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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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Computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Performance Improvement of Blink Types Classification Using a 3D-CNN by Mode Value Correction

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2025年第4期145卷 428-437页

作者： Sato, Hironobu Abe, Kiyohiko Matsuno, Shogo Ohyama, Minoru College of Science and Engineering Kanto Gakuin University 1-50-1 Mutsuura-higashi Kanazawa-ku Kanagawa Yokohama236-8501 Japan School of System Design and Technology Tokyo Denki University 5 Senju Asahi-cho Adachi-ku Tokyo120-8551 Japan Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan

We have developed blink measurement methods that can be applied to input interfaces. To use the eye-blinking information as an input trigger, it is necessary to automatically classify blink types into voluntary and involuntary. A method for blink type classification using a three-dimensional convolutional neural network (3D-CNN) has been proposed. This classification method takes a short image sequence of the periocular area and classifies the blink type. We previously reported on several performance-improving methods that can be applied to this 3D-CNN. Since our classification using 3D-CNN outputs classification results in units of video frames, multiple types of classification results could be mixed together during a period of a single blinking motion. To address this problem, we employ a correction method to calculate the mode value as a representative value for the consecutive blink period. This paper proposes a correction method to improve accuracy based on limiting the aggregation range of the mode to a reliable portion. The evaluation experiment resulted in 97.9% accuracy and 94.0% F-score in the classification results for each short image sequence for 10 subjects. Then, 97.5% accuracy and 97.3% F-score were obtained for the accuracy of blink type classification. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Convolutional neural networks

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Magnitude,distribution and temporal trend of nitrous oxide emissions from China's natural soils over 1980–2022

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science China Earth sciences 2025年第4期68卷 1074-1085页

作者： Songbai HONG Zimeng LI Mingsong TANG Fa LI Yitong YAO Yanzi YAN Mingzhu HE Xuhui WANG Hui ZENG Shilong PIAO School of Urban Planning and Design Shenzhen Graduate SchoolPeking UniversityShenzhen518055China Key Laboratory of Earth Surface System and Human-Earth Relations Ministry of Natural Resources of ChinaShenzhen Graduate SchoolPeking UniversityShenzhen518055China Institute of Carbon Neutrality Sino-French Institute for Earth System ScienceCollege of Urban and Environmental SciencesPeking UniversityBeijing100871China Department of Earth System Science Stanford UniversityStanfordCA94305USA Division of Geological and Planetary Sciences California Institute of TechnologyPasadenaCA91125USA Department of Soil and Environment Swedish University of Agricultural SciencesUppsala75007Sweden Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance Beijing Normal UniversityZhuhai519087China School of National Safety and Emergency Management Beijing Normal UniversityZhuhai519807China

Soils represent the largest contributor to global nitrous oxide(N_(2)O)***,current research efforts predominantly focus on N_(2)O emissions from agricultural soils,while studies on N_(2)O emissions from natural soils,another significant source,remain *** this study,we explored the magnitude,spatiotemporal dynamics,and drivers of N_(2)O emissions from China's natural soils over the period 1980–2022 via machine learning and a compiled novel dataset of 319 field *** results revealed that the N_(2)O flux per unit area was generally higher in the southeast compared to the northwest,and higher in forests than in *** spatial and biome heterogeneity was strongly regulated by local hydrothermal conditions and nitrogen *** averaged N_(2)O emissions over the study period were 646.2±27 Gg N_(2)O yr^(-1),with approximately equivalent contributions from forests and ***,there exhibited a significant and increasing trend from 1980 to 2022,with a rate of 2.7 Gg N_(2)O yr^(-2).Such an increase was primarily driven by the expansion of forested area and elevated nitrogen *** data-driven study presents a long-term and gridded estimate of N_(2)O emissions from China's natural soils,emphasizing the need for future research on changes of greenhouse gas emissions induced by land use change and nitrogen deposition.

关键词： Nitrous oxide Spatiotemporal dynamics Nitrogen deposition Forests Grasslands Data-driven estimates

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An interpretable machine learning-based model of turbulent Prandtl number for supercritical CO2 under buoyancy

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Applied Thermal Engineering 2025年 274卷

作者： Zhang, Ruizeng Yang, Zhengwei Liu, Yu Qiu, Qinggang Zhu, Xiaojing Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education Dalian University of Technology Dalian116024 China 703 Research Institute China State Shipbuilding Corporation Harbin150010 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

