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Flower automata pattern-based discrimination of fibromyalgia from control subjects using fusion of sleep EEG and ECG signals

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IEEE Access 2025年 13卷 99032-99047页

作者： Barua, Prabal Datta Kobayashi, Makiko Dogan, Sengul Baygin, Mehmet Tuncer, Turker Paul, Jose Kunnel Iype, Thomas Acharya, U.R. Australia Kumamoto University Graduate School of Science and Technology Kumamoto860-8555 Japan Kumamoto860-8555 Japan Firat University Technology Faculty Department of Digital Forensics Engineering Elazig Turkey Erzurum Technical University Faculty of Engineering and Architecture Department of Computer Engineering Erzurum Turkey Government Medical College Department of Neurology Kerala Thiruvananthapuram India University of Southern Queensland School of Mathematics Physics and Computing Springfield Australia

Electroencephalogram (EEG) and electrocardiogram(ECG) signals provide vital insights into brain and heart activity and are widely used in automated medical diagnostics. This study introduces a novel, multimodal fibromyalgia detection system developed by the fusion of EEG and ECG signals recorded during sleep stages 2 and 3. The novelty of the model is the use of dynamic and interpretable feature engineering framework comprising of two innovations: (i) Flower Automata Pattern (FAP) for self-organized pattern-based feature extraction, and (ii) Attention-Driven Wavelet Transform and Absolute Maximum Pooling (ADWTAMP) method for signal decomposition and compression. Three feature selection strategies—Neighborhood Component Analysis (NCA), Chi2, and the intersection of NCA and Chi2 (NCA˄Chi2) —are employed to generate robust feature vectors, which are classified using k-nearest neighbors (kNN) and support vector machine (SVM) under the leave-one-record-out cross-validation (LORO CV) scheme. The final decision is derived through an iterative voting and greedy fusion approach. The proposed model achieved classification accuracies of 99.36% and 98.37% for sleep stages 2 and 3, respectively. Key advantages of the model include its high accuracy, low computational requirements (CPU-only execution), and explainable architecture. To the best of our knowledge, this is the first multimodal automata-based classification framework designed for fibromyalgia detection. © 2013 IEEE.

关键词： Electrocardiography

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Research on fault arc detection method based on multimodal fusion strategy

Research on fault arc detection method based on multimodal f...

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2024 International Conference on Energy technology and Electrical Power, ETEP 2024

作者： Li, Zhaoqiang Liu, Yang Liang, Zhihua Huang, Ying Feng, Pingfa Wang, Xiaohao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518313 China Research Center Sunfly Holding Co. Ltd. Foshan528300 China School of Computer Science & Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9781510690400

With the increasing number of electrical fires caused by fault arc, effectively identifying the fault has become a very meaningful thing. According to actual product testing, it has been found that many fault arc detection products on the current market have high level of both false positive rate (FPR) and false negative rate (FNR). A high FPR refers to frequent misleading alarms, which may lead to unexpected power outages. And a high FNR means that fault arc has occurred but the system has not detected it, which may lead to a fire. The contribution of this study is the proposal of a multimodal fault arc recognition algorithm, which effectively reduces the FPR and FNR of the arc detection system, and helps to improve the comprehensive performance of the electrical safety warning system. © 2025 SPIE.

关键词： Fire alarm systems

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Ubi-Care: An Elderly Life Support Healthcare Framework Based on Ubiquitous Personal Online Data Stores

Ubi-Care: An Elderly Life Support Healthcare Framework Based...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Hong Zhou, Tao Wu, Bo Graduate School of Human Sciences Waseda University Saitama359-1192 Japan Daiichi Institute of Technology Department of Information and Electronics System Engineering Taito Tokyo110-0005 Japan School of Computer Science Tokyo University of Technology Hachioji Tokyo192-0982 Japan

ISBN: (纸本)9781665410205

In recent years, the global aging population has intensified, leading to a sharp increase in social security benefits and caregiving costs. The elderly face greater health risks, and their behaviors often indicate signs of crises and illnesses. The rapid development of IoT technology offers new solutions for detecting abnormal signs, thereby promoting healthy aging, independent living, and social participation for the elderly. However, the diversity of IoT devices has led to the phenomenon of personal data silos. When the elderly leave their usual IoT environment, the issue of continuity in healthcare services becomes more pronounced. To address this issue, this study proposes a decentralized Ubi-Care framework. The framework aims to achieve complete separation of data and applications. It is based on the ActivityPub protocol and integrates various data from wearable devices, smart home sensors, and social networks, storing this data in a ubiquitous personal online data store (UPOD) and assigning different roles based on data type. UPOD applications support bidirectional following, allowing users to access data associated with relevant roles, addressing issues related to data categorization, sharing, privacy, and security. Additionally, this study proposes a method for utilizing complete UPOD data to perform anomaly detection based on the hidden Markov model (HMM). By effectively integrating IoT data into UPOD and enhancing data interoperability. The data-sharing model proposed in this study also facilitates elderly individuals and their family members in sharing relevant data as needed, while ensuring privacy protection. The methods proposed in this study will help prevent accidental injuries and enable early diagnosis of diseases, providing strong technical support for elderly healthcare. © 2024 IEEE.

