wafaaSalem's Stars
vaasha/Machine-leaning-in-examples
Contains notebooks which can explain machine learning problem on examples
WillKoehrsen/machine-learning-project-walkthrough
An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!
Msanjayds/Machine_Learning_Projects
Various Machine Learning Projects on Classification, Regression etc
lkj8389/PR-impwork
This is some implements of pattern classificaion course including perceptron,relaxation procedure,MSE,Fisher,Ho-kashyap,SVM,KNN
halam189/Gait_Recognition_LSTM
Source code for the Gait Recognition using LSTM, presented in the paper "Multi-model Long Short-term Memory Network for Gait Recognition using Window-based Data Segment"
ravikiran-mane/FBCNet
FBCNet: An Efficient Multi-view Convolutional Neural Network for Brain-Computer Interface
nyukat/breast_cancer_classifier
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
WillKoehrsen/feature-selector
Feature selector is a tool for dimensionality reduction of machine learning datasets
parrt/dtreeviz
A python library for decision tree visualization and model interpretation.
naveenv92/python-science-tutorial
Series of notebooks to illustrate different plotting features using Python
maciejskorski/BreakingPointDetection
This python code illustrates modelling change points in a time series.
taspinar/siml
Machine Learning algorithms implemented from scratch
aymericdamien/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
margitantal68/featurelearning
Sage-Bionetworks/mHealthIdentityConfounding
pablo-git8/Breast-Cancer-Detection-Tool-Project-BCDT-
In this project I will be developing a software tool for predicting breast cancer based on patient´s public clinical information retrieved from the records found at BCSC web page. The purpose of this project is to help physicians on determining whether a patient can be diagnosed with breast cancer based on its demographic data, prior studies evaluation, historical records, technologist assessment (BI-RADS score) and family medical records. The scope of the project is to develop and deploy a tool that can be used in a radiography software that may classify breast images and, together (as a complete solution) be able to work as Artificial Intelligence for medical decision support in mammography. All text included in _italics_ is retrieved from public relevant web pages. And its proper references are included in [#] with direct access to them through their http link.
h-gokul/ParkinsonsGaitAssist
Parkinson disease patients suffer from "Freezing of Gait"(FOG) events during daily normal activities. Medical research has shown Rhythmic auditory cueing can recover normal gait when freezing events occur. The occurence of FOG events are detected using body worn accelerometer based systems. This system can be made more cheaper and scalable with implementation of low sized ML models.
mittrayash/Parkinson-s-Disease-Detection-using-Gait-Analysis
A research project that aims to detect Parkinson's disease in patients using Gait Analysis data. Subsequently, the project may make use of Gait Data Analysis to make powerful inferences which would help in genralizing the most common groups affected by this disease.
swzCuroverse/PGPGraphics
Various python scripts to analyze and graph characteristics of PGP participants
pranjalmedhi/Ensemble_Technique_Predicting_Parkinson-Disease
### Data Description & Context: Parkinson’s Disease (PD) is a degenerative neurological disorder marked by decreased dopamine levels in the brain. It manifests itself through a deterioration of movement, including the presence of tremors and stiffness. There is commonly a marked effect on speech, including dysarthria (difficulty articulating sounds), hypophonia (lowered volume), and monotone (reduced pitch range). Additionally, cognitive impairments and changes in mood can occur, and risk of dementia is increased. Traditional diagnosis of Parkinson’s Disease involves a clinician taking a neurological history of the patient and observing motor skills in various situations. Since there is no definitive laboratory test to diagnose PD, diagnosis is often difficult, particularly in the early stages when motor effects are not yet severe. Monitoring progression of the disease over time requires repeated clinic visits by the patient. An effective screening process, particularly one that doesn’t require a clinic visit, would be beneficial. Since PD patients exhibit characteristic vocal features, voice recordings are a useful and non-invasive tool for diagnosis. If machine learning algorithms could be applied to a voice recording dataset to accurately diagnosis PD, this would be an effective screening step prior to an appointment with a clinician ### Objective Objective behind leveraging Machine Learning Algorithm for predicting Parkinson Disease in subjects: PD patients exhibit characteristic vocal features and voice recordings are a useful and non-invasive tool for diagnosis. So this case study aims at identifying if Machine learning algorithms can be applied to a voice recording dataset to accurately diagnosis PD; if this is successful then this would be an effective screening step prior to an appointment with a clinician; ### Domain: Medicine
alexcaselli/Federated-Learning-for-Human-Mobility-Models
Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility
jakevdp/PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
viswanath-puttagunta/bkk16_MLPrimer
Jupyter ipynb file and associated data prepared specifically for Linaro Connect Bangkok 2016 (BKK16) talk title "Data Analytics and Machine Learning Primer"
blue-yonder/tsfresh
Automatic extraction of relevant features from time series:
malloch/Arduino_IMU
Arduino sensor-fusion firmware for estimating orientation using accelerometers, rate-gyroscopes, and magnetometers.
clemaitre58/Accelerometer_integration
DS-73/Data-OverfittingVisualization
mainkoon81/Study-09-MachineLearning-B
**Supervised-Learning** (with some Kaggle winning solutions and their reason of Model Selection for the given dataset).
sho-87/sensormotion
Package for analyzing human motion data (e.g. PA, gait)
matt002/GaitPy