Deep Learning
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.
Recognition of Fish in Aqua Cage by Machine Learning with Image Enhancement
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
Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications
Traffic Signs Recognition using CNN and Keras
Whale and Dolphin Classification
The proposed method involves a multi-step process to classify whale and dolphin species from images. First, the dataset is collected and pre-processed to ensure high-quality input data. The VGG16 model is used to extract features from the images, capturing complex patterns and details. These features are then used to train a Support Vector Machine (SVM) model, which excels in binary and multi-class classification tasks.