Discover our Team’s recent Publications

The publications presented on this page highlight the department’s research activity and scholarly contributions across a wide range of scientific venues. Our work includes articles in international peer-reviewed journals, book chapters, refereed conference proceedings, and publications presented at both international and national conferences. These contributions reflect our ongoing commitment to advancing scientific knowledge, fostering international collaboration, and supporting innovation through academic research.
Browse Publications by Category

International Journals

Book Chapters

Springer / ACM

International Conferences

National Conferences

Publications in International Journals

2025

A. Mystakidis, P. Koukaras, C. Tjortjis, Advances in Traffic Congestion Prediction: An Overview of Emerging Techniques and Methods, Smart Cities, 8(1), 25; 2025, (MDPI), Scimago Q1.

P. Koukaras and C. Tjortjis, Data Preprocessing and Feature Engineering for Data Mining: Techniques, Tools, and Best Practices, AI, 6(10), 257; 2025 (MDPI), Scimago Q2.

G. Papageorgiou, V. Sarlis, M. Maragoudakis, C. Tjortjis, A Multimodal Framework Embedding Retrieval-Augmented Generation with MLLMs for Eurobarometer Data, AI, 6(3), 50; 2025, (MDPI), Scimago Q2.

K. Stathakis, G. Papageorgiou, and C. Tjortjis, Identifying Methodological Language in Psychology Abstracts: A Machine Learning Approach Using NLP and Embedding-Based Clustering, Big Data and Cognitive Computing, 9(9), 224; 2025, (MDPI), Scimago Q1.

D. Iatropoulos, V. Sarlis, C. Tjortjis, A Data Mining Approach to Identify NBA Player Quarter-by-Quarter Performance Patterns, Big Data and Cognitive Computing, 9(4), 74; 2025, (MDPI) Scimago Q1.

G. Papageorgiou, C. Tjortjis, Adaptive Sliding Window Normalization, Information Systems, Vol. 129, 102515, March 2025, (Elsevier), Scimago Q1.

V. Sarlis, G. Papageorgiou, C. Tjortjis, Sports Analytics for Evaluating Injury Impacts on NBA Performance, Information, 16(8), 699; 2025, (MDPI), Scimago Q2.

M. Patsiarikas, G. Papageorgiou, and C. Tjortjis, Using Machine Learning on Macroeconomic Factors, Technical and Sentiment Indicators for Stock Market Forecasting. Information, 16(7), 584; 2025, (MDPI), Scimago Q2.

A. Mystakidis, C. Tjortjis, Traffic congestion prediction with missing data: a Classification approach using weather information, Int’l Journal of Data Science and Analytics, Vol. 20, no 3, pp. 2387-2406, 2025, (Springer), Scimago Q2.

G. Papageorgiou, V. Sarlis, C. Tjortjis, An Innovative Method for Accurate NBA Player Performance Forecasting and Line-up Optimization in Daily Fantasy Sports, Int’l Journal of Data Science and Analytics, Vol. 20, no. 2, pp. 1215-1238, 2025, (Springer), Scimago Q2

A. Mystakidis, N. Tsalikidis, P. Koukaras, G. Skaltsis, D. Ioannidis, C. Tjortjis and D. Tzovaras, EV Charging Forecasting Exploiting Traffic, Weather and User Information, Int’l Journal of Machine Learning and Cybernetics, 2025, (Springer-ΟΑ) Scimago Q2.

G. Papageorgiou, V. Sarlis, M. Maragoudakis, C. Tjortjis, Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant, Applied Sciences, 15(11), 6315; 2025, (MDPI) Scimago Q2.

A. Mystakidis, E. Ntozi, P. Koukaras, N. Katsaros, D. Ioannidis, C. Tjortjis and D. Tzovaras, A Multi-Energy Meta-Model Strategy for Multi-step Ahead Energy Load Forecasting, Electrical Engineering, 2025, (Springer), Scimago Q2.

