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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Konstantina-Helen Tsarapatsani Antonis Sakellarios Vasilis D Tsakanikas Henrik Rudolf Hans J. Trampisch Vasileios C. Pezoulas George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH) Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina GR 45110 Ioannina Greece University Medical Center Rostock Institute for Biostatistics and Informatics in Medicine and Ageing Research Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis’s results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided.

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DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring

arXiv

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arXiv 2021年

作者： Dong, Jiangxin Roth, Stefan Schiele, Bernt School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Germany Department of Computer Science TU Darmstadt Germany

We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning. In contrast to existing methods that deblur the image directly in the standard image space, we propose to perform an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale cascaded feature refinement module then predicts the deblurred image from the deconvolved deep features, progressively recovering detail and small-scale structures. The proposed model is trained in an end-to-end manner and evaluated on scenarios with simulated Gaussian noise, saturated pixels, or JPEG compression artifacts as well as real-world images. Moreover, we present detailed analyses of the benefit of the feature-based Wiener deconvolution and of the multi-scale cascaded feature refinement as well as the robustness of the proposed approach. Our extensive experimental results show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts and quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin. Copyright © 2021, The Authors. All rights reserved.

关键词： Deconvolution

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Explainable AI chatbots towards XAI ChatGPT: A review

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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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Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review

arXiv

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arXiv 2023年

作者： Azad, Reza Kazerouni, Amirhossein Heidari, Moein Aghdam, Ehsan Khodapanah Molaei, Amirali Jia, Yiwei Jose, Abin Roy, Rijo Merhof, Dorit Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Electrical Engineering Shahid Beheshti University Tehran Iran School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://***/mindflow-institue/Awesome-Transformer. © 2023, CC BY.

关键词： Deep neural networks

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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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Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-based Classifier

Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-b...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Supantha Das Arnab Hazra Soumen Kumar Pati Soumadip Ghosh Saurav Mallik Suharta Banerjee Ayan Mukherji Aimin Li Zhongming Zhao Department of Information Technology Academy of Technology Hooghly West Bengal India Department of Computer Science and Engineering Future Institute of Technology Kolkata West Bengal India Department of Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal Haringhata Nadia West Bengal India Department of Computer Science and Engineering Sister Nivedita University New Town Kolkata West Bengal India Department of Environmental Health Harvard T H Chan School of Public Health Boston MA USA Department of Computer Science and Engineering Meghnad Saha of Institute of Technology Kolkata West Bengal India Department of Computer Science and Engineering Pailan College of Management & Technology Kolkata West Bengal India School of Computer Science & Engineering Xi’an University of Technology Xi’an Shaanxi China Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center Houston TX USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Chronic kidney disease (CKD) is as severe as cancer in today s world. It may even lead to the permanent failure of kidney. The initial detection of this disease is needed for timely cure. Our work presents a classifier (named ANFIS) in accordance with the notion of neuro-fuzzy in order to detect the existence of chronic kidney disease. We use blood test results of several patients for our research study. We compare our proposed classifier with some conventional classifiers such as Multi-layer Perceptron, Support Vector Machine, Logistic Regression and Decision Tree. Experimental results indicates that our proposed neuro-fuzzy rule-based classifier performs better than the other classifiers used here. ANFIS has given 3% to 4% better accuracy compared to the other classifiers.

关键词： Support vector machines Chronic kidney disease Bioinformatics Regression tree analysis Blood Logistics Cancer

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Winding number and circular 4-coloring of signed graphs

arXiv

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arXiv 2023年

作者： Gujgiczer, Anna Naserasr, Reza Rohini, S. Taruni, S. Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics MTA-BME Lendület Arithmetic Combinatorics Research Group Hungary Université de Paris IRIF CNRS ParisF-75006 France Department of Mathematics Indian Institute of Technology Madras Chennai600 036 India Department of Mathematics Indian Institute of Technology Dharwad Dharwad580011 India

Concerning the recent notion of circular chromatic number of signed graphs, for each given integer k we introduce two signed bipartite graphs, each on 2k2 − k + 1 vertices, having shortest negative cycle of length 2k, and the circular chromatic number 4. Each of the construction can be viewed as a bipartite analogue of the generalized Mycielski graphs on odd cycles, M(C2k+1). In the course of proving our result, we also obtain a simple proof of the fact that M(C2k+1) and some similar quadrangulations of the projective plane have circular chromatic number 4. These proofs have the advantage that they illuminate, in an elementary manner, the strong relation between algebraic topology and graph coloring problems. © 2023, CC BY.

