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-in-mobile-networks
Data-driven Anomaly Detection with Traffic Pattern Categorization in Mobile Cellular Networks
mahyad55/awesome-fraud-detection-papers
A curated list of data mining papers about fraud detection.
mahyad55/Awesome-Video-Datasets
Video datasets
mahyad55/Coursera
This repository contains different Coursera Specilazation Assignment Solutions in Deep Learning, Big Data, Machine Learning and Data Science
mahyad55/cs-video-courses
List of Computer Science courses with video lectures.
mahyad55/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.
mahyad55/DeepFaceLab
DeepFaceLab is the leading software for creating deepfakes.
mahyad55/DeOldify
A Deep Learning based project for colorizing and restoring old images (and video!)
mahyad55/End-to-End-Guide-to-Building-a-Credit-Scorecard-Using-Machine-Learning
Open-Source Tools for Real World Problem Series
mahyad55/evidently
Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
mahyad55/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.
mahyad55/ghesta_credit_scoring
Credit Scoring for ghesta
mahyad55/Image-Processing-OpenCV
learning image processing using python
mahyad55/InstaPy
📷 Instagram Bot - Tool for automated Instagram interactions
mahyad55/linkedin-skill-assessments-quizzes
Full reference of LinkedIn answers 2021 for skill assessments, LinkedIn test, questions and answers (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, css, python, machine-learning, power-poin, excel ...) linkedin ответы на квиз, linkedin ответы на тесты, LinkedIn quiz lösungen, linkedin quiz las respuestas, linkedin odpovědi na otázky
mahyad55/models
Models and examples built with TensorFlow
mahyad55/modern-pyqt
Source Code for 'Modern PyQt' by Joshua Willman
mahyad55/msda
multi-dimensional time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly predictor
mahyad55/my_ml_service
My Machine Learning Web Service
mahyad55/Nlp_Instagram_Comments_Analysis
This repository contains code to scrape instagram comments and then perform NLP analysis using different techniques.
mahyad55/OAD
Collection of tools and scripts useful to automate microscopy workflows in ZEN Blue using Python and Open Application Development tools.
mahyad55/Paper-with-Code-of-Wireless-communication-Based-on-DL
无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL
mahyad55/PlotNeuralNet
Latex code for making neural networks diagrams
mahyad55/Probability-Calibration-Imbalanced
This repository implements Pozzolo, et al., (2015)'s probability calibration for imbalanced data.
mahyad55/python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
mahyad55/pytse-client
work with Tehran stock exchange data in Python
mahyad55/Reproducible-Deep-Compressive-Sensing
Collection of reproducible deep learning for compressive sensing
mahyad55/Telegram-Anti-Revoke
Telegram anti-revoke plugin - Telegram 防撤回插件
mahyad55/whylogs
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
mahyad55/yolo-license-plate-detection
A License-Plate detecttion application based on YOLO