Malaiappan59

S.MALAIAPPANSRIKANTH's starred repositories

tasks-list

Tasks list sorted on priority. Used styled components + local storage

Language:JavaScript700

fullstack

MEAN stack practice codes

Language:HTML200

react-starter-kit

The web's most popular Jamstack front-end template (boilerplate) for building web applications with React

Language:TypeScriptMIT2264600

leet-code

Java Solutions to problems on LintCode/LeetCode

Language:Java435600

Microsoft-Activation-Scripts

A Windows and Office activator using HWID / Ohook / KMS38 / Online KMS activation methods, with a focus on open-source code and fewer antivirus detections.

Language:BatchfileGPL-3.09275000

passwordGenrator.github.io

this project utlizies a highly uncrackable algorithm to make your password, that's can't even opened by fbi

Language:CSS100

erxes

Source available experience management infrastructure. Pioneering the future of experiences with XOS (Experience Operating System). Hubspot + Qualtrics alternative

Language:TypeScriptNOASSERTION348600

free-tshirts-stickers-and-swag-for-developers

List of free tshirts, stickers and swags available for developers

MIT104800

30-days-of-react

30 Days of React Content and Source Code

Language:JavaScript215100

Firearm_Detection_In_CCTVs_Using_YOLOv5

Detects firearms in real-time from CCTV videos, highlighting them within bounding boxes for immediate attention

Language:Jupyter NotebookMIT700

iCrime

This is our project for the Artificial Intelligence Project Management subject.

100

Care4ther-

This code will help the users to get an idea of the accidents well before the accident took place

Language:HTMLMIT6800

Deep-Learning-Demo-Apps

The intention here is to create a generic repositories which contain various practical applications in the field of deep learning.

Language:Jupyter Notebook200

This AIM of this repository is to create real time / video application using Deep Learning based Object Detection using YOLOv3 with OpenCV YOLO trained on the COCO datasets. The COCO dataset consists of 80 labels.

Language:Python900