关键词： Electronic health record

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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

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作者： Yuto Inokuchi Pedro Henrique González Masayoshi Aritsugi Israel Mendonça Kumamoto University Graduate School of Science and TechnologyKumamotoJapan Federal University of Rio de Janeiro System Engineering and Computer Science ProgramRio de JaneiroBrazil Kumamoto University Faculty of Advanced Science and TechnologyKumamotoJapan

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy.

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AI-Enhanced Resource Allocation for LPWAN-based LoRaWAN:A Hybrid TinyML and Deep Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Lodhi, Muhammad Ali Sun, Xiaobing Mahmood, Khalid Lodhi, Anum Park, Youngho Hussain, Majid Yangzhou University School of Information Engineering Yangzhou China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of computer science and engineering China Kyungpook National University School of Electronics Engineering Daegu41566 Korea Republic of The University of Faisalabad Department of Computer Science Faisalabad38850 Pakistan

The integration of Artificial Intelligence (AI) with Low Power Wide Area Networks (LPWAN) offers a promising approach to address resource constraints and dynamic network conditions inherent in these networks. However, deploying complex AI algorithms on resource-limited edge devices presents significant challenges due to their limited computational capabilities. In this study, we propose a hybrid Tiny Machine Learning (TinyML) and Deep Neural Network (DNN)-based solution for optimizing resource allocation in LPWAN-based LoRaWAN networks, targeting both static and mobile applications. Our approach leverages the strengths of a 1-D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model implemented on the network server, combined with TinyML models deployed on edge devices. The CNN-LSTM model predicts optimal spreading factor and transmission power by analyzing spatial and temporal patterns from real-time data, while the TinyML models enable edge devices to autonomously adjust communication parameters in resource-constrained and disconnected scenarios. This hybrid framework enhances network performance by improving the packet success ratio (PSR), maximizing energy efficiency, and addressing the challenges posed by dynamic IoT environments. © 2014 IEEE.

关键词： Deep neural networks

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Handling Problems of Credit Data for Imbalanced Classes using SMOTEXGBoost 3

Handling Problems of Credit Data for Imbalanced Classes usin...

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3rd International Conference on Computational science and Information Management, ICoCSIM 2019

作者： Mardiansyah, Heru Widia Sembiring, Rahmat Efendi, Syahril Graduate Program of Computer Science Department of Information Technology Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medam Indonesia

Some researchers find data with imbalanced class conditions, where there are data with a number of minorities and a majority. SMOTE is a data approach for an imbalanced classes and XGBoost is one algorithm for an imbalanced data problems. This research uses SMOTE and XGBoost or abbreviated as SMOTEXGBoost for handling data with an imbalanced classes. The results showed almost the same accuracy value between SMOTE and SMOTEXGBoost at 99%. While the value of AUC SMOTEXBoost has a more stable value than SMOTE that is equal to 99.89% for training and 98.51% for testing. © Published under licence by IOP Publishing Ltd.

关键词： Data handling

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A Systematic Literature Review of Recent Trends and Challenges in Digital Twin Implementation 10

A Systematic Literature Review of Recent Trends and Challeng...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Arin, Ikrar Adinata Meyliana Warnars, Harco Leslie Hendric Spits Murad, Dina Fitria Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350339543

This paper reviews the latest trends and challenges in implementing digital twin technology. A digital twin is a tool used in various industries to improve efficiency, optimize processes, and enable advanced analysis. The review involved searching major research databases and search engines for articles published between 2018 and 2023. The findings reveal several important trends, including the development of different types of digital twin dimensions, each with its own advantages and limitations. The benefits of digital twin implementation include improved decision-making, increased productivity, and operational efficiency. However, there are challenges, such as data integration, security and privacy concerns, a lack of standardization, and the need for experts to effectively design and operate digital twins. The implications of these trends and challenges are discussed regarding their impact on the successful adoption and implementation of digital twin technology. The review also highlights the need to address these challenges and explore new approaches for maximizing the benefits of digital twin technology. Overall, this comprehensive review is a valuable resource for researchers, practitioners, and organizations seeking to understand the current landscape, identify areas for improvement, and make informed decisions when implementing digital twin technology. © 2023 IEEE.

关键词： Decision making

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UAV Trajectory Optimization Considering User Pattern and Communication Coverage Fairness

UAV Trajectory Optimization Considering User Pattern and Com...

