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Antonis C. Kakas

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Nicoletta Prentzas

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Publications

2023
[c90]
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  dblp key:
  - conf/caip/NicolaouPLPKP23
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  - https://dblp.org/rec/conf/caip/NicolaouPLPKP23
Andria Nicolaou, Nicoletta Prentzas, Christos P. Loizou, Marios Pantzaris, Antonis C. Kakas, Constantinos S. Pattichis:
A Comparative Study of Explainable AI models in the Assessment of Multiple Sclerosis. CAIP (2) 2023: 140-148
[c87]
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  - conf/xai/PrentzasPK23
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Nicoletta Prentzas, Constantinos S. Pattichis, Antonis C. Kakas:
Explainable Machine Learning via Argumentation. xAI (3) 2023: 371-398
[i13]
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  - journals/corr/abs-2308-05411
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Nicoletta Prentzas, Antonis C. Kakas, Constantinos S. Pattichis:
Explainable AI applications in the Medical Domain: a systematic review. CoRR abs/2308.05411 (2023)
2022
[c86]
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  - electronic edition @ ceur-ws.org (open access)
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  - conf/comma/PrentzasGNK22
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Nicoletta Prentzas, Athena Gavrielidou, Marios Neofytou, Antonis C. Kakas:
Argumentation-based Explainable Machine Learning (ArgEML): a Real-life Use Case on Gynecological Cancer. ArgML@COMMA 2022: 1-13
2021
[c80]
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  - conf/bibe/PrentzasPKNKP21
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Nicoletta Prentzas, Marios Pitsiali, Efthyvoulos Kyriacou, Andrew Nicolaides, Antonis C. Kakas, Constantinos S. Pattichis:
Model Agnostic Explainability Techniques in Ultrasound Image Analysis. BIBE 2021: 1-6
2020
[c78]
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  - conf/bibe/AchilleosLPKKP20
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Kleo G. Achilleos, Stephanos Leandrou, Nicoletta Prentzas, Panayiotis A. Kyriacou, Antonis C. Kakas, Constantinos S. Pattichis:
Extracting Explainable Assessments of Alzheimer's disease via Machine Learning on brain MRI imaging data. BIBE 2020: 1036-1041
2019
[c77]
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  - conf/bibe/PrentzasNKKP19
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Nicoletta Prentzas, Andrew Nicolaides, Efthyvoulos Kyriacou, Antonis C. Kakas, Constantinos S. Pattichis:
Integrating Machine Learning with Symbolic Reasoning to Build an Explainable AI Model for Stroke Prediction. BIBE 2019: 817-821

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