An RFID Based Door Access System is an electronic security system that allows only authorized users to unlock a door using an RFID card or key tag. The system reads the unique ID (UID) of the RFID tag and compares it with the stored authorized IDs. If the UID matches, the door lock opens automatically for a few seconds and then locks again. Unauthorized cards are denied access, making the system suitable for homes, offices, laboratories, libraries, and industrial applications.
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The IoT Based Smart Farm Monitoring System is designed to automate irrigation by monitoring the soil moisture level using an ESP32 microcontroller. The ESP32 hosts a simple web dashboard that can be accessed through its IP address while connected to the same Wi-Fi network. Farmers can monitor real-time soil moisture, temperature, humidity, and pump status, as well as manually control the water pump from any smartphone, tablet, or computer on the local network
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The RFID Automatic Car Gate Controller is an intelligent vehicle access control system that uses RFID technology to identify authorized vehicles and automatically open a gate.
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The Gas Leakage Detection System is a safety project designed to detect the leakage of LPG, methane, propane, and other combustible gases using an MQ-2 Gas Sensor. When the gas concentration exceeds a predefined threshold, the Arduino activates a buzzer
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The Automatic Temperature-Based DC Motor Controller is an embedded system that continuously monitors temperature using an LM35 temperature sensor. The measured temperature is displayed on a 16×2 LCD. When the temperature exceeds 40°C, the Arduino automatically activates a relay to switch ON a DC motor (or cooling fan). A
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The Smart Blind Stick is an assistive device designed for visually impaired people to help them detect obstacles while walking. It uses an HC-SR04 ultrasonic sensor to measure the distance to nearby objects. When an obstacle is detected within a predefined range (for example, less than 50 cm), the system alerts the user through a buzzer, vibration motor, or LED. This enables users to avoid obstacles safely and move more independently.
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Detect a hand through your webcam and count raised fingers in real time using Python, OpenCV, and Google's MediaPipe Hands model — no external hardware needed, just a laptop or USB webcam.
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Build a machine learning model that predicts house prices based on house size using Linear Regression. This project introduces supervised learning and regression.
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A machine learning system that predicts a patient's risk of diabetes using clinical parameters (glucose, BMI, blood pressure, age, etc.), with a focus on interpretability so healthcare workers understand why a patient is flagged as high-risk — not just the prediction itself.
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An exploratory data analysis project examining hospital admission records to uncover patterns in patient readmissions, length of stay, department-wise bed utilization, and seasonal disease trends — turning raw hospital records into actionable operational insights.
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