Education

Research Interests

Teaching Activities

Journals


Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.


N. Kyriakou, E. Loukis, M. Maragoudakis, Predicting firms’ resilience to economic crisis using artificial intelligence for optimizing economic stimulus programσ, Transforming Government: People, Process and Policy, 2023, (to_appear), , indexed in SCI-E, IF =
 

Abstract
Purpose – This study aims to develop a methodology for predicting the resilience of individual firms to economic crisis, using historical government data to optimize one of the most important and costly interventions that governments undertake, the huge economic stimulus programs that governments implement for mitigating the consequences of economic crises, by making them more focused on the less resilient and more vulnerable firms to the crisis, which have the highest need for government assistance and support. Design/methodology/approach – The authors are leveraging existing firm-level data for economic crisis periods from government agencies having competencies/ responsibilities in the domain of economy, such as Ministries of Finance and Statistical Authorities, to construct prediction models of the resilience of individual firms to the economic crisis based on firms’ characteristics (such as human resources, technology, strategies, processes and structure), using artificial intelligence (AI) techniques from the area of machine learning (ML). Findings – The methodology has been applied using data from the Greek Ministry of Finance and Statistical Authority about 363 firms for the Greek economic crisis period 2009–2014 and has provided a satisfactory prediction of a measure of the resilience of individual firms to an economic crisis. Research limitations/implications – The authors’ study opens up new research directions concerning the exploitation of AI/ML in government for a critical government activity/intervention of high importance that mobilizes/ spends huge financial resources. The main limitation is that the abovementioned first application of the proposed methodology has been based on a rather small data set from a single national context (Greece), so it is necessary to proceed to further application of this methodology using larger data sets and different national contexts. Practical implications – The proposed methodology enables government agencies responsible for the implementation of such economic stimulus programs to proceed to radical transformations of them by predicting the resilience to economic crisis of the firms applying for government assistance and then directing/focusing the scarce available financial resources to/on the ones predicted to be more vulnerable, ιncreasing substantially the effectiveness of these programs and the economic/social value they generate. Originality/value – To the best of the authors’ knowledge, this study is the first application of AI/ML in government that leverages existing data for economic crisis periods to optimize and increase the effectiveness of the largest and most important and costly economic intervention that governments repeatedly have to make: the economic stimulus programs for mitigating the consequences of economic crises.

E. Loukis, M. Maragoudakis, N. Kyriakou, Artificial Intelligence based Public Data Analytics for Economic Crisis Policy Making, Transforming Government: People, Process and Policy, 2020, Emerald, (to_appear), indexed in SCI-E
 

Abstract
Purpose: Public sector has started exploiting artificial intelligence (AI) techniques, however mainly for operational and to a lower extent for tactical level tasks. The purpose of this study is to exploit AI for the highest strategic level task of government: to develop an AI-based public sector data analytics methodology for supporting policy-making for one of the most serious and large-scale challenges that governments repeatedly face: the economic crises giving rise to economic recessions (though the proposed methodology has much wider applicability) Design/Methodology/Approach: A public sector data analytics methodology has been developed, which enables the exploitation of existing public and private sector data, through advanced processing of them using a big data oriented AI technique, ‘all-relevant’ feature selection, in order to identify characteristics of firms and their external environment that affect (positively or negatively) their resilience to economic crisis. Findings: A first application of the proposed public sector data analytics methodology has been conducted, using Greek firms’ data concerning the economic crisis period 2009-2014, which has led to interesting conclusions and insights, revealing some factors that affect the extent of sales revenue decrease in Greek firms during the above crisis period, and providing a first validation of our methodology. Research Implications: Our research contributes to the advancement of two emerging highly important for the society, but minimally researched, digital government research domains: public sector data analytics (and especially policy analytics) and government exploitation of AI. It exploits an AI feature selection algorithm, the Boruta ‘all-relevant’ variables identification one, which has been minimally exploited in the past for public sector data analytics, in order to support the design of public policies for addressing one of the most serious and large-scale economic challenges that governments repeatedly face: the economic crises. Practical Implications: The proposed methodology allows the identification of characteristics of firms and their external environment that affect positively or negatively their resilience to economic crisis. This enables a better understanding of the kinds of firms that are more strongly hit by the crisis, which is quite useful for the design of public policies for supporting them; and at the same time reveals firms’ resources, capabilities and practices that enhance their ability to cope with economic crisis, in order to design policies for promoting them through educational and support activities. Social Implications: This methodology can be very useful for the design of more effective public policies for reducing the negative impacts of economic crises on firms, and therefore mitigating their negative consequences for the society, such as unemployment, poverty and social exclusion. Originality/Value: Our research develops a novel approach to the exploitation of public and private sector data, based on a minimally exploited for such purposes AI technique (‘all-relevant’ feature selection), in order to support the design of public policies for addressing one of the most threatening disruptions that modern economies and societies repeatedly face the economic crises.

C. Kalyvas, M. Maragoudakis, A Skyline-Based Decision Boundary Estimation Method for Binominal Classification in Big Data, Computation , Vol. 8, No. 3, pp. 1-22, 2020, MDPI, https://www.mdpi.com/2079-3197/8/3/80/ht...
[4]
M. Maragoudakis, Bayesian Feature Construction for the Improvement of Classification Performance, International Journal of Data Analysis Techniques and Strategies, Vol. 12, No. 1, 2019, INDERSCIENCE, (to_appear)
[5]
M. Maragoudakis, Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data, Algorithms-Special Issue "Modeling Computing and Data Handling for Marine Transportation", Vol. 12, No. 1, pp. 27-49, 2019, MDPI
J. Gomez, L. Jaccheri, M. Maragoudakis, K. Sharma, Digital Storytelling for good with Tappetina Game, Entertainment Computing, 2019, Elsevier, (to_appear), https://doi.org/10.1016/j.entcom.2019.10...
 

Abstract
Storytelling is an important asset in today\\\'s society. Digital platforms for storytelling can facilitate collaborative development of stories. The storytelling process, if properly facilitated, can lead to the creation of stories that improve the relations between the players. Moreover, stories convey important information about the players and their interaction. Extended knowledge and better tools are needed about how to facilitate storytelling for good and analysis to exploit the power of the generated data. Research Question: How to facilitate Digital Storytelling for good? Method: The investigation is based on a case study approach in which participants have been engaged in the creation of stories. The study is based on empirical data collection and analysis: from the stories recorded, we extract the storytelling features and performance. We have provided qualitative (Domain Expert) and quantitative (Machine Learning) analysis of the stories. In total, 58 users played the game in 15 sessions. Results and Conclusions: The main result is a framework for analysing digital stories. The analysis gives an indication of which game building blocks lead to stories for good. Future work will include a redesign of the game and its building blocks which lead to stories for good and further analyses.

