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50 results on '"Tsadiras, Athanasios"'

1. Using Boosting and Neural Networks Methods to Detect Healthcare Fraud

Author

Fourkiotis, Konstantinos Panagiotis, Tsadiras, Athanasios, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, Avlonitis, Markos, editor, and Papaleonidas, Antonios, editor

Published
2024
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2. Pre-launch Fashion Product Demand Forecasting Using Machine Learning Algorithms

Author

Arampatzis, Marios, Theodoridis, G eorgios, Tsadiras, Athanasios, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, MacIntyre, John, editor, and Dominguez, Manuel, editor

Published
2023
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3. Comparing Machine Learning Techniques for House Price Prediction

Author

Fourkiotis, Konstantinos Panagiotis, Tsadiras, Athanasios, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, MacIntyre, John, editor, and Dominguez, Manuel, editor

Published
2023
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5. Comparing Boosting and Deep Learning Methods on Multivariate Time Series for Retail Demand Forecasting

Author

Theodoridis, Georgios, Tsadiras, Athanasios, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, and Cortez, Paulo, editor

Published
2022
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7. Using Machine Learning Methods to Predict Subscriber Churn of a Web-Based Drug Information Platform

Author

Theodoridis, Georgios, Tsadiras, Athanasios, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Macintyre, John, editor, and Iliadis, Lazaros, editor

Published
2021
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8. Retail Demand Forecasting: A Multivariate Approach and Comparison of Boosting and Deep Learning Methods.

Author

Theodoridis, Georgios and Tsadiras, Athanasios

Subjects
*DEMAND forecasting, *ARTIFICIAL neural networks, *DEEP learning, *TRANSFORMER models, *MACHINE learning, *STATISTICAL smoothing, *TIME series analysis
Abstract

Retail demand forecasting is a highly intricate and multilevel problem as it may include numerous products, under multiple categories, and potentially sold in many different stores. Forecasts may also process products in isolation or collectively so as to detect mutual correlations. In technical terms, retail demand forecasting may be broken down to a multivariate time series forecasting problem where the time axis is prevalent and introduces added complexity which necessitates proper usage of advanced methodologies with machine/deep learning backgrounds. Popular boosting regressors, such as XGBoost and LightGBM, are excellent machine learning candidates with extensive bibliographic backgrounds covering multiple scientific fields and applications including generic multivariate time series forecasts. The Temporal Convolutional Network and the Temporal Fusion Transformer are recently proposed deep neural network architectures that found success in specific multivariate scenarios but are yet to be tested in a variety of related fields such as retail demand forecasting. Therefore, within this paper, these novel in-the-field models are compared to the aforementioned boosting approaches alongside popular statistical univariate methods, namely Exponential Smoothing and SARIMA. To properly compare the selected models and attempt to generalize their usability, two separate datasets are analyzed and forecasted; an item sales dataset with 500 time series and a category sales dataset with 540 time series. The findings suggest that the deep learning solutions are the better predictors, with the Transformer model surpassing the Boosting solutions by up to 16.8% sMAPE decrease and the statistical approaches by up to 28.6% decrease, further substantiating the notion that deep learning techniques are exceptionally promising in handling large-scale, non-linear and outlier data whilst suggesting that retail demand forecasting does benefit from multivariate approaches and the application of advanced machine learning methods. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Decision Making on Container Based Logistics Using Fuzzy Cognitive Maps

Author

Tsadiras, Athanasios, Zitopoulos, George, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Iliadis, Lazaros, editor, and Jayne, Chrisina, editor

Published
2015
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15. Using Rules to Develop a Personalized and Social Location Information System for the Semantic Web

Author

Viktoratos, Iosif, Tsadiras, Athanasios K., Bassiliades, Nick, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bikakis, Antonis, editor, Fodor, Paul, editor, and Roman, Dumitru, editor

Published
2014
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19. Personalizing Location Information through Rule-Based Policies

Author

Viktoratos, Iosif, Tsadiras, Athanasios, Bassiliades, Nick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bikakis, Antonis, editor, and Giurca, Adrian, editor

Published
2012
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23. Using Fuzzy Cognitive Maps as a Decision Support System for Political Decisions: The Case of Turkey’s Integration into the European Union

Author

Tsadiras, Athanasios K., Kouskouvelis, Ilias, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bozanis, Panayiotis, editor, and Houstis, Elias N., editor

Published
2005
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43. Teaching logic programming from a distance: an approach

