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Explainable AI chatbots towards XAI ChatGPT: A review

Heliyon 2025年第2期11卷 e42077页

作者： Kovari, Attila Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eszterhazy ter 1 Eger 3300 Hungary Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Tancsics M. 1/A Dunaujvaros 2400 Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki u. 10 Kecskemét 6000 Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Óbuda University Bécsi street 96/B Budapest 1034 Hungary

Advances in artificial intelligence (AI) have had a major impact on natural language processing (NLP), even more so with the emergence of large-scale language models like ChatGPT. This paper aims to provide a critical review of explainable AI (XAI) methodologies for AI chatbots, with a particular focus on ChatGPT. Its main objectives are to investigate the applied methods that improve the explainability of AI chatbots, identify the challenges and limitations within them, and explore future research directions. Such goals emphasize the need for transparency and interpretability of AI systems to build trust with users and allow for accountability. While integrating such interdisciplinary methods, such as hybrid methods combining knowledge graphs with ChatGPT, enhancing explainability, they also highlight industry needs for explainability and user-centred design. This will be followed by a discussion of the balance between explainability and performance, then the role of human judgement, and finally the future of verifiable AI. These are the avenues through which insights can be used to guide the development of transparent, reliable and efficient AI chatbots. © 2025 The Author

关键词： AI chatbots ChatGPT Controllable AI Explainable AI (XAI) Natural language processing (NLP) Transparency

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Ranking matters: Does the new format select the best teams for the knockout phase in the UEFA Champions League?

arXiv

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arXiv 2025年

作者： Csató, László Devriesere, Karel Goossens, Dries Gyimesi, András Lambers, Roel Spieksma, Frits Laboratory on Engineering and Management Intelligence Research Group of Operations Research and Decision Systems Budapest Hungary Institute of Operations and Decision Sciences Department of Operations Research and Actuarial Sciences Hungary Ghent University Department of Business Informatics and Operations Management Belgium FlandersMake@UGent – core lab CVAMO Ghent Belgium University of Pécs Hungary Eindhoven University of Technology Department of Mathematics and Computer Science Netherlands

Starting in the 2024/25 season, the Union of European Football Associations (UEFA) has fundamentally changed the format of its club competitions: the group stage has been replaced by a league phase played by 36 teams in an incomplete round robin format. This makes ranking the teams based on their results challenging because teams play against different sets of opponents, whose strengths vary. In this research note, we apply several well-known ranking methods for incomplete round robin tournaments to the 2024/25 UEFA Champions League league phase. Our results show that it is doubtful whether the currently used point-based system provides the best ranking of the *** Codes 15A06, 91B14 Copyright © 2025, The Authors. All rights reserved.

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Generic Drone Simulator: Design, Development and User Testing in a Virtual Search and Rescue Mission

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IEEE computer Graphics and Applications 2025年 PP卷 PP页

作者： Pappas, Georgios Lappas, Dimitrios Karampelas, Panagiotis Siegel, Joshua E. Templalexis, Ioannis Stavrou, Stavros Krishnan, Sriram Open University of Cyprus Lab of Educational Material and Methodology Latsia Nicosia Cyprus University of the Aegean Learning Technology and Educational Engineering Laboratory Rhodes Greece Hellenic Air Force Academy Department of Informatics & Computers Dekelia Attiki Greece Michigan State University Department of Computer Science and Engineering East LansingMI United States Hellenic Air Force Academy Department of Thermodynamics Propulsion and Power Systems Dekelia Attiki Greece Open University of Cyprus Cybersecurity and Telecommunications Research Lab Latsia Nicosia Cyprus Indian Institute of Technology The Energy Consortium Madras India

This study addresses the need for scenario-specific drone simulators to enhance research, education, pilot training, and performance evaluation across diverse mission contexts. A gamified simulator was developed for the joint master's degree program "Security and Defence" between the Open University of Cyprus (OUC) and the Hellenic Air Force Academy (HAFA), with a focus on Search and Rescue mission training and planning. The simulator balances interface design, physics accuracy, and environmental realism to support specialized professional training and research. A survey-based evaluation involving 57 participants –pilots, engineers, and air traffic controllers –demonstrated the tool's ability to provide realistic experiences for trained pilots and meaningful learning opportunities for novices. Statistical analysis revealed significant correlations between gaming familiarity, task completion times, and participant expertise. © 1981-2012 IEEE.

