Research on Virus Detection Technology Based on Ensemble Neural Network and SVM

2012 ◽  
pp. 367-372
Author(s):  
Boyun Zhang ◽  
Jianping Yin ◽  
Shulin Wang
Keyword(s):  
Neural Network ◽  
Virus Detection ◽  
Detection Technology ◽  
Ensemble Neural Network

Research on virus detection technique based on ensemble neural network and SVM

2014 ◽  
Vol 137 ◽  
pp. 24-33 ◽  
Author(s):  
Bo-yun Zhang ◽  
Jian-ping Yin ◽  
Shu-Lin Wang ◽  
Xi-ai Yan
Keyword(s):  
Neural Network ◽  
Virus Detection ◽  
Detection Technique ◽  
Ensemble Neural Network

scholarly journals Neural Network for Metal Detection Based on Magnetic Impedance Sensor

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4456
Author(s):  
Sungjae Ha ◽  
Dongwoo Lee ◽  
Hoijun Kim ◽  
Soonchul Kwon ◽  
EungJo Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Field ◽  
Deep Learning ◽  
Short Term Memory ◽  
Magnetic Impedance ◽  
Detection Technology ◽  
Metal Detection ◽  
Long Short Term Memory ◽  
Impedance Sensor ◽  
The Magnetic Field

The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.

Predicting Synthetic Lethality in Human Cancers via Multi-Graph Ensemble Neural Network

2021 ◽  
Author(s):  
Mincai Lai ◽  
Guangyao Chen ◽  
Haochen Yang ◽  
Jingkang Yang ◽  
Zhihao Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Synthetic Lethality ◽  
Human Cancers ◽  
Ensemble Neural Network

scholarly journals Virus detection and identification in minutes using single-particle imaging and deep learning

2020 ◽  
Author(s):  
Nicolas Shiaelis ◽  
Alexander Tometzki ◽  
Leon Peto ◽  
Andrew McMahon ◽  
Christof Hepp ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Single Particle ◽  
Virus Detection ◽  
Diagnostic Methods ◽  
Clinical Samples ◽  
Promising Alternative ◽  
Detection And Identification ◽  
Particle Imaging ◽  
Single Particle Imaging

AbstractThe increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive viral diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes and does not require any lysis, purification or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses, with high accuracy. Single-particle imaging combined with deep learning offers a promising alternative to traditional viral diagnostic methods, and has the potential for significant impact.

scholarly journals Colorimetric Reverse Transcription–Loop-Mediated Isothermal Amplification Assay for Rapid Detection of SARS-CoV-2

2021 ◽  
Author(s):  
Meng Yee Lai ◽  
Soo Nee Tang ◽  
Yee Ling Lau
Keyword(s):  
Sensitivity And Specificity ◽  
Reverse Transcription ◽  
Virus Detection ◽  
Lamp Assay ◽  
Isothermal Amplification ◽  
Clinical Sensitivity ◽  
Loop Mediated Isothermal Amplification ◽  
Resource Limited ◽  
Detection Technology ◽  
Simple Detection

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been spreading rapidly all over the world. In the absence of effective treatments or a vaccine, there is an urgent need to develop a more rapid and simple detection technology of COVID-19. We describe a WarmStart colorimetric reverse transcription–loop-mediated isothermal amplification (RT-LAMP) assay for the detection of SARS-CoV-2. The detection limit for this assay was 1 copy/µL SARS-CoV-2. To test the clinical sensitivity and specificity of the assay, 37 positive and 20 negative samples were used. The WarmStart colorimetric RT-LAMP had 100% sensitivity and specificity. End products were detected by direct observation, thereby eliminating the need for post-amplification processing steps. WarmStart colorimetric RT-LAMP provides an opportunity to facilitate virus detection in resource-limited settings without a sophisticated diagnostic infrastructure.

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