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Integration of FDOSM and FWZIC under Homogeneous Fermatean Fuzzy Environment: A Prioritization of COVID-19 Patients for Mesenchymal Stem Cell Transfusion

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International Journal of Information technology and Decision Making 2024年第4期23卷 1559-1599页

作者： Alsattar, H.A. Qahtan, Sara Mohammed, R.T. Zaidan, A.A. Albahri, O.S. Kou, Gang Alamoodi, A.H. Albahri, A.S. Zaidan, B.B. Al-Samarraay, Mohammed S. Malik, R.Q. Jasim, Ali Najm Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim35900 Malaysia Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq Department of Computer Center Middle Technical University's Baghdad Iraq Sulaymaniyah Iraq Faculty of Engineering and IT The British University in Dubia United Arab Emirates Computer Techniques Engineering Department Mazaya University College Nassiriya Thi-Qar Iraq School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Foundation of Alshuhda Baghdad Iraq

Mesenchymal stem cell (MSC) transfusion has shown promising results in treating COVID-19 cases despite the limited availability of these MSCs. The task of prioritizing COVID-19 patients for MSC transfusion based on multiple criteria is considered a multi-Attribute decision-Analysis (MADA) problem. Although literature reviews have assessed the prioritization of COVID-19 patients for MSCs, issues arising from imprecise, unclear and ambiguous information remain unresolved. Compared with the existing MADA methods, the robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy-weighted zero inconsistency (FWZIC) is proven. This study adopts and integrates FDOSM and FWZIC in a homogeneous Fermatean fuzzy environment for criterion weighting followed by the prioritization of the most eligible COVID-19 patients for MSC transfusion. The research methodology had two phases. The decision matrices of three COVID-19 emergency levels (moderate, severe, and critical) were adopted based on an augmented dataset of 60 patients and discussed in the first phase. The second phase was divided into two subsections. The first section developed Fermatean FWZIC (F-FWZIC) to weigh criteria across each emergency level of COVID-19 patients. These weights were fed to the second section on adopting Fermatean FDOSM (F-FDOSM) for the purpose of prioritizing COVID-19 patients who are the most eligible to receive MSCs. Three methods were used in evaluating the proposed works, and the results included systematic ranking, sensitivity analysis, and benchmarking checklist. © 2024 World Scientific Publishing Company.

关键词： COVID-19

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Application of ARIS and Adaptive Filtering Algorithm in Underwater Acoustic Communication

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The International Journal of Intelligent Control and Systems 2024年第3期29卷 101-107页

作者： Tianrui Zhang Shuangshuang Han Ziyuan Yang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Key Laboratory of Ocean Observation Technology Ministry of Natural ResourcesTianjin 300112China Research Development and International Cooperation Department National Ocean Technology CenterTianjin 300112China

As the core of marine communication,underwater acoustic communication technology significantly improves the performance of underwater communication by using technologies such as modulation,multiple access,and *** them,adaptive filtering technology plays a key role in eliminating noise and channel *** paper explores the novel application of acoustic reconfigurable intelligence surface(ARIS)and adaptive filtering in underwater acoustic *** introduce the concept of ARIS assisted underwater acoustic communication,integrating the least mean squares(LMS)algorithm to dynamically estimate and mitigate oceanic *** approach further enhances signal clarity and communication quality by leveraging orthogonal frequency division multiplexing(OFDM)*** results show that the underwater acoustic channel with ARIS is superior to the traditional channel without ARIS in terms of bit error rate and anti-interference *** technology significantly enhances the reception quality of underwater communication signals,making it suitable for applications in marine monitoring,military communication,and other complex environments.

关键词： Underwater acoustic communication adaptive filtering technology acoustic reconfigurable intelligence surface orthogonal frequency division multiplexing

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Amodal Segmentation for Laparoscopic Surgery Video Instruments

arXiv

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arXiv 2024年

作者： Shi, Ruohua Liu, Zhaochen Duan, Lingyu Jiang, Tingting National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China AI Innovation Center School of Computer Science Peking University China National Biomedical Imaging Center Peking University China

Segmentation of surgical instruments is crucial for enhancing surgeon performance and ensuring patient safety. Conventional techniques such as binary, semantic, and instance segmentation share a common drawback: they do not accommodate the parts of instruments obscured by tissues or other instruments. Precisely predicting the full extent of these occluded instruments can significantly improve laparoscopic surgeries by providing critical guidance during operations and assisting in the analysis of potential surgical errors, as well as serving educational purposes. In this paper, we introduce Amodal Segmentation to the realm of surgical instruments in the medical field. This technique identifies both the visible and occluded parts of an object. To achieve this, we introduce a new Amoal Instruments Segmentation (AIS) dataset, which was developed by reannotating each instrument with its complete mask, utilizing the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset. Additionally, we evaluate several leading amodal segmentation methods to establish a benchmark for this new dataset. © 2024, CC BY.

