Python
A Novel Integrated Approach for Stock Prediction Based on Modal Decomposition Technology and Machine Learning
An Integrated Multi-Task Model for Fake News Detection
KiRTi: A Blockchain-based Credit Recommender System for Financial Institutions
LSTM Based Phishing Detection for Big Email Data
Predicting Market Performance Using Machine and Deep Learning Techniques
The aim of this study is to evaluate the effectiveness of various machine learning and deep learning algorithms, including LSTM networks, ARIMA models, and traditional machine learning techniques, for forecasting market prices. We analyze the performance of these models on stock historical datasets and compare their predictive accuracy to determine the most suitable approach for real-time market analysis. This research seeks to provide insights into the predictability of markets and support informed decision-making for investors
