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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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Scrutiny In-Utero to recognize Fetal Brain MRI Anomalies 3

Scrutiny In-Utero to recognize Fetal Brain MRI Anomalies

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3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Durgadevi, P. Vijayalakshmi, S. Sabbharwal, Munish Galgotias University School of Computing Science and Engineering India Computer Science and Engineering Galgotias College of Engineering and Technology India Christ University Department of Data Science India

ISBN: (纸本)9781665438117

In utero MRI distinguishes fbrain irregularities high precisely compared to ultrasonography as well as gives extra medical data during the pregnancies. fMRI is medically performed to get the knowledge of the brain in conditions where the inconsistency are perceived with the help of pre-birth sonography. These are common regularly solidify ventriculomegaly, not regular of the corpus callosum, and oddities of the back fossa. Fbrain inconsistencies can cause authentic brain hurt. Therefore, it is vital to recognize them from the get-go in their course so treatment can be managed to the mother, if conceivable. The job of imaging is to decide the presence, assuming any, and the degree of brain harm in the contaminated hatchling. Even though MRI is most generally utilized as a subordinate to sonography when clinical doubt is high in the setting of a typical ultrasound or to all the more likely characterize irregularities recognized by ultrasound, MRI is regularly utilized in toxoplasmosis seroconversion to conclusively preclude brain injuries, in any event, when the ultrasound examination is viewed as ordinary. X-ray is likewise utilized sequentially all through the pregnancy to check for the improvement of brain anomalies;clinical treatment brings about the astounding clinical result if the brain is typical. Intracranial irregularities are ordinarily speculated discoveries on antenatal US that are needed for assessment which is used by MRI. This audit portrays numerous irregularities imaged as a way to direct clinicians' inappropriate determination. © 2021 IEEE.

关键词： Magnetic resonance imaging

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Differences of communication activity and mobility patterns between urban and rural people

arXiv

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arXiv 2023年

作者： Ogushi, Fumiko Roy, Chandreyee Kaski, Kimmo Center for Mathematical Modeling and Data Science Osaka University Japan Department of Computer Science Aalto University School of Science Espoo Finland The Alan Turing Institute London United Kingdom

Human mobility and other social activity patterns influence various aspects of society such as urban planning, traffic predictions, crisis resilience, and epidemic prevention. The behaviour of individuals, like their communication frequencies and movements, are shaped by societal and socio-economic factors. In addition, the differences in the geolocation of people as well as their gender and age cast effects on their activity patterns. In this study we focus on investigating these patterns by using mobile phone data, specifically the call detail records (CDRs), to analyze the social communication and mobility patterns of people. This dataset can provide us insight into the individual and population-level behaviours in rural and urban environments on a daily, weekly and seasonal basis. The results of our analyses show that in the urban areas people have high calling activity but low mobility, while in the rural areas they show the opposite behaviour, i.e. low calling activity combined with high mobility. Overall, there is a decreasing trend in people’s mobility through the year even though their calling activity remained consistent except for the holidays during which time the communication frequency drops markedly. We have also observed that there are significant differences in the mobility between the work days and free days. Finally, the age and gender of individuals have also been observed to play a role in the seasonal patterns differently in urban and rural areas. Copyright © 2023, The Authors. All rights reserved.

关键词： Rural areas

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Contextual Combinatorial Beam Management via Online Probing for Multiple Access mmWave Wireless Networks

arXiv

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arXiv 2024年

作者： Li, Zhizhen Luo, Xuanhao Chen, Mingzhe Xu, Chenhan Mao, Shiwen Liu, Yuchen The Department of Computer Science North Carolina State University United States The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Due to the exponential increase in wireless devices and a diversification of network services, unprecedented challenges, such as managing heterogeneous data traffic and massive access demands, have arisen in next-generation wireless networks. To address these challenges, there is a pressing need for the evolution of multiple access schemes with advanced transceivers. Millimeter-wave (mmWave) communication emerges as a promising solution by offering substantial bandwidth and accommodating massive connectivities. Nevertheless, the inherent signaling directionality and susceptibility to blockages pose significant challenges for deploying multiple transceivers with narrow antenna beams. Consequently, beam management becomes imperative for practical network implementations to identify and track the optimal transceiver beam pairs, ensuring maximum received power and maintaining high-quality access service. In this context, we propose a Contextual Combinatorial Beam Management (CCBM) framework tailored for mmWave wireless networks. By leveraging advanced online probing techniques and integrating predicted contextual information, such as dynamic link qualities in spatial-temporal domain, CCBM aims to jointly optimize transceiver pairing and beam selection while balancing the network load. This approach not only facilitates multiple access effectively but also enhances bandwidth utilization and reduces computational overheads for real-time applications. Theoretical analysis establishes the asymptotically optimality of the proposed approach, complemented by extensive evaluation results showcasing the superiority of our framework over other state-of-the-art schemes in multiple dimensions. © 2024, CC BY.