Existing studies identify the failure of turbulent heat flux modeling—particularly the constant turbulent Prandtl number (Prt) assumption—as the primary cause of inaccurate heat transfer deterioration (HTD) predictions in supercritical fluids. To address this, we propose a dynamic Prt prediction framework based on interpretable machine learning (ML) to elucidate Prt evolution under buoyancy effects. The model comprises a Prt distribution framework integrating the thermophysical parameter (Pr) and a comprehensive factor fsp that captures system parameter influences, and an ML module that nonlinearly maps multi-parameter effects into fsp, overcoming the limitations of traditional empirical correlations. Results show that the model significantly improves HTD prediction accuracy and reveals that strong buoyancy necessitates a lower Prt to compensate for the turbulent diffusion attenuation. SHAP-based interpretability analysis further quantifies parameter influence hierarchies: heat flux and mass flow rate dominate HTD, inlet temperature, and pressure act independently without interfering with the effects of other parameters on heat transfer, and diameter amplifies the negative effects of heat flux and mass flow rate through synergistic interactions. This study establishes a high-accuracy, mechanically interpretable modeling paradigm for optimizing supercritical systems. © 2025 Elsevier Ltd

关键词： Buoyancy

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Deformation Analysis and Prediction of Drop-Stitch Reinforced Inflatable Robot Link for 1DOF and 2DOF Motion

Deformation Analysis and Prediction of Drop-Stitch Reinforce...

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2025 IEEE/SICE International Symposium on system Integration, SII 2025

作者： Gubbala, Gangadhara Naga Sai Nagashima, Masato Mori, Hiroki Seong, Young Ah Sato, Hiroki Niiyama, Ryuma Suga, Yuki Ogata, Tetsuya Waseda University School of Fundamental Science and Eng. Dept. of Intermedia Art and Science Tokyo165-8555 Japan Waseda University Institute for AI and Robotics Future Robotics Organization Tokyo165-8555 Japan Hosei University Faculty of Eng. and Design Dept. of System Design Tokyo102-8160 Japan Miyagi University School of Project Design Miyagi981-3298 Japan Meiji University School of Science and Technology Tokyo214-8571 Japan Tokyo165-8555 Japan Tokyo100-8921 Japan

ISBN: (纸本)9798331531614

In this study, we observe the dynamic behavior of an inflatable robot arm with an internally reinforced drop-stitch structure. We examine the deformation during motion of 1 and 2 degrees of freedom (DOF) for an inflatable body. The inflatable robot arm has a soft inflatable body as links and rigid servo actuators as joints. We implemented a sinusoidal motion for inflatable links for various payload conditions and analyzed them using a Motion Capture system. To estimate the dynamic deformation of the balloon in motion, we have defined a Deformation Index (DI) metric. Angle, current of the actuator (servo), and DI are used as input to polynomial regression to predict the end effector position. With this analysis, we can understand the complexity of modeling the nonlinear behavior of inflatable links for multi-DOF motion. We observed DI helps improve the prediction of the end effector position by including deformation information. However, the results demonstrate the limitations of polynomial regression analysis of an internally reinforced inflatable robot arm link. © 2025 IEEE.

关键词： End effectors

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Fractional Order Sliding-Mode Observer for Speed and Flux of Induction Motor 11th

Fractional Order Sliding-Mode Observer for Speed and Flux of...

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11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024

作者： Zhou, Minghao Jia, Mingzhe Wang, Dong Chen, Jiaxin Wu, Weiwei Chi, Ying Yu, Shaohuo School of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin150080 China Suzhou Huichuan United Power System Co. Ltd. Suzhou215128 China The China Ship Development & Design Institute Wuhan430064 China Zhejiang Fengli Grinding Equipment Co. LTD Shengzhou312400 China

ISBN: (纸本)9789819788231

A speed-flux observer for the induction motor (IM) based on the fractional order sliding mode (FOSM) is proposed in this paper. Aiming at the problems of system parameter variation, external disturbance, and chattering, a new FOSM observer is designed. The anti-interference of the observer is improved, at the same time, the flux and speed of the motor are estimated, and the smoothness of the output of the estimation curve is improved, so that the estimation results haven shown high accuracy and advantages in dynamic response. Finally, the effectiveness and flexibility of the proposed FOSM method are proved by experiment results. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Sliding mode control

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Multiple Sclerosis Detection with Reinforcement Learning and Differential Evolution

Multiple Sclerosis Detection with Reinforcement Learning and...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Jing Yang, Jin Wu, Chenwei Jiang, Gaozhe Lv, Yaning Liu, Bingyan Xu, Feng Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis United States Evident Battery Inc. Massachusetts United States Institute of Operations Research and Analytics National University of Singapore Singapore School of Mathematics and Statistics Xi'an University of Electronic Science and Technology Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering and Design Technical University of Munich Munich Germany

ISBN: (纸本)9798350368741

Multiple Sclerosis (MS) disrupts nerve communication, potentially leading to permanent damage. Convolutional Neural Networks (CNNs) are commonly recommended to accelerate magnetic resonance imaging (MRI) analysis for MS. Traditional CNN-based methods often face challenges with feature selection, imbalanced classification, and hyperparameter optimization, which can reduce their effectiveness. To address these challenges, this paper introduces a reinforcement learning (RL) strategy for tackling feature selection and class imbalance, complemented by an advanced Differential Evolution (DE) algorithm for hyperparameter tuning. The RL algorithm dynamically adapts, enhancing feature selection and class balance, and consistently outperforms traditional static methods by continually optimizing its response to the complex patterns in MS detection. Furthermore, the DE is enhanced with a novel mutation mechanism that utilizes k-means clustering to identify significant clusters. Tested on the SNAC-MS and ISBI datasets, our model demonstrates robust accuracy with average F-measures of 92.873% and 93.783%, respectively, highlighting its effectiveness and adaptability in diagnosing MS. © 2025 IEEE.

