scholarly journals Classification of Tweets based on Emotions using Word Embedding and Random Forest Classifiers

2017 ◽  
Vol 178 (3) ◽  
pp. 1-7
Author(s):  
Parth Vora ◽  
Mansi Khara ◽  
Kavita Kelkar
Keyword(s):  
Random Forest ◽  
Word Embedding

scholarly journals Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45993-45999
Author(s):  
Ung Yang ◽  
Seungwon Oh ◽  
Seung Gon Wi ◽  
Bok-Rye Lee ◽  
Sang-Hyun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Network Models ◽  
Convolution Neural Network ◽  
Neural Network Models

Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest

2021 ◽  
Author(s):  
Balajee Alphonse ◽  
Venkatesan Rajagopal ◽  
Sudhakar Sengan ◽  
Kousalya Kittusamy ◽  
Amudha Kandasamy ◽  
...  
Keyword(s):  
Random Forest ◽  
Time Domain ◽  
Multi Class Classification

A data mining approach to the diagnosis of failure modes for two serial fastened sandwich composite plates

2016 ◽  
Vol 51 (20) ◽  
pp. 2853-2862 ◽  
Author(s):  
Serkan Ballı
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Failure Modes ◽  
Composite Plates ◽  
Study Data ◽  
Sandwich Composite ◽  
Support Vector ◽  
Geometrical Parameters ◽  
Mining Methods

The aim of this study is to diagnose and classify the failure modes for two serial fastened sandwich composite plates using data mining techniques. The composite material used in the study was manufactured using glass fiber reinforced layer and aluminum sheets. Obtained results of previous experimental study for sandwich composite plates, which were mechanically fastened with two serial pins or bolts were used for classification of failure modes. Furthermore, experimental data from previous study consists of different geometrical parameters for various applied preload moments as 0 (pinned), 2, 3, 4, and 5 Nm (bolted). In this study, data mining methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, three geometrical parameters and 100 test data were used for classification by utilizing support vector machine, Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest methods. According to experiments, Random Forest method achieved better results than others and it was appropriate for diagnosing and classification of the failure modes. Performances of all data mining methods used were discussed in terms of accuracy and error ratios.

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