[7]
C. Kalyvas, M. Maragoudakis, Skyline and Reverse Skyline Query Processing in SpatialHadoop, Data & Knowledge Engineering, 2019, (to_appear), IF = 1.547
Foteini Kollintza-Kyriakoulia, M. Maragoudakis, Anastasia Krithara, Measuring the Impact of Financial News and Social Media on Stock Market Modeling Using Time Series Mining Techniques, Algorithms, Vol. 11, No. 11, pp. 181-205, 2018, MDPI, https://www.mdpi.com/1999-4893/11/11/181
[9]
V. Athanasiou, M. Maragoudakis, A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek, Algorithms , Vol. 10, No. 1, pp. 1-21, 2017, MDPI, http://www.mdpi.com/1999-4893/10/1/34
[10]
Luciana Mariñelarena-Dondena, Edgardo Ferretti, M. Maragoudakis, Maximiliano Sapino, Marcelo Luis Errecalde, Predicting Depression: a comparative study of machine learning approaches based on language usage, Panamerican Journal of Neuropsychology, 2017, (to_appear),
[11]
K. Kontos, M. Maragoudakis, Machine Learning for Water bodies Identification from Satellite Images, International Journal of Data Mining, Modelling and Management, 2017, (to_appear),
[12]
N. Potha, M. Maragoudakis, D. Lyras, A Biology-Inspired, Data Mining Framework for Extracting Patterns in Sexual Cyberbullying Data, Knowledge-Based Systems, 2016, Elsevier, (to_appear), http://dx.doi.org/10.1016/j.knosys.2015...., indexed in SCI-E, IF = 2.947
[13]
Y. Markou, M. Maragoudakis, Visual Data Mining in Social Media, International Journal of Social Network Mining, 2016, (to_appear),
[14]
K. Kontos, M. Maragoudakis, Automated Pool Detection from Satellite Images using Data Mining Techniques, International Journal of Image Mining , 2016, (to_appear),
[15]
D. Drossos, M. Maragoudakis, F. Kokkinaki, Buying behavior on daily-deal sites: The role of face value, product involvement, information and website quality, Journal of Internet Commerce, Vol. 14, No. 2, pp. 200-232, 2015, Taylor & Francis, http://www.tandfonline.com/doi/full/10.1...
[16]
M. Maragoudakis, D. Serpanos, Exploiting Financial News and Social Media Opinions for Stock Market Analysis using MCMC Bayesian Inference, Computational Economics, 2015, Elsevier, (to_appear), http://dx.doi.org/10.1007/s10614-015-949..., indexed in SCI-E, IF = 0.483
[17]
E. Linardos, K. Kermanidis, M. Maragoudakis, Using Financial News Articles with Minimal Linguistic Resources to Forecast Stock Behavior, International Journal of Data Mining, Modelling and Management, Vol. 7, No. 3, pp. 185-212, 2015, INDERSCIENCE
A. Panteli, M. Maragoudakis, S. Gritzalis, Privacy-Preserving Data Mining using Radial Basis Functions on Horizontally-partitioned Databases in the Malicious Model, International Journal on Artificial Intelligence Tools, Vol. 23, No. 5, pp. 1450007:1-22, 2014, World Scientific Publishing, http://www.worldscientific.com/toc/ijait..., indexed in SCI-E, IF = 0.436
 

Abstract
This paper presents a privacy preserving protocol for the computation of a Radial Basis Function (RBF) neural network model between N participants which share horizontally partitioned datasets. The RBF model is used for regression analysis tasks. The novel aspect of the proposed protocol lies to the fact that it assumes a malicious user model and does not use homomorphic cryptographic methods, which are inherently only suited for a semi-trusted user environment. The performance analysis shows that the communication overhead is low enough to warranty its use while the computational complexity is identical in most cases with the centralized computation scenario (e.g. a trusted third party). The accuracy of the output model is only marginally subpar to a centralized computation on the union of all datasets.

M. Maragoudakis, E. Loukis, Heart Sound Screening in Real-Time Assistive Environments through MCMC Bayesian Data Mining, Universal Access in the Information Society , Vol. 13, pp. 73-88, 2014, Springer, indexed in SCI-E
 

Abstract
Emerging pervasive assistive environment applications for remote home healthcare monitoring of the elderly, disabled and also patients with various chronic diseases generate massive amounts of sensor signal data, which are transmitted from numerous homes to local health centers or hospitals. While it is critical to process this data efficiently (in a fast and accurate manner) and cost-effectively, in a large scale application of the above technologies it is not possible to do so manually by specialized human resources. This paper proposes a methodology for automatic real-time screening of heart sound signals (one of the most widely acquired signals from the human body for diagnostic purposes)and identification of those that are abnormal and require some action to be taken, which can be applied to many other similar types of bio-signals generated in assistive environments.It is based on a novel Markov Chain Monte Carlo (MCMC) Bayesian Inference approach, which estimates conditional probability distributions in structures obtained from a Tree-Augmented Naïve Bayes (TAN) algorithm. It has been applied and validated in a highly ‘difficult’ heterogeneous dataset of 198 heart sound signals, which comes from both healthy medical cases and unhealthy ones having Aortic Stenosis, Mitral Regurgitation, Aortic Regurgitation or Mitral Stenosis. The proposed methodology achieved high classification performance in this difficult screening problem. It performs higher than other widely used classifiers, showing great potential for contributing to a cost-effective large scale application of ICT-based assistive environment technologies.

[20]
K. Kermanidis, M. Maragoudakis, Political Sentiment Analysis of Tweets before and after the Greek Elections of May 2012, International Journal of Social Network Mining, Vol. 1, No. 3/4, pp. 298-317, 2013, InderScience Publishers
[21]
M. Maragoudakis, D. Lyras, K. Sgarbas, Bayesian Retrieval of Greek Texts using a Similarity-based Lemmatizer, International Journal on Artificial Intelligence Tools, Vol. 21, No. 05, 2012, World Scientific, indexed in SCI-E, IF = 0.436
M. Maragoudakis, E. Loukis, Using Ensemble Random Forests Classifiers for the Extraction and Exploitation of Knowledge on Gas Turbine Blading Faults Identification, Operational Research Insight , Vol. 25, No. 2, pp. 80-104, 2012, Palgrave Macmillan
 

Abstract
The extraction and exploitation of existing knowledge assets for supporting decision making and increasing the effectiveness of various internal and external interventions is of critical importance for the success of modern organizations. The use of advanced Operational Research based quantitative methods in combination with high capabilities information systems can be very useful for this purpose. In this paper we are investigating the use of Ensemble Random Forests for extracting, codifying and exploiting existing organizational knowledge on gas turbine blading faults identification, in the form of a large number of decision trees (called a ‘forest’); each of them has internal nodes corresponding to various tests on features of signals acquired from the gas turbine and leaf nodes corresponding to classifications to the healthy condition or particular faults. Two heterogeneous kinds of inserting randomness to the development of these forest trees, based on different theoretical assumptions, have been examined (Random Input Forests and Random Combination Forests). Using data from a large power gas turbine the performance of Ensemble Random Forests has been evaluated, and also compared against other machine learning classification methods, such as Neural Networks, Classification and Regression Trees and K-Nearest Neighbor. The Ensemble Random Forests reached a level of 97% in terms of precision and recall in engine condition diagnosis from new signals acquired from the gas turbine, which was higher than the performance of all the other examined classification methods. These results provide some first evidence that Ensemble Random Forest can be an effective tool for the extraction, codification and exploitation of the technological knowledge assets of modern organizations, and contribute significantly to the improvement of organizations’ decision making and interventions in this area. The extraction and exploitation of existing knowledge assets for supporting decision making and increasing the effectiveness of various internal and external interventions is of critical importance for the success of modern organizations. The use of advanced Operational Research based quantitative methods in combination with high capabilities information systems can be very useful for this purpose. In this paper we are investigating the use of Ensemble Random Forests for extracting, codifying and exploiting existing organizational knowledge on gas turbine blading faults identification, in the form of a large number of decision trees (called a ‘forest’); each of them has internal nodes corresponding to various tests on features of signals acquired from the gas turbine and leaf nodes corresponding to classifications to the healthy condition or particular faults. Two heterogeneous kinds of inserting randomness to the development of these forest trees, based on different theoretical assumptions, have been examined (Random Input Forests and Random Combination Forests). Using data from a large power gas turbine the performance of Ensemble Random Forests has been evaluated, and also compared against other machine learning classification methods, such as Neural Networks, Classification and Regression Trees and K-Nearest Neighbor. The Ensemble Random Forests reached a level of 97% in terms of precision and recall in engine condition diagnosis from new signals acquired from the gas turbine, which was higher than the performance of all the other examined classification methods. These results provide some first evidence that Ensemble Random Forest can be an effective tool for the extraction, codification and exploitation of the technological knowledge assets of modern organizations, and contribute significantly to the improvement of organizations’ decision making and interventions in this area.

