Nada Khater's Projects
A simple responsive chatbot web design using HTML, CSS and Java Script for a 3D character bot.
This project automates the setup of Jenkins, Nexus, Nginx on localhost as well as on AWS instances. creating 2 AWS instances (one with Nexus and another with Jenkins), installing Nginx on one of them, configuring Nginx to redirect to Jenkins using only the IP, creating DNS entries in /etc/hosts, creating self-signed certificates for enabling HTTPS.
Predicting the price of house in boston, given a set of features that describe the house using different machine learning models.
A car racing game using HTML, CSS and JavaScript.
A simple interactive chatbot using deep learning and keras.
The project aims to automate the infrastructure provisioning and deployment process using Terraform, Jenkins, and Ansible. It focuses on creating a pipeline to manage the infrastructure as code (IaC), configure application servers, and deploy applications efficiently.
Database Management System (DBMS) implemented in Bash scripting which enables users to store and retrieve the data from Hard-disk.
Django-Crowd Funding Web App
Django_Lab3
Face mask detection model using CNN and Gradio.
Extract features from different images with multiple algorithms "SURF, SIFT". Display matches on both images using bfmatcher. OpenCV contrib python version: 3.4.2.17
Apply image preprocessing techniques to remove hair from images using Blackhat and Tophat filters. - Testing model with PAD-UFES-20 dataset.
This project automates the CI/CD pipeline using Jenkins for deploying a Node.js application on Kubernetes. The setup involves creating and managing local Kubernetes instances with Minikube, deploying Jenkins and Nexus, and running Node.js and MySQL applications in a Kubernetes cluster.
Name verification model using tensorflow v2 and word embedding that classifies the input name into real and fake name with accuracy of 99%
Object detection using random forest dataset link on drive: https://drive.google.com/drive/folders/1DaqYJfsNs8SDBD5W-Qi2lAvJ7vWP7DnP?usp=sharing
Sign language classification model using CNN, Gradio and Hugging Face.
Applying Deep Hybrid Model to PAD-UFES-20 dataset. - Increasing the accuracy with 95.7% among other studies.
Utilized python to apply semantic segmentation techniques using U-NET to segment lesions from clinical images. Training model with ISIC19 dataset and testing it with PAD-UFES-20 dataset.
This project aims to automate the setup of networking and compute resources in AWS using Terraform. It creates a VPC with public and private subnets, an internet gateway, route tables, security groups, EC2 instances, RDS instance, and Elasticache instance
Using Pandas to analysis Titanic dataset. Cleaning the data by handling null values and removing unnecessary columns from it. Dealing with outliers in data and visualizing it using matplotib.