MortadhaMannai

A-SHIP-OR-AN-ICEBERG-CAN-YOU-DECIDE-FROM-SPACE-

The remote sensing systems used to detect icebergs are housed on satellites over 600 kilometers above the Earth. The Sentinel-1 satellite constellation is used to monitor Land and Ocean. Orbiting 14 times a day, the satellite captures images of the Earth's surface at a given location, at a given instant in time. The C-Band radar operates at a frequency that "sees" through darkness, rain, cloud and even fog. Since it emits it's own energy source it can capture images day or night. Satellite radar works in much the same way as blips on a ship or aircraft radar. It bounces a signal off an object and records the echo, then that data is translated into an image. An object will appear as a bright spot because it reflects more radar energy than its surroundings, but strong echoes can come from anything solid - land, islands, sea ice, as well as icebergs and ships. The energy reflected back to the radar is referred to as backscatter. Here we see challenging objects to classify. We have given you the answer, but can you automate the answer to the question .... Is it a Ship or is it an Iceberg?

BERT-Neural-Language-Interface-Explainability-Explorer

Welcome to the Neural Language Interface (NLI) Explain project! This repository is dedicated to exploring and explaining the decision-making process of BERT models in the context of Natural Language Inference (NLI) tasks. We employ Feature Interaction methods to shed light on why BERT makes specific predictions in NLI.

Comparative-Analysis-of-Equity-Crowdfunding-Expenditure-Across-OECD-Nations-Python-and-R-Approach

This project aims to perform a comprehensive analysis of the differences in money spent on "Equity Crowdfunding" projects across OECD countries.

Disaster-Risk-Monitoring-Using-Satellite-Imagery-and-Nvidia-Libraries

Learn how to build and deploy a deep learning model to automate the detection of flood events using satellite imagery. This workflow can be applied to lower the cost, improve the efficiency, and significantly enhance the effectiveness of various natural disaster management use cases.

EvolveGCN-Evolving-Graph-Convolutional-Networks-for-Dynamic-Graphs-

This repository contains the code for EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs, published in AAAI 2020.

Fake-news-detection-with-LSTMs-and-BERT

well Social media has affected society during the last decade by providing free milieus for everyone to share their thoughts, ideas, and also news. As a negative effect, these environments have been used for propagation of low-quality, content-bare, and even outright “Fake” news. Spreading of Fake news has extreme effects on people’s minds and societies, such as decreasing one’s trust to all sources of news, making readers defensive against most news channels,The Fight Against Fake News with Deep Learning Fake news detection with LSTMs and BERT etc. This is why recently the detection of Fake news has become one of the top trends in the field of research. Referring to one of the latest works on this area, detecting Fake news in social media has exclusive features which cannot be found in traditional methods and approaches for reality detection so this work represent

Graph-Convolution-over-Pruned-Dependency-Trees-for-Relation-Extraction

Graph Convolution over Pruned Dependency Trees for Relation Extraction" is a sophisticated approach that leverages graph convolutional networks (GCNs) on pruned dependency trees to enhance the accuracy of relation extraction tasks

MIC-Workshop-Application-of-Generative-IA-in-the-Real-Word

Welcome to the MIC-Workshop-Application-of-Generative-IA-in-the-Real-Word repository! Here, you'll find comprehensive resources, code, and instructions for exploring and building AI applications using cutting-edge language models (LLMs).

Natural-Language-Processing-Analyzing-GitHub-Pull-Requests

This project covers the concepts of : Topic Modelling using LDA Clustering through tf-idf and BoW Dimension reduction through t-SNE and truncated SVD Classification and Regression algorithms

VOCAL-TRACK-EXTRACTION-USING-NEURAL-NETWORKS

There are four models in this project: Deep Clustering Model, Hybrid Deep Clustering Model, U-net Model and UH-net Model. Models are trained on DSD100 dataset. The project is based on PyTorch.