Author

Kargidis, Theodoros, Kefalas, Petros, Stamatis, Demosthenes, Tsadiras, Athanasios K., Learning in Physics Group, University of Cyprus, Constantinou, Constantinos P., Constantinou, Constantinos P. [0000-0003-3183-4131], and Kefalas, Petros [0000-0002-6846-7374]

Subjects
Teaching material and aids, Learners and teachers, Evaluating learning and teaching, ComputingMilieux_COMPUTERSANDEDUCATION, Teaching methods, Logic programming
Abstract

Περιέχει το πλήρες κείμενο In this paper a Networked Learning environment is presented for teaching Logic Programming from a Distance. After a short introduction to the Networked Learning approach to Open and Distance Learning, we present the reasons why Logic Programming background is regarded as necessary for a computer scientist. Technological and pedagogical aspects of the design of the Networked Learning course are then discussed and the designing choices are justified together with the implementation of the environment, by giving descriptions of the syllabus and the course structure. We discuss our experience acquired by both the web based teaching environment and the teaching process. The Networked Learning course was delivered for two successive academic years to undergraduate students under a SOCRATES-MINERVA program. Finally, we briefly present the results of the networked learning process based on student evaluation questionnaires and students’ learning performance and we propose ways to improve the programme delivery and outcomes.

Published
2003

44. Developing Personalized E-Books: A Multi-Layered Approach.

Author

Maglogiannis, Ilias, Karpouzis, Kostas, Bramer, Max, Chatzara, Konstantina, Mpantsos, Athanasios, Stamatis, Demosthenes, and Tsadiras, Athanasios

Abstract

Users of hypermedia systems are heterogeneous, came from different backgrounds and have different level of knowledge in the use of information systems. This paper discusses a multi-layered approach for developing personalized e-books. The layers regard both Human Computer Interaction/Navigation level and the Content of the e-book. These layers are defined and described in detail. The tei-Book architecture is also presented in the paper and it is proposed as suitable for providing personalized e-book views to the various e-book readers. This is done by an E-Book manager that manage to provide the appropriate e-book view according to the User Profiles and Book Profile Components that dynamically are developed by the tei-Book. Future work includes the evaluation of the personalized e-book views and also the evaluation of system's usability. [ABSTRACT FROM AUTHOR]

Published
2006
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45. Using Fuzzy Cognitive Maps as a Decision Support System for Political Decisions: The Case of Turkey's Integration into the European Union.

Author

Bozanis, Panayiotis, Houstis, Elias N., Tsadiras, Athanasios K., and Kouskouvelis, Ilias

Abstract

In this paper we use Fuzzy Cognitive Maps (FCMs), a well-established Artificial Intelligence technique that incorporates ideas from Artificial Neural Networks and Fuzzy Logic, to create a dynamic model of Turkey's course towards its integration into the European Union, after the decision of December 18, 2004, according to which in October 3, 2005, Turkey will start negotiating its access to the European Union. FCMs create models as collections of concepts and the various causal relations that exist between these concepts. The decision capabilities of the FCM structure are examined and presented using a model developed based on the beliefs of a domain expert in the political situation in Turkey & European Union. The model is examined both statically using graph theory techniques and dynamically through simulations. Scenarios are introduced and predictions are made by viewing dynamically the consequences of the corresponding actions. [ABSTRACT FROM AUTHOR]

Published
2005
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50. Evaluating time variations to identify valuable association rules in market basket analysis.

Author

Papavasileiou, Vasilios and Tsadiras, Athanasios

Subjects
MARKET basket analysis, ASSOCIATION rule mining, RETAIL industry, DATA mining, TIME-varying systems, CONSUMER behavior
Abstract

Data mining techniques are extensively applied to retail market to discover association rules between products. Indicators such as lift and confidence have been defined to evaluate and promote valuable association rules. The aim of this paper is to propose a method of identifying the most valuable association rules among the large set of discovered association rules. To achieve the above, our study proposes new indicators, concerning a) the variability of an association rule, that expresses the average variability of both lift and confidence indicators of an association rule and b) the variability of a product, that expresses the average variability of the association rules that the specific product participates. Based on these indicators, our proposed method identifies the most inflexible association rules (rules that present low degree of variability). Our method examines these inflexible rules, and based also on their confidence and lift indicators, provides the most valuable association rules regarding consumer behavior. The above method is applied to annual customer transaction data that have been collected by the use of loyalty cards from a known Greek supermarket chain. To discover the association rules, the apriori algorithm is used. In the paper, the most valuable rules that are identified by the proposed method are compared to those identified by the classical approach that uses only the confidence and lift indicators. The comparison exhibits the advantages of our proposed method. [ABSTRACT FROM AUTHOR]

Published
2013
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