关键词： Transceivers

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Preprocessing technology of consuming big data based on user interest with internet of location mining

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International Journal of computers and Applications 2021年第2期43卷 147-152页

作者： Dagao, Duan Yang, Gao Department of Computer Science and Information Engineering Beijing Technology and Business University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China

The current group discovery research mainly uses the communication relationship to mine user groups and fails to make full use of the user social relationships implied in the network, so that the mining groups can not truly reflect the user’s social relations in the social life. In this paper, the consumer big data preprocessing technology based on user interest location mining is proposed. The sequence pattern mining algorithm is used to mine user’s positional regularity sequence, and the position sequence correlation metrics are established. The positional regularity correlation is used to reveal the social relationship of the user’s social activities. Second, this paper combines the local similarity measure method to calculate the user’s communication distance index, which reflects the degree of understanding between users. Finally, using the communication distance index to weight the position correlation to calculate the user group correlation and then use the split clustering algorithm to mine user groups with communication relationship and social relationship. The experimental results showed that this method can effectively mine the communication relevance and position correlation in the user social activities and reflect the user’s group relations in real social activities. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： data mining

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Enhancing Cloud Security Implementing AI-Based Intrusion Detection Systems 1

Enhancing Cloud Security Implementing AI-Based Intrusion Det...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Santharam, Vinoth Thoguluva Yallamelli, Akhil Raj Gaius Altemimi, Mohammed Alaa H. Arunachalam, Mahesh Prabu Rudrank, Shukla Ghule, Gauri Data Analytics Organization-Comcast Chennai India Amazon Web Services Inc Seattle United States Al-Khwarizmi College of Engineering The University of Baghdad Department of Information and Communication Engineering Baghdad Iraq Department of Information Technology and Computer Science AustinTX United States Kalasalingam Academy of Research and Education Deemed to be University Tamilnadu India Department of E and TC Pune India

ISBN: (纸本)9798331518578

The increasing complexity of assaults necessitates the use of innovative intrusion detection systems (IDS) to safeguard critical assets and data. There is a higher risk of cyberattacks like data breaches and unauthorised access since cloud services have been used more frequently. The project's goal is to find out how Artificial Intelligence (AI) could enhance the IDS's ability to identify and classify network traffic and identify anomalous activities. Online dangers could be identified with IDS. An intrusion detection system, or IDS, is required to keep networks secure. We must create efficient IDS for the cloud platform as well, since it is constantly growing and permeating more aspects of our daily life. However, using standard intrusion detection systems in the cloud may provide challenges. The pre-established IDS design may overburden a cloud segment due to the additional detection overhead. Within the framework of an adaptively designed networked system. We demonstrate how to fully use available resources without placing undue load on any one cloud server using an intrusion detection system (IDS) based on neural networks. To even more successfully detect new threats, the suggested IDS make use of neural network machine learning (ML). © 2025 IEEE.

关键词： Network intrusion

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Active Model Learning for Software Interrogation and Diagnosis

Active Model Learning for Software Interrogation and Diagnos...

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Adam A. Porter Alan F. Karr Department of Computer Science USA and Fraunhofer USA Center Mid-Atlantic University of Maryland College Park Department of Statistics Operations and Data Science USA and Fraunhofer USA Center Mid-Atlantic Temple University Philadelphia

ISBN: (数字)9798350344790

ISBN: (纸本)9798350344806

We propose extension of the concept of software correctness, and the associated task of software testing, to include interrogation and diagnosis, whereby actionable knowledge about the limitations and performance of the software is gleaned. The new paradigm is especially relevant for AI-based systems such as classifiers and large language models (LLMs) that involve interactions among complex software, underlying training/reference data and the analysis/input data. In this context, the need for both interrogation-to identify problematic outputs when there may be no measure of correctness-and diagnosisto determine where the problem lies-is evident. To make our rhetoric concrete, we present two case studies. Classifier boundaries enable characterization of robustness of classifier output and fragility of inputs. Cliques in metagenomic assembly provide novel insight into the software as well as the potential for performance improvement.

