Python
Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning
Predicting Heart Diseases Using Machine Learning and Different Data Classification Techniques
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
Predictive Analysis of Network based Attacks by Hybrid Machine Learning Algorithms
Predictive Analytics on Diabetes Data using Machine Learning Techniques
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.
Ransomware Classification and Detection with Machine Learning Algorithms
RanViz Ransomware Visualization and Classification Based on Time-series Categorical Representation of API Calls
Real-Time Plant Disease Dataset Development and Detection of Plant Disease Using Deep Learning
Recent Advances in Deep-Learning Based SAR Image Target Detection and Recognition
