Publikationen
Nazemi, Kawa Artificial Intelligence in Visual Analytics Proceedings Article In: Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award, S. 230 - 237, IEEE CPS, 2023. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Visual Analytical Reasoning, Visual analytics, Visual tasks Secco, Cristian A.; Sina, Lennart B.; Blazevic, Midhad; Nazemi, Kawa Visual Analytics for Forecasting Technological Trends from Text Proceedings Article In: Proceedings of the 27th International Conference Information Visualisation (IV2023), S. 251-258, IEEE CPS, 2023. Abstract | Links | BibTeX | Schlagwörter: Sina, Lennart B.; Secco, Cristian A.; Blazevic, Midhad; Nazemi, Kawa Visual Analytics for Corporate Foresight - A Conceptual Approach Proceedings Article In: Proceedings of the 27th International Conference Information Visualisation (IV2023), S. 244-250, IEEE CPS, 2023. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Visual analytics Blazevic, Midhad; Sina, Lennart B.; Secco, Cristian A.; Nazemi, Kawa Recommendations in Visual Analytics - An Analytical Approach for Elaboration in Science Proceedings Article In: Proceedings of the 27th International Conference Information Visualisation (IV 2023), S. 259- 267, IEEE CPS, 2023. Abstract | Links | BibTeX | Schlagwörter: Artifical Intelligence Banissi, Ebad; Siirtola, Harri; Ursyn, Anna; Pires, João Moura; Datia, Nuno; Nazemi, Kawa; Kovalerchuk, Boris; Andonie, Razvan; Nakayama, Minoru; Temperini, Marco; Sciarrone, Filippo; Nguyen, Quang Vinh; Mabakane, Mabule Samuel; Rusu, Adrian; Cvek, Urska; Trutschl, Marjan; Mueller, Heimo; Francese, Rita; Boua-li, Fatma; Venturini, Gilles (Hrsg.) Proceedings of 2023 27th International Conference Information Visualisation Konferenzberichte 2023, ISBN: 979-8-3503-4161-4. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Data Analytics, Data Science, Visual analytics, Visual Knowledge Discovery Sina, Lennart B.; Secco, Cristian A.; Blazevic, Midhad; Nazemi, Kawa Hybrid Forecasting Methods - A Systematic Review Artikel In: Electronics, Bd. 12, Nr. 7, 2023. Abstract | Links | BibTeX | Schlagwörter: hybrid forecsting, PRISMA study Blazevic, Midhad; Sina, Lennart B.; Secco, Cristian A.; Nazemi, Kawa In: Electronics, Bd. 12, Nr. 7, 2023, ISSN: 2079-9292. Abstract | Links | BibTeX | Schlagwörter: latex editor, publication recommendations, recommendation systems, similarity algorithms, topics modeling Kaupp, Lukas; Nazemi, Kawa; Humm, Bernhard In: Electronics, Bd. 11, Nr. 23, 2022, ISSN: 2079-9292. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Case study, Data Analytics, Data Science, Data visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics Kaupp, Lukas; Humm, Bernhard; Nazemi, Kawa; Simons, Stephan In: Sensors, Bd. 22, Nr. 21, 2022, ISSN: 1424-8220. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Machine Leanring, Machine learning, smart factory, Smart manufacturing Banissi, Mark W. McK. Bannatyne Anna Ursyn Ebad; Geroimenko, Vladimir (Hrsg.) Proceedings of 2022 26th International Conference Information Visualisation (IV) Konferenzberichte 2022, ISBN: 978-1-6654-9007-8. Abstract | Links | BibTeX | Schlagwörter: Nazemi, Kawa; Feiter, Tim; Sina, Lennart B.; Burkhardt, Dirk; Kock, Alexander Visual Analytics for Strategic Decision Making in Technology Management Buchkapitel In: Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Hrsg.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, S. 31–61, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Abstract | Links | BibTeX | Schlagwörter: Blazevic, Midhad; Sina, Lennart B.; Nazemi, Kawa Visual Collaboration - An Approach for Visual Analytical Collaborative Research Proceedings Article In: 2022 26th International Conference Information Visualisation (IV), S. 293 - 299, IEEE, 2022. Abstract | Links | BibTeX | Schlagwörter: Kaupp, Lukas; Nazemi, Kawa; Humm, Bernhard Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model Buchkapitel In: Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Hrsg.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, S. 403–436, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Hrsg.) Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery Buch SPRINGER NATURE, 2022, ISBN: 3030931188. Abstract | Links | BibTeX | Schlagwörter: Kovalerchuk, Boris; Andonie, Răzvan; Datia, Nuno; Nazemi, Kawa; Banissi, Ebad Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions Buchkapitel In: Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Hrsg.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, S. 1–27, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Abstract | Links | BibTeX | Schlagwörter: Kaupp, Lukas; Nazemi, Kawa; Humm, Bernhard Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model Buchkapitel In: Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Hrsg.