GT2 Decision Support Systems and Methods

Leontios Hadjileontiadis's picture
Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)
Spiros Denaxas's picture
Spiros Denaxas
University College London (UK)
Conference room
Session time
Thursday, June 22, 2017 - 16:00 to 17:15
EEG Signal Analysis of Real-word Reading and Nonsense-word Reading between Adults with Dyslexia and without Dyslexia
Abstract: 
The evolution in technology plays a major role in improving diagnostic accuracies. Pattern recognition and classification are techniques that may help uncover answers that are not always obvious. This paper attempts to discover such patterns found in brain wave signals in people with dyslexia using classifiers. Electroencephalogram (EEG) signals captured during real-word and nonsense-word reading activities from individuals with dyslexia are compared with normal controls.
Harshani Perera's picture
Harshani Perera
Murdoch University (AU)
Mohd Fairuz Shiratuddin's picture
Mohd Fairuz Shiratuddin
Kok Wai Wong's picture
Kok Wai Wong
Kelly Fullarton's picture
Kelly Fullarton
Emotional state recognition using advanced machine learning techniques on EEG data
Abstract: 
This study investigates the discrimination between calm, exciting positive and exciting negative emotional states using EEG signals. Towards this direction, a publicly available dataset from eNTERFACE Workshop 2006 was used having as stimuli emotionally evocative images. At first, EEG features were extracted based on literature review. Then, a computational framework is proposed using machine learning techniques, performing feature selection and classification into two at a time emotional states.
Katerina Giannakaki's picture
Katerina Giannakaki
University of Crete (GR)
Giorgos Giannakakis's picture
Giorgos Giannakakis
Christina Farmaki's picture
Christina Farmaki
Vangelis Sakkalis's picture
Vangelis Sakkalis
Estimation of Heart Failure Patients Medication Adherence through the Utilization of Saliva and Breath Biomarkers and Data Mining Techniques
Abstract: 
The aim of this work is to estimate the medication adherence of patients with heart failure through the application of a data mining approach on a dataset including information from saliva and breath biomarkers. The method consists of two stages. In the first stage, a model for the estimation of adherence risk of a patient, exploiting anamnestic and instrumental data, is applied.
Evanthia Tripoliti's picture
Evanthia Tripoliti
Theofilos Papadopoulos's picture
Theofilos Papadopoulos
Georgia Karanasiou's picture
Georgia Karanasiou
FORTH (GR)
Fanis Kalatzis's picture
Fanis Kalatzis
Yorgos Goletsis's picture
Yorgos Goletsis
Aris Bechlioulis's picture
Aris Bechlioulis
Silivia Ghimenti's picture
Silivia Ghimenti
Tommaso Lomonaco's picture
Tommaso Lomonaco
Francesca Bellagambi's picture
Francesca Bellagambi
Roger Fuoco's picture
Roger Fuoco
Mario Marzilli's picture
Mario Marzilli
Maria Chiara Scali's picture
Maria Chiara Scali
Katerina Naka's picture
Katerina Naka
Abdelhamid Errachid's picture
Abdelhamid Errachid
Dimitris Fotiadis's picture
Dimitris Fotiadis
University of Ioannina (GR)
Exploiting active microRNA interactions for diagnosis from expression profiling experiments
Abstract: 
In silico diagnosis through microRNA expression profiling experiments is a promising direction in the clinical practices of bioinformatics science. The task is computationally defined as a classification problem where a query experiment is required to be assigned into one of the predefined diseases using a learned model from previously labeled samples. While several powerful machine learning models exist to perform this task, the challenging issue is how to feed these models by effectively encoded samples. This encoding requires a sensible representation of experiment content.
Erdem Corapcioglu's picture
Erdem Corapcioglu
Hasan Ogul's picture
Hasan Ogul
Baskent University (TR)
The Effect of Mammogram Preprocessing on Microcalcification Detection with Convolutional Neural Networks
Abstract: 
"Microcalcifications are an early mammographic indicator of breast cancer. To assist screening radiologists in reading mammograms, machine learning techniques have been developed for the automated detection of microcalcifications. In the last few years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision and medical image analysis applications. A key step in CNN-based detection is image preprocessing, including brightness and contrast variations.
Agnese Marchesi's picture
Agnese Marchesi
University of Cassino and Southern Latium (IT)
Alessandro Bria's picture
Alessandro Bria
Claudio Marrocco's picture
Claudio Marrocco
Mario Molinara's picture
Mario Molinara
Francesco Tortorella's picture
Francesco Tortorella
University of Cassino and South Lazio (IT)
Jan-Jurre Mordang's picture
Jan-Jurre Mordang
Nico Karssemeijer's picture
Nico Karssemeijer