关键词： Drones

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SLC6A7 as a Novel Prognostic and Diagnostic Biomarker in the Ovarian Serous Cystadenocarcinoma by Bioinformatics Approaches

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Indian Journal of Gynecologic Oncology 2025年第2期23卷 1-11页

作者： Mahmoudabadi, Ali Ghaderi, Shiva Aldaghi, Tahmineh Fathi, Sina Nazari, Elham Department of Medical Genetics Faculty of Medicine Kerman University of Medical Sciences Kerman Iran Department of Computer Engineers Faculty of Engineering AL-Zahra University Tehran Iran Institute of Biophysics and Informatics First Faculty of Medicine Charles University Prague Czech Republic Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Proteomics Research Center Faculty of Paramedical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran

Ovarian serous cystadenocarcinoma (OSC), accounting for approximately 90% of ovarian cancer cases, poses a significant global health issue due to the lack of effective screening methods. SLC6A7, a member of the solute carrier family, has emerged as a potential biomarker in cancer research, though its role in OSC remains underexplored. RNA expression profiling was performed using data from ovarian cancer patients in the TCGA database. Differentially expressed genes (DEGs) were identified using the Limma package in R. Prognostic biomarkers, and a risk model was developed using the Cox proportional hazard regression model and Kaplan–Meier survival analysis. Molecular pathways, protein–protein interaction (PPI) networks, and clinical and disease data correlations were further analyzed. Key findings include the identification of 393 DEGs, with eight genes (AVP, C8B, NACAP1, NHLH2, PTTG2, RRH, UBE2NL, and SLC6A7) overlapping as prognostic markers. SLC6A7 exhibited prognostic relevance (hazard ratio [HR] = 1.32, 95% CI 1.10–1.58, P = 0.0027) and diagnostic potential (AUC = 0.854). Enrichment analysis linked these genes to histone modifications and fatty acid omega oxidation pathways. Combining DEGs and prognostic genes provides insights into potential biomarkers for OSC. These findings suggest the possibility of early diagnosis, targeted therapy, and improved patient outcomes. SLC6A7 shows promise as a prognostic and diagnostic biomarker in OSC, though further experimental validation is needed to confirm its clinical utility.

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A SIP spatial audio server for the EVE platform

A SIP spatial audio server for the EVE platform

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3rd International Conference on computer Graphics Theory and Applications

作者： Bouras, Ch. Triglianos, V. Tsiatsos, Tb. Research Academic Computer Technology Institute (CTI) Greece Computer Engineering and Informatics Department (CEID) University of Patras Greece Department of Informatics Aristotle University of Thessaloniki Research Academic Greece

ISBN: (纸本)9789898111203

When it comes to 3D Virtual Environments it is well known that 3D sound is of great importance to the whole interactive experience. The percept of sound is a major counterpart for the eyesight, since it describes the location, the momentum and the attitude towards the listener, of each surrounding entity. The sound can offer precision of spatial perception that cannot be achieved by the eye itself Thus the support of 3D surround sound of high fidelity is mandatory for a 3D Virtual platform. The evolution of Internet telephony led to the introduction of new session establishment and management protocols. The most important of them, the Session Initiation Protocol (SIP), is a robust, lightweight reliable and fast application-layer control (signaling) protocol that is highly adopted for creating, modifying and terminating sessions. This protocol can be of extreme importance in establishing audio sessions for multi-user platforms. This paper presents the work done for developing a SIP 3D spatial audio server for a multi-user virtual environments platform, called EVE, in order to support 3D spatial audio.

