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07/20/2023
A Markov Chain Model for Identifying Changes in Daily Activity Patterns of People Living with Dementia
Malnutrition and dehydration are strongly associated with increased cogn...
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02/22/2023
Information Theory Inspired Pattern Analysis for Time-series Data
Current methods for pattern analysis in time series mainly rely on stati...
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12/06/2022
Loss Adapted Plasticity in Deep Neural Networks to Learn from Data with Unreliable Sources
When data is streaming from multiple sources, conventional training meth...
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10/04/2022
Using Entropy Measures for Monitoring the Evolution of Activity Patterns
In this work, we apply information theory inspired methods to quantify c...
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10/19/2021
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
Agitation is one of the neuropsychiatric symptoms with high prevalence i...
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09/10/2021
Multimodal Federated Learning
Federated learning is proposed as an alternative to centralized machine ...
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03/03/2021
A Hamiltonian Monte Carlo Model for Imputation and Augmentation of Healthcare Data
Missing values exist in nearly all clinical studies because data for a v...
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01/18/2021
An attention model to analyse the risk of agitation and urinary tract infections in people with dementia
Behavioural symptoms and urinary tract infections (UTI) are among the mo...
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12/08/2020
Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots
We introduce the conceptual formulation, design, fabrication, control an...
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11/27/2020
Machine learning for risk analysis of Urinary Tract Infection in people with dementia
The Urinary Tract Infections (UTIs) are one of the top reasons for unpla...
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11/02/2020
Semi-supervised Federated Learning for Activity Recognition
The proliferation of IoT sensors and edge devices makes it possible to u...
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10/19/2020
Verifying the Causes of Adversarial Examples
The robustness of neural networks is challenged by adversarial examples ...
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05/08/2020
Continual Learning Using Task Conditional Neural Networks
Conventional deep learning models have limited capacity in learning mult...
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10/09/2019
Continual Learning Using Bayesian Neural Networks
Continual learning models allow to learn and adapt to new changes and ta...
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05/20/2019
Continual Learning in Deep Neural Network by Using a Kalman Optimiser
Learning and adapting to new distributions or learning new tasks sequent...
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05/20/2019
Continual Learning in Deep Neural Networks by Using Kalman Optimiser
Learning and adapting to new distributions or learning new tasks sequent...
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05/20/2019
Continual Learning in Deep Neural Networks by Using a Kalman Optimiser
Learning and adapting to new distributions or learning new tasks sequent...
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11/06/2018
Kalman Filter Modifier for Neural Networks in Non-stationary Environments
Learning in a non-stationary environment is an inevitable problem when a...
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02/17/2018
Machine learning for Internet of Things data analysis: A survey
Rapid developments in hardware, software, and communication technologies...
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10/26/2017
Segment Parameter Labelling in MCMC Mean-Shift Change Detection
This work addresses the problem of segmentation in time series data with...
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05/28/2017