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Correction: Structural and shielding effectiveness properties of (NiFe2O4/chopped strands) composites for 6.5–18 GHz applications

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Journal of Materials science: Materials in Electronics 2024年第30期35卷 1-2页

作者： Sahin, Ethem İlhan Ibrahim, Jamal-Eldin F. M. Emek, Mehriban Sinsoysal, Bahaddin Amanzholova, Alina Advanced Technology Research and Application Center Adana Alparslan Türkes ̧ Science and Technology University Adana Turkey Doctoral School of Material Science and Technology Institute of Ceramic and Polymer Engineering Faculty of Material Science and Engineering University of Miskolc Miskolc Hungary Department of Physics Golbasi Vocational School Computer Technologies Adiyaman University Adıyaman Turkey Department of Mathematics and Computer Engineering Faculty of Engineering İstanbul Gedik University Istanbul Turkey Department of Mathematics Faculty of Science Khoja Akhmet Yassawi International Kazakh-Turkish University Turkistan Kazakhstan

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Detection of Bleeding Events In EHR Notes Using A Graph Dynamic Autoencoder Based Riemannian Residual Neural Network 4

Detection of Bleeding Events In EHR Notes Using A Graph Dyna...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Niveditha, V.R. Ali, H. Mohammed Rammohan, S. Radha Rajababu, D. Maranan, R. Kaliappan, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Semmancheri Chennai600119 India Srm Institute of Science and Technology Department of Mechanical Engineering Ramapuram Campus Chennai89 India Hindustan University Center for Open and Digital Education Padur Chennai India Sr University Department of Electrical and Electronics Engineering Telangana Warangal506371 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Phagwara144411 India

ISBN: (纸本)9798331518981

Identifying occurrences of abnormal bleeding based on unstructured and structured medical data, such as patient notes, vitals, and laboratory findings, is the problem of recognising bleeding events in Electronic Health Records (EHR). The problem is in accurately recognising these occurrences by analysing temporal patterns, extracting significant information, and distinguishing catastrophic bleeding occasions from typical medical fluctuations given the complexity of EHR data, which is frequently longitudinal, heterogeneous, and packed with noise. The goal is to create a Graph Dynamic Autoencoder combined with a Riemannian Residual Neural Network (GDA-RRNN) with Elk Herd optimizer (EHO) model that can identify and display bleeding incidents in electronic medical data. The EHO optimizes the hyperparameters GDA-RRNN to improve the performance of the suggested method. We extracted 300 electronic health records that contained leukaemia or bleeding diagnostic codes from the International categorization of Diseases, Tenth Revision. The electronic health record has annotations indicating whether a sentence was positive or negative for bleeding. A proposed model that can identify bleeding at the sentence and note levels was created using the annotated texts. The best-performing suggested approach achieved a sensitivity of 0.99, specificity of 0.99, and a negative prediction value of 0.99 on an equal test set of 1178 phrases. © 2024 IEEE.

关键词： Electronic health record

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Knitted fabric simulation: A survey

Knitted fabric simulation: A survey

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作者： Hu, Xinrong Wang, Meng Liu, Junping Liang, Jinxing Yang, Kai Luo, Ruiqi Fang, Fei Peng, Tao School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei China Engineering Research Center of Hubei Province for Clothing Information Wuhan Textile University Hubei China State Key Laboratory of New Textile Material and Advanced Processing Technologies Wuhan Textile University Hubei China

Knitted fabric simulation seeks to create lifelike virtual representations of various knitted items like sweaters and socks using mathematical models and advanced simulation techniques. Significant advancements have been achieved in this area. Visual simulations of these fabrics now boast enhanced details, textures, and colors, closely resembling real-world counterparts. Additionally, physical simulations have improved the movement and deformation aspects of virtual knitted fabrics, enhancing their realism. However, challenges remain. One major concern is the computational demands of these simulations, which can hinder real-time interactions. Another issue is refining the accuracy of material models, especially when simulating diverse fibers and textures. This paper aims to review the current state of knitted fabric simulation and speculate on its future directions. © 2024 John Wiley & Sons Ltd.

关键词： Knit fabrics

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Leakage Minimization in Network-Assisted Full-Duplex Cell-Free Massive MIMO Systems

Leakage Minimization in Network-Assisted Full-Duplex Cell-Fr...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Koushi Okui Kengo Ando Giuseppe Abreu Koji Ishibashi Advanced Wireless & Communication Research Center (AWCC) The University of Electro-Communications Tokyo Japan School of Computer Science and Engineering Constructor University Bremen Germany

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

We propose a new scheme for the joint design of access point clustering and leakage-minimization beamforming in a network-assisted full-duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) system. In the proposed method, each user considers all other users as eavesdroppers or malicious users, with the corresponding secrecy capacity formulated as the difference between the channel capacity of the intended user and the maximum leakage to the other users. By maximizing this secrecy capacity, the proposed minimizes information leakage to other users while maintaining high spectral efficiency for each user. As a result, the proposed approach achieves approximately double spectral efficiency and confidentiality compared to conventional CF-mMIMO based on time division duplex.

