1) AGROSENSE: Implemented using ML techniques and deployed with gradio

It is a machine learning project implemented to determine the crops suitabble to grow under specific soil condition.

LINK : CROP PREDICTION PREDICTION

2) BREAST CANCER ABNORMALITY PREDICTION: IMPLEMENTED USING DL TECHNIQUES (CNN)

It is a CNN project implemented to classify the given input x ray image to find the abnormality of the cancer(mass or calcification).

DATASET : CBIS -DDSM images

LINK : BREAST CANCER ABNORMALITY CLASSIFICATION

3) EMOTION CLASSIFICATION: IMPLEMENTED USING DL TECHNIQUES (CNN)

IIt is a CNN project implemented to classify the emotions from the given input image.

LINK: EMOTION CLASSIFICATION

4) Road safety accident severity prediction: Implemented using ML techniques and deployed with GRADIO.

It is a machine learning project implemented to get greater information about the accident severity from the dataset.

LINK : ACCIDENT SEVERITY PREDICTION

5) Machine fault Detection: Implemented using ML techniques and deployed with GRADIO.

It is a machine learning project implemented to determine the condition of the motor, whether it is healthy or faulty. A sound signal has been processed, and the features are extracted to form the sound recorded.

LINK : MACHINE FAULT DETECTION

6) ENTRY PORTAL: Implemented using Frontend web development tools

The SIMPLE ENTRY PORTAL is a simple and easy to use website for people to sign up and log in. It has a clean and modern look, making it easy for users to create accounts or log in without any trouble.

LINK : SIGNU UP / LOGIN PORTAL

7) OBJECT HUMAN DETECTION:Implemented using yoloV3

Developed and implemented an image processing project to detect humans and objects using machine learning algorithms. Utilized computer vision techniques to accurately identify and classify objects in real-time. Enhanced system efficiency through advanced preprocessing and optimization methods.

LINK : OBJ-HUM DETECTOR

8) Multi-Layered Camera Surveillance: IOT

Proposed a multi-layered IoT-based security system for robust access control.This project uses Firebase as the database and ESP32 camera for basic surveillance. It includes two-factor authentication via RFID and fingerprint scanning, facial recognition as the third layer, and a master website for monitoring entry times fetched from Firebase.