检索结果-内蒙古大学图书馆

Journal of Magnetism and Magnetic Materials 2024年 610卷

作者： Tao Hai Kamal Sharma Riyam K. Marjan Babak Farhang Mohamed H. Mahmoud Hassan Fouad Walid El-Shafai School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China School of Electronics and Information Engineering Ankang University China State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang 550025 China Institute of Engineering and Technology GLA University Mathura UP 281406 India Department of Intelligent Medical Systems Al-Mustaqbal University College Babylon 51001 Iraq Department of Energy Technology Aalborg University Denmark Department of Biochemistry College of Science King Saud University Riyadh Saudi Arabia Biomedical Engineering Department Faculty of Engineering Helwan University Cairo P.O. Box 11795 Egypt Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

Accelerated Latent Factor Analysis for Recommender Systems via PID Controller

学校读者我要写书评

暂无评论

Accelerated Latent Factor Analysis for Recommender Systems v...

IEEE International Conference on Networking, Sensing and Control

作者： Jinli Li Xuke Wu Ye Yuan Yajuan Wu Kangkang Ma Yue Zhou Computer School China West Normal University Nanchong Sichuan China Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of information and software engineering University of Electronic Science and Technology of China Chengdu Sichuan China University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

High-dimensional and sparse (HiDS) matrices generated by recommender systems (RSs) contain rich knowledge. A latent factor (LF) model can address such data effectively. Stochastic gradient descent (SGD) is an efficient algorithm for building a LF model on an HiDS matrix. However, it suffers slow convergence. To address this issue, this study proposes to implement a LF model with a proportional integral derivative (PID) controller. The main idea is to continuously apply a correction for SGD to accelerate the training process. Based on such design, a PID-based LF (PLF) model is proposed. Empirical studies on two HiDS matrices from RSs indicate that a PLF model outperforms an LF model in terms of both convergence rate and prediction accuracy for missing data.

关键词： Training Predictive models data models Sparse matrices Acceleration Recommender systems Convergence

RPF-Net: A multimodal model for the postoperative UISS risk stratification of non-metastatic ccRCC based on CT and whole-slide images

学校读者我要写书评

暂无评论

computer Methods and Programs in Biomedicine 2025年 268卷 108836-108836页

作者： Mao, Jiahui Li, Wuchao Sun, Xinhuan Fu, Bangkang He, Junjie Yan, Chongzhe Zhu, Jianguo Zhang, Zhuxue Mao, Jiahui Hong, Zhangxin Tang, Qi Liu, Zhen Li, Pinhao Zhang, Yan Wang, Rongpin Medical College Guizhou University Guiyang550025 China Department of Radiology International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment Guizhou Provincial People's Hospital Guiyang550002 China CETC Big Data Research Institute Co. Ltd. Guizhou550022 China National Engineering Research Center of Big Data Application to Improvement of Governance Capacity Guizhou550022 China Department of Pathology Guizhou Provincial People's Hospital Guiyang550002 China Department of Urinary Guizhou Provincial People's Hospital Guiyang550002 China Department of Pathology Affiliated Hospital of Guizhou Medical University Guiyang550002 China Department of Imaging Affiliated Hospital of Guizhou Medical University Guiyang550002 China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou215163 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200082 China

Background and objectives: Postoperative non-metastatic clear cell renal cell carcinoma (nccRCC) patients face the risk of tumor recurrence and metastasis. However, prognosis assessment for nccRCC remains time-consuming and subjective. In the current diagnostic landscape, computed tomography (CT) images provide macro-scale anatomical information, and whole-slide images (WSIs) offer micro-scale details that are inaccessible to CT imaging. To address this gap, the study proposes a multimodal approach that leverages both CT and WSI data to develop an automated model for postoperative risk stratification in nccRCC. Methods: This study proposes a multimodal model named the Radiology-Pathology Fusion Network (RPF-Net), which employs self-attention, graph-attention, and dynamic attention fusion mechanisms to integrate CT images and WSIs for classifying nccRCC patients into low-risk and intermediate-high-risk groups per the University of California, Los Angeles, Integrated Staging System (UISS) criteria. The proposed model is divided into three steps. First, the ResNet-50 and 3D ResNet-50 are used as feature extractors to respectively extract representative feature maps from WSIs and CT images. Second, a dual-branch module is designed to extract global and local features of the WSIs. Finally, a multilayer dynamic attention fusion (MDAF) module is developed to facilitate cross-modal feature interaction and predict the risk stratification results. Results: The area under the curve (AUC), accuracy, precision, and F1 Score of the RPF-Net on the internal validation set are 0.949±0.013, 0.894±0.019, 0.895±0.020, and 0.894±0.019, respectively. Furthermore, the RPF-Net shows robust generalization, achieving an AUC of 0.901 on the external validation set and 0.924 on the public dataset. Conclusions: The RPF-Net models the diagnostic process of multimodal data and shows strong generalization and excellent performance. This model may be a potential tool to facilitate clinical risk str