[23]
M. Maragoudakis, I. Maglogiannis, A medical ontology for intelligent web-based skin lesion image retrieval, health informatics journal, Vol. 17, No. 2, pp. 140-157, 2011, Sage
C. Kolias, G. Kambourakis, M. Maragoudakis, Swarm Intelligence in Intrusion Detection: A Survey, Computers & Security, Vol. 30, No. 8, pp. 625-642, 2011, Elsevier, www.elsevier.com/locate/cose, indexed in SCI-E, IF = 0.868
 

Abstract
Intrusion Detection Systems (IDS) have nowadays become a necessary component of almost every security infrastructure. So far, many different approaches have been followed in order to increase the efficiency of IDS. Swarm Intelligence (SI), a relatively new bioinspired family of methods, seeks inspiration in the behavior of swarms of insects or other animals. After applied in other fields with success SI started to gather the interest of researchers working in the field of intrusion detection. In this paper we explore the reasons that led to the application of SI in intrusion detection, and present SI methods that have been used for constructing IDS. A major contribution of this work is also a detailed comparison of several SI-based IDS in terms of efficiency. This gives a clear idea of which solution is more appropriate for each particular case.

[25]
K. Kotis, A. Papasalouros, M. Maragoudakis, Mining Query Logs for Learning Useful Ontologies: an Incentive to SW Content Creation, International Journal for Knowledge Engineering and Data Mining (IJKEDM), issue Special Issue on Incentives for Semantic Content Creation, Vol. 1, No. 4, pp. 303-330, 2011, InderScience Publishers
[26]
M. Maragoudakis, D. Serpanos, Robust Stock Market Mining, incorporating Financial News and Technical Analysis Data, Engineering Intelligent Systems, Vol. 18, No. 3, 2010, CRL Publishing
E. Magkos, M. Maragoudakis, V. Chryssikopoulos, S. Gritzalis, Accurate and Large-Scale Privacy-Preserving Data Mining using the Election Paradigm, Data and Knowledge Engineering, Vol. 68, No. 11, pp. 1224-1236, 2009, Elsevier, http://www.sciencedirect.com/science/art..., indexed in SCI-E, IF = 1.745
 

Abstract
With the proliferation of the Web and ICT technologies there have been concerns about the handling and use of sensitive information by data mining systems. Recent research has focused on distributed environments where the participants in the system may also be mutually mistrustful. In this paper we discuss the design and security requirements for large-scale privacy-preserving data mining (PPDM) systems in a fully distributed setting, where each client possesses its own records of private data. To this end we argue in favor of using some well-known cryptographic primitives, borrowed from the literature on Internet elections. More specifically, our framework is based on the classical homomorphic election model, and particularly on an extension for supporting multi-candidate elections. We also review a recent scheme [Z. Yang, S. Zhong, R.N. Wright, Privacy-preserving classification of customer data without loss of accuracy, in: SDM’ 2005 SIAM International Conference on Data Mining, 2005] which was the first scheme that used the homomorphic encryption primitive for PPDM in the fully distributed setting. Finally, we show how our approach can be used as a building block to obtain Random Forests classification with enhanced prediction performance.

[28]
G. N. Yannakakis, M. Maragoudakis, J. Hallam, Preference Learning for Cognitive Modeling: A Case Study on Entertainment Preferences, IEEE Systems, Man and Cybernetics; Part A: Systems and Humans, Vol. 39, No. 6, pp. 1165 - 1175, 2009, indexed in SCI-E, IF = 2.123
[29]
C.Fidas, M. Maragoudakis, N. Avouris, Intelligent - Miner: The Conceptual and Architectural Design of a Web Based Data Mining Service, International Journal on Human-Computer Interaction, Vol. 1, No. 4, pp. 23-43, 2008, eMinds,
[30]
M. Maragoudakis, A. Thanopoulos, N. Fakotakis, MeteoBayes: Effective Plan Recognition in a Weather Dialogue System, IEEE Intelligent Systems , Vol. 22 , No. 1 , pp. 66-78, 2007, IEEE Educational Activities Department ,
[31]
K. Kermanidis, M. Maragoudakis, N. Fakotakis, G. Kokkinakis, Learning Verb Complements for Modern Greek: Balancing the Noisy Data Set, Natural Language Engineering , Vol. 12, No. 4, pp. 1-30, 2006,
[32]
N. Tselios, A. Stoica, M. Maragoudakis, N. Avouris, V. Komis, Enhancing user support in open problem solving environments through Bayesian Network inference techniques, Journal of Educational Technology & Society, Vol. 9, pp. 150-165, 2006,
[33]
M. Maragoudakis, K. Kermanidis, A. Tasikas, N. Fakotakis, G. Kokkinakis, Bayesian Induction of Verb Subcategorization Frames in Imbalanced Heterogeneous Data, Journal of Quantitative , Vol. 12, No. 2, pp. 185-211, 2005,
[34]
M. Maragoudakis, A. Thanopoulos, K. Sgarbas, N. Fakotakis, Domain knowledge acquisition and plan recognition by probabilistic reasoning, International Journal on Artificial Intelligence Tools, Vol. 13, No. 2, pp. 333-365, 2004,
[35]
M. Maragoudakis, N. Fakotakis, G. Kokkinakis, Imposing Classification Bias in Bayesian Network Learning, Pattern Recognition and Image Analysis, Vol. 14, No. 3, pp. 455-470, 2004,
[36]
M. Maragoudakis, N. Fakotakis, A Bayesian Network Model for Stochastic Tagging of Natural Language Texts, Journal of Applied Linguistics, Vol. 19, No. 1, pp. 63-82, 2003,

Conferences


Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.


Maria Eleni Skarkala, M. Maragoudakis, S. Gritzalis, L. Mitrou, PP-TAN: a Privacy Preserving Multi-party Tree Augmented Naive Bayes Classifier, SEEDA CECNSM 2020 5th South East Europe Design, Automation, Computer Engineering, Computer Networks and Social Media Conference, Sep, 2020, Corfu, Greece, IEEE CPS Conference Publishing Services, https://hilab.di.ionio.gr/seeda2020/
 

Abstract
The rapid growth of Information and Communication Technologies emerges deep concerns on how data mining techniques and intelligent systems parse, analyze and manage enormous amount of data. Due to sensitive information contained within, data can be exploited by potential aggressors. Previous research has shown the most accurate approach to acquire knowledge from data while simultaneously preserving privacy is the exploitation of cryptography. In this paper we introduce an extension of a privacy preserving data mining algorithm designed and developed for both horizontally and vertically partitioned databases. The proposed algorithm exploits the multi-candidate election schema and its capabilities to build a privacy preserving Tree Augmented Naive Bayesian classifier. Security analysis and experimental results ensure the preservation of private data throughout mining processes.

E. Loukis, N. Kyriakou, M. Maragoudakis, Using Government Data and Machine Learning for Predicting Firms’ Vulnerability to Economic Crisis, EGOV-CEDEM-EPART 2020, (ed), (eds), (to_appear), Sep, 2020, Linköping, Sweden, Springer Verlag,
 

Abstract
The COVID-19 pandemic is expected to lead to a severe recessionary economic crisis with quite negative consequences for large numbers of firms and citizens; however, this is an ‘old story’: recessionary economic crises appear re-peatedly in the last 100 years in the market-based economies, and they are rec-ognized as one of the most severe and threatening weaknesses of them. They can result in closure of numerous firms, and decrease of activities of many more, as well as poverty and social exclusion for large parts of the population, and finally lead to political upheaval and instability; so they constitute one of the most threatening and difficult problems that governments often face. For the above reasons it is imperative that governments develop effective public policies and make drastic interventions for addressing these economic crises. Quite useful for these interventions can be the prediction of the vulnerability of individual firms to recessionary economic crisis, so that government can focus its attention as well as its scarce economic resources on the most vulnerable ones. In this direction our pa-per presents a methodology for using existing government data in order to predict the vulnerability of individual firms to economic crisis, based on Artificial Intelligence (AI) Machine Learning (ML) algorithms. Furthermore, a first application of the proposed methodology is presented, based on existing data from the Greek Ministry of Finance and Statistical Authority concerning 363 firms for the economic crisis period 2009-2014, which gives encouraging results.

