ackerson, Joseph M.; Dave, Rushit; Seliya, Naeem

doi:10.3390/info12070272

Cited by 45 publications

(18 citation statements)

References 49 publications

(103 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study used a recurrent neural network (RNN) to perform volumetric fluorescence microscopy [45]. In addition, biometric authentication and anomaly detection were performed using recurrent neural networks [46].…”

Section: Research Backgroundmentioning

confidence: 99%

SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images

Taha

Al-Jumaily

Mashhadany

et al. 2022

Viruses

View full text Add to dashboard Cite

The SARS-CoV-2 virus is responsible for the rapid global spread of the COVID-19 disease. As a result, it is critical to understand and collect primary data on the virus, infection epidemiology, and treatment. Despite the speed with which the virus was detected, studies of its cell biology and architecture at the ultrastructural level are still in their infancy. Therefore, we investigated and analyzed the viral morphometry of SARS-CoV-2 to extract important key points of the virus’s characteristics. Then, we proposed a prediction model to identify the real virus levels based on the optimization of a full recurrent neural network (RNN) using transmission electron microscopy (TEM) images. Consequently, identification of virus levels depends on the size of the morphometry of the area (width, height, circularity, roundness, aspect ratio, and solidity). The results of our model were an error score of training network performance 3.216 × 10−11 at 639 epoch, regression of −1.6 × 10−9, momentum gain (Mu) 1 × 10−9, and gradient value of 9.6852 × 10−8, which represent a network with a high ability to predict virus levels. The fully automated system enables virologists to take a high-accuracy approach to virus diagnosis, prevention of mutations, and life cycle and improvement of diagnostic reagents and drugs, adding a point of view to the advancement of medical virology.

show abstract

Section: Research Backgroundmentioning

confidence: 99%

SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images

Taha

Al-Jumaily

Mashhadany

et al. 2022

Viruses

View full text Add to dashboard Cite

show abstract

“…At present, feature extraction methods are mainly divided into two categories: one is to manually extract short-term features from audio signals, such as Meir cepstrum coefficient, pitch and energy, and then apply short-term features to traditional classifiers, such as GMM, matrix decomposition and HMM, etc. The other is automatic feature extraction using NN, such as Convolutional Neural Network (CNN) (Feng, 2022), auto-encoder, Recurrent Neural Network (RNN) (Ackerson et al, 2021), LSTM (Liu et al, 2022), CNN + LSTM, etc. Gao et al (2021) and Mandić (2022) show that these methods have achieved good results in speech classification tasks.…”

Section: Related Workmentioning

confidence: 99%

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

Shen

Xiao-xi²,

Jiang³

et al. 2022

Front. Neurorobot.

View full text Add to dashboard Cite

Dance emotion recognition is an important research direction of automatic speech recognition, especially in the robot environment. It is an important research content of dance emotion recognition to extract the features that best represent speech emotion and to construct an acoustic model with strong robustness and generalization. The dance emotion data set is small in size and high in dimension. The traditional recurrent neural network (RNN) has the problem of long-range dependence disappearance, and due to the focus on local information of convolutional neural network (CNN), the mining of potential relationships between frames in the input sequence is insufficient. To solve the above problems, this paper proposes a novel linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory (LSTM) for dance emotion recognition. In this paper, the linear prediction coefficient (LPC) and Meier frequency cepstrum coefficient (MFCC) are combined to obtain a new feature, namely the linear prediction Meier frequency cepstrum coefficient (LPMFCC). Then, the combined feature obtained by combining LPMFCC with energy feature is used as the extracted dance feature. The extracted features are input into the bidirectional LSTM network for training. Finally, support vector machine (SVM) is used to classify the obtained features through the full connection layer. Finally, we conduct experiments on public data sets and obtain the better effectiveness compared with the state-of-art dance motion recognition methods.

show abstract

“…In any case, it was once found in this article that these social features did never again work appropriately while clients had been moving, in like manner, diminishing the exactness of the application. This issue with aggregating social records while the customer was once in real life used to be seen to be an issue in various articles as appropriately [14][15][16][17][18][19] and [5]. Likewise, many examination do now not ponder thought on how a client's ways of behaving can substitute over the long haul, for example, the buyer going downhill and composing more slow, the shopper now not being in that frame of mind to utilize their prevailing hand, and different long haul social changes [20].…”

Section: Authentication Schemes Using Machine Learningmentioning

confidence: 99%

“…In light of going before forecasts made in 2018, an expected 50 billion units are by and by connected to the web [4,5]. The majority of the devices connected with the net are presently not standard PCs, PCs, or cell phones, in any case, on the other hand are contraptions that are smartwatches, shrewd fridges, indoor regulators, voice administrations, security cameras, and parcels more.…”

Section: Introductionmentioning

confidence: 99%

Machine learning Applications of Data Security and Privacy A Systemic Review

Dave¹

2022

Preprint

Self Cite

View full text Add to dashboard Cite

<p><em>With the present day trends in technology, greater and more human beings rely on their non-public devices to preserve their sensitive information. Concurrently, the environment in which these devices are linked have grown to develop to be higher dynamic and complex. This opens the communicate of if the modern authentication techniques being used in these devices are reliable adequate to hold these user’s files safe. This paper examines the wonderful purchaser authentication schemes proposed to make larger the safety of great devices. This article is wreck up into two one of a variety avenues discussing authentication schemes that use each be- havioral biometrics or bodily layer authentication. This survey will discuss about every the advantages and challenges that show up with the accuracy, usability, and widespread safety of computing machine getting to recognize techniques in these authentication systems. This article pursuits to beautify in addition look up in this challenge by means of exhibiting the extra than a few existing day authentication models, their schematics, and their results.</em></p>

show abstract

Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

Cited by 45 publications

References 49 publications

SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images

SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

Machine learning Applications of Data Security and Privacy A Systemic Review

Contact Info

Product

Resources

About