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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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The conceptual approach of system for automatic vehicle accident detection and searching for life signs of casualties 5th

The conceptual approach of system for automatic vehicle acci...

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5th International Doctoral Symposium on applied Computation and Security Systems, ACSS 2018

作者： Lupinska-Dubicka, Anna Tabedzki, Marek Adamski, Marcin Rybnik, Mariusz Omieljanowicz, Miroslaw Omieljanowicz, Andrzej Szymkowski, Maciej Gruszewski, Marek Klimowicz, Adam Rubin, Grzegorz Saeed, Khalid Faculty of Computer Science Bialystok University of Technology Bialystok Poland Faculty of Mathematics and Informatics University of Bialystok Bialystok Poland Faculty of Mechanical Engineering Department of Automatic Control and Robotics Bialystok University of Technology Bialystok Poland Faculty of Computer and Food Science Lomza State University of Applied Sciences Lomza Poland

ISBN: (纸本)9789811337017

The European eSafety initiative aims to improve the safety and efficiency of road transport. The main element of eSafety is the pan-European eCall project—an in-vehicle system which idea is to inform about road collisions or serious accidents. As estimated by the European Commission, the implemented system will reduce services’ response time by 40%. This will save 2,500 people a year. In 2015, the European Parliament adopted the legislation that from the end of March 2018 all new cars from the EU should be equipped with the eCall system. The limitation of this idea is that only small part of cars driven in UE are brand new (about 3.7% brand new cars were sold in 2015). This paper presents the first concept of an onboard eCall device which can be installed at the owners’ request in used vehicles. The proposed system will be able to detect a road accident, indicate the number of vehicle’s occupants, report their vital functions, and send that information to dedicated emergency services via duplex communication channel. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Emergency services

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Why is the winner the best?

arXiv

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arXiv 2023年

作者： Eisenmann, M. Reinke, A. Weru, V. Tizabi, M.D. Isensee, F. Adler, T.J. Ali, S. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, S. Balu, N. Bano, S. Bernal, J. Bodenstedt, S. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D.G. Engelhardt, S. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulmé, A. Jannin, P. Kavur, A.E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martín-Isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, S. Payette, K. Rädsch, T. Rafael-Patiño, J. Bawa, V. Singh Speidel, S. Sudre, C.H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M.H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Brüngel, R. Cho, J. Choi, C. Dou, Q. Ezhov, I. Friedrich, C.M. Fuller, C. Gaire, R.R. Galdran, A. Faura, Á. García Grammatikopoulou, M. Hong, S. Jahanifar, M. Jang, I. Kadkhodamohammadi, A. Kang, I. Kofler, F. Kondo, S. Kuijf, H. Li, M. Luu, M. Martinčič, T. Morais, P. Naser, M.A. Oliveira, B. Owen, D. Pang, S. Park, J. Park, S. Plotka, S. Puybareau, E. Rajpoot, N. Ryu, K. Saeed, N. Shephard, A. Shi, P. Štepec, D. Subedi, R. Tochon, G. Torres, H.R. Urien, H. Vilaça, J.L. Wahid, K.A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, S. Yang, Y. Zhao, Z. Maier-Hein, K. Jäger, P.F. Kopp-Schneider, A. Maier-Hein, L. Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Germany Heidelberg Germany School of Computing Faculty of Engineering and Physical Sciences University of Leeds Leeds United Kingdom Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington SeattleWA United States Department of Computer Science University College London London United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Sierre Switzerland Harvard Medical School Brigham and Women’s Hospital BostonMA United States School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Essen Germany University of Nebraska Medical Center OmahaNE United States Department of Internal Medicine III Heidelberg University Hospital Heidelberg Germany Neurobiology Research Unit Copenhagen University Hospital Rigshospitalet C

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. © 2023, CC BY.

关键词： Benchmarking

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Digital Twins of Business Processes: A Research Manifesto

arXiv

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arXiv 2024年

作者： Fornari, Fabrizio Compagnucci, Ivan De Donato, Massimo Callisto Bertrand, Yannis Beyel, Harry H. Carrión, Emilio Franceschetti, Marco Groher, Wolfgang Grüger, Joscha Kilic, Emre Koschmider, Agnes Leotta, Francesco Li, Chiao-Yun Lugaresi, Giovanni Malburg, Lukas Mangler, Juergen Mecella, Massimo Pastor, Oscar Riss, Uwe Seiger, Ronny Serral, Estefania Torres, Victoria Valderas, Pedro University of Camerino School of Science and Technology Computer Science Department Via Madonna delle Carceri 7 Camerino62032 Italy Gran Sasso Science Institute Computer Science Department Viale Francesco Crispi 7 L’Aquila67100 Italy University of St.Gallen Institute of Computer Science Rosenbergstrasse 30 St.Gallen9000 Switzerland University of Bayreuth Business Informatics and Process Analytics Wittelsbacherring 8 Bayreuth95444 Germany Fraunhofer Institute for Applied Information Technology Schloss Birlinghoven Sankt Augustin53757 Germany RWTH Aachen University Ahornstraße 55 Aachen52074 Germany Mercadona Tech Mercadona Plaza de Amèrica 2 Valencia46004 Spain PROS Research Center - VRAIN Institute Universitat Politècnica de València Camino de Vera Valencia46022 Spain TU Dortmund University Leonhard-Euler-Str. 5 Dortmund44227 Germany KU Leuven Warmoesberg 26 Brussels1000 Belgium Sapienza Università di Roma DIAG via Ariosto 25 Rome00185 Italy KU Leuven Faculty of Engineering Technology Celestijnenlaan 300 Leuven3001 Belgium Branch Trier University Behringstraße 21 Trier54296 Germany Artificial Intelligence and Intelligent Information Systems Trier University Universitätsring 15 Trier54296 Germany OST Eastern Switzerland University of Applied Sciences Rosenbergstrasse 59 St. Gallen9001 Switzerland TUM School of Computation Information and Technology Boltzmannstraße 3 Garching bei München85748 Germany