2024

V. Sarlis, D. Gerakas, C. Tjortjis, A Data Science and Sports Analytics Approach to Decode Clutch Dynamics in the Last-Minutes of NBA Games, Machine Learning and Knowledge Extraction, 6(3), pp. 2074–2095. 2024 (MDPI), Scimago Q1.

C. Markopoulou, G. Papageorgiou, C. Tjortjis, Diverse Machine Learning for Forecasting Goal-Scoring Likelihood in Elite Football Leagues, Machine Learning and Knowledge Extraction, 2024, (MDPI), Scimago Q1

N. Tsalikidis, A. Mystakidis, P. Koukaras, M. Ivaškevičius, L. Morkūnaitė, D. Ioannidis, P.A. Fokaides, C. Tjortjis, D. Tzovaras, Urban traffic congestion prediction A multi-step approach utilizing sensor data and weather information, Smart Cities, Vol. 7, No. 1, pp. 233–253. 2024, (MDPI), Scimago Q1.

A. Kousis, C. Tjortjis, Investigating the key aspects of a smart city through topic modeling and thematic analysis, Future Internet, Vol. 16, No. 1: 3. 2024, (MDPI), Scimago Q2.

P. Koukaras, K. Afentoulis, P. Gkaidatzis, A. Mystakidis, D. Ioannidis, S. Vagropoulos and C. Tjortjis, Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions, Energies, 2024, (MDPI), Scimago Q1.

A. Mystakidis, P. Koukaras, N. Tsalikidis, D. Ioannidis and C. Tjortjis, Energy Forecasting: A Comprehensive Review of Techniques and Technologies, Energies, 17, 1662, 2024, (MDPI), Scimago Q1.

P. Koukaras, A. Mustapha, A. Mystakidis, and C. Tjortjis, Optimizing Building Short-Term Load Forecasting A Comparative Analysis of Machine Learning Models, Energies, 17, 1450, 2024, (MDPI), Scimago Q1.

G. Papageorgiou, V. Sarlis, C. Tjortjis, Evaluating the Effectiveness of Machine Learning Models for Performance Forecasting in Basketball: A Comparative Study, Knowledge and Information Systems, 2024, (Springer), Scimago Q2.

V. Sarlis, C. Tjortjis, Sports Analytics: Data Mining to uncover NBA Player Position, Age, and Injuries Impact on Performance and Economics, Information, 15(4), 242; (MDPI), Scimago Q2

G. Papageorgiou, V. Sarlis, C. Tjortjis, Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts, Information, Vol. 15, No. 1, 61, 2024, (MDPI), Scimago Q2.

N. Tsalikidis, A. Mystakidis, C. Tjortjis, P. Koukaras, D. Ioannidis, Energy Load Forecasting: One-Step Ahead Hybrid Model utilizing ensembling, Computing, Vol. 106, No. 1, pp. 241-273, 2024, (Springer), Scimago Q1.

A. Mystakidis, C. Koukaras, P. Koukaras, K. Kaparis, S. Stavrinides and C. Tjortjis, Optimizing Nurse Rostering: A Case Study Using Integer Programming to Enhance Operational Efficiency and Care Quality, Healthcare, 12(24), 2545; 2024, (MDPI), Scimago Q2.

G. Papageorgiou, D. Gkaimanis, C. Tjortjis, Enhancing Stock Market Forecasts with Double Deep Q-Network in Volatile Stock Market Environments, Electronics, 13(9), 1629; 2024, (MDPI), Scimago Q2.

G. Papageorgiou, V. Sarlis, M. Maragoudakis, C. Tjortjis, Enhancing E-Government Services through a state of the art, modular and reproducible architecture over Large Language Models, Applied Sciences, 14(18), 8259; 2024, (MDPI), Scimago Q2.

K.V. Tompra, G. Papageorgiou, C. Tjortjis, Strategic Machine Learning Optimization for Cardiovascular Disease Prediction and High-Risk Patient Identification, Algorithms, 17(5), 178; 2024, (MDPI), Scimago Q2.

