Browsing by Subject "Machine learning"
Now showing items 1-20 of 28
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An Active Data Representation of Videos for Automatic Scoring of Oral Presentation Delivery Skills and Feedback Generation
(2020)Public speaking is an important skill, the acquisition of which requires dedicated and time consuming training. In recent years, researchers have started to investigate automatic methods to support public speaking skills ... -
Applying machine learning to model radon using topsoil geochemistry
(2023)Radon is classified as a Class 1 carcinogen, being the leading cause of lung cancer in non-smokers. Understanding the prominent sources of radon helps to mitigate against the adverse effects of radon exposure. Considering ... -
Big Data and Machine Learning for Materials Science
(Springer, 2021-04-19)Herein we review aspects of leading-edge research and innovation in Materials Sciencethatexploits big data and Machine Learning(ML), two computer science conceptsthat combine to yield computationalintelligence. ML can ... -
Commentary on Afzali et al. (2019): Two data sets are better than one
(Society for the Study of Addiction, 2019)The use of large data sets in addiction research iswelcome, because statistical power is increased. Whenapplied to large data sets, machine learning can help withinterpreting variable importance and with ... -
Data-driven time propagation of quantum systems with neural networks
(2022)We investigate the potential of supervised machine learning to propagate a quantum system in time. While Markovian dynamics can be learned easily, given a sufficient amount of data, non-Markovian systems are nontrivial and ... -
Data-driven time propagation of quantum systems with neural networks
(2022)We investigate the potential of supervised machine learning to propagate a quantum system in time. While Markovian dynamics can be learned easily, given a sufficient amount of data, non-Markovian systems are nontrivial and ... -
Digital signal processing approaches to bird song analysis
(Trinity College Dublin. School of Engineering. Discipline of Electronic & Elect. Engineering, 2017)The ability to automatically analyze bird vocalizations would provide major assistance to zoologists in their behavioral and ecological studies. Revisions of taxonomy need to be made in cases where a population of species ... -
Drone image segmentation using machine and deep learning for mapping Irish bog vegetation communities
(2020)The application of drones has recently revolutionised the mapping of wetlands due to their high spatial resolution and the flexibility in capturing images. In this study, the drone imagery was used to map key vegetation ... -
Fingerprinting and assessing the reactivity of past and future aggregates
(Trinity College Dublin. School of Natural Sciences. Discipline of Geology, 2021)The Irish construction boom of the early 2000s generated an unprecedented demand for sub floor rock aggregate. However, due to a lack of residential specific material standardisation, unacceptable documentation practices ... -
Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios
(2021)Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models ... -
Improving palliative care with machine learning and routine data: a rapid review
(2019)Introduction: Improving palliative care is a priority worldwide as this population experiences poor outcomes and accounts disproportionately for costs. In clinical practice, physician judgement is the core method of ... -
Investigating the use of recurrent motion modelling for speech gesture generation
(2018)The growing use of virtual humans demands generating increasingly realistic behavior for them while minimizing cost and time. Gestures are a key ingredient for realistic and engaging virtual agents and consequently automatized ... -
The Jacobi-Legendre potential
(2022)As the go-to method to solve the electronic structure problem, Kohn-Sham density functional theory (KS-DFT) can be used to obtain the ground-state charge density, total energy, and several other key materials’ properties. ... -
A machine learning approach to understanding patterns of engagement with internet-delivered mental health interventions
(American Medical Association, 2020)Importance: The mechanisms by which engagement with internet-delivered psychological interventions are associated with depression and anxiety symptoms are unclear. Objective: To identify behavior types based on how ... -
Machine-learning approach for quantified resolvability enhancement of low-dose STEM data
(2023)High-resolution electron microscopy is achievable only when a high electron dose is employed, a practice that may cause damage to the specimen and, in general, affects the observation. This drawback sets some limitations ... -
Machine-learning semilocal density functional theory for many-body lattice models at zero and finite temperature
(2021)We introduce a machine-learning density-functional-theory formalism for the spinless Hubbard model in one dimension at both zero and finite temperature. In the zero-temperature case this establishes a one-to-one ... -
Optimising Energy Efficiency in UAV-Assisted Networks using Multi-Agent Reinforcement Learning
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Computer Science, 2023)The demand for cellular connectivity continues to witness unprecedented growth over the years. Unmanned Aerial Vehicles (UAVs) equipped with small cells can provide ubiquitous connectivity to static and mobile ground users ... -
An Overview on Application of Machine Learning Techniques in Optical Networks
(2019)Today’s telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users’ ... -
Predicting cognitive load levels from speech data
(Springer, 2015)An analysis of acoustic features for a ternary cognitive load classification task and an application of a classification boosting method to the same task are presented. The analysis is based on a data set that ...
