Discover @basharbme Open Source projects

Bashar Shami (@basharbme)

basharbme

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Global Rank 70,952 (Top 3 %)
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Registered over 6 years ago
Most used languages

Jupyter Notebook
31.3 %

Python
18.8 %

Java
12.5 %

C++
6.3 %

OpenSCAD 6.3 %

JavaScript
6.3 %

R
6.3 %

Kotlin
6.3 %

Julia
6.3 %
Location 🇯🇴 Jordan
Country Total Rank 10
Country Ranking

Julia
1

Kotlin
9

Jupyter Notebook
12

Java
54

Python
74

EEG_Classification_Deeplearning

EEG Signal Classification using LSTM on various datasets

Jupyter Notebook

TMJ-prosthesis-Blender

Open source TMJ Prosthesis Customizable to patient anatomy

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.

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.

Android-Cholesterol-Checker

Android-Application-Cholesterol-Checker in Java

anatomic-implants

OnlineDentalClinic-AndroidApp

2048-console

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

Julia-Knaspsack

Knapsack is a problem in dynamic programming who try to get the best option to take

Vehicle-Detection-Image-Set

Jupyter Notebook

MONAI-3D-Pelvic-Bone-cancer-segmentation-and-classification

Author : Bashar Shami , [email protected] Team: Mona Shouman

Jupyter Notebook

GymManagement

A java swing Gym Management project that consists of three types of user logins--Admin, Manager, Customer.

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.