Author Details

Drones are now used in a wide range of industries, including delivery services and agriculture. Notwithstanding, controlling robots in powerful conditions can be testing, particularly while performing complex assignments. Conventional strategies for drone mechanization depend on pre-customized directions, restricting their adaptability and versatility. Drones can learn from their interactions with their environment and improve their performance over time with the help of reinforcement learning (RL), which has emerged as a promising method for drone automation in recent years. This paper looks at how RL can be used to automate drones and how it can be used in different industries. In addition, the difficulties of RL-based drone automation and potential directions for future research are discussed in the paper.

Keywords

Reinforcement Learning, Drone Automation, Machine Learning, Navigation, Obstacle Avoidance, Object Tracking, Safety, Reliability, Training Data, Dynamic Environments, Decision-Making.

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Hate Speech Detection in Social Media Using the Ensemble Learning Technique

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Authors

Ahshanul Haque ¹, Md Naseef-Ur-Rahman Chowdhury ¹, Hamdy Soliman ¹

Affiliations
1 Department of Computer Science, New Mexico Tech, Socorro, New Mexico, US

Source

International Journal of Advanced Networking and Applications, Vol 15, No 1 (2023), Pagination: 5815-5821

Abstract

Our lives have become intertwined with social media platforms such as Twitter, Facebook, LinkedIn, etc. They provide us with a platform to express our opinions and share our thoughts with the world. However, some individuals abuse the freedom of expression afforded to them by these platforms and utilize them to disseminate content that is derogatory and promotes hate speech. This has become a significant problem today, and detecting such content is a challenging task. In this research paper, we propose a solution for hate speech detection in social media using natural language processing techniques. We use a publicly available dataset provided by CrowdFlower and perform text pre-processing to clean the dataset. We then conduct feature engineering to extract key features that can be used in machine learning classification algorithms. We compare the performance of various algorithms about each feature set and conduct an in-depth analysis of the results obtained.

Keywords

Hate Speech Detection, Social Media, Natural Language Processing, Machine Learning, Artificial Intelligence, Sentiment Analysis.

Full Text

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