63 citations · 76 across the 12 of their papers we have counts for
13 papers
Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET
Loukas Ilias, Anthi-Maria Vozinaki, Christos Ntanos +1
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a leading cause of death worldwide. Early diagnosis plays an important part especially at the Mild Cognit…
Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention
George Theodosiou, Loukas Ilias, Dimitris Askounis
Parkinson's disease (PD) is a progressive neurodegenerative disorder that frequently causes speech impairments associated with hypokinetic dysarthria. As speech production relies o…
A Multimodal Framework for Dementia Detection via Linguistic and Acoustic Representation Learning
Loukas Ilias, Dimitris Askounis
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia, affecting memory, reasoning, communication, and daily functioning. Early dia…
Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows
Vasilis Michalakopoulos, Christoforos Menos-Aikateriniadis, Elissaios Sarmas +3
This study investigates the performance of machine learning models in forecasting electricity Day-Ahead Market (DAM) prices using short historical training windows, with a focus on…
A Trustworthy By Design Classification Model for Building Energy Retrofit Decision Support
Panagiota Rempi, Sotiris Pelekis, Alexandros Menelaos Tzortzis +4
Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings, which contribut…
Recognition of Dysarthria in Amyotrophic Lateral Sclerosis patients using Hypernetworks
Loukas Ilias, Dimitris Askounis
Amyotrophic Lateral Sclerosis (ALS) constitutes a progressive neurodegenerative disease with varying symptoms, including decline in speech intelligibility. Existing studies, which…
Mixture of Experts for Recognizing Depression from Interview and Reading Tasks
Loukas Ilias, Dimitris Askounis
Depression is a mental disorder and can cause a variety of symptoms, including psychological, physical, and social. Speech has been proved an objective marker for the early recogni…
AI4EF: Artificial Intelligence for Energy Efficiency in the Building Sector
Alexandros Menelaos Tzortzis, Georgios Kormpakis, Sotiris Pelekis +4
AI4EF, Artificial Intelligence for Energy Efficiency, is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimizat…
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond
Loukas Ilias, George Doukas, Vangelis Lamprou +2
The advent of 6G/NextG networks comes along with a series of benefits, including extreme capacity, reliability, and efficiency. However, these networks may become vulnerable to new…
Trustworthy artificial intelligence in the energy sector: Landscape analysis and evaluation framework
Sotiris Pelekis, Evangelos Karakolis, George Lampropoulos +4
The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAI) within the European Union (EU), with a specific focus on the energy sector. The analysis enco…
A Graph Neural Architecture Search Approach for Identifying Bots in Social Media
Georgios Tzoumanekas, Michail Chatzianastasis, Loukas Ilias +3
Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots-automated programs disseminating misinformation and ideologies with…
A multi-dimensional unsupervised machine learning framework for clustering residential heat load profiles
Vasilis Michalakopoulos, Elissaios Sarmas, Viktor Daropoulos +4
Central to achieving the energy transition, heating systems provide essential space heating and hot water in residential and industrial environments. A major challenge lies in effe…
Depression detection in social media posts using transformer-based models and auxiliary features
Marios Kerasiotis, Loukas Ilias, Dimitris Askounis
The detection of depression in social media posts is crucial due to the increasing prevalence of mental health issues. Traditional machine learning algorithms often fail to capture…