[3]
Nikiforos, Vergis, Stylidou, Augoustis, K. Kermanidis, M. Maragoudakis, Fake News Detection Regarding the Hong Kong Events from Tweets, MHDW 2020 and 5G-PINE 2020, Maglogiannis, Ilias, Iliadis, Lazaros, Pimenidis, Elias, (eds), pp. 177-186, Jun, 2020, Neos Marmaras, Greece, Springer, https://www.springer.com/gp/book/9783030...
[4]
Zervopoulos, Alvanou, Bezas, Papamichail, M. Maragoudakis, K. Kermanidis, Hong Kong Protests: Using Natural Language Processing for Fake News Detection on Twitter, Artificial Intelligence Applications and Innovations, pp. .408-419, Dec, 2020, Springer, https://link.springer.com/chapter/10.100...
E. Loukis, M. Maragoudakis, N. Kyriakou, Economic Crisis Policy Analytics Based on Artificial Intelligence, EGOV-CEDEM-EPART 2019 Conference, Sep, 2019, San Benedetto Del Tronto, Italy, Springer Verlag
 

Abstract
An important trend in the area of digital government is its expansion beyond the support of internal processes and operations, as well as transactions and consultations with citizens and firms, which were the main objectives of its first generations, towards the support of higher-level functions of government agencies, with main emphasis on public policy making. This gives rise to the gradual development of policy analytics. Another important trend in the area of digital government is the increasing exploitation of artificial intelligence techniques by government agencies, mainly for the automation, support and enhancement of operational tasks and lower-level decision making, but only to a very limited extent for the support of higher-level functions, and especially policy making. Our paper contributes towards the advancement and the combination of these two important trends: it proposes a policy analytics methodology for the exploitation of existing public and private sector data, using a big data oriented artificial intelligence technique, feature selection, in order to support policy making concerning one of the most serious problems that governments face, the economic crises. In particular, we present a methodology for exploiting existing data of taxation authorities, statistical agencies, and also of private sector business information and consulting firms, in order to identify characteristics of a firm (e.g. with respect to strategic directions, resources, capabilities, practices, etc.) as well as its external environment (e.g. with respect to competition, dynamism, etc.) that affect (positively or negatively) its resilience to the crisis with respect to sales revenue; for this purpose an advanced artificial intelligence feature selection algorithm, the Boruta ‘all-relevant’ variables identification one, is used. Furthermore, an application of the proposed economic crisis policy analytics methodology is presented, which provides a first validation of the usefulness of our methodology.

L. Spiliotopoulou, D. Damopoulos, Y. Charalabidis, M. Maragoudakis, S. Gritzalis, Europe in the shadow of financial crisis: Policy Making via Stance Classification, HICSS-50 2017 Hawaii International Conference on System Sciences, T. Bui, R. Sprague, (eds), pp. 2835–2844, Jan, 2017, Hawaii, USA, IEEE CPS Conference Publishing Services, http://www.hicss.org/
 

Abstract
Since 2009, the European Union (EU) is phasing a multi–year financial crisis affecting the stability of its involved countries. Our goal is to gain useful insights on the societal impact of such a strong political issue through the exploitation of topic modeling and stance classification techniques. \ \ To perform this, we unravel public’s stance towards this event and empower citizens’ participation in the decision making process, taking policy’s life cycle as a baseline. The paper introduces and evaluates a bilingual stance classification architecture, enabling a deeper understanding of how citizens’ sentiment polarity changes based on the critical political decisions taken among European countries. \ \ Through three novel empirical studies, we aim to explore and answer whether stance classification can be used to: i) determine citizens’ sentiment polarity for a series of political events by observing the diversity of opinion among European citizens, ii) predict political decisions outcome made by citizens such as a referendum call, ii) examine whether citizens’ sentiments agree with governmental decisions during each stage of a policy life cycle.

N. Kyriakou, M. Maragoudakis, E. Loukis, M. Themistocleus, Prediction of Propensity for Enterprise Cloud Computing Adoption, Hawaii International Conference on System Sciences (HICSS), Jan, 2017, Hawaii, Big Island
 

Abstract
Cloud computing (CC) can offer significant benefits to enterprises. However, it can pose some risks as well, and this has led to a lower adoption than the initial expectations. For this reason, it would be very useful to predict which enterprises will exhibit a propensity for CC adoption. In this direction, we investigate the use of six well-established classifiers (fast large margin Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, k-Nearest Neighbor, and Linear Regression) for the prediction of enterprise level propensity for CC adoption. Having as our theoretical foundation the Technology – Organization – Environment (TOE) framework, we are using for this purpose of set of technological (concerning enterprise information systems), organizational and environmental characteristics. Our first results, using a dataset of 676 manufacturing firms of the glass, ceramic and cement sectors from six European countries (Germany, France, Italy, Poland, Spain, and UK), collected through the e-Business W@tch Survey of the European Commission, are encouraging. It is concluded that among the examined characteristics the technological ones, concerning enterprise systems, seem to be the most important predictors.

[8]
M. Maragoudakis, K. Kermanidis, S. Vossinakis, Modeling Promotion Factors using Bayesian Networks and Video Games, 7th International Conference on Information Science and Applications 2016, (to_appear), Dec, 2016, Vietnam,
[9]
V. Athanasiou, M. Maragoudakis, Dealing With High Dimensional Sentiment Data Using Gradient Boosting Machines, Mining Humanistic Data Workshop 2016, AIAI 2016, (to_appear), Dec, 2016,
Y. Charalabidis, M. Maragoudakis, E. Loukis, Opinion Mining and Sentiment Analysis in Policy Formulation Initiatives: The EU-Community Approach, IFIP eParticipation Conference EPART2015, Sep, 2015, Thessaloniki,
 

Abstract
In the last decade there is extensive and continuously growing creation of political content in the Internet, and especially in the Web 2.0 social media, which can be quite useful for government agencies in order to understand the needs and problems of societies and formulate effective public policies for addressing them. So a variety of ICT-based methods have been developed for the exploitation of this political content by governments (‘citizensourcing’), initially simpler and later more sophisticated ones. These ICT-based methods are increasingly based on the use of opinion mining (OM) and sentiment analysis (SA) techniques, in order to process the extensive political content collected from numerous sources. This paper describes a novel approach to OM and SA use, created as part of an advanced ICT-based method of exploiting political content created in the Internet, and especially in social media, by experts (‘expertsourcing’), aiming to leverage the extensive policy community of the European Union, which is developed in the European EU-Community project. Furthermore, some first experimental results of it are presented.

A. Ramfos, A. Kiousi, M. Kokkonidis, C . Leclercq, D. Mekkaoui, M. Sattonnay, M. Maragoudakis, A. Androutsopoulou, Y. Charalabidis, J. Kohlhammer, The "EU Community Project" - Coupling the Power of Data with Community Expertise, 14th IFIP Electronic Government (EGOV) and 7th Electronic Participation (ePart) Conference 2015, Workshop on Enabling Effective Policy Making 2015 (EEPM), Aug, 2015, Thessaloniki, Greece
 

Abstract
The EU Community project seeks to promote, facilitate, and ultimately exploit the synergy of a cutting-edge intelligent collaboration platform with a community of institutional actors, stakeholders, scientists, consultants, media analysts and other individuals that can make valuable contributions to EU policy debates. Its ultimate goal is to effectuate a transformation in the modus operandi of EU politics and move closer to achieving the illusive goals of improved transparency, efficiency, awareness and engagement, ultimately leading to better policies for a better European Union.

