Special Issue "Advanced Machine Learning and Deep Networks for Psycho-Physiological Signals Processing, Modelling, and Classification"
Deadline for manuscript submissions: 31 August 2019
Prof. Dr. George Magoulas
Department of Computer Science and Information Systems, Birkbeck College, University of London, London WC1E 7HX, UK
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Interests: intelligent optimization; context awareness; neural computing; deep learning; data-driven modelling; intelligent systems; machine learning; nature-inspired computing; user modelling; computational models of learning and cognition
Psycho-physiological signals have been demonstrated as being useful in several applications for assessing emotional experiences, modelling cognitive processes, user and player modelling, human activity recognition, classification of facial expressions, detection of behavioural changes and so on. Signals come from a wide range of sensors, such as wearable sensors, mobile sensors, cameras, heart rate monitoring devices, EEG headcaps and headbands, ECG sensors, breathing monitors, EMG sensors, and temperature sensors. However, the use of these signals poses several challenges for reliable data processing, modelling and classification, as it is influenced by different types of environmental and biological sources of noise, artefacts and interference. Methods that employ machine learning and deep learning appear eminently suitable for these challenging tasks.
This Special Issue will present state-of-the-art machine learning and deep learning approaches for data processing, modelling, pattern recognition, and the classification of psycho-physiological signals, and for the development of intelligent systems that use psycho-physiological signals.
Prof. Dr. George Magoulas
Manuscript Submission Information
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- Data transformation
- Dimensionality reduction
- Feature selection
(2) Modelling, classification and pattern recognition methods:
- Bio-inspired computing
- Decision trees
- Deep networks
- Ensemble learning
- Fuzzy logic
- Genetic and evolution algorithms
- Kernel methods
- Machine learning
- Neural networks
- Random forests
- Support vectors
(3) Visualisation of psycho-physiological signals and time-series
(4) Intelligent systems and human-machine systems that use psycho-physiological signals