Discover mohamed-said-ibrahem/Applicant-Tracking-System Open Source

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PHP
Created about 7 years ago
Updated about 6 years ago

mohamed-said-ibrahem

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A Fully integrated Application that is used by Managers and Recruiters to manage the applicants requestes and current employees status, The application can be easily modified to fit your usage and to fit your requirements, you can add, edit, delete and search any employee or applicant, The application has alot of amazing features such as the page pagination which uses a smart query so it will get only the required data only from the database by using the offset and that will not case any overhead on the application performance, The application is totally secure from CSRF, XSS, SQL injection, Brute-Force and dictionary attacks, The application has JWT (Jason web token) (Generate-Update-Revoke) too which can be used so easily but i dont used it as the main method, The application can be modified easily to used as an API to fit your requirements.

Computer-Vision

Multiple Computer Vision course projects and labs such as: (Image cartoonifier, Coin detection, Image mosaics, Stereo vision, Lucas Kanade tracker for 3D object detection). Used multiple algorithms and techniques such as (Hough transform, Dynamic programming...)

Jupyter Notebook

Computer-Networks-III_Routing_Protocols

Create a script that simulates a specific topology using NS3 which helps to create various virtual nodes. Using NS3 we can create PointToPoint, Wireless, CSMA, etc connections between nodes. Algorithm used is Open Shortest Path First (OSPF).

C++

MRNET-For-Knee-Diagnosis

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

Jupyter Notebook

PV-RCNN_Point-Voxel-Feature-Set-Abstraction-For-3D-Object-Detection

PointVoxel-RCNN (PV-RCNN), is a two-stage 3D detection framework aiming at more accurate 3D object detection from point clouds. 3D detection approaches are based on either 3D voxel CNN with sparse convolution or PointNet-based networks as the backbone. 3D voxel CNNs with sparse convolution are more efficient and are able to generate high-quality 3D object proposals. PointNet-based methods can capture more accurate contextual information with flexible receptive fields.

Jupyter Notebook