Now showing items 1-6 of 6
A novel wearable sensor-based human activity recognition approach using artificial hydrocarbon networks
(MDPI AG, 2016)
Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of ...
Artificial hydrocarbon networks for freezing of gait detection in Parkinson’s disease
(Institute of Electrical and Electronics Engineers Inc., 2020-11)
Freezing of gait (FoG) is one of the most impairing phenomenon experienced by Parkinson's disease (PD) patients. This phenomenon is associated with falls and is an important factor that limits autonomy and impairs quality ...
Interpretability of artificial hydrocarbon networks for breast cancer classification
(Institute of Electrical and Electronics Engineers Inc., 2017)
In machine learning, interpretability refers to understand the underlying behavior of the prediction of a model in order to identify diagnosis criteria and/or new rules from its output. Interpretability contributes to ...
A reinforcement learning method for continuous domains using artificial hydrocarbon networks
(Institute of Electrical and Electronics Engineers Inc., 2018)
Reinforcement learning in continuous states and actions has been limitedly studied in ocassions given difficulties in the determination of the transition function, lack of performance in continuous-to-discrete relaxation ...
Human activity recognition on mobile devices using artificial hydrocarbon networks
(Springer Verlag, 2018)
Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. ...
Versatility of artificial hydrocarbon networks for supervised learning
(Springer Verlag, 2019)
Surveys on supervised machine show that each technique has strengths and weaknesses that make each of them more suitable for a particular domain or learning task. No technique is capable to tackle every supervised learning ...