关键词： Non-metastatic clear cell renal cell carcinoma Computed tomography Whole-slide image UISS Multimodal model

RETRACTED ARTICLE: An enhanced pairing-based authentication scheme for smart grid communications

学校读者我要写书评

暂无评论

Journal of Ambient Intelligence and Humanized Computing 2021年第1期15卷 165-165页

作者： Wu, Tsu-Yang Lee, Yu-Qi Chen, Chien-Ming Tian, Yuan Al-Nabhan, Najla Abdulrahman College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Computer Engineering Nanjing Institute of Technology Nanjing China Computer Science Department College of Computer and Information Sciences King Saud University Riyadh Kingdom of Saudi Arabia

Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Cheng, Lei Chen, Zhongtao Shi, Qingjiang Wu, Yik-Chung Theodoridis, Sergios The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The School of Software Engineering Tongji University Shanghai201804 China The Shenzhen Research Institute of Big Data Shenzhen518172 China The National and Kapodistrian University of Athens Greece Greece The Department of Electronic Systems Aalborg University Denmark

Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as an essential yet challenging problem. In particular, since the tensor rank controls the complexity of the CPD model, its inaccurate learning would cause overfitting to noise or underfitting to the signal sources, and even destroy the interpretability of model parameters. However, the optimal determination of a tensor rank is known to be a non-deterministic polynomial-time hard (NP-hard) task. Rather than exhaustively searching for the best tensor rank via trial- and-error experiments, Bayesian inference under the Gaussian-gamma prior was introduced in the context of probabilistic CPD modeling, and it was shown to be an effective strategy for automatic tensor rank determination. This triggered flourishing research on other structured tensor CPDs with automatic tensor rank learning. On the other side of the coin, these research works also reveal that the Gaussian-gamma model does not perform well for high-rank tensors and/or low signal-to-noise ratios (SNRs). To overcome these drawbacks, in this paper, we introduce a more advanced generalized hyperbolic (GH) prior to the probabilistic CPD model, which not only includes the Gaussian-gamma model as a special case, but also is more flexible to adapt to different levels of sparsity. Based on this novel probabilistic model, an algorithm is developed under the framework of variational inference, where each update is obtained in a closed-form. Extensive numerical results, using synthetic data and real-world datasets, demonstrate the significantly improved performance of the proposed method in learning both low as well as high tensor ranks even for low SNR cases. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

Deep Anomaly Detection for Time-series data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Liu, Yi Garg, Sahil Nie, Jiangtian Zhang, Yang Xiong, Zehui Kang, Jiawen Shamim Hossain, M. School of Data Science and Technology Heilongjiang University Harbin China Electrical Engineering Department École de technologie supérieure Uni-versité du Québec Montréal Canada Energy Research Institute Interdisciplinary Graduate Programme School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Technology Wuhan University of Technology China Alibaba-NTU Joint Research Institute School of Computer Science and Engineering NTU Singapore Singapore Energy Research Institute Nanyang Technological University Singapore Department of Software Engineering College of Computer and Information Sciences King Saud University Saudi Arabia

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user’s private data, which is challenging the current detection approaches as user privacy is calling for the public concern in recent years. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based CNN units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of LSTM unit in predicting time series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-k selection to improve communication efficiency. Extensive experiment studies on four real-world datasets demonstrate that the proposed framework can accurately and timely detect anomalies and also reduce the communication overhead by 50% compared to the federated learning framework that does not use gradient compression scheme. Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

