Showing 37–43 of 43 results

Product Recommendation System Using Large Language Model Llama 3

5,500.00
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

5,500.00
Aim: The aim of this project is to propose a system to automate the process of fish population monitoring in aquaculture environments by utilizing the YOLOv8 deep learning-based object detection model, combined with image enhancement techniques.

Silent Alert: Advancing Women’s Security through Smart Sign Recognition and AI

5,500.00
Aim: To develop a real-time video-level-sign classification system that identifies rescue and emergency hand signs using BiLSTM, enabling automated alert messages to guardians via Twilio SMS.

Sleep Apnea Detection From Single-Lead ECG: A Comprehensive Analysis of Machine Learning and Deep Learning Algorithms

5,500.00
Aim:           We proposed detecting Sleep Apnea Detection From Single-Lead ECG. The advancement of smart wearables technologies has provided a

Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications

5,500.00
Aim:           To detect and identify the Violation detection using Deep-Learning techniques. Abstract:         The widespread deployment of surveillance cameras,

Traffic Signs Recognition using CNN and Keras

5,500.00
Aim:            To detect and identify the Traffic Signs detection using CNN.  Abstract:          Traffic sign recognition and detection are

Whale and Dolphin Classification

5,500.00
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.