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, S. 403–436, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery Banissi, Ebad; Ursyn, Anna; Bannatyne, Mark W. McK.; Pires, João Moura; Datia, Nuno; Huang, Mao Lin Huang Weidong; Nguyen, Quang Vinh; Nazemi, Kawa; Kovalerchuk, Boris; Nakayama, Minoru; Counsell, John; Agapiou, Andrew; Khosrow-shahi, Farzad; Chau, Hing-Wah; Li, Mengbi; Laing, Richard; Bouali, Fatma; Venturini, Gilles; Temperini, Marco; Sarfraz, Muhammad (Hrsg.) Proceedings of 2021 25th International Conference Information Visualisation (IV) Konferenzberichte IEEE, New York, USA, 2021, ISBN: 978-1-6654-3827-8. Abstract | Links | BibTeX | Schlagwörter: Information visualization Nazemi, Kawa; Burkhardt, Dirk; Kock, Alexander In: Multimedia Tools and Applications, Bd. 1198, 2021, ISSN: 1573-7721, (Springer Nature). Abstract | Links | BibTeX | Schlagwörter: Emerging Trend Identification, Information visualization, Innovation Management, Interaction Design, Multimedia Interaction, Technology Management, Visual analytics, Visual Trend Analytics Blazevic, Midhad; Sina, Lennart B.; Burkhardt, Dirk; Siegel, Melanie; Nazemi, Kawa Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data Proceedings Article In: 2021 25th International Conference Information Visualisation (IV), S. 211-217, IEEE , 2021. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Collaboration, Information visualization, Similarity, Visual analytics Schütz, Mina; Schindler, Alexander; Siegel, Melanie; Nazemi, Kawa Automatic Fake News Detection with Pre-trained Transformer Models Proceedings Article In: Bimbo, Alberto Del; Cucchiara, Rita; Sclaroff, Stan; Farinella, Giovanni Maria; Mei, Tao; Bertini, Marco; Escalante, Hugo Jair; Vezzani, Roberto (Hrsg.): Pattern Recognition. ICPR International Workshops and Challenges, S. 627–641, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-68787-8. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, datamining, Decision Making, Fake News, Machine Leanring, Transformer2023
@inproceedings{Nazemi2023,
title = {Artificial Intelligence in Visual Analytics},
author = {Kawa Nazemi},
doi = {10.1109/IV60283.2023.00048},
year = {2023},
date = {2023-11-30},
urldate = {2023-11-30},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award},
journal = {Proceedings of the 27th International Conference Information Visualisation (IV2023) - Best Paper Award-},
pages = {230 - 237},
publisher = {IEEE CPS},
abstract = {Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics can be used to interpret black-box artificial intelligence models and provide a visual explanation of those models. In this paper, we provide an overview of the state-of-the-art of artificial intelligence techniques used in Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics.},
keywords = {Artificial Intelligence, Visual Analytical Reasoning, Visual analytics, Visual tasks},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Secco2023,
title = {Visual Analytics for Forecasting Technological Trends from Text},
author = {Cristian A. Secco and Lennart B. Sina and Midhad Blazevic and Kawa Nazemi},
doi = {10.1109/IV60283.2023.00051},
year = {2023},
date = {2023-11-30},
urldate = {2023-11-30},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023)},
pages = {251-258},
publisher = {IEEE CPS},
abstract = {Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows reacting to market, political, and societal changes and challenges at an appropriate time. In our previous works, we presented approaches for the early identification and analysis of emerging trends. Although our previous approaches are detecting emerging trends appropriately, they lack the ability to predict the potential future course of a trend or technology. We present in this work a novel Visual Analytics approach for forecasting emerging trends that combines interactive visualizations with machine learning techniques and statistical approaches to detect, analyze, and predict trends from textual data. We extend our previous work on analyzing technological trends from text and propose an advanced approach that includes forecasting through hybrid techniques consisting of neural networks and established statistical methods. Our approach offers insights from enormous data sets and the potential future course of trends based on their occurrence in textual data. We contribute with a novel approach for identifying and forecasting trends, a hybrid forecasting method to predict trends from text, and interactive visualization techniques on
macro level, micro level, and monitoring topics of interest.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
macro level, micro level, and monitoring topics of interest.@inproceedings{SinaIV2023,
title = {Visual Analytics for Corporate Foresight - A Conceptual Approach},
author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},
doi = {10.1109/IV60283.2023.00050},
year = {2023},
date = {2023-11-29},
urldate = {2023-11-29},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023)},
pages = {244-250},
publisher = {IEEE CPS},
abstract = {Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potential scenarios, and creating strategies to address them to ensure long-term success and sustainability. Visual Analytics approaches have been introduced to cover parts of the Corporate Foresight process. These concepts present different approaches to integrate machine learning methods and artificial intelligence with interactive visualizations to solve tasks such as identifying emerging trends. A holistic concept for synthesizing Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics approach that covers the main aspects of Corporate Foresight by including strategic management and considers different organizational forms. Our model goes beyond the state-of-the-art by providing, besides foresight also, hindsight and insight. Our main contributions are the revised Visual Analytics model and its proof of concept through implementation as a web-based system with real data.},
