Maria Nefeli Nikiforos is a PhD candidate supervised by Professor Adamantia Pateli, working on vocational education in the tourism sector with machine learning techniques. Her research interests include vocational education and language learning, natural language processing, linguistic data mining, machine learning and artificial intelligence.
Publications
Journal Articles
Tzimiris, S., Nikiforos, S., Nikiforos, M. N., Mouratidis, D., & Kermanidis, K. L. (2025). A Comparative Evaluation of Transformer-Based Language Models for Topic-Based Sentiment Analysis. Electronics, 14(15), 2957. https://doi.org/10.3390/electronics14152957
@article{tzimiris2025comparative,
title = {A Comparative Evaluation of Transformer-Based Language Models for Topic-Based Sentiment Analysis},
author = {Spyridon Tzimiris and Stefanos Nikiforos and Maria Nefeli Nikiforos and Despoina Mouratidis and Katia Lida Kermanidis},
url = {https://doi.org/10.3390/electronics14152957},
doi = {10.3390/electronics14152957},
year = {2025},
date = {2025-01-01},
journal = {Electronics},
volume = {14},
number = {15},
pages = {2957},
publisher = {MDPI AG},
abstract = {This research investigates topic-based sentiment classification in Greek educational-related data using transformer-based language models. A comparative evaluation is conducted on GreekBERT, XLM-r-Greek, mBERT, and Palobert using three original sentiment-annotated datasets representing parents of students with functional diversity, school directors, and teachers, each capturing diverse educational perspectives. The analysis examines both overall sentiment performance and topic-specific evaluations across four thematic classes: (i) Material and Technical Conditions, (ii) Educational Dimension, (iii) Psychological/Emotional Dimension, and (iv) Learning Difficulties and Emergency Remote Teaching. Results indicate that GreekBERT consistently outperforms other models, achieving the highest overall F1 score (0.91), particularly excelling in negative sentiment detection (F1 = 0.95) and showing robust performance for positive sentiment classification. The Psychological/Emotional Dimension emerged as the most reliably classified category, with GreekBERT and mBERT demonstrating notably high accuracy and F1 scores. Conversely, Learning Difficulties and Emergency Remote Teaching presented significant classification challenges, especially for Palobert. This study contributes significantly to the field of sentiment analysis with Greek-language data by introducing original annotated datasets, pioneering the application of topic-based sentiment analysis within the Greek educational context, and offering a comparative evaluation of transformer models. Additionally, it highlights the superior performance of Greek-pretrained models in capturing emotional detail, and provides empirical evidence of the negative emotional responses toward Emergency Remote Teaching.},
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This research investigates topic-based sentiment classification in Greek educational-related data using transformer-based language models. A comparative evaluation is conducted on GreekBERT, XLM-r-Greek, mBERT, and Palobert using three original sentiment-annotated datasets representing parents of students with functional diversity, school directors, and teachers, each capturing diverse educational perspectives. The analysis examines both overall sentiment performance and topic-specific evaluations across four thematic classes: (i) Material and Technical Conditions, (ii) Educational Dimension, (iii) Psychological/Emotional Dimension, and (iv) Learning Difficulties and Emergency Remote Teaching. Results indicate that GreekBERT consistently outperforms other models, achieving the highest overall F1 score (0.91), particularly excelling in negative sentiment detection (F1 = 0.95) and showing robust performance for positive sentiment classification. The Psychological/Emotional Dimension emerged as the most reliably classified category, with GreekBERT and mBERT demonstrating notably high accuracy and F1 scores. Conversely, Learning Difficulties and Emergency Remote Teaching presented significant classification challenges, especially for Palobert. This study contributes significantly to the field of sentiment analysis with Greek-language data by introducing original annotated datasets, pioneering the application of topic-based sentiment analysis within the Greek educational context, and offering a comparative evaluation of transformer models. Additionally, it highlights the superior performance of Greek-pretrained models in capturing emotional detail, and provides empirical evidence of the negative emotional responses toward Emergency Remote Teaching.