V. Sarlis, G. Papageorgiou, C. Tjortjis, Leveraging Sports Analytics and Association Rule Mining to Uncover Recovery and Economic Impacts in NBA Basketball, Data, 9(83). 2024, (MDPI), Scimago Q2.

V. Sarlis, G. Papageorgiou, C. Tjortjis, Injury Patterns and Impact on Performance in the NBA League using Sports Analytics, Computation, Vol. 12, No. 2, 36. 2024, (MDPI), Scimago Q2.

2023

P. Koukaras, D. Rousidis and C. Tjortjis, Unraveling Microblog Sentiment Dynamics: A Twitter Public Attitudes Analysis Towards COVID-19 Cases and Deaths, Informatics, Vol. 10, No. 4, 88, 2023, (MDPI), Scimago Q2.

A. Mystakidis, E. Ntozi, K. Afentoulis, P. Koukaras, P. Gkaidatzis, D. Ioannidis, C. Tjortjis and D. Tzovaras, Energy generation forecasting: Elevating performance with machine and deep learning, Computing, Vol. 105, pp. 1623–1645, 2023, (Springer), Scimago Q2.

F. Shaban, P. Siskos and C. Tjortjis, Electromobility prospects in Greece by 2030: a regional perspective on strategic policy analysis Energies, Vol. 16, No. 16, 6083, 2023, (MDPI), Scimago Q1.

M.T. Siddique, P. Koukaras, D. Ioannidis, C. Tjortjis, A Methodology Integrating Quantitative As-sessment of Energy Efficient Operation and Occupant needs into the Smart Readiness Indicator, Energies, Vol. 16, No. 19, 7007; 2023, (MDPI), Scimago Q1.

V. Sarlis, G. Papageorgiou, C. Tjortjis, Sports Analytics and Text Mining NBA Data to Assess Recovery from Injuries and their Economic Impact, Computers, Vol. 12, No. 12, 261, 2023, (MDPI), Scimago Q2.

M.T. Siddique, P. Koukaras, D. Ioannidis, C. Tjortjis, SmartBuild RecSys: A Recommendation System based on the Smart Readiness Indicator for Energy Efficiency in Buildings, Algorithms, Vol. 16, No. 10, 482, 2023 (MDPI), Scimago Q2.

N. Stasinos, A. Kousis, V. Sarlis, A. Mystakidis, D. Rousidis, P. Koukaras, I. Kotsiopoulos, C. Tjortjis, A Tri-model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study, Algorithms, Vol. 16, No. 3: 140, 2023, (MDPI), Scimago Q2.

2022

D. P. Kasseropoulos, P. Koukaras and C. Tjortjis, Exploiting textual information for fake news detection, Int’l Journal of Neural Systems, Vol. 32, No. 12, 2022, (World Scientific Publishing), Scimago Q1.

P. Koukaras, C. Nousi and C. Tjortjis, Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning, Telecom, 3(2), 358-378, 2022, (MDPI), Scimago Q2.

Book Chapters

The massive growth of Big Data kickstarted a new era for data analytics and knowledge discovery. Data mining algorithms are employed to analyze different types of data, which reside in complex information networks. Researchers focus on producing usable knowledge by taking advantage of opportunities in various domains (e.g., healthcare, social media, energy etc.). Epidemics and disease outbreaks raised concerns about effective infectious disease management in communities around the world. Therefore, they encourage the use of AI methods for management and prevention, in order to mitigate disease spread, and contain outbreaks. This work engages in predictive analytics, utilizing classification, as well as descriptive analytics utilizing association rule mining and clustering, which are widely used in healthcare and medicine, either for predicting outbreaks or for extracting usable information from healthcare and medical data. Certain steps need to be considered when attempting to perform data analysis, such as data extraction, cleaning, preprocessing, transformation, interpretation and evaluation. The experimental part of this chapter integrates widely used datasets retrieved from the UCI Machine Learning Repository related with the healthcare domain. This chapter offers a literature review on data mining in epidemics, while thoroughly discussing all the aforementioned concepts. It also presents a complete process/cycle of the required steps to analyze data retrieved from healthcare and medical sources. Hence, the research questions addressed can be summarized to the following: Q1. Which are the pervasive types of analytics involving the domains of medicine and healthcare? Q2. How is data mining performed in the fields of healthcare and medicine? Q3. Which are the widespread techniques and methods utilized? These questions are discussed and elaborated, through a concise, informative and educational narration.