keywords = {Artificial Intelligence, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{blaz2023,
title = {Recommendations in Visual Analytics - An Analytical Approach for Elaboration in Science},
author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},
doi = {10.1109/IV60283.2023.00052},
year = {2023},
date = {2023-11-24},
urldate = {2023-11-24},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV 2023)},
pages = {259- 267},
publisher = {IEEE CPS},
abstract = {The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using recommendations in various application domains, the full potential of recommendation systems is not yet fully utilized. Particularly, there are missing approaches that combine interactive visualizations with recommendation systems to enable an analytical investigation of the current state of technology and science. We, therefore, propose in this work a novel Visual Analytics approach that integrates recommendation methods as the model and provides a seamless integration of both interactive visualizations and recommendation systems. We utilize MAE and RMSE metrics and human validation to identify the best approach out of eight approaches that differ in vectorization and similarity algorithms to recommend scientific items. We contribute novel approaches for recommending scientific publications, venues, and projects, based on comparing traditional and deep-learning-based recommendation approaches. Furthermore, we propose a Visual Analytics approach that uses recommendation methods for analytical elaboration. This work shows the potential of integrating recommendation systems into scientific research and identifies potential future directions for improving the proposed model.},
keywords = {Artifical Intelligence},
pubstate = {published},
tppubtype = {inproceedings}
}
@proceedings{Banissi2023,
title = {Proceedings of 2023 27th International Conference Information Visualisation},
editor = {Ebad Banissi and Harri Siirtola and Anna Ursyn and João Moura Pires and Nuno Datia and Kawa Nazemi and Boris Kovalerchuk and Razvan Andonie and Minoru Nakayama and Marco Temperini and Filippo Sciarrone and Quang Vinh Nguyen and Mabule Samuel Mabakane and Adrian Rusu and Urska Cvek and Marjan Trutschl and Heimo Mueller and Rita Francese and Fatma Boua-li and Gilles Venturini},
doi = {10.1109/IV60283.2023.00001},
isbn = {979-8-3503-4161-4},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
issue = {IV2023},
abstract = {Do aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era? Does the infrastructure of any information-dependent society rely on the quality of data, information, and analysis of such entities from past and present and projected future activities and, most importantly, how it is intended to be applied? Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are state-of-the-art developments that effectively enhance understanding of these well-established drivers. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From raw data to knowledge, processing the relationship between these phases has added new impetus to understanding and communicating these. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to knowledge discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains to simply storytelling through data. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the processes, from raw data to the knowledge acquisition stage. But there is a new twist: fast-developing generative AI with ever-increasing access to data outsmarting humans in decision-making. A new evolutionary step in the human journey, no doubt.},
keywords = {Artificial Intelligence, Data Analytics, Data Science, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {proceedings}
}
@article{SSB*23,
title = {Hybrid Forecasting Methods - A Systematic Review},
author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},
url = {https://www.mdpi.com/2079-9292/12/9/2019},
doi = {10.3390/electronics12092019},
year = {2023},
date = {2023-04-27},
urldate = {2023-04-27},
journal = {Electronics},
volume = {12},
number = {7},
abstract = {Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization.},
keywords = {hybrid forecsting, PRISMA study},
pubstate = {published},
tppubtype = {article}
}
@article{electronics12071699,
title = {Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System},
author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},
url = {https://www.mdpi.com/2079-9292/12/7/1699},
doi = {10.3390/electronics12071699},
issn = {2079-9292},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Electronics},
volume = {12},
number = {7},
abstract = {Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities.},
keywords = {latex editor, publication recommendations, recommendation systems, similarity algorithms, topics modeling},
pubstate = {published},
tppubtype = {article}
}
2022
@article{electronics11233942,
title = {Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories},
author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Kawa Nazemi and Egils Ginters and Michael Bazant},
url = {https://www.mdpi.com/2079-9292/11/23/3942},
doi = {10.3390/electronics11233942},
issn = {2079-9292},