关键词： Graphic methods

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Robust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multi-Agent DRL Approach

arXiv

引用

arXiv 2023年

作者： Li, Bin Yang, Rongrong Liu, Lei Wang, Junyi Zhang, Ning Dong, Mianxiong Nanjing University of Information Science and Technology Nanjing210044 China The Guangzhou Institute of Technology Xidian University Guangzhou510555 China The School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada The Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

For multiple Unmanned-Aerial-Vehicles (UAVs) assisted Mobile Edge Computing (MEC) networks, we study the problem of combined computation and communication for user equipments deployed with multi-type tasks. Specifically, we consider that the MEC network encompasses both communication and computation uncertainties, where the partial channel state information and the inaccurate estimation of task complexity are only available. We introduce a robust design accounting for these uncertainties and minimize the total weighted energy consumption by jointly optimizing UAV trajectory, task partition, as well as the computation and communication resource allocation in the multi-UAV scenario. The formulated problem is challenging to solve with the coupled optimization variables and the high uncertainties. To overcome this issue, we reformulate a multi-agent Markov decision process and propose a multi-agent proximal policy optimization with Beta distribution framework to achieve a flexible learning policy. Numerical results demonstrate the effectiveness and robustness of the proposed algorithm for the multi-UAV-assisted MEC network, which outperforms the representative benchmarks of the deep reinforcement learning and heuristic algorithms. © 2023, CC BY.

关键词： Reinforcement learning

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Enhancing Production Planning in the Internet of Vehicles: A Transformer-based Federated Reinforcement Learning Approach

Enhancing Production Planning in the Internet of Vehicles: A...

引用

IEEE Conference on Vehicular technology (VTC)

作者： Jinhua Chen Zihan Zhao Keping Yu Shahid Mumtaz Joel J. P. C. Rodrigues Mohsen Guizani Takuro Sato Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Applied Informatics Silesian University of Technology Gliwice Poland Departement of Computer Sciences Nottingham Trent University Nottingham UK Amazonas State University Manaus AM Brazil Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE Research Institute for Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

The Internet of Vehicles (10V) brings significant economic benefits to countries. However, large-scale smart vehicle production planning remains challenging in the 10V. Currently, heuristic algorithms and solvers commonly used for these problems often lack scalability and fall into local optima. Moreover, security concerns about wireless data transfer arising from multi-factory manufacturing processes are garnering attention. To address these issues, this paper introduces an algorithm, TRL, which is a Transformer-based Reinforcement Learning for vehicle production planning problems. Furthermore, we propose a Transformer-based Federated Reinforcement Learning algorithm, named TFRL, tailored for large-scale manufacturing and secure wireless communication. Experimental results showcase the high performance and security of TFRL. It schedules 1000 orders in about 14 seconds and avoids exchanging plaintext during the interaction. Compared to Non-dominated Sorting Genetic Algorithm II(NSGA-II), the TFRL enhances computational speed by 95.12% and reduces constraint violation scores by 93.18%.

关键词： Wireless communication Vehicular and wireless technologies Schedules Production planning Reinforcement learning Transformers Manufacturing

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Embedding-aided network dismantling

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Physical Review Research 2023年第1期5卷 013076-013076页

作者： Saeed Osat Fragkiskos Papadopoulos Andreia Sofia Teixeira Filippo radicchi Max Planck Institute for Dynamics and Self-Organization (MPIDS) 37077 Göttingen Germany Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 33 Saripolou Street 3036 Limassol Cyprus LASIGE Departamento de Informática Faculdade de Ciências Universidade de Lisboa 1749-016 Lisboa Portugal Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA

Optimal percolation concerns the identification of the minimum-cost strategy for the destruction of any extensive connected components in a network. Solutions of such a dismantling problem are important for the design of optimal strategies of disease containment based either on immunization or social distancing. Depending on the specific variant of the problem considered, network dismantling is performed via the removal of nodes or edges, and different cost functions are associated to the removal of these microscopic elements. In this paper, we show that network representations in geometric space can be used to solve several variants of the network dismantling problem in a coherent fashion. Once a network is embedded, dismantling is implemented using intuitive geometric strategies. We demonstrate that the approach well suits both Euclidean and hyperbolic network embeddings. Our systematic analysis on synthetic and real networks demonstrates that the performance of embedding-aided techniques is comparable to, if not better than, the one of the best dismantling algorithms currently available on the market.

关键词： Network phase transitions Robustness

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