ELPINIKI PAPAGEORGIOU

Associate Professor

Greece

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About

Elpiniki Papageorgiou is an Associate Professor at the University of Thessaly, Geopolis Campus, Greece. Her research interests span artificial intelligence applications in healthcare, human-robot interaction, and agricultural robotics. Recent articles reflect her focus on machine learning methodologies for medical diagnostics and optimization techniques in various operational contexts.

Articles (20)

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2025

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2022

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2022

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2022

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2022

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2022

Collaborators (5)

George Karras

Assistant Professor

University of Thessaly

GREECE

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Nikolaos Papandrianos

University of Thessaly

GREECE

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Aspassia Daskalopulu

Associate Professor

University of Thessaly

GREECE

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Remigio BERRUTO

Associate professor

University of Turin

ITALY

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Katarzyna Poczęta

Kielce University of Technology

POLAND

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ELPINIKI PAPAGEORGIOU

Research Interests

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About

Articles (20)

Experimental Comparative Analysis of Centralized vs. Decentralized Coordination of Aerial–Ground Robotic Teams for Agricultural Operations

Explainable AI Methods for Identification of Glue Volume Deficiencies in Printed Circuit Boards

Neural-FCM: a deep learning approach for weight matrix optimization in Fuzzy Cognitive Map classifiers

Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis

Artificial Intelligence in Decoding Ocular Enigmas: A Literature Review of Choroidal Nevus and Choroidal Melanoma Assessment

Human–Robot Interaction through Dynamic Movement Recognition for Agricultural Environments

Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis

A Multi-Modal Machine Learning Methodology for Predicting Solitary Pulmonary Nodule Malignancy in Patients Undergoing PET/CT Examination

Energy Cost Optimization for Incorporating Energy Hubs into a Smart Microgrid with RESs, CHP, and EVs

Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening

Innovative Attention-Based Explainable Feature-Fusion VGG19 Network for Characterising Myocardial Perfusion Imaging SPECT Polar Maps in Patients with Suspected Coronary Artery Disease

Distributed Genetic Algorithm for Community Detection in Large Graphs with a Parallel Fuzzy Cognitive Map for Focal Node Identification

Edge-Based Short-Term Energy Demand Prediction

An Auction Pricing Model for Energy Trading in Electric Vehicle Networks

Uncovering the Black Box of Coronary Artery Disease Diagnosis: The Significance of Explainability in Predictive Models

Double Auction Offloading for Energy and Cost Efficient Wireless Networks

Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification

Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks

Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities

Artificial Intelligence Methods for Identifying and Localizing Abnormal Parathyroid Glands: A Review Study

Collaborators (5)

George Karras

Nikolaos Papandrianos

Aspassia Daskalopulu

Remigio BERRUTO

Katarzyna Poczęta

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