Modern organizations necessitate continuous business processes improvement to maintain efficiency, adaptability, and competitiveness. In the last few years, the Internet of Things, via the deployment of sensors and actuators, has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes influencing and enhancing how people and organizations work. Such advancements are now pushed forward by the rise of the Digital Twin paradigm applied to organizational processes. Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process - a virtual replica with real-time capabilities of a real process occurring in an organization. Combining business process models with real-time data and simulation capabilities promises to provide a new way to guide day-to-day organization activities. However, integrating Digital Twins and business processes is a non-trivial task, presenting numerous challenges and ambiguities. This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins, identifying ongoing research and open challenges, thereby shedding light on and driving future exploration of this innovative interplay. © 2024, CC BY-NC-ND.

关键词： Virtual corporation

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BDoTs: Blockchain-based Evaluation Scheme for Online Teaching under COVID-19 Environment

BDoTs: Blockchain-based Evaluation Scheme for Online Teachin...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Arpit Shukla Nirav Patel Sudeep Tanwar Balqies Sadoun Mohammad S. Obaidat Nirma University India Department of Computer Science and Engineering Nirma University India College of Engineering Sharjah Universiiy UAE College of Engineering Al-Balqa' Applied Universiiy Jordan College of Computing and Informatics University of Sharjah UAE King Abdullah II School of IT University of Jordan Jordan University of Science and Technology Beijing China

ISBN: (数字)9781728165448

ISBN: (纸本)9781728165455

Online teaching has become mandatory across the globe during the COVID-19 pandemic situations. Hence, there is a need to uplift the online teaching technology for data privacy preservation and transparency in the system. Various online teaching schemes have been proposed by various authors, but they lack in handling decentralised governance, transparency, trust and communication issues. Blockchain (BC) Technology has emerged to provide decentralised solution in solving the real-time problems. Motivated by these facts, in this paper, we propose a BC-based decentralised online teaching scheme known as BDoTs. The security and data privacy issues in BDoTs are resolved by developing smart contracts (SCs) over BC. Moreover, the data storage cost issues are handled by the Inter Planetary File System (IPFS) protocol for Off-Chain data storage. Moreover, we present a real-time BC simulation and deployment of SC in Truffle suite. Results show that the proposed scheme performs better in comparison to the state-of-the-art schemes in terms of scalability, data storage cost, and packet loss.

关键词： Education Peer-to-peer computing Internet Distributed databases COVID-19 Security Scalability

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A new class of scaling matrices for scaled trust region algorithms

arXiv

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arXiv 2019年

作者： Ayanzadeh, Aydin Yazdanian, Shokoufeh Shahamatnia, Ehsan Department of Applied Informatics Informatics Institute Istanbul Technical University Istanbul Turkey Department of Computer Science University of Tabriz Tabriz Iran Department of Computer and Electrical Engineering Universidade Nova de Lisboa Lisbon Portugal

A new class of affine scaling matrices for the interior point Newton-type methods is considered to solve the nonlinear systems with simple bounds. We review the essential properties of a scaling matrix and consider several wellknown scaling matrices proposed in the literature. We define a new scaling matrix that is the convex combination of these matrices. The proposed scaling matrix inherits those interesting properties of the individual matrices and satisfies additional desired requirements. The numerical experiments demonstrate the superiority of the new scaling matrix in solving several important test problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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ICGM-FRAX: Iterative Cross Graph Matching for Hip Fracture Risk Assessment using Dual-energy X-ray Absorptiometry Images

arXiv

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arXiv 2025年

作者： Zhao, Chen Shaik, Anjum Keyak, Joyce H. Lane, Nancy E. Deng, Jeffrey D. Su, Kuan-Jui Sha, Qiuying Shen, Hui Deng, Hong-Wen Zhou, Weihua Department of Computer Science Kennesaw State University 680 Arntson Dr MariettaGA30060 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Radiological Sciences Department of Biomedical Engineering Department of Mechanical and Aerospace Engineering University of California IrvineCA92697 United States Department of Internal Medicine Division of Rheumatology UC Davis Health SacramentoCA95817 United States Geisel School of Medicine Dartmouth College HanoverNH03755 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Mathematical Sciences Michigan Technological University HoughtonMI49931 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States