关键词： Software testing Analytical models Uncertainty Measurement uncertainty Training data Software systems data models

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EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence 23

EdgeMove: Pipelining Device-Edge Model Training for Mobile I...

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2023 World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computing Macquarie University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

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Enhancing Fetal Care and Maternal Health with Machine Learning: A Systematic Review

Enhancing Fetal Care and Maternal Health with Machine Learni...

引用

Intelligent Computing and Emerging Communication Technologies (ICEC), International Conference on

作者： Chetan Puri K. T. V Reddy Department of Computer Science and Design Datta Meghe Institute of Higher Education and Research(DU) Wardha India Department of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research(DU) Wardha India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

Fetal growth restriction and preterm delivery proceed to be major around the world wellbeing concerns, with serious consequences for the wellbeing of moms and babies. Provoke and exact estimating of these issues is basic for prompt action. In spite of the fact that progresses have been made in conventional clinical assessment approaches, the potential of computational apparatuses to back clinical decision-making is getting to be progressively clear. With its capacity to assess complex data patterns, machine learning (ML) has gotten to be a reasonable procedure for making strides pre-birth care. The review's conclusions highlight ML's potential to upgrade fetal care. Various examinations yielded empowering discoveries, as machine learning models outperformed routine risk evaluation instruments in terms of anticipated exactness. Numerous pregnancy issues, counting fetal growth limitation and untimely conveyance, have been viably anticipated by machine learning calculations. Healthcare experts can reduce unfavorable results by utilizing focused medicines to distinguish high-risk pregnancies early on. In any case, obstacles and confinements anticipate machine learning from being broadly utilized in restorative settings. Given that solid machine learning models require expansive and representative datasets, information accessibility and quality stay significant concerns. It is vital to ensure information security and take moral issues into consideration. Besides, ML models regularly have destitute interpretability, which makes it challenging to comprehend the basic components directing predictions. Future considers ought to concentrate on making and assessing machine learning models over a extend of demographics, improving the interpretability of models, and incorporating ML into clinical strategies. By handling these issues, machine learning (ML) has the potential to make strides the wellbeing of expectant women and their babies.

关键词： Pregnancy Pediatrics Visualization Solid modeling Temperature Machine learning Medical services Solids Hazards Systematic literature review

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BiSHop: bi-directional cellular learning for tabular data with generalized sparse modern hopfield model 24

BiSHop: bi-directional cellular learning for tabular data wi...

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Proceedings of the 41st International Conference on Machine Learning

作者： Chenwei Xu Yu-Chao Huang Jerry Yao-Chieh Hu Weijian Li Ammar Gilani Hsi-Sheng Goan Han Liu Department of Computer Science Northwestern University Evanston IL Department of Physics and Center for Theoretical Physics National Taiwan University Taipei Taiwan Department of Physics and Center for Theoretical Physics and Center for Quantum Science and Engineering National Taiwan University Taipei Taiwan and Physics Division National Center for Theoretical Sciences Taipei Taiwan Department of Computer Science and Department of Statistics and Data Science Northwestern University Evanston IL

We introduce the Bi-Directional Sparse Hopfield Network (BiSHop), a novel end-to-end framework for tabular learning. BiSHop handles the two major challenges of deep tabular learning: non-rotationally invariant data structure and feature sparsity in tabular data. Our key motivation comes from the recently established connection between associative memory and attention mechanisms. Consequently, BiSHop uses a dual-component approach, sequentially processing data both column-wise and row-wise through two interconnected directional learning modules. Computationally, these modules house layers of generalized sparse modern Hopfield layers, a sparse extension of the modern Hopfield model with learnable sparsity. Methodologically, BiSHop facilitates multi-scale representation learning, capturing both intra-feature and inter-feature interactions, with adaptive sparsity at each scale. Empirically, through experiments on diverse real-world datasets, BiSHop surpasses current SOTA methods with significantly fewer HPO runs, marking it a robust solution for deep tabular learning. The code is available on GitHub; future updates are on arXiv.

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