Handbook of Artificial Intelligence in Healthcare, 2022

The rapid growth of Social Media Networks (SMN) initiated a new era for data analytics. We use various data mining and machine learning algorithms to analyze different types of data generated within these complex networks, attempting to produce usable knowledge. When engaging in descriptive analytics, we utilize data aggregation and mining techniques to provide an insight into the past or present, describing patterns, trends, incidents etc. and try to answer the question “What is happening or What has happened”. Diagnostic analytics come with a pack of techniques that act as tracking/monitoring tools aiming to understand “Why something is happening or Why it happened”. Predictive analytics come with a variety of forecasting techniques and statistical models, which combined, produce insights for the future, hopefully answering “What could happen”. Prescriptive analytics, utilize simulation and optimization methodologies and techniques to generate a helping/support mechanism, answering the question “What should we do”. In order to perform any type of analysis, we first need to identify the correct sources of information. Then, we need APIs to initialize data extraction. Once data are available, cleaning and preprocessing are performed, which involve dealing with noise, outliers, missing values, duplicate data and aggregation, discretization, feature selection, feature extraction, sampling. The next step involves analysis, depending on the Social Media Analytics (SMA) task, the choice of techniques and methodologies varies (e.g. similarity, clustering, classification, link prediction, ranking, recommendation, information fusion). Finally, it comes to human judgment to meaningfully interpret and draw valuable knowledge from the output of the analysis step. This chapter discusses these concepts elaborating on and categorizing various mining tasks (supervised and unsupervised) while presenting the required process and its steps to analyze data retrieved from the Social Media (SM) ecosystem.

Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems, Springer, 2019

Social media (SM) is establishing a new era of tools with multi-usage capabilities. Governments, businesses, organizations, as well as individuals are engaging in, implementing their promotions, sharing opinions and propagating decisions on SM. We need filters, validators and a way of weighting expressed opinions in order to regulate this continuous data stream. This chapter presents trends and attempts by the research community regarding: (a) the influence of SM on attitudes towards a specific domain, related to public health and safety (e.g. diseases, vaccines, mental health), (b) frameworks and tools for monitoring their evolution and (c) techniques for suggesting useful interventions for nudging public sentiment towards best practices. Based on the state of the art, we discuss and assess whether SM can be used as means of prejudice or esteem regarding online opinions on health care. We group the state of the art in the following categories: virus–illness outbreaks, anti-vaccination, mental health, social trends and food and environment. Furthermore, we give more weight to virus–illness outbreaks and the anti-vaccination issues/trends in order to examine disease outbreak prevention methodologies and vaccination/anti-vaccination incentives, whilst discussing their performance. The goal is to consolidate the state of the art and give well-supported directions for future work. To sum up, this chapter discusses the aforementioned concepts and related biases, elaborating on forecasting and prevention attempts using SM data.

Advanced Computational Intelligence in Healthcare, 2020

Referred publications in Springer-Verlag Lecture Notes and ACM Int’l Conf. Proc. Series

This paper presents research on Information Network (IN) modeling
using graph mining. The theoretical background along with a review of relevant
literature is showcased, pertaining the concepts of IN model types, network schemas and graph measures. Ongoing research involves experimentation and evaluation on bipartite and star network schemas, generating test subjects using Social
Media, Energy or Healthcare data. Our contribution is showcased by two proof of-concept simulations we plan to extend.