关键词： Laparoscopy

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Multi-Keypoint Affordance Representation for Functional Dexterous Grasping

arXiv

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arXiv 2025年

作者： Yang, Fan Luo, Dongsheng Chen, Wenrui Lin, Jiacheng Cai, Junjie Yang, Kailun Li, Zhiyong Wang, Yaonan School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China College of Computer Science and Electronic Engineering Hunan University Changsha China

Functional dexterous grasping requires precise hand-object interaction, going beyond simple gripping. Existing affordance-based methods primarily predict coarse interaction regions and cannot directly constrain the grasping posture, leading to a disconnection between visual perception and manipulation. To address this issue, we propose a multi-keypoint affordance representation for functional dexterous grasping, which directly encodes task-driven grasp configurations by localizing functional contact points. Our method introduces Contact-guided Multi-Keypoint Affordance (CMKA), leveraging human grasping experience images for weak supervision combined with Large Vision Models for fine affordance feature extraction, achieving generalization while avoiding manual keypoint annotations. Additionally, we present a Keypoint-based Grasp matrix Transformation (KGT) method, ensuring spatial consistency between hand keypoints and object contact points, thus providing a direct link between visual perception and dexterous grasping actions. Experiments on public real-world FAH datasets, IsaacGym simulation, and challenging robotic tasks demonstrate that our method significantly improves affordance localization accuracy, grasp consistency, and generalization to unseen tools and tasks, bridging the gap between visual affordance learning and dexterous robotic manipulation. The source code and demo videos will be publicly available at https://***/PopeyePxx/MKA. Copyright © 2025, The Authors. All rights reserved.

关键词： Vision

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RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network

RAHP: A Redundancy-aware Accelerator for High-performance Hy...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Hui Yu Yu Zhang Ligang He Yingqi Zhao Xintao Li Ruida Xin Jin Zhao Xiaofei Liao Haikun Liu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Hypergraph Neural Network (HyperGNN) has emerged as a potent methodology for dissecting intricate multilateral connections among various entities. Current software/hardware solutions leverage a sequential execution model that relies on hyperedge and vertex indices for conducting standard matrix operations for HyperGNN inference. Yet, they are impeded by the dual challenges of redundant computation and irregular memory access overheads. This is primarily due to the frequent and repetitive access and updating of a number of feature vectors corresponding to the same hyperedges and vertices. To address these challenges, we propose the first redundancy-aware accelerator, RAHP, which enables high performance execution of HyperGNN inference. Specifically, we present a redundancy-aware asynchronous execution approach into the accelerator design for HyperGNN to reduce redundant computations and off-chip memory accesses. To unveil opportunities for data reuse and unlock the parallelism that existing HyperGNN solutions fail to capture, it prioritizes vertices with the highest degree as roots, prefetching other vertices along the hypergraph structure to capture the common vertices among multiple hyperedges, and synchronizing the computations of hyperedges and vertices in real-time. By such means, this facilitates the concurrent processing of relevant hyperedge and vertex computations of the common vertices along the hypergraph topology, resulting in smaller redundant computations overhead. Furthermore, by efficiently caching intermediate results of the common vertices, it curtails memory traffic and off-chip communications. To fully harness the performance potential of our proposed approach in the accelerator, RAHP incorporates a topology-driven data loading mechanism to minimize off-chip memory accesses on the fly. It is also endowed with an adaptive data synchronization scheme to mitigate the effects of conflicting updates of both hyperedges and vertices. Moreover, RAHP emplo

关键词： Computational modeling Neural networks Memory management Loading Energy conservation Vectors Software Topology Synchronization Standards

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Review of Nodule Mineral Image Segmentation Algorithms for Deep-Sea Mineral Resource Assessment

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computers, Materials & Continua 2022年第10期73卷 1649-1669页

作者： Wei Song Lihui Dong Xiaobing Zhao Jianxin Xia Tongmu Liu Yuxi Shi School of Information and Engineering Minzu University of ChinaBeijing100081China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural ResourceGuangzhou510300China National Language Resource Monitoring&Research Center of Minority Languages Minzu University of ChinaBeijing100081China School of Ocean Science China University of GeosciencesBeijing100191China Department of Buoy Engineering South China Sea Marine Survey and Technology Center of State Oceanic AdministrationGuangzhou510310China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ07102USA

A large number of nodule minerals exist in the deep *** on the factors of difficulty in shooting,high economic cost and high accuracy of resource assessment,large-scale planned commercial mining has not yet been *** experimental mining has been carried out in areas with high mineral density and obvious benefits after mineral resource *** an efficient method for deep-sea mineral resource assessment,the deep towing system is equipped with a visual system for mineral resource analysis using collected images and videos,which has become a key component of resource ***,high accuracy in deep-sea mineral image segmentation is the primary goal of the segmentation *** this paper,the existing deep-sea nodule mineral image segmentation algorithms are studied in depth and divided into traditional and deep learning-based segmentation methods,and the advantages and disadvantages of each are compared and *** deep learning methods show great advantages in deep-sea mineral image segmentation,and there is a great improvement in segmentation accuracy and efficiency compared with the traditional ***,the mineral image dataset and segmentation evaluation metrics are ***,possible future research topics and improvement measures are discussed for the reference of other researchers.

