Work Place Safety: Machine Learning Techniques for Assessing Workplace Incident Severity
Objective
The main objectives of this project are to assess the severity of workplace incidents using machine learning techniques for enhanced occupational safety. It analyzes historical safety data to predict risk levels and prioritize preventive measures in industrial environments. By identifying patterns in incident reports, the system supports data-driven safety planning and faster response strategies
Abstract
This project presents a workplace safety monitoring system using Arduino Uno, DHT11, MQ135, MQ2 sensors, LCD display, buzzer, relay, and water pump with a Random Forest machine learning model. The system continuously monitors temperature, humidity, air quality, and gas levels to assess workplace safety conditions. The collected data is analyzed using Random Forest to classify incident severity. When hazardous conditions are detected, the buzzer alerts users and the relay activates a water pump for safety response. The system provides a low-cost, real-time solution to improve industrial safety and prevent accidents.
Keywords: Arduino Uno, Random Forest, Workplace Safety, MQ135, MQ2, DHT11, Industrial MonitoringNOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware components:
- Arduino UNO
- LCD
- MQ135 Sensor
- Mq2 Sensor
- DHT11 Sensor
- Buzzer
- Red LED
- Relay
- DC Water Pump
- connectors-10
Software requirements:
- Arduino IDE
- Embedded C
- Python IDLE
Learning Outcomes
- Arduino pin diagram and architecture
- How to install Arduino IDE and required software
- Setting up and installation procedure for Arduino IDE
- Introduction to Arduino development environment
- Basics of Embedded C / Python programming
- Basics of IoT platforms
- Working of power supply
- About Project Development Life Cycle:
- Planning and Requirement Gathering (software’s, tools, hardware components, etc.,)
- Schematic preparation
- Code development and debugging
- Hardware development and debugging
- Development of the Project and Output testing
- Practical exposure to:
- Hardware and software tools
- Solution providing for real-time problems
- Working with team / individual
- Work on creative ideas
- Project development Skills:
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