Communications in Computer & Information Science , Springer, 2020

This paper describes and evaluates T3, an algorithm that builds trees of depth at most three, and results in high accuracy whilst keeping the size of the tree reasonably small. T3 is an improvement over T2 in that it builds larger trees and adopts a less greedy approach. T3 gave better results than both T2 and C4.5 when run against publicly available data sets: T3 decreased classification error on average by 47% and generalisation error by 29%, compared to T2; and T3 resulted in 46% smaller trees and 32% less classification error compared to C4.5. Due to its way of handling unknown values, T3 outperforms C4.5 in generalisation by 99% to 66%, on a specific medical dataset.

Lecture Notes Computer Science, Vol. 2412, pp. 50-55, Springer-Verlag, 2002

Information Networks (INs) are abstract representations of realworld interactions among different entities. This paper focuses on a special
type of Information Networks, namely Heterogeneous Information
Networks (HINs). First, it presents a concise review of the recent work on
this field. Then, it proposes a novel method for querying such networks,
using a bi-functional machine learning algorithm for clustering and ranking.
It performs and elaborates on supervised and unsupervised, proof-ofconcept modelling experiments on multi-typed, interconnected data, while
retaining their semantic importance. The results show that this method
yields promising results and can be extended and utilized, using larger, realworld datasets.

Intelligent Data Communication Technologies and Internet of Things,2020

In this paper, we present PRICES, an efficient algorithm for mining association rules, which first identifies all large itemsets and then generates association rules. Our approach reduces large itemset generation time, known to be the most time-consuming step, by scanning the database only once and using logical operations in the process. Experimental results and comparisons with the state of the art algorithm Apriori shows that PRICES very efficient and in some cases up to ten times as fast as Apriori.

Lecture Notes Computer Science, Vol. 3177, pp. 352-358, Sprienger-Verlag, 2004

An estimated 2.5 quintillion bytes of data are created every day. This
data explosion, along with new datatypes, objects, and the wide usage of social
media networks, with an estimated 3.8 billion users worldwide, make the exploitation and manipulation of data by relational databases, cumbersome and problematic. NoSQL databases introduce new capabilities aiming at improving the
functionalities offered by traditional SQL DBMS. This paper elaborates on ongoing research regarding NoSQL, focusing on the background behind their development, their basic characteristics, their categorization and the noticeable increase in popularity. Functional advantages and data mining capabilities that
come with the usage of graph databases are also presented. Common data mining
tasks with graphs are presented, facilitating implementation, as well as efficiency.
The aim is to highlight concepts necessary for incorporating data mining techniques and graph database functionalities, eventually proposing an analytical
framework offering a plethora of domain specific analytics. For example, a virus
outbreak analytics framework allowing health and government officials to make
appropriate decisions.

Communications in Computer & Information Science , Springer, 2020

With increasing in amount of available data, researchers try to propose new approaches for extracting useful knowledge. Association Rule Mining (ARM) is one of the main approaches that became popular in this field. It can extract frequent rules and patterns from a database. Many approaches were proposed for mining frequent patterns; however, heuristic algorithms are one of the promising methods and many of ARM algorithms are based on these kinds of algorithms. In this paper, we improve our previous approach, ARMICA, and try to consider more parameters, like the number of database scans, the number of generated rules, and the quality of generated rules. We compare the proposed method with the Apriori, ARMICA, and FP-growth and the experimental results indicate that ARMICA-Improved is faster, produces less number of rules, generates rules with more quality, has less number of database scans, it is accurate, and finally, it is an automatic approach and does not need predefined minimum support and confidence values.