[12]
N. Potha, M. Maragoudakis, Time Series Forecasting in Cyberbullying Data, 16th international conference on Engineering Applications of Neural Networks, EANN 2015, (eds), Dec, 2015, Rodos, Greece
[13]
K. Kermanidis, M. Maragoudakis, S. Vossinakis, House of Ads: a Multiplayer Action Game for Annotating Ad Video Content , 16th international conference on Engineering Applications of Neural Networks, EANN 2015, (to_appear), Dec, 2015, Rodos, Greece,
Y. Charalabidis, M. Maragoudakis, E. Loukis, Opinion Mining and Sentiment Analysis in Policy Formulation Initiatives: The EU Community Approach, 14th IFIP Electronic Government (EGOV) and 7th Electronic Participation (ePart) Conference 2015, Dec, 2015, Thessaloniki,
 

Abstract
In the last decade there is extensive and continuously growing creation of political content in the Internet, and especially in the Web 2.0 social media, which can be quite useful for government agencies in order to understand the needs and problems of societies and formulate effective public policies for addressing them. So a variety of ICT-based methods have been developed for the exploitation of this political content by governments (‘citizensourcing’), initially simpler and later more sophisticated ones. These ICT-based methods are increasingly based on the use of opinion mining (OM) and sentiment analysis (SA) techniques, in order to process the extensive political content collected from numerous sources. This paper describes a novel approach to OM and SA use, created as part of an advanced ICT-based method of exploiting political content created in the Internet, and especially in social media, by experts (‘expertsourcing’), aiming to leverage the extensive policy community of the European Union, which is developed in the European EU-Community project. Furthermore, some first experimental results of it are presented.

M. Chalaris, S. Gritzalis, M. Maragoudakis, C. Sgouropoulou, A. Lykeridou, Examining students' graduation issues using data mining techniques - The case of TEI of Athens , IC-ININFO 4th International Conference on Integrated Information, Sep, 2014, Madrid, Spain, AIP Publishing, https://aip.scitation.org/doi/abs/10.106...
 

Abstract
One of the major issues that Greek Higher Education Institutes face is the delayed completion of studies of their students. For example, in the case of the Technological Educational Institute of Athens, in the academic year 2012-2013, the percentage of graduates with a length of studies of more than 6 years was 53%. This "problem" becomes harder if we consider that according to the new legislation, the Greek Higher Education Institutes (HEI) must cut off access to the students who "linger" too long. This means that many of these graduateswouldn't be able to complete their studies. While many institutes have systems to quantify and report the length of studies of all graduates, far less attention is typically paid to each student's reason(s) for delayed graduation. In this paper, we focus on examining the question of why students delay in the completion of their studies using several data mining techniques. Through the application of data mining techniques new knowledge will be provided to the administration of a HEI that could be used for solving this problem. The data used in our case study come from a questionnaire distributed to graduates of the institute but also from educational data stored in the Institute's student database.

[16]
K. Kermanidis, M. Maragoudakis, S. Vossinakis, Crowdsourcing for the Development of a Hierarchical Ontology in Modern Greek for Creative Advertising, LREC 2014 Workshop “CCURL 2014 - Collaboration and Computing for Under-Resourced Languages in the Linked Open Data Era”, (to_appear), Dec, 2014, Reykiavik, Iceland,
N. Potha, M. Maragoudakis, Cyberbullying Detection using Time Series Modeling, IEEE ICDM 2014 - SENTIRE: 4th edition of Sentiment Elicitation from Natural Text for Information Retrieval and Extraction, (to_appear), Dec, 2014
 

Abstract
Cyberbullying is a new phenomenon resulting from the advance of new communication technologies including the Internet, cell phones and Personal Digital Assistants. It is a challenging bullying problem occurring in a new territory. Online bullying can be particularly damaging and upsetting because it's usually anonymous or hard to trace. In this paper, the proposed method is utilizing a dataset of real world conversations (i.e. pairs of questions and answers between cyber predator and the victim), in which each predator question is manually annotated in terms of severity using a numeric label. We approach the issue as a sequential data modelling approach, in which the predator’s questions are formulated using a Singular Value Decomposition representation. The motivation of this procedure is to study the accuracy of predicting the level of cyberbullying attack using classification methods and also to examine potential patterns between the lingustic style of each predator. More specifically, unlike previous approaches that consider a fixed window of a cyber-predator’s questions within a dialogue, we exploit the whole question set and model it as a signal, whose magnitude depends on the degree of bullying content. Using feature weighting and dimensionality reduction techniques, each signal is straightforwardly parsed by a neural network that forecasts the level of insult within a question given a window between two and three previous questions. Throughout the time series modeling experiments, an interesting discovery was made. By applying SVD on the time series data and taking into account the second dimension (since the first is usually modeling trivial dependencies between instances and attributes) we observed that its plot was very similar to the plot of the class attribute. By applying a Dynamic Time Warping algorithm, the similarity of the aforementioned signals was proved to exist, providing an immediate indicator for the severity of cyberbullying within a given dialogue.

[18]
M. Maragoudakis, K. Kermanidis, S. Vossinakis, Extracting knowledge from collaboratively annotated Ad video content, IFIP Advances in Information and Communication Technology, pp. 85-95, Dec, 2014,
[19]
K. Kermanidis, M. Maragoudakis, S. Vossinakis, N. Exadaktylos, Designing a Support Tool for Creative Advertising by Mining Collaboratively Tagged Ad Video Content: The Architecture of PromONTotion. , Artificial Intelligence Applications and Innovations, pp. 10-19, Oct, 2013, Paphos, Cyprus,
[20]
K. Kontos, M. Maragoudakis, Breast Cancer Detection in Mammogram Medical Images with Data Mining Techniques, Artificial Intelligence Applications and Innovations, pp. 336-347, Oct, 2013, Paphos, Cyprus,
M. Chalaris, S. Gritzalis, M. Maragoudakis, C. Sgouropoulou, A. Tsolakidis, Improving quality of educational processes providing new knowledge using data mining techniques, 3rd IC-ININFO International Conference on Integrated Information, D. Sakas, (ed), pp. 390-397, Sep, 2013, Prague, Czech, Procedia, Elsevier, http://www.icininfo.net/
 

Abstract
One of the biggest challenges that Higher Education Institutions (HEI) faces is to improve the quality of their educational processes. Thus, it is crucial for the administration of the institutions to set new strategies and plans for a better management of the current processes. Furthermore, the managerial decision is becoming more difficult as the complexity of educational entities increase. The purpose of this study is to suggest a way to support the administration of a HEI by providing new knowledge related to the educational processes using data mining techniques. This knowledge can be extracted among other from educational data that derive from the evaluation processes that each department of a HEI conducts. These data can be found in educational databases, in students’ questionnaires or in faculty members’ records. This paper presents the capabilities of data mining in the context of a Higher Education Institute and tries to discover new explicit knowledge by applying data mining techniques to educational data of Technological Educational Institute of Athens. The data used for this study come from students’ questionnaires distributed in the classes within the evaluation process of each department of the Institute.

[22]
V. Politopoulou, M. Maragoudakis, On Mining Opinions from Social Media, Communications in Computer and Information Science, Engineering Applications of Neural Networks, Lazaros Iliadis, Harris Papadopoulos, Chrisina Jayne , pp. 474-484, Sep, 2013, Halkidiki, Greece, Springer,
Maria Eleni Skarkala, Hannu Toivonen, Pirjo Moen, M. Maragoudakis, S. Gritzalis, L. Mitrou, Privacy Preservation by k-Anonymization of Weighted Social Networks, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, P. Yu, K. Carley et al., (eds), pp. 423-428, Aug, 2012, Istanbul, Turkey, IEEE CPS Conference Publishing Services, http://dl.acm.org/ft_gateway.cfm?id=2457...
 

Abstract
Privacy preserving analysis of a social network aims at a better understanding of the network and its behavior, while at the same time protecting the privacy of its individuals. We propose an anonymization method for weighted graphs, i.e., for social networks where the strengths of links are important. This is in contrast with many previous studies which only consider unweighted graphs. Weights can be essential for social network analysis, but they pose new challenges to privacy preserving network analysis. In this paper, we mainly consider prevention of identity disclosure, but we also touch on edge and edge weight disclosure in weighted graphs. We propose a method that provides k-anonymity of nodes against attacks where the adversary has information about the structure of the network, including its edge weights. The method is efficient, and it has been evaluated in terms of privacy and utility on real word datasets.

