Results of training as well as validation: The matrix of confusion of all ML models.

Results of training as well as validation: The matrix of confusion of all ML models.

<p>(a) Stacking, (b) AdaBoost, (c) Bagging, (d) K-NN, (e) LR, (f) MLP, (g) RF, (h) SGD, (i) Voting, (j) XGBoost.</p>

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Bibliographic Details
Main Author:	Khandaker Mohammad Mohi Uddin (13124356) (author)
Other Authors:	Hasibul Hamim (19797121) (author), Mst. Nishat Tasnim Mim (19797124) (author), Arnisha Akhter (16323159) (author), Md Ashraf Uddin (14855902) (author)
Published:	2024
Subjects:	Biological Sciences not elsewhere classified Information Systems not elsewhere classified using mathematical visualization stochastic gradient descent social media platforms safeguard psychological wellness reverse document frequency online bullying could natural language processing free online environment eliminate online harassment combined two sets become much easier cnn ), convolutional deep neural networks binary classification model categorize bengali comments 000 bengali comments bidirectional long short 34 %, precision mlp ), k bengali comments long short lstm ), hybrid model deep learning 33 %, xlink "> writing aims term memory reaching consequences rapid adoption producing 94 nearest neighbors machine learning layer perceptron large number large amount label class f1 score evaluation accuracy earlier stage different points detected quickly count vectorizer contemporary web based approach
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