关键词： feature selection hyperparameter optimization imbalanced classification Multiple sclerosis reinforcement learning

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Integration of SDN and Digital Twin for the Intelligent Detection of DoC Attacks in WRSNs

arXiv

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arXiv 2025年

作者： Qaisar, Muhammad Umar Farooq Yuan, Weijie Han, Guangjie Ahmed, Adeel Liu, Chang Piran, Md. Jalil School of Computer Science Northwestern Polytechnical University China School of System Design and Intelligent Manufacturing Southern University of Science and Technology China Department of Internet of Things Engineering Hohai University China School of Computer Science and Technology University of Science and Technology of China China School of Information Engineering Guangdong University of Technology Guangzhou China Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of

Wireless rechargeable sensor networks (WRSNs), supported by recent advancements in wireless power transfer (WPT) technology, hold significant potential for extending network lifetime. However, traditional approaches often prioritize scheduling algorithms and network optimization, overlooking the security risks associated with the charging process, which exposes the network to potential attacks. This paper addresses this gap by integrating Software-Defined Networking (SDN) and Digital Twin technologies for the intelligent detection of Denial of Charging (DoC) attacks in WRSNs. First, it leverages the flexibility and intelligent control of SDN, in combination with Digital Twin, to enhance real-time detection and mitigation of DoC attacks. Second, it employs four key metrics to detect such attacks including charging request patterns, energy consumption, behavioral and reputation scores, and charging behavior and efficiency. The numerical results demonstrate the superior performance of the proposed protocol in terms of energy usage efficiency, survival rate, detection rate, and travel distance. © 2025, CC BY.

关键词： State of charge

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Event-Camera Based Relative Navigation systems for a Rocket Upper-Stage

Event-Camera Based Relative Navigation Systems for a Rocket ...

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AIAA science and technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Murayama, Shingo Takahashi, Masaki Fukuda, Seisuke School of Science for Open and Environmental Systems Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Faculty of Science and Technology Department of System Design Engineering Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama Japan Department of Spacecraft Engineering Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa Sagamihara252-5210 Japan

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Active debris removal has become crucial. The removal of the upper stages of the rocket, which are the most common type of debris, is receiving increased attention. In the case of debris removal from the upper stage of a rocket, it is considered to recognize the circular structure of the upper stage of the rocket, which appears as an ellipse on the image plane, as a payload attach fitting (PAF) using the navigation sensor, and to approach it. Approaching the debris requires tracking the ellipses during the approach after initially recognizing the inner and outer ellipses of the PAF. Thus, the navigation sensor must be robust to sunlight for stable ellipse recognition. Consequently, we propose an initial PAF recognition method using an event camera due to its robust features as a navigation sensor. In the initial recognition, image processing is applied to event frames that accumulate asynchronous output events. As the inner ellipse of the PAF is suitable for feature extraction, we proposed a method to recognize the outer ellipse using the value of the inner ellipse after initial recognition of the inner ellipse. Additionally, when the recognition of the inner or outer ellipse fails, we propose extending the event accumulation time and repeating the process. The effectiveness of the proposed method was verified by numerical analysis using a simulator. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Rockets

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Lattice Boltzmann method for simulation of time-dependent neutral particle transport

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Nuclear science and Techniques 2017年第3期28卷 74-84页

作者： Ya-Hui Wang Li-Ming Yan Bang-Yang Xia Yu Ma School of Energy Science and Engineering Harbin Institute of Technology Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China

In this paper, a novel model is proposed to investigate the neutron transport in scattering and absorbing medium. This solution to the linear Boltzmann equation is expanded from the idea of lattice Boltzmann method(LBM) with the collision and streaming process. The theoretical derivation of lattice Boltzmann model for transient neutron transport problem is proposed for the first *** fully implicit backward difference scheme is used to ensure the numerical stability, and relaxation time and equilibrium particle distribution function are obtained. To validate the new lattice Boltzmann model, the LBM formulation is tested for a homogenous media with different sources, and both transient and steady-state LBM results get a good agreement with the benchmark solutions.

关键词： Transient neutron transport Lattice Boltzmann method Linear Boltzmann equation

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