Robust Detection and Classification of Regional Seismic Signals Using a Two Mode/Two Stage Cascaded Adaptive Arma (CAARMA) Model

1985 ◽  
Author(s):  
B. Schnitta-Israel
Keyword(s):  
Seismic Signals ◽  
Two Stage ◽  
Robust Detection

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

scholarly journals Two-Stage Deep Learning Approach to the Classification of Fine-Art Paintings

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 41770-41781 ◽  
Author(s):  
Catherine Sandoval ◽  
Elena Pirogova ◽  
Margaret Lech
Keyword(s):  
Deep Learning ◽  
Fine Art ◽  
Learning Approach ◽  
Two Stage

scholarly journals Robust Detection of Atrial Fibrillation Using Classification of a Linearly-Transformed Window of R-R Intervals Tachogram

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110012-110022 ◽  
Author(s):  
Md Saiful Islam ◽  
Mohamed Maher Ben Ismail ◽  
Ouiem Bchir ◽  
Mohammed Zakariah ◽  
Yousef Ajami Alotaibi
Keyword(s):  
Atrial Fibrillation ◽  
Robust Detection

Robust Detection and Classification of Longitudinal Changes in Color Retinal Fundus Images for Monitoring Diabetic Retinopathy

2006 ◽  
Vol 53 (6) ◽  
pp. 1084-1098 ◽  
Author(s):  
H. Narasimha-Iyer ◽  
A. Can ◽  
B. Roysam ◽  
C.V. Stewart ◽  
H.L. Tanenbaum ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Fundus Images ◽  
Longitudinal Changes ◽  
Robust Detection ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Improved Classification of Recent HIV-1 Infection by Employing a Two-Stage Sensitive/Less-Sensitive Test Strategy

2003 ◽  
Vol 32 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Niel T. Constantine ◽  
Anne M. Sill ◽  
Noreen Jack ◽  
Kristen Kreisel ◽  
Jeffrey Edwards ◽  
...  
Keyword(s):  
Sensitive Test ◽  
Two Stage ◽  
Test Strategy ◽  
Hiv 1

Discriminative Feature Selection for Automatic Classification of Volcano-Seismic Signals

2012 ◽  
Vol 9 (2) ◽  
pp. 151-155 ◽  
Author(s):  
I. Alvarez ◽  
L. Garcia ◽  
G. Cortes ◽  
C. Benitez ◽  
Ángel De la Torre
Keyword(s):  
Feature Selection ◽  
Automatic Classification ◽  
Seismic Signals ◽  
Discriminative Feature ◽  
Selection For
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