Discover @hasnainnaeem Open Source projects

Hasnain Naeem (@hasnainnaeem)

hasnainnaeem

Stars
84
Global Rank 235,692 (Top 9 %)
Followers 46
Following 106
Registered over 7 years ago
Most used languages

Python
25.0 %

Java
25.0 %

C
12.5 %

JavaScript
12.5 %

Jupyter Notebook
12.5 %

C++
12.5 %
Location 🇵🇰 Pakistan
Country Total Rank 439
Country Ranking

Python
59

Jupyter Notebook
133

C
196

Java
568

C++
650

JavaScript
1,722

Violence-Detection-in-Videos

Violence detection in videos using Deep Learning (CNNs + LSTMs). 98.5% video accuracy and 97.81% frame level accuracy (with threshold=3) was achieved through the proposed model by Joshua on HockeyFight dataset. Joshua's project was extended with real-time predictions on video feed coming from camera. Moreover, notebook is added to easily setup and run code on Google Colab.

Gunshot-Detection-in-Audio

Audio classification deep learning model using TensorFlow 2.0 to detect Gunshots. 97.5% test set accuracy and 99% training set accuracy was achieved on Binary-Urban8K. This work was done during my summer internship at TUKL-NUST lab.

Jupyter Notebook

Product-Manager

NodeJS powered product managing website created for learning purposes. It has admin, buyer and seller panels created using ExpressJS on server-side and Jquery for client-side.

EPatwari

Blockhain and MYSQL based immutable and decentralized storage for secure data storage and mining.

Quizify

Offline Entrance test preparation application made using Java for Pakistani Students who don't have access to Internet. Features include multiple test options (chapterwise, halfbook etc.), performance tracking using charts and aggregate calculators.

End-to-End-Autopilot-Steering-Model

Implementation of end to end steering model using deep learning (https://arxiv.org/pdf/1604.07316.pdf)

DSA-Assignments-Labs

DSA Labs & Assignments

Mood-Detector-and-Enhancer

Artificially Intelligent software to detect user's mood based on the text entered. After that it entertains user based on his/her mood. In case of happy mood, it asks for reason and saves that. When user is not happy it suggests user about activities which made him/her happy previously. Moreover, it has a diary option which stores good experiences of life and then displays to user in case of unhappiness.