Kermanidis, K. L., Tzimiris, S., Nikiforos, S., Nikiforos, M. N., & Mouratidis, D. (2025). ICT Adoption in Education: Unveiling Emergency Remote Teaching Challenges for Students with Functional Diversity Through Topic Identification in Modern Greek Data. Applied Sciences, 15(9), 4667. https://doi.org/10.3390/app15094667
@article{kermanidis2025ict,
title = {ICT Adoption in Education: Unveiling Emergency Remote Teaching Challenges for Students with Functional Diversity Through Topic Identification in Modern Greek Data},
author = {Katia Lida Kermanidis and Spyridon Tzimiris and Stefanos Nikiforos and Maria Nefeli Nikiforos and Despoina Mouratidis},
url = {https://doi.org/10.3390/app15094667},
doi = {10.3390/app15094667},
year = {2025},
date = {2025-01-01},
journal = {Applied Sciences},
volume = {15},
number = {9},
pages = {4667},
publisher = {MDPI AG},
abstract = {This study explores topic identification using text analysis techniques in Modern Greek interviews with parents of students with functional diversity during Emergency Remote Teaching. The analysis focused on identifying key educational themes and addressing challenges in processing Greek educational data. Machine learning models, combined with Natural Language Processing techniques, were applied for topic identification, utilizing cross-validation and data balancing methods to enhance reliability. The findings revealed the impact of linguistic complexity on topic modeling and highlighted the educational implications of analyzing qualitative data in this context. Among the models tested, the Naïve Bayes (Kernel) algorithm performed best when combined with lemmatization-based preprocessing, confirming that text normalization significantly enhances classification accuracy in Greek educational data. The proposed framework contributes to the analysis of qualitative educational data by identifying key parental concerns related to Emergency Remote Teaching. It demonstrates how text analysis techniques could support data-driven decision-making and help guide policy development for the inclusive and effective integration of Information and Communication Technology in education.},
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This study explores topic identification using text analysis techniques in Modern Greek interviews with parents of students with functional diversity during Emergency Remote Teaching. The analysis focused on identifying key educational themes and addressing challenges in processing Greek educational data. Machine learning models, combined with Natural Language Processing techniques, were applied for topic identification, utilizing cross-validation and data balancing methods to enhance reliability. The findings revealed the impact of linguistic complexity on topic modeling and highlighted the educational implications of analyzing qualitative data in this context. Among the models tested, the Naïve Bayes (Kernel) algorithm performed best when combined with lemmatization-based preprocessing, confirming that text normalization significantly enhances classification accuracy in Greek educational data. The proposed framework contributes to the analysis of qualitative educational data by identifying key parental concerns related to Emergency Remote Teaching. It demonstrates how text analysis techniques could support data-driven decision-making and help guide policy development for the inclusive and effective integration of Information and Communication Technology in education.
Tzimiris, S., Nikiforos, S., Nikiforos, M. N., Mouratidis, D., & Kermanidis, K. L. (2025). Topic Classification of Interviews on Emergency Remote Teaching. Information, 16(4), 253. https://doi.org/10.3390/info16040253
@article{tzimiris2025topic,
title = {Topic Classification of Interviews on Emergency Remote Teaching},
author = {Spyridon Tzimiris and Stefanos Nikiforos and Maria Nefeli Nikiforos and Despoina Mouratidis and Katia Lida Kermanidis},
url = {https://doi.org/10.3390/info16040253},
doi = {10.3390/info16040253},
year = {2025},
date = {2025-01-01},
journal = {Information},
volume = {16},
number = {4},
pages = {253},
publisher = {MDPI AG},
abstract = {This study explores the application of transformer-based language models for automated Topic Classification in qualitative datasets from interviews conducted in Modern Greek. The interviews captured the views of parents, teachers, and school directors regarding Emergency Remote Teaching. Identifying key themes in this kind of interview is crucial for informed decision-making in educational policies. Each dataset was segmented into sentences and labeled with one out of four topics. The dataset was imbalanced, presenting additional complexity for the classification task. The GreekBERT model was fine-tuned for Topic Classification, with preprocessing including accent stripping, lowercasing, and tokenization. The findings revealed GreekBERT’s effectiveness in achieving balanced performance across all themes, outperforming conventional machine learning models. The highest evaluation metric achieved was a macro-F1-score of 0.76, averaged across all classes, highlighting the effectiveness of the proposed approach. This study contributes the following: (i) datasets capturing diverse educational community perspectives in Modern Greek, (ii) a comparative evaluation of conventional ML models versus transformer-based models, (iii) an investigation of how domain-specific language enhances the performance and accuracy of Topic Classification models, showcasing their effectiveness in specialized datasets and the benefits of fine-tuned GreekBERT for such tasks, and (iv) capturing the complexities of ERT through an empirical investigation of the relationships between extracted topics and relevant variables. These contributions offer reliable, scalable solutions for policymakers, enabling data-driven educational policies to address challenges in remote learning and enhance decision-making based on comprehensive qualitative evidence.},
