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A novel method for detecting malware using machine learning is proposed. The approach leverages feature extraction techniques to identify malicious patterns within software code. Experimental results demonstrate that the method achieves high accuracy and outperforms existing techniques in detecting previously unseen malware variants. The proposed system offers a proactive solution for enhancing cybersecurity defenses against evolving malware threats.

This document discusses the application of machine learning techniques to predict stock market movements. It explores various algorithms, including recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, for analyzing historical stock data and identifying patterns. The study aims to develop a predictive model that can assist investors in making more informed trading decisions and