mahyad55 Goto Github PK
Name: Mahmood Yadegari
Type: User
Company: toospolicy
Location: tehran
Name: Mahmood Yadegari
Type: User
Company: toospolicy
Location: tehran
Advanced Data Science with IBM Specialization
Anomaly detection in Intel Lab sensor data
Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the application of anomaly detection in the field of data mining is very extensive.
Data-driven Anomaly Detection with Traffic Pattern Categorization in Mobile Cellular Networks
Anomaly detection method for wireless sensor networks based on time series data
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 curated list of data mining papers about fraud detection.
Video datasets
Computer Vision and Deep Learning techniques to accurately classify vehicle damage to facilitate claims triage by training convolution neural networks
Detect dents and scratches in cars. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow.
Detect location and draw boundary of nuclei from microscopic images
Code of content-adaptive superpixel segmentation, published in TIP, 2018.
This repository contains different Coursera Specilazation Assignment Solutions in Deep Learning, Big Data, Machine Learning and Data Science
Machine learning-Stanford University
List of Computer Science courses with video lectures.
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
Machine leaning projects
Simulation code for “Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning,” by Özlem Tugfe Demir, Emil Björnson, IEEE Open Journal of the Communications Society, To appear.
Deep Learning Specialization by Andrew Ng on Coursera.
Implementation related to the Deep Complex Networks
DeepFaceLab is the leading software for creating deepfakes.
A Deep Learning based project for colorizing and restoring old images (and video!)
2D convolutional autoencoder and variational autoencoder implementation for tutorials.
Extended Isolation Forest for Anomaly Detection
Electricity theft detection using Self-Attention mechanisms
The main objective of this project is to solve the manual billing of electricity and tackle the electricity leakage by finding the charlatan via their regular electricity consumption using data analysis and machine learning algorithms.
Open-Source Tools for Real World Problem Series
Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
Simple project of eye-writing, using machine learning-based facial mapping (landmarks).
I have used different machine learning algorithm such as Support Vector Machine (SVM), Random forest, K-Nearest Neighbor (KNN), Decision Tree, Gradient Boost, XG Boost and Recurrent Neural Network (RNN) for classification.
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.