aouedions11 Goto Github PK
Name: Ons Aouedi
Type: User
Company: University of Luxembourg
Bio: Associate Researcher
Twitter: AouediO
Location: Luxembourg
Blog: https://scholar.google.com/citations?user=JMOBDusAAAAJ&hl=fr&oi=ao
Name: Ons Aouedi
Type: User
Company: University of Luxembourg
Bio: Associate Researcher
Twitter: AouediO
Location: Luxembourg
Blog: https://scholar.google.com/citations?user=JMOBDusAAAAJ&hl=fr&oi=ao
5G Network Slicing for Wi-Fi Networks
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
A collection of research papers and software related to explainability in graph machine learning.
:books: :eyeglasses: A collection of research papers, codes, tutorials and blogs on Federated Computing/Learning.
Federated Learning Library: https://fedml.ai
Papers about graph transformers.
The reference P4 software switch
Analysis of real cellular traffic captured on a device
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
In PyTorch Learing Neural Networks Likes CNN(Convolutional Neural Networks for Sentence Classification (Y.Kim, EMNLP 2014) 、LSTM、BiLSTM、DeepCNN 、CLSTM、CNN and LSTM
Official implementation of our work "Collaborative Fairness in Federated Learning."
Implementation of Convolutional LSTM in PyTorch.
List of papers, code and experiments using deep learning for time series forecasting
Deep learning framework for wearable activity recognition based on convolutional and LSTM recurretn layers
EdgeCloudSim: An Environment for Performance Evaluation of Edge Computing Systems
Compilation of high-profile real-world examples of failed machine learning projects
A MNIST-like fashion product database. Benchmark :point_down:
Source code for 'Dual Attention Based FL for Wireless Traffic Prediction'
A collection of Google research projects related to Federated Learning and Federated Analytics.
Everything about Federated Learning (papers, tutorials, etc.) -- 联邦学习
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.