Meta-cognitive neural network method for classification of diabetic retinal images

2016 ◽  
Author(s):  
Rubeena Banu ◽  
Vanishri Arun ◽  
N Shankaraiah ◽  
Shyam V
Keyword(s):  
Neural Network ◽  
Retinal Images ◽  
Neural Network Method ◽  
Network Method

scholarly journals Automatic Classification of Pararubber Trees in Thailand from LANDSAT-8 Images Using Neural Network Method

2021 ◽  
Author(s):  
C. Supunyachotsakul ◽  
N. Suksangpanya
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Satellite Images ◽  
Ground Truth ◽  
Classification Model ◽  
Landsat 8 ◽  
Neural Network Method ◽  
Network Method ◽  
Convolutional Encoder

Classifying features from satellite images has been a time-consuming manual process which requires lots of manpower. This work exploits deep convolutional encoder-decoder neural network to develop an algorithm that can automatically classify the extents of the Pararubber tree-growing areas from the LANDSAT-8 images. The ground truth of the areas of the Pararubber tree was manually prepared and was separated into training datasets and the validation datasets. The classification model from this approach obtained using the training datasets was verified with the classification accuracy of70.90%, precision of 67.66%, recall of 80.80%, and F1 score of 73.59%.

scholarly journals INDONESIAN LANGUAGE CLASSIFICATION OF CYBERBULLING WORDS ON TWITTER USING ADABOOST AND NEURAL NETWORK METHODS

2021 ◽  
Vol 3 (2) ◽  
pp. 93-100
Author(s):  
Kristiawan Nugroho
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Negative Consequences ◽  
Accuracy Rate ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Methods ◽  
Network Method ◽  
Cyberbullying Detection

Cyberbullying is a very interesting research topic because of the development of communication technology, especially social media, which causes negative consequences where people can bully each other, causing victims and even suicide. The phenomenon of Cyberbullying detection has been widely researched using various approaches. In this study, the AdaBoost and Neural Network methods were used, which are machine learning methods in classifying Cyberbullying words from various comments taken from Twitter. Testing the classification results with these two methods produces an accuracy rate of 99.5% with Adaboost and 99.8% using the Neural Network method. Meanwhile, when compared to other methods, the results obtained an accuracy of 99.8% with SVM and Decision Tree, 99.5% with Random Forest. Based on the research results of the Neural Network method, SVM and Decision Tree are tested methods in detecting the word cyberbullying proven by achieving the highest level of accuracy in this study

Sign in / Sign up

Export Citation Format

Share Document