Design of a Search and Rescue Robot Based on Fire hazards

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Aim:

Ā  Ā  Ā  Ā  The Mainstay of the project isĀ  to develop an autonomous firefighting robot that integrates AI for person detection, real-time video streaming, and obstacle avoidance. The system enhances firefighter safety and efficiency by providing autonomous navigation and fire suppression in hazardous environments.

Design of Wearable Device for Child Safety

13,500.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā Ā  Aim of the project is to develop a wearable device for Child safety within a child care environment

Eye care device for facial paralyzed patients

13,000.00
Aim: Aim of this project is automatic raspberry pi based automatic eye blinking system for facial paralyzed patients. Abstract:          

IoT Sensor Initiated Healthcare Data Security

14,000.00
Objective Ā Ā Ā Ā Ā Ā Ā Ā  Ā Ā  Aim of the project is to develop a secure data transmission system between healthcare devices and end-user

Mo-SSS: A Motorcycle Smart Security System Using Raspberry Pi Based on the Internet of Things

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Aim:

Ā  Ā  Ā  Ā The Mainstay of the project is to develop a smart motorcycle security system using Raspberry Pi and IoT, ensuring enhanced theft prevention, real-time alerts, and accident detection. The system integrates advanced features like location tracking, photo capture, and power management for efficient and secure vehicle operation.

Real-Time Object Recognition with Voice Feedback for Visually Impaired Based on Raspberry Pi

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Aim:

Ā Ā Ā Ā Ā Ā Ā Ā Ā Ā Ā  The Main Objective of the project is to build an assist device for visually impaired people to the techniques involved in condition based deep learning process.

 

Smart Wheelchair Controlled Through a Vision-Based Autonomous System

12,800.00
AIM: Ā Ā Ā Ā Ā Ā Ā  The mainstay of the project is to design and develop a wheelchair that uses eye movement with the

Tree-Based Personalized Clustered Federated Learning A Driver Stress Monitoring Through Physiological Data Case Study

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Aim:

Ā  Ā  Ā  Ā  Ā The aim of this study is to develop a privacy-preserving and personalized driver stress monitoring system using a Tree-Based Personalized Clustered Federated Learning (TPCFL) approach, which effectively addresses the challenges of non-IID physiological data by grouping drivers based on similarities in their data characteristics, optimizing cluster selection, and enabling accurate stress detection for both existing and new unlabeled drivers without compromising sensitive information.