A survey that ranks phishing URL classifiers by averaging published accuracy values, with naive Bayes lowest and random forest highest, plus an unvalidated proposal for a two-stage RF-CNN detector.
AI Powered Anti-Cyber Bullying System using Machine Learning Algorithm of Multinomial Naive Bayes and Optimized Linear Support Vector Machine
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
"Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue." ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation messaging system to test our model leading to the development of Artificial Intelligence powered anti-cyber bullying system using machine learning algorithm of Multinomial Naive Bayes (MNB) and optimized linear Support Vector Machine (SVM). Our model is able to detect and intercept bullying outgoing and incoming bullying messages and take immediate action.
fields
cs.CR 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey
A survey that ranks phishing URL classifiers by averaging published accuracy values, with naive Bayes lowest and random forest highest, plus an unvalidated proposal for a two-stage RF-CNN detector.