basharbme

2048-console

A clone of the game 2048 in the console written in vanilla python without any imports.

2048-DeepQLearning

Implementation of an agent able to play 2048. Report, evaluation and implementation details in the GitHub repository. During the project I gained experience with Reinforcement learning technologies such as the gym framework and the Keras-rl library.

3D-Bioprinter-parts-and-accessories

A collection of STL files used for building or modifying a FDM printer to print gels

bone-segmentation

Source code behind the paper Fully automatic and fast segmentation of the femur bone

EEG_Classification_Deeplearning

EEG Signal Classification using LSTM on various datasets

Health_Discernment_System

An efficient and user-friendly application with GUI based (Tkinter) front-end and various custom CNN models as back-end which detects various human diseases such as Malaria, Pneumonia, Breast Cancer and Skin Cancer using cell, tissue, x-ray or skin images.

Intelligent-Estimation-of-Speed-of-Induction-Motor

speed tracking capability of model reference adaptive system (MRAS) with model-based flux/speed observers and artificial neural network (ANN)-based adaptive speed estimators for sensorless induction motor (IM) drives has been analyzed. In model-based technique, mathematical model of IM is used to estimate the rotor speed. The current and flux observers are used as the reference model to estimate the rotor flux. The estimated rotor flux signals are used as the input signal for the adaptive observer to estimate the speed. In ANN-based method, adaptive model is constructed with a feedforward neural network to estimate the rotor speed. Feedforward ANN algorithm is used to train the network. The training algorithm decides the learning speed, stability, and dynamic performance of the system. Both methods have good speed tracking capability. Simulation results are presented to know the accuracy of the proposed methods. The proposed speed estimation techniques have great potential in industrial applications.

MONAILabel

MONAI Label is an intelligent open source image labeling and learning tool.

SmileToCast-Blender

Blender console easily convert dentist planning smile to 3D printable customized cast for veneers \ or cosmetics procedure planning .. Cast to be then scaled to real size impression.

TMJ-prosthesis-Blender

Open source TMJ Prosthesis Customizable to patient anatomy

basharbme/TMJ-prosthesis-Blender

Open source TMJ Prosthesis Customizable to patient anatomy

basharbme/3D-Bioprinter-parts-and-accessories

A collection of STL files used for building or modifying a FDM printer to print gels

basharbme/Android-Cholesterol-Checker

Android-Application-Cholesterol-Checker in Java

basharbme/anomaly-detection

Some Jupyter Notebooks with encoding models for image anomaly detection

basharbme/backgroundremover

Remove Background from Video and Images with a simple command line interface

basharbme/bone-cancer-classifier

Diseases Prediction from MRI-CT data

basharbme/cellblender

Create, Simulate, Visualize, and Analyze Realistic 3D Cell Models

basharbme/DC_GAN

An implementation of the research paper "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks".

basharbme/modeling-of-the-impulse-response-of-a-mechano-acoustical-system

The following repository contains programs written in MATLAB language, used to model the impulse response of a tuning fork.

basharbme/PubLayNet

basharbme/PyTorch-2D-3D-UNet-Tutorial

basharbme/vehicle-classification

Image classificator using PyTorch

basharbme/watershed-segmentation-Python-OpenCV

Watershed Segmentation with Python and OpenCV

basharbme/2048Game

deep learning for 2048Game

basharbme/3D-MiniNet

Official Implementation in Pytorch and Tensorflow of 3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

basharbme/active_reinforcement_learning

Active reinforcement learning for semantic segmentation of nuclei in medical imagery

basharbme/alien-signal-detection

Detecting Extra-terrestrial signals with the help of Patch-based Image Classification Deep Learning models.

basharbme/Brain-Tumor-VSegmentation-Using-3D-CNN

In this work, two neural networks architectures based on the Unet network have been designed and trained to automatically segment different tumor substructures using the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset

basharbme/face_mask_detection

Face mask detection system using Deep learning.

basharbme/final_year_project

A web application which integrates a student tracking system, report submission system and NSS accomodation finder

basharbme/Ludo

An interactive, offline version of the popular Ludo board game using OpenGL

basharbme/Ludo-Game

Ludo game using GLUT and Threads in C++

basharbme/Lung_Nodule_Detection_And_3D_Reconstruction

Lung nodule detection using UNET (from CT scan images) & 3D Reconstruction

basharbme/MIA

Repository for 'Automatic Skull Defect Restoration and Cranial Implant Generation for Cranioplasty'. Skull Shape Completion Using Deep Learning (High resolution Volumetric Shape Completion)

basharbme/multi_modal_3d_unet

MRI's have been one of the go-to methods for diagnosis of brain tumors by radiologists. In this repository, we implement a multi-modal (T2 FLAIR, T1w, T1Gd and T2w) 3D semantic segmentation model (3D Unet) to automatically segment whole tumor, tumor core and enhancing tumor.

basharbme/MURA_Classification

basharbme/numerical_computation

estimation of integrals, solving of system of linear equations and IVPS and BVP of ODEs

basharbme/Routing-on-Resource-Allocation-in-Free-Space-Optical-Network

basharbme/SwinDetr

Integration of Swin Transformer to DETR for Robust Object Detection (DEMO)

basharbme/T20-WorldCup-Analysis

Checkout my project on T20 World Cup Data Analysis. First of all used selenium for webscraping, pandas for reading csv, grouping, filtering, label encoding, etc and plotly for visualization. I have divided Visualization as All time records, Season wise Records), Records in Knockout & Final, First & Last 3 season records and Indian Cricket Team records, further sub-divided into Most Runs, Most 6s, 4s, 50s, catches, etc.