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2022 International Symposium on Networks, computers and Communications, ISNCC 2022

作者： Zhang, Jianfang Liu, Yaxi Huangfu, Wei University of Science and Technology Beijing Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services School of Computer & Communication Engineering Beijing China Shunde Graduate School of University of Science and Technology Beijing Guangdong China

ISBN: (数字)9781665485449

ISBN: (纸本)9781665485449

Optimizing the trajectory of Unmanned Aerial Vehicle (UAV) Base Station (BS) is an important operational task to improve the Quality of Service (QoS) for remote areas. However, existing works mainly neglect the fair coverage and dynamic Ground Users (GUs). In this paper, we propose a novel coverage fairness index (CFI) to measure whether dynamic GUs are served as fairly as possible. Then, we formulate the problem as a constrained problem with the objective of maximizing fair coverage and minimizing energy consumption while satisfying the bound constraints. An accurate and efficient Soft-Actor-Critic (SAC)-based UAV trajectory optimization algorithm is proposed to solve the complex constrained problem based on deep reinforcement learning. Experiments are executed to prove the feasibility and efficiency of the proposed algorithm. The results manifest that the performance of the proposed algorithm is better than that of the two existing baseline methods. © 2022 IEEE.

关键词： Energy utilization

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Intensity-Dependent Tacs Entrainment Effects in a Cortical Microcircuit: A Computational Study

SSRN

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SSRN 2025年

作者： Park, Kyeongseop Chung, Hyeyeon Seo, Hyeon Jun, Sung Chan School of Electrical Engineering and Computer Science Gwangju Institute of Science & Technology Gwangju Korea Republic of Department of Computer Science and Engineering Gyeongsang National University Jinju Korea Republic of AI Graduate School Gwangju Institute of Science & Technology Gwangju Korea Republic of

Transcranial alternating current stimulation (tACS) is a promising noninvasive technique for modulating disrupted neural oscillations in psychiatric disorders and enhancing cognitive functions. However, its efficacy remains debated, partly because neuronal morphology and other microscopic factors critically affect the response to external electric fields. To address this issue, we developed a cortical microcircuit model integrating realistic neuronal morphologies, synaptic connectivity, and intrinsic oscillatory dynamics. This model enabled us to investigate cellular- and network-level mechanisms underlying tACS-induced neural entrainment. Using the NEURON simulation environment, we constructed a microcircuit consisting of five biophysical cortical neuron models—including layer 1 neurogliaform cell, layer 4 large basket cell, and pyramidal cells from layers 2/3, 5, and 6. Neural responses to a range of tACS intensities were assessed with metrics based on cross-correlation, phase coherence, and phase-locking value (PLV). Our computational study reveals that tACS effects arise from a complex interplay between intrinsic neuronal properties and network dynamics. While tACS modulates spike timing without significantly altering firing rates, pyramidal neurons—owing to their extensive, directionally oriented dendritic trees—are particularly sensitive to external fields compared to interneurons. In addition, tACS can either disrupt or enhance synchronization depending on the endogenous oscillation and stimulation intensity. These findings underscore the importance of neuronal morphology in determining tACS responses and provide insights that may help optimize stimulation parameters for precise neuromodulation in both clinical and research settings. © 2025, The Authors. All rights reserved.

关键词： Neurons

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Dynamic Antenna Control for HAPS Using Mean Field Reinforcement Learning in Multi-Cell Configuration

Dynamic Antenna Control for HAPS Using Mean Field Reinforcem...

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IEEE International Conference on Communications (ICC)

作者： Siyuan Yang Mondher Bouazizi Tomoaki Ohtsuki Graduate School of Science and Technology Keio University Department of Information and Computer Science Faculty of Science and Technology Keio University

In this paper, we propose a Mean Field reinforcement learning (MFRL) method for dynamic antenna control in High-Altitude-Platform-Station (HAPS) communication system with Multi-Cell Configuration. HAPS works at stratospheric altitudes of about 20 km to provide an ultra-wide coverage area. However, the wind pressure caused HAPS movement leads to the degradation of users' throughput. Considering the multi-antenna arrays in the HAPS, to find the optimal antenna parameters of all antenna arrays for reducing the number of low-throughput users, we formulate the antenna control problem into stochastic game equilibrium. Usually, solving the stochastic to find the equilibrium needs very high computation complexity to calculate the transition probability for getting the $\mathcal{Q}$ -value under a certain state and action. Therefore, we use the reinforcement learning (RL) named Deep $\mathcal{Q}$ -Network (DQN) to learn the transition probability and predict the Q-value according to the reward fed backed from the environment. Besides, we employ the Mean field Game theory in conjunction with RL during the training phase of DQN to reduce the complexity of the interactions among agents. To evaluate the proposed method, we compare the proposed method with a genetic algorithm (GA) named Particle Swarm Optimization (PSO), $\mathcal{Q}$ -learning, Fuzzy $\mathcal{Q}$ -learning, and conventional DQN under four realistic user distribution scenarios. The simulation results show that the proposed method achieves comparable throughput performance with a high convergence rate.

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