A5020724277- Time Series Analysis and Forecasting
- Context-Aware Activity Recognition Systems
- Anomaly Detection Techniques and Applications
- Music and Audio Processing
- Digital Transformation in Industry
- Physical Unclonable Functions (PUFs) and Hardware Security
- Internet of Things and AI
- University-Industry-Government Innovation Models
- Plant Physiology and Cultivation Studies
- Growth and nutrition in plants
- COVID-19 epidemiological studies
- Cardiac pacing and defibrillation studies
- IoT and Edge/Fog Computing
- Complex Systems and Time Series Analysis
- Cancer Cells and Metastasis
- Cardiac electrophysiology and arrhythmias
- Innovation and Knowledge Management
- SARS-CoV-2 and COVID-19 Research
- Flexible and Reconfigurable Manufacturing Systems
- Mobile Health and mHealth Applications
- Flowering Plant Growth and Cultivation
- Breast Lesions and Carcinomas
- Infection Control and Ventilation
- Cardiac Arrhythmias and Treatments
- Advanced Text Analysis Techniques
University of Ioannina
2005-2024
University of Western Macedonia
2022
National Public Health Organization
2021
University Hospital of Ioannina
2021
Hebrew University of Jerusalem
1998
Industry 4.0 has risen as an integrated digital manufacturing environment, and it created a novel research perspective that thrust to interdisciplinarity exploitation of ICT advances. This work presents discusses the main aspects how intelligence can be embedded in create smart factory. It briefly describes components 4.0, focuses on security challenges fully interconnected ecosystem meet threats for each component. Preserving crucial role is vital its existence, so directions ensure...
Knowledge and skills in the field of Artificial Intelligence (AI), Internet Things (IoT), Edge Computing (EC) are more important for industry. Therefore, it is crucial to know what current students future employees can offer University develop their knowledge support industry implementing modern technologies future. It be expected that first source information will lectures other activities at university. However, they may obtain from sources. This article presents results research conducted...
We collected data from 10 EU/EEA countries on 240 COVID-19 outbreaks occurring July-October 2021 in long-term care facilities with high vaccination coverage. Among 17,268 residents, 3,832 (22.2%) cases were reported. Median attack rate was 18.9% (country range: 2.8-52.4%), 17.4% of hospitalised, 10.2% died. In fully vaccinated adjusted relative risk for increased outbreak rate. Findings highlight the importance early detection and rapid containment through effective infection prevention...
In this work, we introduce the Multichannel Intelligent Icons, a novel method for producing and presenting essential patterns of multidimensional bio-signals. The proposed approach is an extension Symbolic Aggregate Approximation (SAX) along with innovative variation Icons. innovation on stands grounds creating spatial correlation inherited information in all dimensions so it provides extra features distinguishing human activities. model testing Human Activity recorded data classification...
In this work, we introduce the Slopewise Aggregate Approximation (SAA), an innovative variation of Piecewise Approximation. The (SAA) is used as a novel core step for Symbolic method. SAA efficiently describes trend at time series signal since it incorporates information regarding shape and fluctuation while simultaneously achieving problem's dimensionality reduction. Then, by applying discretization technique, problem transformed into symbolic space problem, Intelligent Icons are features...
Practical Learning of Artificial Intelligence on the Edge Industry 4.0 (PLANET4) is a cross-disciplinary initiative funded by European Commission under Erasmus+ program that embodies triple helix model collaboration between academia, industry, and administration. It aims to bridge gap academic teaching practical applications in context 4.0. PLANET4 focuses developing hard skills artificial intelligence, industrial Internet things, cloud edge computing, along with soft competencies required...
The widespread availability of smartphones and their high processing power have made them powerful mobile tools able to host run various apps. In addition, wearable devices with low cost accurate sensors gathering physiological data information are now available. Meanwhile, automated activity recognition is a rapidly evolving research area directly related the Health (mHealth) field. Rapid advancements in Human Activity Recognition (HAR) field mainly based on combining succeed advancing...
This paper deals with the problem of identifying and recognizing everyday human activities. The main goal is to compare a variety implemented classification models founded on diverse machine learning approaches; one that utilizes features extracted from time frequency domain three others take advantage attributes symbolic space in order extract conclusions regarding performance potential usefulness each them. To guarantee impartiality comparison, we used signals contained free accessible...