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This study explores the application of transformer-based language models for automated Topic Classification in qualitative datasets from interviews conducted in Modern Greek. The interviews captured the views of parents, teachers, and school directors regarding Emergency Remote Teaching. Identifying key themes in this kind of interview is crucial for informed decision-making in educational policies. Each dataset was segmented into sentences and labeled with one out of four topics. The dataset was imbalanced, presenting additional complexity for the classification task. The GreekBERT model was fine-tuned for Topic Classification, with preprocessing including accent stripping, lowercasing, and tokenization. The findings revealed GreekBERT’s effectiveness in achieving balanced performance across all themes, outperforming conventional machine learning models. The highest evaluation metric achieved was a macro-F1-score of 0.76, averaged across all classes, highlighting the effectiveness of the proposed approach. This study contributes the following: (i) datasets capturing diverse educational community perspectives in Modern Greek, (ii) a comparative evaluation of conventional ML models versus transformer-based models, (iii) an investigation of how domain-specific language enhances the performance and accuracy of Topic Classification models, showcasing their effectiveness in specialized datasets and the benefits of fine-tuned GreekBERT for such tasks, and (iv) capturing the complexities of ERT through an empirical investigation of the relationships between extracted topics and relevant variables. These contributions offer reliable, scalable solutions for policymakers, enabling data-driven educational policies to address challenges in remote learning and enhance decision-making based on comprehensive qualitative evidence.
Karagiannis, S., Magkos, E., Karavaras, E., Karnavas, A., Nikiforos, M. N., & Ntantogian, C. (2024). Towards NICE-by-Design Cybersecurity Learning Environments: A Cyber Range for SOC Teams. Journal of Network and Systems Management, 32(2), 42. https://doi.org/10.1007/s10922-024-09816-w
@article{karagiannis2024nice,
title = {Towards NICE-by-Design Cybersecurity Learning Environments: A Cyber Range for SOC Teams},
author = {Stylianos Karagiannis and Emmanouil Magkos and Eleftherios Karavaras and Antonios Karnavas and Maria Nefeli Nikiforos and Christoforos Ntantogian},
url = {https://doi.org/10.1007/s10922-024-09816-w},
doi = {10.1007/s10922-024-09816-w},
year = {2024},
date = {2024-01-01},
journal = {Journal of Network and Systems Management},
volume = {32},
number = {2},
pages = {42},
publisher = {Springer Science and Business Media LLC},
abstract = {Cybersecurity has become an increasingly important field as cyber threats continue to grow in number and complexity. The NICE framework, developed by NIST, provides a structured approach to cybersecurity education. Despite the publication of cybersecurity frameworks, scenario design in cybersecurity is not yet governed by structured design principles, leading to ambiguous learning outcomes. This research uses the NICE framework to provide structure design and development of a cyber range and the relevant scenarios. The proposed methodology and research results can assist the scenario design in cybersecurity and as a methodological procedure for evaluation. Finally, the research provides a better understanding of the NICE framework and demonstrates how it can assist in creating practical cybersecurity scenarios.},
keywords = {},
pubstate = {published},
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Cybersecurity has become an increasingly important field as cyber threats continue to grow in number and complexity. The NICE framework, developed by NIST, provides a structured approach to cybersecurity education. Despite the publication of cybersecurity frameworks, scenario design in cybersecurity is not yet governed by structured design principles, leading to ambiguous learning outcomes. This research uses the NICE framework to provide structure design and development of a cyber range and the relevant scenarios. The proposed methodology and research results can assist the scenario design in cybersecurity and as a methodological procedure for evaluation. Finally, the research provides a better understanding of the NICE framework and demonstrates how it can assist in creating practical cybersecurity scenarios.