[24]
K. Anagnostou, M. Maragoudakis, Player Modeling Using HOSVD towards Dynamic Difficulty Adjustment in Videogames, IFIP International Conference on Artificial Intelligence Applications and Innovations , Iliadis L., Maglogiannis I., Papadopoulos H., Karatzas K., Sioutas S. (eds) , pp. 125-134, Dec, 2012, Springer, https://link.springer.com/chapter/10.100...
[25]
E. Paliulis, M. Maragoudakis, A. Panteli, Privacy preserving neural networks in Iris signature feature extraction, Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environment, Dec, 2012, http://dl.acm.org/citation.cfm?id=241310...
[26]
K. Kermanidis, M. Maragoudakis, PromONTotion: Creating an Advertisement Thesaurus By Semantically Annotating Ad Videos Through Collaborative Gaming, Workshop on Collaborative Resource Development and Delivery, International Conference on Language Resources and Evaluation, Dec, 2012, Istanbul, Turkey,
M. Maragoudakis, E. Loukis, Y. Charalabidis, A Review of Opinion Mining Methods for Analyzing Citizens, Third international conference on eParticipation (ePart 2011), Sep, 2011, Delft, Netherlands, Springer Verlag
 

Abstract
Electronic Participation (eParticipation), both in its traditional form and in its emerging Web 2.0 based form, results in the production of large quantities of textual contributions of citizens concerning government policies and decisions under formation, which contain valuable relevant opinions and knowledge of the society, however are exploited to a limited only extent. It is of critical importance to analyze these contributions in order to extract the opinions and knowledge they contain in a cost-efficient way. This paper reviews a wide range of opinion mining methods, which have been developed for analyzing commercial product opinions and reviews posted on the Web, as to the capabilities they can offer for meeting the above challenges. The review has revealed the great potential of these methods for the analysis of textual citizens’ contributions in public policy debates, both for assessing contributors’ general attitudes-sentiments (positive, negative or neutral) towards the policy/decision under discussion, and also for extracting the main issues they raise (e.g. negative and positive aspects and effects, implementation barriers, improvement suggestions) and the corresponding attitudes-sentiments. Based on the conclusions of this review a basic framework for the use of opinion mining methods in eParticipation has been formulated.

Maria Eleni Skarkala, M. Maragoudakis, S. Gritzalis, L. Mitrou, Privacy Preserving Tree Augmented Naïve Bayesian Multi – party Implementation on Horizontally Partitioned Databases, TrustBus 2011 8th International Conference on Trust, Privacy and Security of Digital Business, S. Furnell, C. Lambrinoudakis, and G. Pernul, (eds), pp. 62 - 73, Aug, 2011, Toulouse, France, Lecture Notes in Computer Science LNCS, Springer, http://link.springer.com/content/pdf/10....
 

Abstract
The evolution of new technologies and the spread of the Internet have led to the exchange and elaboration of massive amounts of data. Simultaneously, intelligent systems that parse and analyze patterns within data are gaining popularity. Many of these data contain sensitive information, a fact that leads to serious concerns on how such data should be managed and used from data mining techniques. Extracting knowledge from statistical databases is an essential step towards deploying intelligent systems that assist in making decisions, but also must preserve the privacy of parties involved. In this paper, we present a novel privacy preserving data mining algorithm from statistical databases that are horizontally partitioned. The novelty lies to the multi-candidate election schema and its capabilities of being a basic foundation for a privacy preserving Tree Augmented Naïve Bayesian (TAN) classifier, in order to obviate disclosure of personal information.

[29]
A. Panteli, M. Maragoudakis, A Random Forests Text Transliteration System for Greek Digraphia, Engineering Applications of Neural Networks / Artificial Intelligence Applications and Innovations, pp. 196-201, Dec, 2011, Corfu, Springer,
M. Maragoudakis, E. Loukis, MCMC Bayesian inference for heart sounds screening in assistive environments, 4th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA) 2011, pp. 16, Dec, 2011, Crete
 

Abstract
The large scale application of ICT-based assistive environment technologies for the home care of elderly and disabled people is going to generate huge numbers of signals transmitted from homes to local health centers or hospitals in order to be monitored by medical personnel. This task is going to be of critical importance and at the same time - if manually performed - quite demanding for specialized human resources and costly. In order to perform it in a cost-efficient manner it is necessary to develop mechanisms and methods for automated screening of these signals in order to identify abnormal ones that require some action to be taken. This paper proposes a method for automatic screening of heart sound signals, which are the most widely acquired signals from the human body for diagnostic purposes in both the „traditional‟ medicine and the emerging ICT-based assistive environments. It is based on a novel Markov Chain Monte Carlo (MCMC) Bayesian Inference approach, which estimates conditional probability distributions in structures obtained from a Tree-Augmented Naïve Bayes (TAN) algorithm. The proposed approach has been applied and validated in a difficult heterogeneous dataset of 198 heart sound signals, which comes from both healthy medical cases and unhealthy ones having Aortic Stenosis, Mitral Regurgitation, Aortic Regurgitation or Mitral Stenosis. The proposed approach achieved a good performance in this difficult screening problem, which is higher than other widely used alternative classifiers, showing great potential for contributing to a cost-effective large scale application of ICT-based assistive environment technologies.

[31]
M. Maragoudakis, I. Maglogiannis, Skin Lesion Diagnosis from Images Using Novel Ensemble Classification Techniques, In Proc of 10th IEEE International Special Topic Conference on Information Technology in Biomedicine (ITAB 2010) , Nov, 2010, Corfu Greece , IEEE,
M. Maragoudakis, E. Loukis, Automated Aortic And Mitral Valves Diseases Diagnosis from Heart Sound Signals using Novel Ensemble Techniques, IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 2010, Oct, 2010, Arras, France
 

Abstract
The development of ‘intelligent’ medical equipment, which can not only acquire various signals from the human body, but also process them and provide recommendations as to probable pathological conditions, will be highly beneficial for both the medical personnel and the patients. However, this necessitates the development and exploitation of advanced highly efficient classification techniques. In this direction this paper presents a novel ensemble classification technique, combining Random Forests with the ‘Markov Blanket’ notion, which is used for the automated diagnosis of aortic and mitral heart valves diseases from low-cost and easily acquired heart sound signals. It has been tested in a highly ‘difficult’ global and heterogeneous dataset of 198 heart sound signals, which been acquired from both healthy and pathological medical cases. The proposed ensemble classification technique exhibited a higher classification performance in comparison with the classical Random Forest algorithms, and also other widely used classification algorithms.

[33]
M. Maragoudakis, D. Serpanos, Towards Stock Market Data Mining Using Enriched Random Forests from Textual Resources and Technical Indicators , Artificial Intelligence Applications and Innovations, 6th IFIP WG 12.5 International Conference, AIAI 2010, Larnaca, Cyprus, October 6-7, 2010. Volume 339/2010, Harris Papadopoulos, Andreas S. Andreou and Max Bramer, (eds), pp. 278-286, Sep, 2010, Larnaca, Cyprus, Springer,
E. Loukis, M. Maragoudakis, Heart Murmurs Identification Using Random Forests in Assistive Environments, The 3rd International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) 2010, Jun, 2010, Samos, Greece, ACM International Conference Proceedings Series
 

Abstract
The aging population in many countries, in combination with high government deficits and financial resources limitations, necessitates new methods for the home care of the elderly at reasonable costs based on the exploitation of modern information and communication technologies (ICT). This requires the installation of assistive environments at the homes of elderly people, which include various types of sensors, generating biosignals of other types of signals, which are transferred through networks to local health centers or hospitals in order to be monitored. However, scaling up the application of such ICTbased methods of elderly home care is going to increase tremendously the workload of the medical staff of local health centers or hospitals. Therefore it is of critical importance to develop capabilities for an automated first screening of these signals and identification of abnormal elements and diseases. In this direction the present paper proposes a system for the automatic identification of murmurs in heart sound signals, and also for the classification of them as systolic or diastolic, using a new generation of advanced Random Forests classification algorithms, which are aggregating the prediction of multiple classifiers (ensemble classification). The proposed system has been applied and validated in a representative global dataset of 198 heart sound signals, which come both from healthy medical cases and from cases having systolic and diastolic murmurs. Also, some alternative classifiers have been applied to the same data for comparison purposes. It has been concluded that the proposed systems shows a good performance, which is higher than the examined alternative classifiers.

