Python
Plant Disease Detection and Classification by Deep Learning: A Review
Plant Disease Detection Using Machine Learning Techniques
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
Privacy-Preserving Outsourced Support Vector Machine Design for Secure Drug Discovery
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.
Quantum Safe Multi-Factor User Authentication Protocol for Cloud-Assisted Medical IoT
Aim:
To design and implement a Quantum-Safe Multi-Factor User Authentication Protocol for Cloud-Assisted Medical IOT systems, ensuring secure, privacy-preserving, and tamper-resistant access to sensitive healthcare data, even against future quantum-computing attacks, by integrating post-quantum cryptography.
Rank Fusion-Based Crime Scene Shoeprint Image Retrieval
Aim:
The aim of the document project is to develop an automated shoeprint image retrieval system for forensic investigation. The system focuses on matching crime-scene shoeprints with reference images using handcrafted visual features. It seeks to improve retrieval accuracy through rank-level fusion of multiple descriptors.