Lecture Notes in Artificial Indigence, Vol. 10412, pp. 296-306, Springer-Verlag, 2017

Recommendation systems offer valuable assistance with selecting products and services. This work checks the hypothesis that taking personality into account can improve recommendation quality. Our main goal is to examine the role of personality in Movie Recommender systems. We introduce the concept of combining collaborative techniques with a personality test to provide more personalized movie recommendations. Previous research attempted to incorporate personality in Recommender systems, but no actual implementation appears to have been achieved. We propose a method and developed the 50/50 recommender system, which combines the Big Five personality test with an existing movie recommender, and used it on a renowned movie dataset. Evaluation results showed that users preferred the 50/50 system 3.6% more than the state of the art method. Our findings show that personalization provides better recommendations, even though some extra user input is required upfront.

CCIS Communications in Computer and Information Science, pp. 498-507, Springer-Verlag, 2017

Source code and metric mining have been used to successfully assist with software quality evaluation. This paper presents a data mining approach which incorporates clustering Java classes, as well as classifying extracted clusters, in order to assess internal software quality. We use Java classes as entities and static metrics as attributes for data mining. We identify outliers and apply K-means clustering in order to establish clusters of classes. Outliers indicate potentially fault prone classes, whilst clusters are examined so that we can establish common characteristics. Subsequently, we apply C4.5 to build classification trees for identifying metrics which determine cluster membership. We evaluate the proposed approach with two well known open source software systems, Jedit and Apache Geronimo. Results have consolidated key findings from previous work and indicated that combining clustering with classification produces better results than stand alone clustering.

LNCS 8445, pp. 273-286, Springer-Verlag, 2014

Publications in refereed International Conferences

2023

A. Mystakidis, N. Tsalikidis, P. Koukaras, C. Kontoulis, P.A. Gkaidatzis, D. Ioannidis, C. Tjortjis, and D. Tzovaras, Power Load Forecasting: A Time-Series Multi-Step Ahead and Multi-Model Analysis, Proc. IEEE 58th Int’l Universities Power Engineering Conference (UPEC 23), 2023.

C. Dontaki, P. Koukaras, and C. Tjortjis, Sentiment Analysis on English and Greek Twitter Data regarding Vaccinations, Proc. 14th Int’l Conf. on Information, Intelligence, Systems and Applications (IISA 23), 2023.

M. Vasileiou, G. Papageorgiou, C. Tjortjis, A Machine Learning Approach for Effective Software Defect Detection, Proc. 14th Int’l Conf. on Information, Intelligence, Systems and Applications (IISA 23), 2023 .

A. Mystakidis, O. Geromichalou, C. Tjortjis, Data Mining for Smart Cities: Traffic Congestion Prediction, Proc. 14th Int’l Conf. on Information, Intelligence, Systems and Applications (IISA 23), 2023.

V. Chouliara, P. Koukaras and C. Tjortjis, Fake News Detection utilizing textual cues, Proc. 19th Int’l Conf. on Artificial Intelligence Applications and Innovations (AIAI 23).

N. Giannakoulas, G. Papageorgiou, C. Tjortjis, Forecasting Goal Performance for Top League Football Players: A Comparative Study, Proc. 19th Int’l Conf. on Artificial Intelligence Applications and Innova-tions (AIAI 23).

2022

A. Mystakidis, E. Ntozi, K. Afentoulis, P. Koukaras, G. Giannopoulos, N. Bezas, P. Gkaidatzis, D. Ioannidis, C. Tjortjis and D. Tzovaras, One Step Ahead Energy Load Forecasting: Multi-model approach utilizing Machine and Deep Learning, Proc. IEEE 57th Int’l Universities Power Engineering Conference (UPEC 2022), 2022.

Ε. Kapoteli, P. Koukaras, C. Tjortjis, Social Media Sentiment Analysis Related to COVID-19 Vaccines: Case studies in English and Greek language, Proc. 18th Int’l Conf. Artificial Intelligence Applications and Innovations (AIAI 22).

M. Karagkiozidou, P. Koukaras, C. Tjortjis, Sentiment Analysis on COVID-19 Twitter Data: A Sentiment Timeline, Proc. 18th Int’l Conf. Artificial Intelligence Applications and Innovations (AIAI 22).