year = {2022},
date = {2022-11-01},
urldate = {2022-01-01},
journal = {Electronics},
volume = {11},
number = {23},
abstract = {Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.},
keywords = {Artificial Intelligence, Case study, Data Analytics, Data Science, Data visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics},
pubstate = {published},
tppubtype = {article}
}
@article{s22218259,
title = {Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis},
author = {Lukas Kaupp and Bernhard Humm and Kawa Nazemi and Stephan Simons},
url = {https://www.mdpi.com/1424-8220/22/21/8259},
doi = {10.3390/s22218259},
issn = {1424-8220},
year = {2022},
date = {2022-10-01},
urldate = {2022-01-01},
journal = {Sensors},
volume = {22},
number = {21},
abstract = {Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.},
keywords = {Artificial Intelligence, Machine Leanring, Machine learning, smart factory, Smart manufacturing},
pubstate = {published},
tppubtype = {article}
}
@proceedings{nokey,
title = {Proceedings of 2022 26th International Conference Information Visualisation (IV)},
editor = {Mark W. McK. Bannatyne Anna Ursyn Ebad Banissi and Vladimir Geroimenko},
doi = {10.1109/IV56949.2022.00001},
isbn = {978-1-6654-9007-8},
year = {2022},
date = {2022-08-08},
urldate = {2022-08-08},
abstract = {Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any information-dependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it is intended to be applied. Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are just a few state-of-the-art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualization forum, compiled for the 26th conference on the Information Visualization incorporating the following: Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2022 provides the opportunity to resonate with many international and collaborative research projects, lectures, and panel discussions from distinguished speakers that channel the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social Networking impact on the social, cultural, and heritage aspects of life, and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 75-plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the
scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts that challenge our beliefs and further encourage our adventure of innovation.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
This collection of papers on this year's information visualization forum, compiled for the 26th conference on the Information Visualization incorporating the following: Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2022 provides the opportunity to resonate with many international and collaborative research projects, lectures, and panel discussions from distinguished speakers that channel the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social Networking impact on the social, cultural, and heritage aspects of life, and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 75-plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the
scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts that challenge our beliefs and further encourage our adventure of innovation.@inbook{Nazemi2022,
title = {Visual Analytics for Strategic Decision Making in Technology Management},
author = {Kawa Nazemi and Tim Feiter and Lennart B. Sina and Dirk Burkhardt and Alexander Kock},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://doi.org/10.1007/978-3-030-93119-3_2},
doi = {10.1007/978-3-030-93119-3_2},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {31--61},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Strategic foresight, corporate foresight, and technology management enable firms to detect discontinuous changes early and develop future courses for a more sophisticated market positioning. The enhancements in machine learning and artificial intelligence allow more automatic detection of early trends to create future courses and make strategic decisions. Visual Analytics combines methods of automated data analysis through machine learning methods and interactive visualizations. It enables a far better way to gather insights from a vast amount of data to make a strategic decision. While Visual Analytics got various models and approaches to enable strategic decision-making, the analysis of trends is still a matter of research. The forecasting approaches and involvement of humans in the visual trend analysis process require further investigation that will lead to sophisticated analytical methods. We introduce in this paper a novel model of Visual Analytics for decision-making, particularly for technology management, through early trends from scientific publications. We combine Corporate Foresight and Visual Analytics and propose a machine learning-based Technology Roadmapping based on our previous work.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{BSN22,
title = {Visual Collaboration - An Approach for Visual Analytical Collaborative Research},
author = {Midhad Blazevic and Lennart B. Sina and Kawa Nazemi},
doi = {10.1109/IV56949.2022.00057},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 26th International Conference Information Visualisation (IV)},
pages = {293 - 299},
publisher = {IEEE},