[35]
K. Anagnostou, M. Maragoudakis, Data Mining for Player Modeling in Videogames, pci, 2009 13th Panhellenic Conference on Informatics, 2009, pp. 30-34, Sep, 2009, Corfu, IEEE,
[36]
K. Kotis, A. Papasalouros, M. Maragoudakis, Mining Web queries to boost semantic content creation, WWW/Internet, Dec, 2009, Rome, IADIS,
E. Magkos, M. Maragoudakis, V. Chryssikopoulos, S. Gritzalis, Accuracy in Privacy-Preserving Data Mining Using the Paradigm of Cryptographic Elections, PSD 2008 Privacy in Statistical Databases, J. Domingo-Ferrer, (ed), pp. 284-297, Sep, 2008, Istanbul, Turkey, Lecture Notes in Computer Science LNCS, Springer, http://link.springer.com/content/pdf/10....
 

Abstract
Data mining technology raises concerns about the handling and use of sensitive information, especially in highly distributed environments where the participants in the system may by mutually mistrustful. In this paper we argue in favor of using some well-known cryptographic primitives, borrowed from the literature on large-scale Internet elections, in order to preserve accuracy in privacy-preserving data mining (PPDM) systems. Our approach is based on the classical homomorphic model for online elections, and more particularly on some extensions of the model for supporting multi-candidate elections. We also describe some weaknesses and present an attack on a recent scheme [1] which was the first to use a variation of the homomorphic model in the PPDM setting. In addition, we show how PPDM can be used as a building block to obtain a Random Forests classification algorithm over a set of homogeneous databases with horizontally partitioned data.

M. Maragoudakis, E. Loukis, P. P. Pantelides, Random Forests Identification of Gas Turbine Faults, IEEE 19th International Conference on Systems Engineering (ICSENG) 2008, Dec, 2008, Las Vegas, USA
 

Abstract
In the present paper, Random Forests are used in a critical and at the same time non trivial problem concerning the diagnosis of Gas Turbine blading faults, portraying promising results. Random forestsbased fault diagnosis is treated as a Pattern Recognition problem, based on measurements and feature selection. Two different types of inserting randomness to the trees are studied, based on different theoretical assumptions. The classifier is compared against other Machine Learning algorithms such as )eural )etworks, Classification and Regression Trees, )aive Bayes and K-)earest )eighbor. The performance of the prediction model reaches a level of 97% in terms of precision and recall, improving the existing state-of-the-art levels achieved by )eural )etworks by a factor of 1.5%-2%. Furthermore, emphasis is given on the pre-processing phase, where feature selection and outliers identification is carried out, in order to provide the basis of a high performance automated diagnostic system. The conclusions derived are of more general interest and applicability.

M. Maragoudakis, E. Loukis, P. P. Pantelides, Gas Turbine Fault Diagnosis using Random Forests, European Conference on Artificial Intelligence (ECAI) 2008, Dec, 2008, Patras, Greece
 

Abstract
In the present paper, Random Forests are used in a critical and at the same time non trivial problem concerning the diagnosis of Gas Turbine blading faults, portraying promising results. Random forests-based fault diagnosis is treated as a Pattern Recognition problem, based on measurements and feature selection. Two different types of inserting randomness to the trees are studied, based on different theoretical assumptions. The classifier is compared against other Machine Learning algorithms such as Neural Networks, Classification and Regression Trees, Naive Bayes and K-Nearest Neighbor. The performance of the prediction model reaches a level of 97% in terms of precision and recall, improving the existing state-of-the-art levels achieved by Neural Networks by a factor of 1.5%-2%.