关键词： virtual reality multimedia collaboration SIP spatial 3D audio networked virtual environments multimedia systems architecture applications

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： Artificial intelligence Big data Data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information

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Cyber-Physical Systems 2025年

作者： Byeon, Haewon AlMahadin, Ghayth Bsoul, Qusay Quraishi, Aadam Soni, Mukesh Khan, Shahi Raza Shabaz, Mohammad Department of AI and Software Inje University Gimhae Korea Republic of Data Science Department College of Information Technology Mutah University Mutah Jordan Department of Cybersecurity College of Computer Sciences and Informatics Amman Arab University Amman Jordan M.D. Research Intervention Treatment Institute HoustonTX United States Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Pune India MJMC Centre for Media and Mass Communication Jamia Hamdard Delhi India Model Institute of Engineering and Technology Jammu India

This paper proposes an improved cyber-physical false information detection model based on cross-modal ambiguous learning. This work introduced the IC2 LFD model with an emphasis on ‘text-image’ ambiguity, focusing on the main goal of intelligent multi-cross modal data fake information identification. The experiment revealed that the significance of text and images differs in intelligent multi-cross modal data fake information detection. The effectiveness of the model is verified on the twitter dataset, showing a 6% accuracy improvement compared to the baseline model and a 1.6% performance improvement over detection methods without dynamic weight allocation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Policies for content filtering in educational networks: The case of Greece

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Telematics and informatics 2003年第1期20卷 71-95页

作者： Avgoulea, M. Bouras, C. Paraskevas, M. Stathakopoulos, G. Department of Computer Engineering and Informatics Computer Technology Institute University of Patras Riga Feraiou 61 26221 Patras Greece

The Internet removes all barriers to sending and receiving information. An increasing number of nations connect their schools on the Internet as an acknowledgment of its importance in education. Despite its undeniable usefulness, it also has certain perils. This study specifies and evaluates these, finding the technologies that are currently available to address filtering issues and comparing them. We present our proposed solution for the Greek School Network, and illustrate to what extent our solution successfully addresses the issues discussed. © 2002 Elsevier Science Ltd. All rights reserved.

关键词： Internet

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Game-Theoretic Incentive Mechanism for Blockchain-Based Federated Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Tang, Wenzheng Liu, Erwu Ni, Wei Qu, Xinyu Huang, Butian Li, Kezhi Niyato, Dusit Jamalipour, Abbas Tongji University College of Electronics and Information Engineering Shanghai201804 China Tongji University Department of Ophthalmology Tongji Hospital School of Medicine China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia Hangzhou Yunphant Network Technology Co. Ltd. Hangzhou China University College London Institute of Health Informatics LondonWC1E 6BT United Kingdom College of Computing and Data Science 639798 Singapore University of Sydney School of Electrical and Computer Engineering NSW2006 Australia

Blockchain-based federated learning (BFL) has gained attention for its potential to establish decentralized trust. While existing research primarily focuses on personalized frameworks for various applications, essential aspects including incentive mechanisms—critical for ensuring stable system operation—remain under-explored. To bridge this gap, we propose a game-theoretic incentive mechanism designed to foster active participation in BFL tasks. Specifically, we model a BFL system comprising a model owner (MO), i.e., task publisher, multiple miners, and training terminals, framing their interactions through two-tier Stackelberg games. In the first-tier game, the MO designs reward strategies to incentivize training terminals to contribute more data, enhancing model accuracy. The second-tier game introduces a multi-leader multi-follower Stackelberg game, enabling miners to set model packaging prices based on competitors' strategies and anticipated user behavior. By deriving the Stackelberg equilibrium, we identify optimal strategies for all participants, leading to an incentive mechanism balancing individual interests with overall performance. Compared to its benchmarks, our incentive mechanism offers 5.8% and 53.4% higher utilities in the two games compared to its alternatives, accelerating convergence and improving accuracy. © 2002-2012 IEEE.

关键词： Federated learning

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