Tzimiris, S., Nikiforos, M. N., Nikiforos, S., & Kermanidis, K. L. (2023). Challenges and Opportunities of Emergency Remote Teaching: Linguistic Analysis on School Directors’ Interviews. European Journal of Engineering and Technology Research, 53–60. https://doi.org/10.24018/ejeng.2023.1.cie.3137
@article{tzimiris2023challenges,
title = {Challenges and Opportunities of Emergency Remote Teaching: Linguistic Analysis on School Directors’ Interviews},
author = {Spyridon Tzimiris and Maria Nefeli Nikiforos and Stefanos Nikiforos and Katia Lida Kermanidis},
url = {https://doi.org/10.24018/ejeng.2023.1.cie.3137},
doi = {10.24018/ejeng.2023.1.cie.3137},
year = {2023},
date = {2023-01-01},
journal = {European Journal of Engineering and Technology Research},
pages = {53–60},
publisher = {European Open Science Publishing},
abstract = {This research delves into the experiences of primary school directors during the abrupt transition to Emergency Remote Teaching (ERT) due to the Covid-19 pandemic. Through semi-structured interviews, the organization and implementation of online classes, associated challenges, and potential improvements were scrutinized. Findings underscored a lack of preparedness, yet acknowledged ERT as a vital tool during the crisis. Recommendations included the improvement of technological support, designing a well-planned strategy, creating appropriate teaching content, comprehensive staff training, and tailoring the educational content to fit students’ learning styles or special needs. A Linguistic Text Analysis approach, employing word clouds, treemaps, and sentiment analysis charts to graphically depict complex patterns in the data, enriched our understanding of the ERT transition, shedding light on subtler nuances and insights. This study contributes valuable knowledge, offering a roadmap for the future development of robust, flexible, and inclusive educational policies and practices, particularly in crisis situations. The dataset is an invaluable asset for policymakers, providing critical insights and highlighting the challenges and opportunities that arise.},
note = {Special issue: Proceedings of the Conference on Informatics in Education (CIE)},
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This research delves into the experiences of primary school directors during the abrupt transition to Emergency Remote Teaching (ERT) due to the Covid-19 pandemic. Through semi-structured interviews, the organization and implementation of online classes, associated challenges, and potential improvements were scrutinized. Findings underscored a lack of preparedness, yet acknowledged ERT as a vital tool during the crisis. Recommendations included the improvement of technological support, designing a well-planned strategy, creating appropriate teaching content, comprehensive staff training, and tailoring the educational content to fit students’ learning styles or special needs. A Linguistic Text Analysis approach, employing word clouds, treemaps, and sentiment analysis charts to graphically depict complex patterns in the data, enriched our understanding of the ERT transition, shedding light on subtler nuances and insights. This study contributes valuable knowledge, offering a roadmap for the future development of robust, flexible, and inclusive educational policies and practices, particularly in crisis situations. The dataset is an invaluable asset for policymakers, providing critical insights and highlighting the challenges and opportunities that arise.