Exploiting shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Zhang, Yichi Liao, Qingcheng Yuan, Lin Zhu, He Xing, Jiezhen Zhang, Jicong School of Biological Science and Medical Engineering Beihang University Beijing China College of Biomedical Engineering Taiyuan University of Technology Taiyuan China School of Computer Science and Engineering Beihang University Beijing China School of Biological Science and Medical Engineering Beihang University Beijing China Hefei Innovation Research Institute Beihang University Hefei China Beijing Advanced Innovation Centre for Biomedical Engineering Beijing China Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine Beijing China

The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infection segmentation from computed tomography (CT) plays an important role in the quantitative analysis of COVID-19. However, the major challenge lies in the inadequacy of annotated COVID-19 datasets. Currently, there are several public non-COVID lung lesion segmentation datasets, providing the potential for generalizing useful information to the related COVID-19 segmentation task. In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation. The model consists of a general encoder to capture general lung lesion features based on multiple non-COVID lesions, and a target encoder to focus on task-specific features based on COVID-19 infections. Features extracted from the two parallel encoders are concatenated for the subsequent decoder part. We develop a collaborative learning scheme to regularize feature-level relation consistency of given input and encourage the model to learn more general and discriminative representation of COVID-19 infections. Extensive experiments demonstrate that trained with limited COVID-19 data, exploiting shared knowledge from non-COVID lesions can further improve state-of-the-art performance with up to 3.0% in dice similarity coefficient and 4.2% in normalized surface dice. In addition, experimental results on large scale 2D dataset with CT slices show that our method significantly outperforms cutting-edge segmentation methods on all evaluation metrics. Our proposed method promotes new insights into annotation-efficient deep learning for COVID-19 infection segmentation and illustrates strong potential for real-world applications in the global fight against COVID-19 in the absence of sufficient high-quality annotations. Copyright © 2020,

关键词： computerized tomography

Android Malware Family Classification and Characterization Using CFG and DFG

学校读者我要写书评

暂无评论

Android Malware Family Classification and Characterization U...

Theoretical Aspects of software engineering (TASE)

作者： Zhiwu XU Kerong Ren Fu Song College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Information Science and Technology ShanghaiTech University Shanghai China

Android malware has become a serious threat for our daily life, and thus there is a pressing need to effectively mitigate or defend against them. Recently, many approaches and tools to analyze Android malware have been proposed to protect legitimate users from the threat. However, most approaches focus on malware detection, while only a few of them consider malware classification or malware characterization. In this paper, we propose an extension of CDGDroid to classifying and characterizing Android malware families automatically. We first perform static analysis used in CDGDroid to extract control-flow graphs and data-flow graphs on the instruction level. Then we encode the graphs into matrices, and use them to build the family classification models via deep learning. For family characterization, we extract the n-gram sequences from the graphs, which are filtered according to the weights of the classification model built for the target family. And then we construct a vector space model and select the top-k sequences as a characterization of the target family. We have conducted some experiments to evaluate our approach and have identified that the family classification model taking the horizontal combination of CFG and DFG as features offers the best performance in terms of accuracy among all the models. Compared with CDGDroid, Drebin and many antivirus tools gathered in VirusTotal, our family classification model gives a better performance. Finally, We have also conducted experiments on family characterization, and the experimental results have shown that our characterization can capture the malicious behaviors of the testing families.

关键词： Malware Static analysis Tools Task analysis Deep learning Testing software engineering

Erratum to “Entropy-based fuzzy support vector machine for imbalanced datasets” [Knowl.-Based Syst. 115 (2017) 87–99]

学校读者我要写书评

暂无评论

Knowledge-Based Systems 2020年 192卷

作者： Salim Rezvani Xizhao Wang Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen 518060 Guangdong China

In this note, we show that the calculation of statistics X F 2 and F F in sections 4.5 and 4.6 of the paper (Fan et al., 2017) is not correct. Also, based on the calculation of Critical Difference (CD) of Bonferroni–Dunn test, we show that the conclusion on ”significantly outperforming the compared algorithms”, drawn by the authors using the P -value test, is not convincing.

关键词： Information entropy fuzzy support vector machine Bonferroni–Dunn test Friedman test