To develop a real-time fire and smoke detection system using the latest YOLOv11 model, providing higher accuracy and faster response in complex environments.
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The aim of this study is to develop a robust and accurate traffic accident risk prediction model by leveraging deep learning techniques such as CNN (Convolutional Neural Network), BiLSTM (Bi-directional Long Short-Term Memory), and GRU (Gated Recurrent Unit) models.
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The aim of this project is to develop a flight delay prediction system that uses a decision tree algorithm to predict delays based on historical data while providing real-time flight tracking and delay updates using Neo4j and live APIs.
Aim: Ā Ā Ā Ā Ā Ā Ā Ā Ā The sentiment analysis for crypto currency-related tweets, Crypto currency market price prediction based on the analyzed sentiments with
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.
Aim: Ā Ā Ā Ā Ā Ā Ā Ā Ā We proposed detecting Sleep Apnea Detection From Single-Lead ECG. The advancement of smart wearables technologies has provided a
Aim:Ā Ā Ā Ā Ā Ā Ā The aim of the project is to develop an automated, real-time attendance system using face recognition technology to enhance accuracy, eliminate manual errors, and streamline attendance tracking in institutions.