Python
Deep Learning
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
Product Recommendation System Using Large Language Model Llama 3
To develop a chatbot that integrates Retrieval-Augmented Generation (RAG) and Llama-3 API for product recommendation by leveraging a vector database with embeddings created using SBERT. This aim involves addressing limitations in traditional recommender systems, such as cold start problems and lack of personalization, by combining state-of-the-art language models with efficient data retrieval mechanisms.
Real-Time Plant Disease Dataset Development and Detection of Plant Disease Using Deep Learning
Recognition of Fish in Aqua Cage by Machine Learning with Image Enhancement
Research on Fire Smoke Detection Algorithm Based on Improved YOLOv8
Road Traffic Accident Risk Prediction and Key Factor Identification Framework Based on Explainable Deep Learning
Silent Alert: Advancing Women’s Security through Smart Sign Recognition and AI
Sleep Apnea Detection From Single-Lead ECG: A Comprehensive Analysis of Machine Learning and Deep Learning Algorithms
Social Media Forensics an Adaptive Cyberbullying-Related Hate Speech Detection Approach Based on Neural Networks with Uncertainty
Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications
Traffic Signs Recognition using CNN and Keras