Nikiforos, M. N., Deliveri, K., Kermanidis, K. L., & Pateli, A. (2023). Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class Balancing †. Computers, 12(6). https://doi.org/10.3390/computers12060111
@article{Nikiforos2023,
title = {Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class Balancing †},
author = {M. N. Nikiforos and K. Deliveri and K. L. Kermanidis and A. Pateli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163617082&doi=10.3390%2fcomputers12060111&partnerID=40&md5=946cf5523adfabb6fc705fe24e9fdba3},
doi = {10.3390/computers12060111},
year = {2023},
date = {2023-01-01},
journal = {Computers},
volume = {12},
number = {6},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Karagiannis, S., Magkos, E., Chalavazis, G., & Nikiforos, M. N. (2022). Analysis and Evaluation of Capture the Flag Challenges in Secure Mobile Application Development. International Journal on Integrating Technology in Education, 11(2), 19–35. https://doi.org/10.5121/ijite.2022.11202
@article{karagiannis2022analysis,
title = {Analysis and Evaluation of Capture the Flag Challenges in Secure Mobile Application Development},
author = {Stylianos Karagiannis and Emmanouil Magkos and George Chalavazis and Maria Nefeli Nikiforos},
url = {https://doi.org/10.5121/ijite.2022.11202},
doi = {10.5121/ijite.2022.11202},
year = {2022},
date = {2022-01-01},
journal = {International Journal on Integrating Technology in Education},
volume = {11},
number = {2},
pages = {19–35},
publisher = {Academy and Industry Research Collaboration Center (AIRCC)},
abstract = {Capture the Flag (CTF) challenges are frequently used as cybersecurity learning environments to engage students in cybersecurity education activities and learning, focusing on technical concepts. CTF challenges cover various learning topics. However, they do not always maintain a clear learning outcome. In this paper, we present a systematic approach to study and evaluate CTF challenges, then apply the evaluation methodology in two CTF challenges that relate to the development of secure mobile applications. For this proof of concept, we used the National Initiative for Cybersecurity Education (NICE) which is a cybersecurity educational framework published by the National Institute of Standards and Technology (NIST). Additional information was used for the evaluation process which included threat, vulnerability, and weakness taxonomies proposed by Open Web Application Security Project® (OWASP) and Mitre Corporation (MITRE). The evaluation methodology could be used to assess and determine the learning outcomes of other existing or upcoming CTF challenges, including though not limited to secure mobile application development.},
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Capture the Flag (CTF) challenges are frequently used as cybersecurity learning environments to engage students in cybersecurity education activities and learning, focusing on technical concepts. CTF challenges cover various learning topics. However, they do not always maintain a clear learning outcome. In this paper, we present a systematic approach to study and evaluate CTF challenges, then apply the evaluation methodology in two CTF challenges that relate to the development of secure mobile applications. For this proof of concept, we used the National Initiative for Cybersecurity Education (NICE) which is a cybersecurity educational framework published by the National Institute of Standards and Technology (NIST). Additional information was used for the evaluation process which included threat, vulnerability, and weakness taxonomies proposed by Open Web Application Security Project® (OWASP) and Mitre Corporation (MITRE). The evaluation methodology could be used to assess and determine the learning outcomes of other existing or upcoming CTF challenges, including though not limited to secure mobile application development.
Mouratidis, D., Nikiforos, M. N., & Kermanidis, K. L. (2021). Deep Learning for Fake News Detection in a Pairwise Textual Input Schema. Computation, 9(2), 20. https://doi.org/10.3390/computation9020020
@article{mouratidis2021deep,
title = {Deep Learning for Fake News Detection in a Pairwise Textual Input Schema},
author = {Despoina Mouratidis and Maria Nefeli Nikiforos and Katia Lida Kermanidis},
url = {https://doi.org/10.3390/computation9020020},
doi = {10.3390/computation9020020},
year = {2021},
date = {2021-01-01},
journal = {Computation},
volume = {9},
number = {2},
pages = {20},
publisher = {MDPI AG},
abstract = {In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.},
note = {MDPI Computation 2021 Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {article}
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In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.
Nikiforos, M. N., Voutos, Y., Drougani, A., Mylonas, P., & Kermanidis, K. L. (2021). The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications. Data, 6(5), 52. https://doi.org/10.3390/data6050052
@article{nikiforos2021modern,
title = {The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications},
author = {Maria Nefeli Nikiforos and Yorghos Voutos and Anthi Drougani and Phivos Mylonas and Katia Lida Kermanidis},
url = {https://doi.org/10.3390/data6050052},
doi = {10.3390/data6050052},
year = {2021},
date = {2021-01-01},
journal = {Data},
volume = {6},
number = {5},
pages = {52},
publisher = {MDPI AG},
abstract = {Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.},
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Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.
