Payam Barnaghi's repositories
TIHM-Dataset
TIHM: An open dataset for remote healthcare monitoring in dementia
Activity_Patterns_Digital_Biomarker
A Digital Biomarker for Identifying Changes in Daily Activity Patterns
Entropy-Analysis-Pipeline
Code for the Entropy Pipeline
AMDRIoT
A prediction-based data reduction method that exploits LMS adaptive filters in the Internet of Things
Basic-ML-Code
Basic ML code in python/mathlab
CancerSymptomNetworks
Demo of my code and expirements on cancer symptoms and their symptom clusters. Please see the following site.
CML-UKDRI
An introduction to the UTI risk analysis in people with dementia project at the Uk DRI Care Research and Technology Centre
continual_learning_papers
Relevant papers in Continual Learning
course-content
NMA Computational Neuroscience course
dcarte
Data ingestion tools to standardise data-driven analysis across data domains e.g., IoT, behavioural, physiological. (UK DRI Care Research & Technology)
F-HMC
Implementation of folded Folded Hamiltonian Monte Carlo for data imputation and augmentation on data collected from cancer patients who self-reported their symptoms experience during chemotherapy by a team in the School of the Nursing University of California.
Generative_CA
This project contains the implementation of using the generative models in continuous authentication on motion sensor data from the H-MOG dataset.
medtimeline
The MedTimeline library aims to provide a standardised process for pre-processing and generating patient journey timelines for the cardiology EHR dataset. Developed by Louise Rigny (Great Ormond Street Hospital for Children)
SPARC
Wellcome Trust Ideathon (Winner of Best Mental Health Entry) - This is an early stage and preliminary work to address the challenges of participant retention in long-term mental health clinical trials by providing researchers with estimation and guidance
IEEE-SIG-TCBDIN
IEEE SIG on Big Data Intelligent Networking
Knowledge-Acquisition-Toolkit
The Knowledge Acquisition Tool (KAT) is a software toolkit that implements the state-of-the-art machine learning and data analytic methods for sensors data. The algorithms and methods implemented in KAT are used for processing and analysing the smart city data in the CityPulse project.
Lagrangian-Multipliers-Roonak_Rezvani
This work proposes a segmentation and aggregation method which represents time series frames as vectors by first applying Piecewise Aggregate Approximation (PAA) and then applying Lagrangian Multipliers. This method allows representing the continuous data as a series of patterns that can be used and processed by various higher-level methods.
LossAdaptedPlasticity
Code for the Loss Adapted Plasticity Algorithm
minder_utils
The library to access the UK DRI minder study data developed by Alex Capstick and Honglin Li.
MSc-BrianSciences
MSc Projects
neuralnet_plasticity
Basic code on synaptic plasticity in neural networks
Non-Conforming-Sources
Code made available for paper on Non-Conforming Data Sources
Task-Conditional-Neural-Networks-TCNN_Honglin_Li
Task Conditional Neural Networks (TCNN) leverage the probabilistic neural networks to estimate the probability density of the training samples. Then produce the task likelihood during the test state to fire the task-specific neurons correspondin to the test sampels. TCNN can detect and learn the new tasks fully-automatically without informing the changes to the model.