Comminution Energy Reduction by Two - Stage Classification

1990 ◽  
pp. 3-17
Author(s):  
D. A. Dahlstrom ◽  
W.-P. Kam
Keyword(s):  
Energy Reduction ◽  
Two Stage ◽  
Stage Classification

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%.

A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

2021 ◽  
Vol 10 (2) ◽  
pp. 736-745
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Feature Selection Method ◽  
Support Vector ◽  
Two Stage ◽  
Machine Learning Classification ◽  
Second Stage ◽  
Testing Data ◽  
Stage Classification ◽  
Positive Dataset

The increased usage of the Internet and social networks allowed and enabled people to express their views, which have generated an increasing attention lately. Sentiment Analysis (SA) techniques are used to determine the polarity of information, either positive or negative, toward a given topic, including opinions. In this research, we have introduced a machine learning approach based on Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) classifiers, to find and classify extreme opinions in Arabic reviews. To achieve this, a dataset of 1500 Arabic reviews was collected from Google Play Store. In addition, a two-stage Classification process was applied to classify the reviews. In the first stage, we built a binary classifier to sort out positive from negative reviews. In the second stage, however we applied a binary classification mechanism based on a set of proposed rules that distinguishes extreme positive from positive reviews, and extreme negative from negative reviews. Four major experiments were conducted with a total of 10 different sub experiments to fulfill the two-stage process using different X-validation schemas and Term Frequency-Inverse Document Frequency feature selection method. Obtained results have indicated that SVM was the best during the first stage classification with 30% testing data, and NB was the best with 20% testing data. The results of the second stage classification indicated that SVM has scored better results in identifying extreme positive reviews when dealing with the positive dataset with an overall accuracy of 68.7% and NB showed better accuracy results in identifying extreme negative reviews when dealing with the negative dataset, with an overall accuracy of 72.8%.

Highly accurate recognition of printed Korean characters through an improved two-stage classification method

1999 ◽  
Vol 32 (12) ◽  
pp. 1935-1945 ◽  
Author(s):  
Jin-Soo Lee ◽  
Oh-Jun Kwon ◽  
Sung-Yang Bang
Keyword(s):  
Classification Method ◽  
Two Stage ◽  
Stage Classification

scholarly journals Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 180618-180632
Author(s):  
Mehdi Abdollahpour ◽  
Tohid Yousefi Rezaii ◽  
Ali Farzamnia ◽  
Ismail Saad
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Sleep Stage ◽  
Two Stage ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Fusion Framework

scholarly journals A two-stage classification method for borehole-wall images with support vector machine

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0199749
Author(s):  
Zhaopeng Deng ◽  
Maoyong Cao ◽  
Laxmisha Rai ◽  
Wei Gao
Keyword(s):  
Support Vector Machine ◽  
Borehole Wall ◽  
Classification Method ◽  
Support Vector ◽  
Two Stage ◽  
Stage Classification

scholarly journals When to Detect Changes in Object-Based Image Analysis: Before or after Classification?

2017 ◽  
Author(s):  
Kaisheng Luo ◽  
Fu-lu Tao ◽  
Juana P. Moiwo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Change Detection ◽  
Coniferous Forest ◽  
Detection Methods ◽  
Landsat Images ◽  
Two Stage ◽  
Smoothing Effects ◽  
Stage Classification ◽  
Elevation Model

This study compared two object-oriented land use change detection methods—detection after classification (DAC) and classification after detection (CAD) —based on a digital elevation model, slope data, and multi-temporal Landsat images (TM image for 2000 and ETM image for 2010). We noted that the overall accuracy of the DAC (86.42%) was much higher than that of the CAD (71.71%). However, a slight difference between the accuracies of the two methods exists for deciduous broadleaf forest, evergreen coniferous forest, mixed wood, upland, paddy, reserved land, and settlement. Owing to substantial spectrum differences, these land use types can be extracted using spectral indexes. The accuracy of DAC was much higher than that of CAD for industrial land, traffic land, green shrub, reservoir, lake, river, and channel, all of which share similar spectrums. The discrepancy was mainly because DAC can completely utilize various forms of information apart from spectrum information during a two-stage classification. In addition, the change-area boundary was not limited at first, but was adjustable in the process of classification. DAC can overcome smoothing effects to a great extent using multi-scale segmentations and multi-characters in detection. Although DAC yielded better results, it was more time-consuming (28 days) because it uses a two-stage classification approach. Conversely, CAD consumed less time (15 days). Thus, a hybrid of the two methods is recommended for application in land use change detection.

scholarly journals Human Attribute Analysis Using a Top-View Camera Based on Two-Stage Classification

2013 ◽  
Vol E96.D (4) ◽  
pp. 993-996 ◽  
Author(s):  
Toshihiko YAMASAKI ◽  
Tomoaki MATSUNAMI ◽  
Tuhan CHEN
Keyword(s):  
Two Stage ◽  
Attribute Analysis ◽  
Stage Classification ◽  
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