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An enhanced SAC algorithm in corporating embedding techniques, semantic generalization, and clustering to efficiently process and anonymize text data is introduced to ensure privacy preservation while retaining the utility of the transformed data, making it suitable for various privacy-sensitive applications.
The research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches.
The system aims to classify URLs as either legitimate or malicious by analyzing various structural and statistical features extracted from the URLs by utilizing a Gradient Boosting Classifier due to its high accuracy and ability to handle complex patterns in data.
The research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches.
This project presents an ML-Based Real-Time UPI Fraud Detection System that uses machine learning algorithms to identify fraudulent transactions efficiently and shows that the Random Forest algorithm achieves the highest accuracy, making it the most effective model for fraud detection.
An optimized machine learning-based Air Quality Forecasting System that integrates Extreme Learning Machines (ELM) and Genetic Algorithms (GA) to predict short-term variations in air quality and demonstrates a robust, scalable, and practical solution for short-term air quality prediction.
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