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/Power_theft_detection-from-smart-meters
This system uses 2 machine learning approaches consequently to detect electricity theft in a locality by analysis of power usage patterns of households
mahyad55/deep-learning-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
mahyad55/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.
mahyad55/Cell-Nuclei-Detection-and-Segmentation
Detect location and draw boundary of nuclei from microscopic images
mahyad55/deep_complex_networks
Implementation related to the Deep Complex Networks
mahyad55/pedagogical
mahyad55/SRR_EIT
Super resolution reconstruction of electrical impedance tomography images
mahyad55/car-damage-detector
Detect dents and scratches in cars. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow.
mahyad55/AnomalyDetection
Anomaly detection method for wireless sensor networks based on time series data
mahyad55/pydata-cookbook
PyData Cookbook Project
mahyad55/anomaly-detection
Anomaly detection in Intel Lab sensor data
mahyad55/wsn-ocsvm-dfn
One-class SVM based anomaly detection for wireless sensor networks
mahyad55/kaggle-house-prices-advanced-regression-techniques
Repository for source code of kaggle competition: House Prices: Advanced Regression Techniques
mahyad55/Loan-Defaulter-Prediction-Machine-Learning
Prediction of loan defaulter based on more than 5L records using Python, Numpy, Pandas and XGBoost
mahyad55/learnFeaturesRS
Demo of "Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM" on the The Prague Texture Segmentation Datagenerator and Benchmark - ALI dataset
mahyad55/linkedIn_crawler
A python script to run search on linkedIn and collect the result in JSON format