Pinned Repositories
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.
mahyad55's Repositories
mahyad55/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.
mahyad55/Advanced_Data-Science_with_IBM_Specialization
Advanced Data Science with IBM Specialization
mahyad55/car-damage-assessment
Computer Vision and Deep Learning techniques to accurately classify vehicle damage to facilitate claims triage by training convolution neural networks
mahyad55/Content-adaptive-superpixel-segmentation
Code of content-adaptive superpixel segmentation, published in TIP, 2018.
mahyad55/Coursera-Machine-Learning-Stanford
Machine learning-Stanford University
mahyad55/DAGAN
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
mahyad55/Datacamp_Machine_Learning_Projects
Machine leaning projects
mahyad55/dimensionality-reduction-autoencoders
2D convolutional autoencoder and variational autoencoder implementation for tutorials.
mahyad55/eif
Extended Isolation Forest for Anomaly Detection
mahyad55/electricity-theft-detection-with-self-attention
Electricity theft detection using Self-Attention mechanisms
mahyad55/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.
mahyad55/eye-writing-easy
Simple project of eye-writing, using machine learning-based facial mapping (landmarks).
mahyad55/generative-compression
TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression
mahyad55/Hybrid-Beamforming-for-Millimeter-Wave-Systems-Using-the-MMSE-Criterion
The Matlab Simulation codes for Hybrid Beamforming for Millimeter Wave Systems Using the MMSE Criterion.
mahyad55/Instagram_Comments_Web_Scraping
Instagram and Youtube comments scraping using selenium and BeautifulSoup
mahyad55/my_ml_service-1
My Machine Learning Web Service
mahyad55/Persian-Sentiment-Resources
Awesome Persian Sentiment Analysis Resources - منابع مرتبط با تحلیل احساسات در زبان فارسی
mahyad55/prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
mahyad55/pyqt5-qtquick2-example
An example of QtQuick 2 providing material and fluent design themes in PyQt5.
mahyad55/python_for_image_processing_APEER
https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG
mahyad55/RMI-AI
mahyad55/Semantic-Segmentation-Suite
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!
mahyad55/SmartGridFraudDetection
Electricity Fraud Detection in Smart Grids
mahyad55/table-parser-opencv
Extract tables from images or PDFs and convert them to Excel files
mahyad55/TecoGAN
This repo will contain source code and materials for the TecoGAN project, i.e. code for a TEmporally COherent GAN
mahyad55/TII_Wide-Deep_Electricity_Theft_Detection
This is the source code of our paper on electricity-theft detection published in TII in the 2017 year.
mahyad55/Time-Series-Analysis-and-Forecasting
End To End Tutorial on Time Series Analysis and Forcasting
mahyad55/Time-Series-Forcasting
In this repository i have implemented various Deep Learning multivariate and multiheaded time series forecasting models . Apart from that i have also uploaded the Ipython file of Grid_Search and Ensemble_Learning technique which i have implemented during my summer intern of IIT-Mandi(May 2019)
mahyad55/Variational-Lstm-Autoencoder
Lstm variational auto-encoder API for time series anomaly detection and features extraction
mahyad55/VideoSuperResolution
A collection of state-of-the-art video or single-image super-resolution architectures, reimplemented in tensorflow.