abstract = {Studies have shown that collaboration in scientific fields is rising and considered enormously important. However, collaboration has proved to be challenging for various reasons, among others, the requirements for human-machine workflows. The importance of scientific collaboration lies in the complexity of the challenges that are faced today. The more complex the challenge, the more scientists should work together. The current form of collaboration in the scientific community is not as intelligent as it should be. Scientists have to multitask with various applications, often losing cognitive focus. Collaboration itself is very nearsighted as it is usually conducted not solely based on expertise but instead on social or local networks. We introduce a single-source visual collaboration approach based on learning methods in this work. We use machine learning and natural language processing approaches to improve the traditional research and development process and create a system that facilitates and encourages collaboration based on expertise, enhancing the research collaboration process in many ways. Our approach combines collaborative Visual Analytics with enhanced collaboration techniques to support researchers from different disciplines.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inbook{Kaupp2022b,
title = {Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model},
author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
doi = {10.1007/978-3-030-93119-3_16},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {403–436},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.},
keywords = {Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {inbook}
}
@book{2022,
title = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://link.springer.com/book/9783030931186, Springer Link},
isbn = {3030931188},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {SPRINGER NATURE},
series = {Studies in Computational Intelligence},
abstract = {This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain.
This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations.
The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.},
key = {SP2022},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations.
The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.@inbook{Kovalerchuk2022b,
title = {Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions},
author = {Boris Kovalerchuk and Răzvan Andonie and Nuno Datia and Kawa Nazemi and Ebad Banissi},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://doi.org/10.1007/978-3-030-93119-3_1},
doi = {10.1007/978-3-030-93119-3_1},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {1--27},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Integrating artificial intelligence (AI) and machine learning (ML) methods with interactive visualization is a research area that has evolved for years. With the rise of AI applications, the combination of AI/ML and interactive visualization is elevated to new levels of sophistication and has become more widespread in many domains. Such application drive has led to a growing trend to bridge the gap between AI/ML and visualizations. This chapter summarizes the current research trend and provides foresight to future research direction in integrating AI/ML and visualization. It investigates different areas of integrating the named disciplines, starting with visualization in ML, visual analytics, visual-enabled machine learning, natural language processing, and multidimensional visualization and AI to illustrate the research trend towards visual knowledge discovery. Each section of this chapter presents the current research state along with problem statements or future directions that allow a deeper investigation of seamless integration of novel AI methods in interactive visualizations.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Kaupp2022,
title = {Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model},
author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://doi.org/10.1007/978-3-030-93119-3_16},
doi = {10.1007/978-3-030-93119-3_16},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {403--436},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.},
keywords = {Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {inbook}
}
2021
@proceedings{Banissi2021,
title = {Proceedings of 2021 25th International Conference Information Visualisation (IV)},
editor = {Ebad Banissi and Anna Ursyn and Mark W. McK. Bannatyne and João Moura Pires and Nuno Datia and Mao Lin Huang Weidong Huang and Quang Vinh Nguyen and Kawa Nazemi and Boris Kovalerchuk and Minoru Nakayama and John Counsell and Andrew Agapiou and Farzad Khosrow-shahi and Hing-Wah Chau and Mengbi Li and Richard Laing and Fatma Bouali and Gilles Venturini and Marco Temperini and Muhammad Sarfraz},
doi = {10.1109/IV53921.2021.00001},
isbn = {978-1-6654-3827-8},
year = {2021},
date = {2021-10-28},
urldate = {2021-10-28},
booktitle = {Information Visualisation: AI & Analytics, Biomedical Visualization, Builtviz, and Geometric Modelling & Imaging},
pages = {1-775},
publisher = {IEEE},
address = {New York, USA},
