Ahmed-Alezzabi's starred repositories
Autoencoder-for-Solar-Energy-Generation
This project comprises of a python project which develops an autoencoder for predicting solar energy produced in photovoltaic solar panels. The dataset employed in this project has been aquired from https://ukpowernetworks.opendatasoft.com/
Active-Solar-Panels-with-Sound-Energy-Capture-for-Streetlights
Here are the datasets and presentation of my final senior design. The project consisted of Active Solar Panels with Sound Energy Capture for Streetlights.
energybeacondataset
Datasets, figures and writing files for "Maximizing energy harvesting with adjustable solar panel for BLE beacon" paper for CPSCom Conference 2019
Solar-Panel-Failure-Prediction-Model
Explore the repository to gain insights into the Solar Panel Failure Prediction Model. Using a verified dataset, this project employs a Supervised ML Algorithm to predict faults, aiming to maximize solar panel efficiency while minimizing human labor.
deep_solar_app
A Machine Learning / Python / Dash geomarketing application based on a Kaggle dataset, providing volume predictions for deploying photovoltaic solar panels.
yolov8s-seg-solar-panels
YOLOv8s-seg trained on solar panels dataset https://app.roboflow.com/rzeszow-university-of-technology/solar-panels-seg/2
EL-images-Dataset
Public dataset of solar panel EL images
Dataset_20WP_SolarPanel
The data capture by arduino mega used INA219 Sensors for mearsurement of Voltage, Current, and Power of Solar Panel
Deep-Neural-Network-Satellite-Image-Classification-in-Google-Colaboratory-iPython-Note-Book-
Deep Neural Network: Satellite Image Classification in Google Colaboratory iPython Note Book
eurosat-classification
Land use and land cover classification performed with logistic regression and XGBoost on aerial satellite images sourced from the EuroSAT benchmark dataset.
Satellite-images-calssification
Noise filtering and classification of areas of real satellite images into segments of (urban - water - desert - plants) using unsupervised learning techniques
Satellite-image-Classification
This is the implemenatation of a Machine Learning Model which classifies various features in satellite image using the k-means clustering Alogrithm.
Damage-Classification-using-Satellite-Images
Classifying post damage level of buildings from satellite imagery, given pre and post-disaster satellite images and building locations
VHRShips
This study focuses on all stages of ship classification in the optical satellite images. The proposed “Hierarchical Design (HieD)” approach, which is based on deep learning techniques, performs Detection, Localization, Recognition and Identification (DLRI) of the ships in the optical satellite images. HieD is an end-to-end approach which allows the optimization of each stage of the DLRI independently. A unique and rich ship dataset (High Resolution Ships, HRShips), which is formed by the Google Earth Pro software, is used in this study. While Xception network is used in detection, recognition and identification stages; YOLOv4 is preferred for the localization of the ships.
Predicting_building_damage_afte_earthquakes
The project focuses on creating a model for the evaluation of the damages suffered in the infrastructures after an earthquake. I apply Python, supervised algorithms and deep learning (CNN).
Building-damage-assessment-with-CNN
Damaged buildings from UAV images were detected and classified by CNN.
PROJECT--Hurricane-Iota-Damage-Classification
Post Damage Building Classification due to hurricane Iota using CNN and Deep Learning Algorithms.
building-damage-classification
Building Disaster Level Classification with CNN on xBD Dataset
BDANet-Building-Damage-Assessment
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
Building-Footprint-Detection-and-Damage
Building Footprint Detection and Damage Assessment from Satellite Images