MortadhaMannai/Disaster-Risk-Monitoring-Using-Satellite-Imagery-and-Nvidia-Libraries

Learn how to build and deploy a deep learning model to automate the detection of flood events using satellite imagery. This workflow can be applied to lower the cost, improve the efficiency, and significantly enhance the effectiveness of various natural disaster management use cases.

MortadhaMannai/Fake-news-detection-with-LSTMs-and-BERT

well Social media has affected society during the last decade by providing free milieus for everyone to share their thoughts, ideas, and also news. As a negative effect, these environments have been used for propagation of low-quality, content-bare, and even outright “Fake” news. Spreading of Fake news has extreme effects on people’s minds and societies, such as decreasing one’s trust to all sources of news, making readers defensive against most news channels,The Fight Against Fake News with Deep Learning Fake news detection with LSTMs and BERT etc. This is why recently the detection of Fake news has become one of the top trends in the field of research. Referring to one of the latest works on this area, detecting Fake news in social media has exclusive features which cannot be found in traditional methods and approaches for reality detection so this work represent

MortadhaMannai/Graph-Convolution-over-Pruned-Dependency-Trees-for-Relation-Extraction

Graph Convolution over Pruned Dependency Trees for Relation Extraction" is a sophisticated approach that leverages graph convolutional networks (GCNs) on pruned dependency trees to enhance the accuracy of relation extraction tasks

MortadhaMannai/BERT-Neural-Language-Interface-Explainability-Explorer

Welcome to the Neural Language Interface (NLI) Explain project! This repository is dedicated to exploring and explaining the decision-making process of BERT models in the context of Natural Language Inference (NLI) tasks. We employ Feature Interaction methods to shed light on why BERT makes specific predictions in NLI.

MortadhaMannai/Breast-Cancer-Detection-Using-Machine-Learning-Classifier.

Breast Cancer Detection Using Machine Learning Classifier Goal of this ML project : I have extracted features of breast cancer patient cells and normal person cells then I create an ML model to classify malignant and benign tumor. To complete this ML project i used the supervised machine learning classifier algorithm. Author: Mannai Mohamed Mortadha

MortadhaMannai/Natural-Language-Processing-Analyzing-GitHub-Pull-Requests

This project covers the concepts of : Topic Modelling using LDA Clustering through tf-idf and BoW Dimension reduction through t-SNE and truncated SVD Classification and Regression algorithms

MortadhaMannai/-Task-2-Prediction-using-Unsupervised-Ml__GRIP-The-Sparks-Foundation

In this regression task I tried to predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables.

MortadhaMannai/Ad_LifeTime_Predection

To be able to predict used cars market value can help both buyers and sellers. So In this Project, we are going to predict the best of Used Cars using various features.

MortadhaMannai/AgentGPT

🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.

MortadhaMannai/Baby-Name-Generation

Baby Name Generation is a fascinating application of artificial intelligence and natural language processing (NLP) that involves the creation of new and unique names for newborns. This task combines linguistic creativity with data-driven approaches to generate names that are both meaningful and culturally resonant.

MortadhaMannai/BMIAppAndroid

BMIAppAndroid (Exercice 1 T-3GII-SSE)

MortadhaMannai/Captcha-Text-Recognition-With-CRNN

Captcha Text Recognition with Convolutional Recurrent Neural Networks (CRNN) is a powerful approach that combines convolutional and recurrent neural network architectures to effectively solve the challenging problem of recognizing text within CAPTCHA images.

MortadhaMannai/Comparative-Analysis-of-Equity-Crowdfunding-Expenditure-Across-OECD-Nations-Python-and-R-Approach

This project aims to perform a comprehensive analysis of the differences in money spent on "Equity Crowdfunding" projects across OECD countries.

MortadhaMannai/Davinci230221-

MortadhaMannai/DESTIANTION_ROBOT

ACADEMIC PROJECT : Optimal placement of a robot according to a task to be carried out in a constrained environment

MortadhaMannai/Handling-Complex-Images---Happy-or-Sad-Dataset

Handling Complex Images - Happy or Sad Dataset: using the happy or sad dataset, which contains 80 images of emoji-like faces, 40 happy and 40 sad. Create a convolutional neural network that trains to 99.9% accuracy on these images, which cancels training upon hitting this training accuracy threshold.