关键词： Polymetallic nodule deep-sea mining image segmentation deep learning

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ARES-WiGR: An Attention-enhanced ResNet based Wi-Fi Gesture Recognition

ARES-WiGR: An Attention-enhanced ResNet based Wi-Fi Gesture ...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Yao, Kexin Li, Han Liu, Ming Gao, Bo Xiong, Ke Fan, Pingyi Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China Beijing Jiaotong University National Engineering Research Center of Advanced Network Technologies Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China

ISBN: (纸本)9781665494274

This paper proposes an Attention-enhanced ResNet based Wi-Fi Gesture Recognition (ARES-WiGR), in which it first extracts Doppler Frequency Shift (DFS) vector parameters from Channel State Information (CSI) via conjugate multiplication and antenna selection. Then, the DFS spectrogram is obtained by Short Time Fourier Transform (STFT), and the DFS spectrogram features are used as the input of the proposed neural network model. Moreover, with the attention mechanism, ARES-WiGR is able to automatically recognize the important information and achieve better recognition effect. The performance is examined in real environment, which shows that the proposed ARES-WiGR effectively extracts gesture features and improves recognition accuracy to about 96%. © 2023 IEEE.

关键词： Channel state information

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Three factor chaotic map based efficient authentication scheme for smart healthcare systems

Three factor chaotic map based efficient authentication sche...

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International Conference on Electronics, Materials engineering and Nano-technology (IEMENTech)

作者： Vinod Kumar Mahor R. Padmavathy Santanu Chatterjee Department of Computer Science and Engineering National Institute of Technology Warangal India Research Center Imarat(RCI) Vignyana Kancha Hyderabad India

Adoption of IoT in healthcare is called Internet of Medical Things(IoMT). IoMT along with device intelligence are helping to realize the concept of smart healthcare. IoMT contains several different type of medical sensors mounted on the patient's body to collect health parameters at defined frequency and transmits to the medical server $MS$ for long term retention and data analytics. This data is accessed by medical professionals using smart terminal connected to $MS$ through insecure channel communication channel. Health related data is both sensitive and critical and any authorized access or manipulation may lead to endanger patient's life. Hence providing secure access and privacy protection of this data is utmost important. Only few literature proposed to address security needs for IoMT. However, most of the protocols in literature fails to resist known attacks and to provide security features for this context. To address this issue, we propose secure and lightweight mutual authentication protocol using Chebyshev Chaotic Map and symmetric key encryption.

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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1612-1626页

作者： Yixin Wang Shuang Qiu Dan Li Changde Du Bao-Liang Lu Huiguang He the Research Center for Brain-inspired Intelligence National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of ScienceBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 the Beijing Institute of Control and Electronic Technology Beijing 100038China the School of Mathematics and Information Sciences Yantai UniversityYantai 264003China the Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceBeijingChina the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in *** attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration *** solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal *** method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common *** adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of *** experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed *** work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

关键词： Cycle-consistency domain adaptation electroencephalograph(EEG) multi modality variational autoencoder

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Intellicare: Improving Healthcare Analysis with Patient-Level Knowledge from Large Language Models

SSRN

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SSRN 2025年

作者： Yu, Zhihao Jin, Yujie Xu, Yongxin Zhang, Xiaoyun Wang, Yasha School of Computer Science Peking University Beijing100091 China National Research and Engineering Center of Software Engineering Beijing100091 China School and Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing100081 China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing100091 China Peking University Information Technology Institute Tianjin300450 China

While pioneering deep learning methods have made great strides in analyzing electronic health record (EHR) data, they often struggle to fully capture the semantics of diverse medical codes from limited data. The integration of external knowledge from Large Language Models (LLMs) presents a promising avenue for improving healthcare predictions. However, LLM analyses may exhibit significant variance due to ambiguity problems and inconsistency issues, hindering their effective utilization. To address these challenges, we propose IntelliCare, a novel framework that leverages LLMs to provide high-quality patient-level external knowledge and enhance existing EHR models. Concretely, IntelliCare identifies patient cohorts and employs task-relevant statistical information to augment LLM understanding and generation, effectively mitigating the ambiguity problem. Additionally, it refines LLM-derived knowledge through a hybrid approach, generating multiple analyses and calibrating them using both the EHR model and perplexity measures. Experimental evaluations on three clinical prediction tasks across two large-scale EHR datasets demonstrate that IntelliCare delivers significant performance improvements to existing methods, highlighting its potential in advancing personalized healthcare predictions and decision support systems. Our code is available at https://***/yzhHoward/IntelliCare. © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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