Conferences
Nikiforos, M. N., Deliveri, K., Kermanidis, K. L., & Pateli, A. (2022). Machine Learning on Wikipedia Text for the Automatic Identification of Vocational Domains of Significance for Displaced Communities. In . https://doi.org/10.1109/SMAP56125.2022.9941803
@conference{Nikiforos2022,
title = {Machine Learning on Wikipedia Text for the Automatic Identification of Vocational Domains of Significance for Displaced Communities},
author = {M. N. Nikiforos and K. Deliveri and K. L. Kermanidis and A. Pateli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143056941&doi=10.1109%2fSMAP56125.2022.9941803&partnerID=40&md5=23c418b4cd3150c5a08fc65dcb5177b6},
doi = {10.1109/SMAP56125.2022.9941803},
year = {2022},
date = {2022-01-01},
journal = {2022 17th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2022},
note = {cited By 1},
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Proceedings Articles
Nikiforos, M. N., Malakopoulou, M., & Exarchos, T. (2021). Development of a Diagnostic Tool for Balance Disorders Based on Machine Learning Techniques. In Vlamos, P. (Ed.), GeNeDis 2020: Computational Biology and Bioinformatics (pp. 47–54). Springer International Publishing. https://doi.org/10.1007/978-3-030-78775-2_7
@inproceedings{nikiforos2021development,
title = {Development of a Diagnostic Tool for Balance Disorders Based on Machine Learning Techniques},
author = {Maria Nefeli Nikiforos and Maria Malakopoulou and Themis Exarchos},
editor = {Panayiotis Vlamos},
url = {https://doi.org/10.1007/978-3-030-78775-2_7},
doi = {10.1007/978-3-030-78775-2_7},
isbn = {9783030787745},
year = {2021},
date = {2021-01-01},
booktitle = {GeNeDis 2020: Computational Biology and Bioinformatics},
volume = {1338},
pages = {47–54},
publisher = {Springer International Publishing},
note = {GeNeDis 2020 (4th World Congress on Genetics, Geriatrics and Neurodegenerative Diseases Research). Advances in Experimental Medicine and Biology, vol. 1338},
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Xipolitopoulos, C., Nikiforos, M. N., & Exarchos, T. (2021). Machine Learning for Autistic Spectrum Disorder Risk Screening. In Vlamos, P. (Ed.), GeNeDis 2020: Computational Biology and Bioinformatics (pp. 81–87). Springer International Publishing. https://doi.org/10.1007/978-3-030-78775-2_10
@inproceedings{xipolitopoulos2021machine,
title = {Machine Learning for Autistic Spectrum Disorder Risk Screening},
author = {Constantine Xipolitopoulos and Maria Nefeli Nikiforos and Themis Exarchos},
editor = {Panayiotis Vlamos},
url = {https://doi.org/10.1007/978-3-030-78775-2_10},
doi = {10.1007/978-3-030-78775-2_10},
isbn = {9783030787745},
year = {2021},
date = {2021-01-01},
booktitle = {GeNeDis 2020: Computational Biology and Bioinformatics},
volume = {1338},
pages = {81–87},
publisher = {Springer International Publishing},
note = {GeNeDis 2020 (4th World Congress on Genetics, Geriatrics and Neurodegenerative Diseases Research). Advances in Experimental Medicine and Biology, vol. 1338},
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Angelis, I., Alvanou, A. G., Avgoustis, N., Vergis, S., Zervopoulos, A., Malakopoulou, M., Bezas, K., Nikiforos, M. N., Papamichail, A., Stylidou, A., Exarchos, T. P., & Vlamos, P. (2021). Mobile Application for Monitoring and Preventing Cognitive Decline Through Lifestyle Intervention. In Vlamos, P. (Ed.), GeNeDis 2020: Computational Biology and Bioinformatics (pp. 89–96). Springer International Publishing. https://doi.org/10.1007/978-3-030-78775-2_11
@inproceedings{angelis2021mobile,
title = {Mobile Application for Monitoring and Preventing Cognitive Decline Through Lifestyle Intervention},
author = {Ioannis Angelis and Aikaterini Georgia Alvanou and Nikolaos Avgoustis and Spiridon Vergis and Alexandros Zervopoulos and Maria Malakopoulou and Konstantinos Bezas and Maria Nefeli Nikiforos and Asterios Papamichail and Andreana Stylidou and Themis P. Exarchos and Panayiotis Vlamos},
editor = {Panayiotis Vlamos},
url = {https://doi.org/10.1007/978-3-030-78775-2_11},
doi = {10.1007/978-3-030-78775-2_11},
isbn = {9783030787745},
year = {2021},
date = {2021-01-01},
booktitle = {GeNeDis 2020: Computational Biology and Bioinformatics},
volume = {1338},
pages = {89–96},
publisher = {Springer International Publishing},
note = {GeNeDis 2020 (4th World Congress on Genetics, Geriatrics and Neurodegenerative Diseases Research). Advances in Experimental Medicine and Biology, vol. 1338},
keywords = {},