MortadhaMannai/Image-Segmentation-with-UNet

Image Segmentation with UNet is a powerful technique in the field of computer vision that involves using the UNet architecture to segment images into distinct regions or objects. Image segmentation is the process of dividing an image into meaningful parts, often with the goal of identifying and delineating objects of interest within the image.

MortadhaMannai/Improve-MNIST-with-Convolutions

Improve MNIST with Convolution : how to enhance the Fashion MNIST neural network with convolutions to make it more accurate !

MortadhaMannai/Multi-Class-Image-Classification-Fashion-MNIST

Multi-Class Image Classification refers to the task of categorizing images into multiple classes or categories. Each image is associated with one specific label from a predefined set of classes.

MortadhaMannai/Multi-Class-Word-Language-Classification

Multi-Class Word Language Classification is a natural language processing task that involves categorizing text documents or sentences into multiple language classes

MortadhaMannai/Neural-Style-Transfer

Neural Style Transfer is a captivating and innovative technique in the field of deep learning and computer vision. It blends the artistic style of one image with the content of another, resulting in visually stunning and creatively expressive outputs.

MortadhaMannai/OpenCVPlayground-Exploring-Diverse-OpenCV-Projects

OpenCVPlayground is a repository dedicated to providing a curated collection of exciting OpenCV projects. Whether you're a beginner looking to learn about computer vision or an experienced developer experimenting with image processing techniques, this repository has something for everyone.

MortadhaMannai/Praccforces

CodeForces Practice Discord Bot

MortadhaMannai/Profitable-App-Profiles-for-the-App-Store-and-Google-Play-Markets

For this project,I pretend I working as data analysts for a company that builds Android and iOS mobile apps. I make our apps available on Google Play and in the App Store. I only build apps that are free to download and install, and our main source of revenue consists of in-app ads. This means that the number of users of our apps determines our revenue for any given app — the more users who see and engage with the ads, the better. Our goal for this project is to analyze data to help our developers understand what type of apps are likely to attract more users.In this project, we went through a complete data science workflow: We started by clarifying the goal of our project. We collected relevant data. We cleaned the data to prepare it for analysis. We analyzed the cleaned data.

MortadhaMannai/Task-3-Explaratory-Data-Analysis-Retail_GRIP_THE_SPARKS_Foundation

The Sparks Foundation: Data Science and Business Analytics Internship GRIP: May 2022 Task 3 : Explaratory Data Analysis: Retail Problem: Perform Explaratory Data Analysis on dataset 'SampleSuperstore' As a business manager, try to find out the weak areas where you can work to make more profit. What all business problems you can derive by exploring the data?

MortadhaMannai/Twitter-sentiment-analysis-bot

This project uses Twitter API v2 with the Tweepy library to request tweet data based on a search keyword and a desired number of tweets both given by the user. the data is then put through an ETL process, and into Pandas dataframe, and then run through a transformer based NLP model for sentiment analysis "BERT"

MortadhaMannai/Unveiling-the-Black-Box-Deciphering-ML-Explainability-with-Statlog-Heart-Data

The goal of this repository is to explore various Explainable AI (XAI) methods on models trained on an classification task on the UCI ML - Statlog (Heart) Data Set.

MortadhaMannai/GRIP_TASK-1---Prediction-using-Supervised-ML

Data Science and Business Analytics GIRP1 by THE SPARKS FOUNDATION_Task1 *Predict the percentage of an student based on the no. of study hours) **Using simple linear regression model, forecasting the marks of a student based on the numbers of hours studied per day. ***Tool(s) Used - Python (Google colab Notebook)

MortadhaMannai/Ultimate-Facebook-Scraper

🤖 A Software that automates your social media interactions to collect posts, photos, videos, interests, friends, followers, and much more on Facebook.

MortadhaMannai/unstructured-api-tools