Search Results for author: Dimosthenis Kyriazis

Found 8 papers, 1 papers with code

XAI for All: Can Large Language Models Simplify Explainable AI?

no code implementations23 Jan 2024 Philip Mavrepis, Georgios Makridis, Georgios Fatouros, Vasileios Koukos, Maria Margarita Separdani, Dimosthenis Kyriazis

The field of Explainable Artificial Intelligence (XAI) often focuses on users with a strong technical background, making it challenging for non-experts to understand XAI methods.

Decision Making Explainable artificial intelligence +3

Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection

no code implementations8 Jan 2024 Georgios Fatouros, Konstantinos Metaxas, John Soldatos, Dimosthenis Kyriazis

This paper introduces MarketSenseAI, an innovative framework leveraging GPT-4's advanced reasoning for selecting stocks in financial markets.

Decision Making In-Context Learning

Enhancing Explainability in Mobility Data Science through a combination of methods

no code implementations1 Dec 2023 Georgios Makridis, Vasileios Koukos, Georgios Fatouros, Dimosthenis Kyriazis

In the domain of Mobility Data Science, the intricate task of interpreting models trained on trajectory data, and elucidating the spatio-temporal movement of entities, has persistently posed significant challenges.

Feature Importance

XAI for time-series classification leveraging image highlight methods

no code implementations28 Nov 2023 Georgios Makridis, Georgios Fatouros, Vasileios Koukos, Dimitrios Kotios, Dimosthenis Kyriazis, Ioannis Soldatos

Although much work has been done on explainability in the computer vision and natural language processing (NLP) fields, there is still much work to be done to explain methods applied to time series as time series by nature can not be understood at first sight.

Time Series Time Series Classification

A Review of Explainable Artificial Intelligence in Manufacturing

no code implementations5 Jul 2021 Georgios Sofianidis, Jože M. Rožanec, Dunja Mladenić, Dimosthenis Kyriazis

The implementation of Artificial Intelligence (AI) systems in the manufacturing domain enables higher production efficiency, outstanding performance, and safer operations, leveraging powerful tools such as deep learning and reinforcement learning techniques.

Decision Making Explainable artificial intelligence +3

STARdom: an architecture for trusted and secure human-centered manufacturing systems

no code implementations2 Apr 2021 Jože M. Rožanec, Patrik Zajec, Klemen Kenda, Inna Novalija, Blaž Fortuna, Dunja Mladenić, Entso Veliou, Dimitrios Papamartzivanos, Thanassis Giannetsos, Sofia Anna Menesidou, Rubén Alonso, Nino Cauli, Diego Reforgiato Recupero, Dimosthenis Kyriazis, Georgios Sofianidis, Spyros Theodoropoulos, John Soldatos

There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5. 0.

Active Learning Decision Making +1

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