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Nevertheless, the main goal of this paper is to explore deep learning approaches using convolutional [6], recurrent [8] and self-attention networks [11] (CNNs,.
Jan 16, 2024 · The study aims to evaluate and compare the performance of various machine learning (ML) classifiers in the context of detecting cyber-trolling behaviors.
Missing: Aggressiveness | Show results with:Aggressiveness
The approach is based on combining Support Vector Machines and Recurrent Neural Network models for analysing a wide-range of character, word, word embeddings, ...
Missing: Aggressiveness | Show results with:Aggressiveness
AI-based violence detection in videos works by training algorithms on labeled video datasets, where violent and non-violent actions are annotated. These ...
Abstract—Aggressive comments on social media negatively impact human life. Such offensive contents are responsible for depression and suicidal-related ...
Missing: Aggressiveness | Show results with:Aggressiveness
The Isistanitos's approach for detecting aggressive content in multiple social media sites is presented, based on combining Support Vector Machines and ...
Missing: Aggressiveness | Show results with:Aggressiveness
Our work focuses on aggression detection by using deep learning methods to classify tweets cyberaggressive (CA) and non-cyber aggressive (NCA). Pipeline of ...
Missing: Aggressiveness | Show results with:Aggressiveness
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In this paper we propose a novel solution to the problem of drivers' behavior classification based on a Long Short Term Memory Fully Convolutional Network (LTSM ...
Our approach is based on a recurrent neural network, more specifically, a bi-directional gated re- current unit (GRU) layer with max pooling and average pooling ...
Missing: Aggressiveness | Show results with:Aggressiveness
The objective of this task is to detect aggressive content and the level of aggressiveness. Thirty teams submitted their test runs. The best system obtained a.