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Nikiforos, M. N., Malakopoulou, M., Stylidou, A., Alvanou, A.-G., Karyotis, V., & Kourouthanassis, P. (2020). Enhancing Collaborative Filtering Recommendations for Web-based Learning Platforms with Genetic Algorithms. In 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA (pp. 1–6). https://doi.org/10.1109/SMAP49528.2020.9248472
@inproceedings{nikiforos2020enhancing,
title = {Enhancing Collaborative Filtering Recommendations for Web-based Learning Platforms with Genetic Algorithms},
author = {Maria Nefeli Nikiforos and Maria Malakopoulou and Andreana Stylidou and Aikaterini-Georgia Alvanou and Vasileios Karyotis and Panos Kourouthanassis},
url = {https://doi.org/10.1109/SMAP49528.2020.9248472},
doi = {10.1109/SMAP49528.2020.9248472},
isbn = {9781728159195},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA},
pages = {1--6},
organization = {IEEE},
abstract = {Web-based learning platforms are now offering a considerable amount of training options, exhibiting great variability, similar to the one encountered by a potential customer in electronic shops. To mitigate this, efficient and effective recommendations engines are needed, capable of satisfying the specific needs of each user, while achieving the best possible promotion of available onlinetraining. This paper discusses the design of a potential generic architecture for online education recommender systems, specifically targeted for promoting online courses and web-based learning material. From an algorithmic perspective, the system relies on item-based and user-based collaborativefiltering approaches. It extends this approach with a genetic algorithm, thus increasing its potential impact. Overall, the paper paves the ground for the specification of generic principles governing the design of personalized online education platforms as well as identifying metrics for evaluating their performance. © 2020 IEEE.},
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pubstate = {published},
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}
Web-based learning platforms are now offering a considerable amount of training options, exhibiting great variability, similar to the one encountered by a potential customer in electronic shops. To mitigate this, efficient and effective recommendations engines are needed, capable of satisfying the specific needs of each user, while achieving the best possible promotion of available onlinetraining. This paper discusses the design of a potential generic architecture for online education recommender systems, specifically targeted for promoting online courses and web-based learning material. From an algorithmic perspective, the system relies on item-based and user-based collaborativefiltering approaches. It extends this approach with a genetic algorithm, thus increasing its potential impact. Overall, the paper paves the ground for the specification of generic principles governing the design of personalized online education platforms as well as identifying metrics for evaluating their performance. © 2020 IEEE.
Kastampolidou, K., Nikiforos, M. N., & Andronikos, T. (2020). A Brief Survey of the Prisoners’ Dilemma Game and Its Potential Use in Biology. In Vlamos, P. (Ed.), GeNeDis 2018: Computational Biology and Bioinformatics (pp. 315–322). Springer International Publishing. https://doi.org/10.1007/978-3-030-32622-7_29
@inproceedings{kastampolidou2020brief,
title = {A Brief Survey of the Prisoners’ Dilemma Game and Its Potential Use in Biology},
author = {Kalliopi Kastampolidou and Maria Nefeli Nikiforos and Theodore Andronikos},
editor = {Panayiotis Vlamos},
url = {https://doi.org/10.1007/978-3-030-32622-7_29},
doi = {10.1007/978-3-030-32622-7_29},
isbn = {9783030326210},
year = {2020},
date = {2020-01-01},
booktitle = {GeNeDis 2018: Computational Biology and Bioinformatics},
volume = {1194},
pages = {315–322},
publisher = {Springer International Publishing},
note = {GeNeDis 2018. Advances in Experimental Medicine and Biology, vol. 1194},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Nikiforos, M. N., & Kermanidis, K. L. (2020). A Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web. In Calzolari, N., Béchet, F., Blache, P., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., & Piperidis, S. (Ed.), Proceedings of the Twelfth Language Resources and Evaluation Conference (LREC 2020) (pp. 3861–3867). Marseille, France: European Language Resources Association.
