Assessment of the Effectiveness of Seven Biometric Feature Normalization Techniques

Friedman, Lee; Komogortsev, Oleg V.

doi:10.1109/tifs.2019.2904844

Cited by 49 publications

(16 citation statements)

References 14 publications

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“…The data preprocessing includes feature normalisation, feature reshaping, label encoding and data splitting. First, each of the values of the network traffic features was scaled to a range of 0 and 1 using the min–max normalisation method given by Equation ( 1 ) [ 75 , 76 ]:

where x is a network traffic feature vector while

and

are the minimum and maximum values of

, respectively. In order to enable development of the DRNN model, an extra dimension was included in the feature set to represent a unit time step (i.e., t = 1).…”

Section: Smote-drnn Algorithm and Model Developmentmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

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Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.

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where x is a network traffic feature vector while

and

are the minimum and maximum values of

, respectively. In order to enable development of the DRNN model, an extra dimension was included in the feature set to represent a unit time step (i.e., t = 1).…”

Section: Smote-drnn Algorithm and Model Developmentmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

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show abstract

“…By further investigation, we found out that a normalization approach can be beneficial to create a smooth image along with increasing the contrast of illumination near the border of the organs. So, to overcome the mentioned problems and enhance the result of the segmentation, a Z score normalization technique is employed so that all the nonzero values inside the image have a unit variance and zero mean ([ 23 – 25 ]; Jafarzadeh [ 26 ]). Equation ( 1 ) outlines how to apply Z score normalization.…”

Section: Methodsmentioning

confidence: 99%

Lung Infection Segmentation for COVID‐19 Pneumonia Based on a Cascade Convolutional Network from CT Images

Ranjbarzadeh

Ghoushchi

Bendechache

et al. 2021

BioMed Research International

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The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c -means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2 % .

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“…Principle component analysis 53 is used to reduce the number of features and allow for the visual inspection of the underlying data. Z-score normalization 54 was used to normalize the VGOPs as a preprocessing measure to aid in the PCA decomposition, though in principle is not necessary.…”

Section: Vector Graph Order Parametermentioning

confidence: 99%

A Physically-informed Graph-based Order Parameter for the Universal Characterization of Atomic Structures

Chapman,

Goldman,

Wood

2021

Preprint

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A new graph-based order parameter is introduced for the characterization of atomistic structures. The order parameter is universal to any material/chemical system, and is transferable to all structural geometries. Three sets of data are used to validate both the generalizability and accuracy of the algorithm: (1) liquid lithium configurations spanning up to 300 GPa, (2) condensed phases of carbon along with nanotubes and buckyballs at ambient and high temperature, and (3) a diverse set of aluminum configurations including surfaces, compressed and expanded lattices, point defects, grain boundaries, liquids, nanoparticles, all at non-zero temperatures. The aluminum configurations are also compared to existing characterization methods for both speed and accuracy. Our order parameter uniquely classifies every configuration and outperforms all crystalline order parameters studied here, opening the door for its use in a multitude of complex application spaces that can require fine configurational characterization of materials.

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Assessment of the Effectiveness of Seven Biometric Feature Normalization Techniques

Cited by 49 publications

References 14 publications

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Lung Infection Segmentation for COVID‐19 Pneumonia Based on a Cascade Convolutional Network from CT Images

A Physically-informed Graph-based Order Parameter for the Universal Characterization of Atomic Structures

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