P. Koukaras, A. Dimara, S. Herrera, N. Zangrando, S. Krinidis, D. Ioannidis, P. Fraternali, C. Tjortjis, C.-N. Anagnostopoulos, D. Tzovaras, Proactive buildings: A prescriptive maintenance approach, Proc. 18th Int’l Conf. Artificial Intelligence Applications and Innovations (AIAI 22).

A. Ahmed, C. Tjortjis, Machine Learning based IoT-BotNet Attack Detection Using Real-time Heterogeneous Data, 2nd Int’l Conf. on Electrical, Computer and Energy Technologies (ICECET 22).

2021

P. Koukaras, V. Tsichli, and C. Tjortjis, Predicting Stock Market Movements with Social Media and Machine Learning, Proc. 17th Int’l Conf. on Web Information Systems and Technologies (WEBIST 21), 2021.

A. Avramidou and C. Tjortjis, Predicting CO2 Emissions for Buildings Using Regression and Classification, Proc. 17th IFIP Int’l Conf. on Artificial Intelligence Applications and Innovations (AIAI 21).

D. P. Kasseropoulos and C. Tjortjis, An Approach Utilizing Linguistic Features for Fake News Detection, Proc. 17th IFIP Int’l Conf. on Artificial Intelligence Applications and Innovations (AIAI 21).

C. Nousi and C. Tjortjis, A Methodology for Stock Movement Prediction Using Sentiment Analysis on Twitter and StockTwits Data, Proc. 6th IEEE South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 21), 2021.

2020

P. Koukaras, C. Berberidis, and C. Tjortjis, A Semi-supervised Learning Approach for Complex Information Networks, 3rd Int’l Conf. Intelligent Data Communication Technologies and Internet of Things (ICICI 2020) 2020.

P. Koukaras, D. Rousidis and C. Tjortjis, An Introduction to Information Network Modeling Capabilities, Utilizing Graphs, 14th Int’l Conf. Metadata and Semantics Research (MTSR2020), Communications in Computer & Information Science (CCIS), Springer 2020.

D. Rousidis, P. Koukaras and C. Tjortjis, Examination of NoSQL Transition and Data Mining capabilities, 14th Int’l Conf. Metadata and Semantics Research (MTSR2020), Communications in Computer & Information Science (CCIS), Springer 2020.

Publications in refereed National Conferences

This paper presents ongoing work on using data mining clustering to facilitate software maintenance, program comprehension and software systems knowledge discovery. We propose a method for grouping Java code elements together, according to their similarity. The method aims at providing practical insights and guidance to maintainers through the specifics of a system, assuming they have little familiarity with it. Our method employs a preprocessing algorithm to identify the most significant syntactical and grammatical elements of Java programs and then applies hierarchical agglomerative clustering to produce correlations among extracted data. The proposed method successfully reveals similarities between classes and other code elements thus facilitating software maintenance and Java program comprehension as shown by the experimental results presented here. The paper concludes with directions for further work

Proc. 10th Pan’c Conf. on Informatics (PCI’2005), 2005

Microarray technology has enabled scientists to monitor and process the expression of thousands of genes in parallel, within a single experiment. However, the efficient interpretation and validation of the analysis results, based on current medical and biological knowledge, remains a challenge. Most gene expression analysis approaches do not incorporate existing background knowledge in the process, thus necessitating laborious manual interpretation. In this paper we propose a novel hybrid knowledge-driven approach for analyzing gene expression data which integrates currently available biological and medical knowledge within the actual clustering process. Existing published scientific information is correlated to create, validate and biologically interpret the resulting clusters. Some preliminary experimental results are supplied using a sample yeast genome data set.

Proc. 10th Pan’c Conf. on Informatics (PCI’2005), 2005

Proc. 3rd Nat’l Conf. of Medical Informatics, 1994