abstract = {Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any informationdependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it intended to be applied. Information Visualization, Analytics, Machine Learning, Artificial Intelligence and Application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the domain of application and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into the processes in all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualization forum, compiled for the 25th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2021 provides the opportunity to resonate with many international and collaborative research projects and lectures and panel discussion from distinguished speakers that channels the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social-Networking impact the social, cultural, and heritage aspects of life and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 75 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts, challenges our beliefs, and further encourages our adventure of innovation.},
keywords = {Information visualization},
pubstate = {published},
tppubtype = {proceedings}
}
This collection of papers on this year's information visualization forum, compiled for the 25th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2021 provides the opportunity to resonate with many international and collaborative research projects and lectures and panel discussion from distinguished speakers that channels the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social-Networking impact the social, cultural, and heritage aspects of life and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 75 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts, challenges our beliefs, and further encourages our adventure of innovation.@article{Nazemi2021,
title = {Visual analytics for Technology and Innovation Management: An interaction approach for strategic decisionmaking},
author = {Kawa Nazemi and Dirk Burkhardt and Alexander Kock},
url = {https://link.springer.com/content/pdf/10.1007/s11042-021-10972-3.pdf, Open Access PDF},
doi = {10.1007/s11042-021-10972-3},
issn = {1573-7721},
year = {2021},
date = {2021-05-20},
urldate = {2021-05-20},
journal = {Multimedia Tools and Applications},
volume = {1198},
abstract = {The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection.},
note = {Springer Nature},
keywords = {Emerging Trend Identification, Information visualization, Innovation Management, Interaction Design, Multimedia Interaction, Technology Management, Visual analytics, Visual Trend Analytics},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{9582711,
title = {Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data},
author = {Midhad Blazevic and Lennart B. Sina and Dirk Burkhardt and Melanie Siegel and Kawa Nazemi},
url = {https://ieeexplore.ieee.org/document/9582711, IEEE Xplore},
doi = {10.1109/IV53921.2021.00041},
year = {2021},
date = {2021-03-01},
urldate = {2021-03-01},
booktitle = {2021 25th International Conference Information Visualisation (IV)},
pages = {211-217},
publisher = {IEEE },
abstract = {Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similarity-based approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics.},
keywords = {Artificial Intelligence, Collaboration, Information visualization, Similarity, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10.1007/978-3-030-68787-8_45,
title = {Automatic Fake News Detection with Pre-trained Transformer Models},
author = {Mina Schütz and Alexander Schindler and Melanie Siegel and Kawa Nazemi},
editor = {Alberto Del Bimbo and Rita Cucchiara and Stan Sclaroff and Giovanni Maria Farinella and Tao Mei and Marco Bertini and Hugo Jair Escalante and Roberto Vezzani},
url = {https://link.springer.com/chapter/10.1007/978-3-030-68787-8_45, Full PDF},
doi = {10.1007/978-3-030-68787-8_45},
isbn = {978-3-030-68787-8},
year = {2021},
date = {2021-02-21},
booktitle = {Pattern Recognition. ICPR International Workshops and Challenges},
pages = {627--641},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The automatic detection of disinformation and misinformation has gained attention during the last years, since fake news has a critical impact on democracy, society, and journalism and digital literacy. In this paper, we present a binary content-based classification approach for detecting fake news automatically, with several recently published pre-trained language models based on the Transformer architecture. The experiments were conducted on the FakeNewsNet dataset with XLNet, BERT, RoBERTa, DistilBERT, and ALBERT and various combinations of hyperparameters. Different preprocessing steps were carried out with only using the body text, the titles and a concatenation of both. It is concluded that Transformers are a promising approach to detect fake news, since they achieve notable results, even without using a large dataset. Our main contribution is the enhancement of fake news' detection accuracy through different models and parametrizations with a reproducible result examination through the conducted experiments. The evaluation shows that already short texts are enough to attain 85% accuracy on the test set. Using the body text and a concatenation of both reach up to 87% accuracy. Lastly, we show that various preprocessing steps, such as removing outliers, do not have a significant impact on the models prediction output.},
keywords = {Artificial Intelligence, datamining, Decision Making, Fake News, Machine Leanring, Transformer},
pubstate = {published},
tppubtype = {inproceedings}
}