[40]
M. Maragoudakis, I. Maglogiannis, D. Lymberopoulos, A medical, description logic based, ontology for skin lesion images, 8th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2008), pp. 1-6, Dec, 2008,
[41]
M. Maragoudakis, D. Lymberopoulos, N. Fakotakis, K. Spiropoulos, A hierarchical, ontology-driven Bayesian concept for ubiquitous medical environments - A case study for pulmonary diseases, Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 3807-3810, Dec, 2008,
[42]
K. Kermanidis, A. Thanopoulos, M. Maragoudakis, N. Fakotakis, Eksairesis: A Domain-Adaptable System for Ontology Building from Unstructured Text, Proceedings of the International Conference on Language Resources and Evaluation, LREC 2008, Dec, 2008, Marrakech, Morocco,
[43]
M. Maragoudakis, N. Fakotakis, Bayesian Feature Construction , 4th Hellenic Conference on Artificial Intelligence, G. Antoniou, G. Potamias, D. Plexousakis, C. Spyropoulos , (eds), May, 2006,
[44]
M. Maragoudakis, K. Kermanidis, A. Garbis, N. Fakotakis, Dealing with Imbalanced Data using Bayesian Techniques, LREC 2006, International Conference on Language Resources and Evaluation, Dec, 2006, Genoa, Italy,
[45]
G. N. Yannakakis, M. Maragoudakis, Player Modeling Impact on Player΄s Entertainment in Computer Games, User Modeling 2005: 10th International Conference, UM 2005, L. Ardissono, P. Brna, A. Mitrovic , (eds), Jul, 2005, Edinburgh, Scotland, UK,
[46]
M. Maragoudakis, T. Ganchev, N. Fakotakis, Bayesian reinforcement for a probabilistic neural net part-of-speech tagger, 7th International Conference on Text, Speech and Dialogue (TSD 2004), P. Sojka, I. Kopecek, K. Pala , (eds), pp. 137-145, Sep, 2004, Springer Verlag,
[47]
K. Kermanidis, M. Maragoudakis, N. Fakotakis, G. Kokkinakis, Learning Greek Verb Complements: Addressing the Class Imbalance, 20th International Conference on Computational Linguistics, COLING 2004, pp. 1065-1071 , Dec, 2004, Geneva, Switzerland,
[48]
M. Maragoudakis, N. Fakotakis, Bayesian Semantics Incorporation to Web Content for Natural Language Information Retrieval, LREC 2004, 4th International Conference on Language Resources and Evaluation, pp. 785-788, Dec, 2004, Lisbon, Portugal,
[49]
M. Maragoudakis, N. Fakotakis, G. Kokkinakis, A Bayesian Model for Shallow Syntactic Parsing of Natural Language Texts, LREC 2004, 4th International Conference on Language Resources and Evaluation, pp. 847-850, Dec, 2004, Lisbon, Portugal,
[50]
P. Zervas, M. Maragoudakis, N. Fakotakis, G. Kokkinakis, Learning to predict Pitch Accents using Bayesian Belief Networks for Greek Language, LREC 2004, 4th International Conference on Language Resources and Evaluation, pp. 2139-2142, Dec, 2004, Lisbon, Portugal,
[51]
P. Zervas, M. Maragoudakis, N. Fakotakis, G. Kokkinakis, Bayesian Induction of intonational phrase breaks, 8th International Conference on Speech Communication and Technology (Eurospeech 2003), pp. 113-116, Sep, 2003, Geneva, Switzerland,
[52]
M. Maragoudakis, K. Kermanidis, N. Fakotakis, A Bayesian Part-Of-Speech and Case Tagger for Modern Greek, 6th International conference of Greek linguistics (ICGL 2003), Sep, 2003, Rethymno, Greece,
[53]
M. Maragoudakis, A. Thanopoulos, N. Fakotakis, User Modeling and Plan Recognition under Conditions of Uncertainty , 6th International Conference on Text, Speech and Dialogue (TSD 2003), Matousek, Mautner , (eds), pp. 372-379, Sep, 2003, Ceske Budejovice, Czech Republic, Springer Verlang,
[54]
M. Maragoudakis, P. Zervas, N. Fakotakis, G. Kokkinakis, A Data-Driven Framework for Intonational Phrase Break Prediction , 6th International Conference on Text, Speech and Dialogue (TSD 2003), Matousek, Mautner , (eds), pp. 189-197, Sep, 2003, Ceske Budejovice, Czech Republic, Springer Verlang ,
[55]
M. Maragoudakis, A. Thanopoulos, K. Sgarbas , N. Fakotakis, Domain Knowledge Acquisition and Plan Recognition by Probabilistic Reasoning , Knowledge-Based Intelligent Information and Engineering Systems, 7th International Conference, KES 2003, V. Palade, R. J. Howlett, L. C. Jain , (eds), Sep, 2003, Oxford, UK, Springer,
[56]
M. Maragoudakis, E. Kavallieratou, N. Fakotakis, G. Kokkinakis, An Effective Stochastic Estimation of Handwritten Character Segmentation Bounds, ISAP 2003: Competitive Environment, Renewable Energy, Distributed Generation, Aug, 2003, Lemnos, Greece,
[57]
M. Maragoudakis, K. Kermanidis, N. Fakotakis, Towards a Bayesian Stochastic Part-Of-Speech and Case Tagger of Natural Language Corpora, Corpus Linguistics 2003, Mar, 2003, Lancaster University (UK),
[58]
I. Zitouni, J. Olive, D. Iskra, K. Choukri, O. Emam, O. Gedge, M. Maragoudakis, H. Tropf, A. Moreno, A. Rodriguez, OrienTel: Speech-Based Interactive Communication Applications for the Mediterranean and the Middle East, ICSLP 2002, 7th International Conference on Spoken Language Processing, pp. 325-328, Sep, 2002, Denver-Colorado, USA,
[59]
M. Maragoudakis, A. Thanopoulos, N. Fakotakis, Statistical Decision Making applied to Text and Dialogue Corpora for Effective Plan Recognition, 5th International Conference on Text, Speech and Dialogue, TSD 2002, P. Sojka, et.al., (eds), pp. 365-372, Sep, 2002, Brno, Czech Republic, Springer-Verlag,
[60]
M. Maragoudakis, K. Kermanidis, N. Fakotakis, G. Kokkinakis, Combining Bayesian and Support Vector Machines Learning to automatically complete Syntactical Information for HPSG-like Formalisms, LREC 2002, 3rd International Conference on Language Resources and Evaluation, May, 2002, Las Palmas, Spain,
[61]
R. Siemund, B. Heuft, K. Choukri, O. Emam, M. Maragoudakis, H. Tropf, O. Gedge, S. Shammass, A. Moreno, A. Nogueiras , OrienTel - Multilingual access to interactive communication services for the Mediterranean and the Middle East, LREC 2002, 3rd International Conference on Language Resources and Evaluation, pp. 887-891, May, 2002, Las Palmas, Spain,
[62]
M. Maragoudakis, N. Tselios, N. Fakotakis, N. Avouris, Improving SMS usability using Bayesian Networks , 2nd Panhellenic Conference on Artificial Intelligence, I. P. Vlahavas, C. D. Spyropoulos , (eds), pp. 179-190, Apr, 2002, Thessaloniki Greece, Springer-Verlag,
[63]
M. Maragoudakis, E. Kavallieratou, N. Fakotakis, Improving handwritten character segmentation by incorporating Bayesian knowledge with support vector machines, IEEE Int. Conf. Audio Speech & Signal Processing ICASSP2002, Dec, 2002, Orlando-Florida,
[64]
M. Maragoudakis, E. Kavallieratou, N. Fakotakis, Incorporating Conditional Independence Assumption with Support Vector Machines to enhance Handwritten Character Segmentation Performance, 16th International Conference of the International Association of Pattern Recognition ICPR 2002, pp. 911-914, Dec, 2002, Quebec-Canada,
[65]
R. Siemund, B. Heuft, K. Choukri, O. Emam, M. Maragoudakis, H. Tropf, O. Gedge, S. Shammass, A. Moreno, A. Nogueiras , OrienTel – Arabic speech resources for the IT market, Arabic workshop held in the Third Int. Conference on Language Resources and Evaluation, Dec, 2002, Las Palmas, Canarias, Spain,
[66]
M. Maragoudakis, B. Kladis, A. Tsopanoglou, K. Sgarbas, N. Fakotakis, Natural Language in Dialogue Systems. A Case Study on a Medical Application, PC-HCI 2001, Panhellenic Conference, with International Participation, in Human–Computer Interaction, pp. 197-201, Dec, 2001, Patras, Greece,
[67]
M. Maragoudakis, K. Kermanidis, N. Fakotakis, G. Kokkinakis, Learning Automatic Acquisition of Subcategorization Frames using Bayesian Inference and Support Vector Machines , ICDM '01, The 2001 IEEE International Conference on Data Mining, pp. 623-625, Nov, 2001, San Jose,
[68]
N. Tselios, M. Maragoudakis, I. M. Avouris, N. Fakotakis, I. Kordaki, Automatic diagnosis of student problem solving strategies using Bayesian Networks, 5th Panhellenic Conference in Mathematics and Informatics in Education, Oct, 2001, Thessalonica,
[69]
M. Maragoudakis, E. Kavallieratou, N. Fakotakis, G. Kokkinakis, How Conditional Independence Assumption Affects Handwritten Character Segmentation, IAPR, ICDAR’2001, 6th International Conference on Document Analysis and Recognition, pp. 246-250, Sep, 2001, Seattle, Washington, U.S.A,
[70]
K. Kermanidis, M. Maragoudakis, N. Fakotakis, G. Kokkinakis, Influence of Conditional Independence Assumption on Verb Subcategorization Detection, 4th International Conference on Text, Speech and Dialog (TSD 2001), V. Matousek et. al., (eds), pp. 62-69, Sep, 2001, Zelezna Ruda, Czech Republic, Springer-Verlag,
[71]
M. Maragoudakis, B. Kladis, A. Tsopanoglou, K. Sgarbas, N. Fakotakis, Human-Computer Interaction using Natural Language. An Application in the Medical Treatment Domain, ΚΤΗΣΙΒΙΟΣ: Πανελλήνιο Συνέδριο Αυτοματισμού, Ρομποτικής και Βιομηχανικής Παραγωγής, Dec, 2001, Σαντορίνη, Ελλάδα,
[72]
M. Maragoudakis, K. Kermanidis, G. Kokkinakis, Learning Subcategorization Frames from Corpora: A Case Study for Modern Greek, COMLEX 2000, Workshop on Computational Lexicography and Multimedia Dictionaries, pp. 19-22, Sep, 2000, Kato Achaia, Greece,

Books


Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.


[1]
M. Maragoudakis, D. Serpanos, Ensemble Learning of High-Dimensional Stock Market Data, 2013, Computational Intelligence for Trading and Investment, Routledge

Chapters in Books


Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.


[1]
E. Magkos, M. Maragoudakis, V. Chryssikopoulos, Προστασία Ιδιωτικότητας σε Κατανεμημένα Συστήματα Εξόρυξης ∆εδομένων, chapter in: ΠΡΟΣΤΑΣΙΑ ΤΗΣ ΙΔΙΩΤΙΚΟΤΗΤΑΣ & ΤΕΧΝΟΛΟΓΙΕΣ ΠΛΗΡΟΦΟΡΙΚΗΣ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΩΝ, ΛΑΜΠΡΙΝΟΥΔΑΚΗΣ – ΜΗΤΡΟΥ – ΓΚΡΙΤΖΑΛΗΣ – ΚΑΤΣΙΚΑΣ, pp. 312-331, 2010, ΠΑΠΑΣΩΤΗΡΙΟΥ,
[2]
M. Maragoudakis, N. Cosmas, A. Garbis, Mining Natural Language Programming Directives with Class-Oriented Bayesian Networks , chapter in: Advanced Data Mining and Applications, Lecture Notes Artificial Intelligence - Lnai 5139, C. Tang, C. X. Ling, X. Zhou, N. J. Cercone, X. Li , (eds), pp. 15-26, 2008, Springer - Verlag Berlin Heidelberg,

Conferences Proceedings Editor


Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.