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Name: Mahmood Yadegari

Type: User

Company: toospolicy

Location: tehran

Mahmood Yadegari's Projects

advanced_data-science_with_ibm_specialization

Advanced Data Science with IBM Specialization

anomaly-detection

Anomaly detection in Intel Lab sensor data

anomaly-detection-based-on-multiple-streaming-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.

anomaly-detection-in-mobile-networks

Data-driven Anomaly Detection with Traffic Pattern Categorization in Mobile Cellular Networks

anomalydetection

Anomaly detection method for wireless sensor networks based on time series data

attack-and-anomaly-detection-in-iot-sensors-in-iot-sites-using-machine-learning-approaches

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.

awesome-fraud-detection-papers

A curated list of data mining papers about fraud detection.

awesome-video-datasets

Video datasets

car-damage-assessment

Computer Vision and Deep Learning techniques to accurately classify vehicle damage to facilitate claims triage by training convolution neural networks

car-damage-detector

Detect dents and scratches in cars. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow.

cell-nuclei-detection-and-segmentation

Detect location and draw boundary of nuclei from microscopic images

content-adaptive-superpixel-segmentation

Code of content-adaptive superpixel segmentation, published in TIP, 2018.

coursera

This repository contains different Coursera Specilazation Assignment Solutions in Deep Learning, Big Data, Machine Learning and Data Science

coursera-machine-learning-stanford

Machine learning-Stanford University

cs-video-courses

List of Computer Science courses with video lectures.

dagan

The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"

datacamp_machine_learning_projects

Machine leaning projects

deep-learning-channel-estimation

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-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep_complex_networks

Implementation related to the Deep Complex Networks

deepfacelab

DeepFaceLab is the leading software for creating deepfakes.

deoldify

A Deep Learning based project for colorizing and restoring old images (and video!)

dimensionality-reduction-autoencoders

2D convolutional autoencoder and variational autoencoder implementation for tutorials.

eif

Extended Isolation Forest for Anomaly Detection

electricity-theft-detection-with-self-attention

Electricity theft detection using Self-Attention mechanisms

electricity_fraud_detection

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.

end-to-end-guide-to-building-a-credit-scorecard-using-machine-learning

Open-Source Tools for Real World Problem Series

evidently

Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b

eye-writing-easy

Simple project of eye-writing, using machine learning-based facial mapping (landmarks).

fault-detection-in-wireless-sensor-networks-using-statistical-machine-learning-methods-and-neural-ne

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.

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