@inproceedings{nikiforos2020supervised,
title = {A Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web},
author = {Maria Nefeli Nikiforos and Katia Lida Kermanidis},
editor = {Nicoletta Calzolari and Frédéric Béchet and Philippe Blache and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
url = {https://aclanthology.org/2020.lrec-1.476/},
isbn = {979-10-95546-34-4},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference (LREC 2020)},
pages = {3861–3867},
publisher = {European Language Resources Association},
address = {Marseille, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Nikiforos, M. N., Vergis, S., Stylidou, A., Augoustis, N., Kermanidis, K. L., & Maragoudakis, M. (2020). Fake News Detection Regarding the Hong Kong Events from Tweets. In Maglogiannis, I., Iliadis, L., & Pimenidis, E. (Ed.), Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops (pp. 177–186). Springer International Publishing. https://doi.org/10.1007/978-3-030-49190-1_16
@inproceedings{nikiforos2020fake,
title = {Fake News Detection Regarding the Hong Kong Events from Tweets},
author = {Maria Nefeli Nikiforos and Spiridon Vergis and Andreana Stylidou and Nikolaos Augoustis and Katia Lida Kermanidis and Manolis Maragoudakis},
editor = {Ilias Maglogiannis and Lazaros Iliadis and Elias Pimenidis},
url = {https://doi.org/10.1007/978-3-030-49190-1_16},
doi = {10.1007/978-3-030-49190-1_16},
isbn = {9783030491895},
year = {2020},
date = {2020-01-01},
booktitle = {Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops},
pages = {177–186},
publisher = {Springer International Publishing},
note = {IFIP Advances in Information and Communication Technology},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Xipolitopoulos, C., Nikiforos, M. N., Malakopoulou, M., & Pateli, A. G. (2020). Success Factors for Crowd-funding Campaigns with Machine Learning Techniques. In ICTO 2020: Smart Technologies for an Inclusive World. Paris, France.
@inproceedings{xipolitopoulos2020success,
title = {Success Factors for Crowd-funding Campaigns with Machine Learning Techniques},
author = {Constantine Xipolitopoulos and Maria Nefeli Nikiforos and Maria Malakopoulou and Adamantia G. Pateli},
url = {https://www.researchgate.net/publication/344254063_Success_Factors_for_Crowd-funding_Campaigns_with_Machine_Learning_Techniques},
year = {2020},
date = {2020-01-01},
booktitle = {ICTO 2020: Smart Technologies for an Inclusive World},
address = {Paris, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Masters Theses
Nikiforos, M. N. (2021). Deep learning techniques in social web text classification. [Master thesis, Department of Informatics, Ionian University].
@mastersthesis{nikiforos2021deep,
title = {Deep learning techniques in social web text classification},
author = {Maria Nefeli Nikiforos},
year = {2021},
date = {2021-01-01},
address = {Corfu, Greece},
school = {Department of Informatics, Ionian University},
note = {MSc Research Directions in Information Technology. Supervisor: Katia Lida Kermanidis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
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Nikiforos, M. N. (2019). Development of a Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web. [Master thesis, Department of Informatics, Ionian University].
@mastersthesis{nikiforos2019development,
title = {Development of a Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web},
author = {Maria Nefeli Nikiforos},
year = {2019},
date = {2019-01-01},
address = {Corfu, Greece},
school = {Department of Informatics, Ionian University},
note = {BSc thesis. Supervisor: Katia Lida Kermanidis},
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pubstate = {published},
tppubtype = {mastersthesis}
}