Hip fractures represent a major health concern, particularly among the elderly, often leading decreased mobility and increased mortality. Early and accurate detection of at risk individuals is crucial for effective intervention. In this study, we propose Iterative Cross Graph Matching for Hip Fracture Risk Assessment (ICGM-FRAX), a novel approach for predicting hip fractures using Dual-energy X-ray Absorptiometry (DXA) images. ICGM-FRAX involves iteratively comparing a test (subject) graph with multiple template graphs representing the characteristics of hip fracture subjects to assess the similarity and accurately to predict hip fracture risk. These graphs are obtained as follows. The DXA images are separated into multiple regions of interest (RoIs), such as the femoral head, shaft, and lesser trochanter. Radiomic features are then calculated for each RoI, with the central coordinates used as nodes in a graph. The connectivity between nodes is established according to the Euclidean distance between these coordinates. This process transforms each DXA image into a graph, where each node represents a RoI, and edges derived by the centroids of RoIs capture the spatial relationships between them. If the test graph closely matches a set of template graphs representing subjects with incident hip fractures, it is classified as indicating high hip fracture risk. We evaluated our method using 547 subjects from the UK Biobank dataset, and experimental results show that ICGM-FRAX achieved a sensitivity of 0.9869, demonstrating high accuracy in predicting hip fractures. © 2025, CC BY.

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Roadmap on Deep Learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Fitting Dense and Sparse Reduced Data 21st

Fitting Dense and Sparse Reduced Data

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21st International Multi-Conference on Advanced computer Systems, ACS 2018

作者： Kozera, Ryszard Wiliński, Artur Faculty of Applied Informatics and Mathematics Warsaw University of Life Sciences - SGGW ul. Nowoursynowska 159 Warsaw02-776 Poland Department of Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway Crawley PerthWA6009 Australia

ISBN: (纸本)9783030033132

This paper addresses the topic of fitting reduced data represented by the sequence of interpolation points (Formula Presented) in arbitrary Euclidean space Em. The parametric curve γ together with its knots (Formula Presented) (for which (Formula Presented)) are both assumed to be unknown. We look at some recipes to estimate T in the context of dense versus sparse M for various choices of interpolation schemes (Formula Presented). For M dense, the convergence rate to approximate γ with (Formula Presented) is considered as a possible criterion to force a proper choice of new knots (Formula Presented). The latter incorporates the so-called exponential parameterization "retrieving" the missing knots T from the geometrical spread of M. We examine the convergence rate in approximating γ by commonly used interpolants (Formula Presented) based here on M and exponential parameterization. In contrast, for M sparse, a possible optional strategy is to select (Formula Presented) which optimizes a certain cost function depending on the family of admissible knots (Formula Presented). This paper focuses on minimizing "an average acceleration" within the family of natural splines (Formula Presented) fitting M with (Formula Presented) admitted freely in the ascending order. Illustrative examples and some applications listed supplement theoretical component of this work. © 2019, Springer Nature Switzerland AG.

关键词： Interpolation

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A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale

Artificial Intelligence in Geosciences

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Artificial Intelligence in Geosciences 2025年第1期6卷

作者： Khadija Meghraoui Teeradaj Racharak Kenza Ait El Kadi Saloua Bensiali Imane Sebari Research Unit of Geospatial Technologies for A Smart Decision. IAV Hassan II Rabat 10101 Morocco Advanced Institute of So-Go-Chi (Convergence Knowledge) Informatics Tohoku University Miyagi Japan School of Information Science Japan Advanced Institute of Science and Technology Ishikawa Japan School of Geomatics and Surveying Engineering. IAV Hassan II Rabat 10101 Morocco Department of Applied Statistics and Computer Science. IAV Hassan II Rabat 10101 Morocco

Crop-yield is a crucial metric in agriculture, essential for effective sector management and improving the overall production process. This indicator is heavily influenced by numerous environmental factors, particularly those related to soil and climate, which present a challenging task due to the complex interactions involved. In this paper, we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction. This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods, specifically representation learning techniques, along with predictions derived from remote sensing imagery. We tested our proposed methodology on a public dataset centered on corn, aiming to predict crop-yield. Our developed smart model achieved promising results in terms of crop-yield prediction, with a root mean squared error (RMSE) of 1.72, outperforming the baseline models. The ontology-based approach achieved an RMSE of 1.73, while the remote sensing-based method yielded an RMSE of 1.77. This confirms the superior performance of our proposed approach over those using single modalities. This integrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence (AI) represents a significant advancement in agricultural applications. It is particularly effective for crop-yield prediction at the field scale, thus facilitating more informed decision-making in advanced agricultural practices. Additionally, it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.

关键词： Crop-yield prediction Neuro-symbolic AI Ontology Ontology embedding Satellite imagery Machine learning

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