关键词： Spectral efficiency Channel capacity Array signal processing Full-duplex system Massive MIMO Minimization Information leakage Convex functions

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Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication 7

Towards Cluster-Based Split Federated Learning Approach for ...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Wazzeh, Mohamad Arafeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Trois-Rivières Canada Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon New York University Division of Science Abu Dhabi United Arab Emirates Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security. © 2023 IEEE.

关键词： Internet of things

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A Fourth-Order Unstructured NURBS-Enhanced Finite Volume WENO Scheme for Steady Euler Equations in Curved Geometries

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Communications on Applied mathematics and Computation 2023年第1期5卷 315-342页

作者： Xucheng Meng Yaguang Gu Guanghui Hu Research Center for Mathematics Beijing Normal University at ZhuhaiZhuhai 519087GuangdongChina Division of Science and Technology BNU-HKBU United International CollegeZhuhai 519087GuangdongChina School of Mathematical Sciences Ocean University of ChinaQingdao 266100ShandongChina Department of Mathematics University of MacaoMacao S.A.R.China Zhuhai UM Science and Technology Research Institute Zhuhai 519000GuangdongChina Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Dynamics and Engineering Applications University of MacaoMacao S.A.R.China

In Li and Ren(*** Fluids 70:742–763,2012),a high-order k-exact WENO finite volume scheme based on secondary reconstructions was proposed to solve the two-dimensional time-dependent Euler equations in a polygonal domain,in which the high-order numerical accuracy and the oscillations-free property can be *** this paper,the method is extended to solve steady state problems imposed in a curved physical *** numerical framework consists of a Newton type finite volume method to linearize the nonlinear governing equations,and a geometrical multigrid method to solve the derived linear *** achieve high-order non-oscillatory numerical solutions,the classical k-exact reconstruction with k=3 and the efficient secondary reconstructions are used to perform the WENO reconstruction for the conservative *** non-uniform rational B-splines(NURBS)curve is used to provide an exact or a high-order representation of the curved wall ***,an enlarged reconstruction patch is constructed for every element of mesh to significantly improve the convergence to steady state.A variety of numerical examples are presented to show the effectiveness and robustness of the proposed method.

关键词： Steady Euler equations Curved boundary NURBS-enhanced finite volume method WENO reconstruction Secondary reconstruction

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Classifying Activities of Electrical Line Workers Based on Deep Learning Approaches Using Wrist-Worn Sensor 20

Classifying Activities of Electrical Line Workers Based on D...

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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350300505

The field o f human activity recognition (HAR) is a priority for cutting-edge study because of its potential to revolutionize the way we understand and improve our everyday lives. A large variety of ordinary, everyday tasks has been classified using H AR. Nevertheless, inc ontrast to basic human actions, the increasing demands of numerous real-world applications have attracted the interest of the HAR area of study. Electrical line workers (ELWs) face a variety of challenges, including long hours, working in isolated locations, and performing particularly hazardous tasks. Wearable sensor-based HAR allows for unobtrusive tracking of ELW efficiency and security. This study explores deep learning strategies for automatically categorizing ELWs' complicated actions through sensor data collected through a wrist-worn device. We propose ResNeXt, a deep residual neural network, and evaluate it with other deep learning networks for their ability to categorize ELW activities effectively. We employ a publicly available benchmark dataset that includes 10 ELW tasks. The results of the experiment demonstrate that the proposed ResNeXt achieved the highest accuracy (98.74%) and F1-score (98.81%) compared to other deep learning networks studied. © 2023 IEEE.

关键词： Wearable sensors

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Relative intensity noise minimization in an all-polarization-maintaining ultrafast fibre laser

Relative intensity noise minimization in an all-polarization...

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2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Dai, Maolin Liu, Bowen Tian, Haochen Shirahata, Takuma Yamaguchi, Naoki Set, Sze Yun Yamashita, Shinji Department of Electrical Engineering and Information Systems The University of Tokyo Bunkyo-ku Tokyo113-8656 Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo153-8904 Japan Division of Time and Frequency Metrology National Institute of Metrology Beijing100029 China

We experimentally demonstrate the minimization of relative intensity noise (RIN) in an all-polarization-maintaining (all-PM) ultrafast fibre laser mode-locked by carbon nanotube (CNT), which will benefit for high-prec... 详细信息

关键词： Polarization

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Recent advances and applications of deep learning methods in materials science

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npj Computational Materials 2022年第1期8卷 548-573页

作者： Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA Theiss Research La JollaCA92037USA DeepMaterials LLC Silver SpringMD20906USA Material Measurement Science Division National Institute of Standards and TechnologyGaithersburgMD20899USA Department of NanoEngineering University of California San DiegoSan DiegoCA92093USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Materials Science and Engineering Carnegie Mellon UniversityPittsburghPA15213USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL60208USA Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied SciencesColumbia UniversityNew YorkNY10027USA

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data *** allows analysis of unstructured data and automated identification of *** recent development of large materials databases has fueled the application of DL methods in atomistic prediction in *** contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL *** this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language *** each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and *** conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

关键词： learning limitations textual

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