Zobrazeno 1 - 10
of 205
pro vyhledávání: '"Kalles, Dimitris"'
Autor:
Zafeiropoulos, Vasilis, Anastassakis, George, Orphanoudakis, Theophanis, Kalles, Dimitris, Fanariotis, Anastasios, Fotopoulos, Vassilis
Publikováno v:
MobileHCI '23 Companion: Proceedings of the 25th International Conference on Mobile Human-Computer Interaction (2023)
This paper presents the V-Lab, a VR application development framework for educational scenarios mainly involving scientific processes executed in laboratory environments such as chemistry and biology laboratories. This work is an extension of the Onl
Externí odkaz:
http://arxiv.org/abs/2407.07698
Advances in AI have led to new types of technical debt in software engineering projects. AI-based competition platforms face challenges due to rapid prototyping and a lack of adherence to software engineering principles by participants, resulting in
Externí odkaz:
http://arxiv.org/abs/2405.11825
Autor:
Zafeiriou, Theodoros, Kalles, Dimitris
Our study focuses on comparing the performance and resource requirements between different Long Short-Term Memory (LSTM) neural network architectures and an ANN specialized architecture for forex market prediction. We analyze the execution time of th
Externí odkaz:
http://arxiv.org/abs/2405.10679
Autor:
Zafeiriou, Theodoros, Kalles, Dimitris
The present document delineates the analysis, design, implementation, and benchmarking of various neural network architectures within a short-term frequency prediction system for the foreign exchange market (FOREX). Our aim is to simulate the judgmen
Externí odkaz:
http://arxiv.org/abs/2405.08045
Autor:
Lygizou, Zoi, Kalles, Dimitris
Recent work on decentralized computational trust models for open Multi Agent Systems has resulted in the development of CA, a biologically inspired model which focuses on the trustee's perspective. This new model addresses a serious unresolved proble
Externí odkaz:
http://arxiv.org/abs/2404.18296
Autor:
Lygizou, Zoi, Kalles, Dimitris
Current trust and reputation models continue to have significant limitations, such as the inability to deal with agents constantly entering or exiting open multi-agent systems (open MAS), as well as continuously changing behaviors. Our study is based
Externí odkaz:
http://arxiv.org/abs/2404.10014
Autor:
Triantafyllopoulos, Loukas, Feretzakis, Georgios, Tzelves, Lazaros, Sakagianni, Aikaterini, Verykios, Vassilios S., Kalles, Dimitris
Publikováno v:
In Computers in Human Behavior December 2024 161
Autor:
Feretzakis, Georgios, Karlis, George, Loupelis, Evangelos, Kalles, Dimitris, Chatzikyriakou, Rea, Trakas, Nikolaos, Karakou, Eugenia, Sakagianni, Aikaterini, Tzelves, Lazaros, Petropoulou, Stavroula, Tika, Aikaterini, Dalainas, Ilias, Kaldis, Vasileios
Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare. Material and
Externí odkaz:
http://arxiv.org/abs/2106.12921
Autor:
Kiourt, Chairi, Feretzakis, Georgios, Dalamarinis, Konstantinos, Kalles, Dimitris, Pantos, Georgios, Papadopoulos, Ioannis, Kouris, Spyros, Ioannakis, George, Loupelis, Evangelos, Antonopoulos, Petros, Sakagianni, Aikaterini
The main objective of this work is to utilize state-of-the-art deep learning approaches for the identification of pulmonary embolism in CTPA-Scans for COVID-19 patients, provide an initial assessment of their performance and, ultimately, provide a fa
Externí odkaz:
http://arxiv.org/abs/2105.11187
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