Automated classification of non-functional requirements

2007 ◽  
Vol 12 (2) ◽  
pp. 103-120 ◽  
Author(s):  
Jane Cleland-Huang ◽  
Raffaella Settimi ◽  
Xuchang Zou ◽  
Peter Solc
Keyword(s):  
Automated Classification ◽  
Functional Requirements

Automated classification of copy number variants based on 2019 ACMG standards

2021 ◽  
Vol 132 ◽  
pp. S287-S288
Author(s):  
Jianling Ji ◽  
Ryan Schmidt ◽  
Westley Sherman ◽  
Ryan Peralta ◽  
Megan Roytman ◽  
...  
Keyword(s):  
Copy Number ◽  
Copy Number Variants ◽  
Automated Classification

scholarly journals Ensemble-based Bag of Features for Automated Classification of Normal and COVID-19 CXR Images

2021 ◽  
pp. 102656
Author(s):  
Amira S. Ashour ◽  
Merihan M. Eissa ◽  
Maram A. Wahba ◽  
Radwa A. Elsawy ◽  
Hamada Fathy Elgnainy ◽  
...  
Keyword(s):  
Automated Classification ◽  
Bag Of Features

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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