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Augmenting Internet of Medical Things security:Deep Ensemble Integration and Methodological Fusion

computer Modeling in Engineering & sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani Farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long short-Term Memory(LsTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUsTL-EHMs-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUsTL-EHMs-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

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Estimating Key Phenological Dates of Multiple Rice Accessions Using Unmanned Aerial Vehicle-based Plant Height Dynamics for Breeding

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Rice science 2024年第5期31卷 617-628,I0066-I0070页

作者： HONG Weiyuan LI Ziqiu FENG Xiangqian QIN Jinhua WANG Aidong JIN shichao WANG Danying CHEN song State Key Laboratory of Rice Biology and Breeding China National Rice Research InstituteHangzhou 311400China College of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China College of Agriculture Yangtze UniversityJingzhou 434025China Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjing 210095China

Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DsM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DsM for extracting rice PH was the 95th(R^(2) = 0.934, RMsE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMsE = 4.344 d), IH(R^(2) = 0.877, RMsE = 2.721 d), and FH(R^(2) = 0.883, RMsE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.

关键词： phenological date plant height unmanned aerial vehicle machine learning rice breeding

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Machine learning-based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to springer Nature switzerland AG 2025.

关键词： Adversarial machine learning

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An autoencoder-based feature level fusion for speech emotion recognition

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Digital Communications and Networks 2024年第5期10卷 1341-1351页

作者： Peng shixin Chen Kai Tian Tian Chen Jingying National Engineering Research Center for E-Learning National Engineering Laboratory for Educational Big DataCentral China Normal UniversityHubei430079China

Although speech emotion recognition is challenging,it has broad application prospects in human-computer *** a system that can accurately and stably recognize emotions from human languages can provide a better user ***,the current unimodal emotion feature representations are not distinctive enough to accomplish the recognition,and they do not effectively simulate the inter-modality dynamics in speech emotion recognition *** paper proposes a multimodal method that utilizes both audio and semantic content for speech emotion *** proposed method consists of three parts:two high-level feature extractors for text and audio modalities,and an autoencoder-based feature *** audio modality,we propose a structure called Temporal Global Feature Extractor(TGFE)to extract the high-level features of the timefrequency domain relationship from the original speech *** that text lacks frequency information,we use only a Bidirectional Long short-Term Memory network(BLsTM)and attention mechanism to simulate an intra-modal *** these steps have been accomplished,the high-level text and audio features are sent to the autoencoder in parallel to learn their shared representation for final emotion *** conducted extensive experiments on three public benchmark datasets to evaluate our *** results on Interactive Emotional Motion Capture(IEMOCAP)and Multimodal EmotionLines Dataset(MELD)outperform the existing ***,the results of CMU Multi-modal Opinion-level sentiment Intensity(CMU-MOsI)are ***,experimental results show that compared to unimodal information and autoencoderbased feature level fusion,the joint multimodal information(audio and text)improves the overall performance and can achieve greater accuracy than simple feature concatenation.

关键词： Attention mechanism Autoencoder Bimodal fusion Emotion recognition

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Quantitative GPR Imaging via U-NET: Radargrams Versus Microwave Tomographic Inputs

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IEEE TRANsACTIONs ON GEOsCIENCE AND REMOTE sENsING 2025年 63卷

作者： Esposito, Giuseppe soldovieri, Francesco Gennarelli, Gianluca Natl Res Council Italy CNR Inst Electromagnet Sensing Environm IREA I-80128 Naples Italy

Ground penetrating radar (GPR) imaging is mostly tackled by resorting to approximate linear inversion algorithms that provide only qualitative maps of the probed scene in terms of location and approximate geometry of the buried anomalies. Deep-learning (DL) techniques have recently been proposed to retrieve quantitative information as an alternative to classical non-linear inversion approaches. Indeed, deep neural networks can effectively learn to map the input data into spatial maps describing the electromagnetic (EM) properties of the targets. In this frame, the present article considers the popular U-NET topology for performing quantitative subsurface imaging. Two different training strategies differing for the type of input data are examined and compared. The first one assumes the radargram in the time domain as the network input;differently, in the second one, the network takes as input a microwave tomographic image of the subsurface scene. Numerical results based on full-wave synthetic data and some experimental tests are reported to assess and compare the reconstruction performance of both training schemes.

关键词： Imaging Radar imaging Tomography Training soil Inverse problems Permittivity Microwave theory and techniques Microwave imaging Image reconstruction Deep learning (DL) ground penetrating radar (GPR) imaging microwave tomography (MWT) U-NET

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FCLA-DT: Federated Continual learning with Authentication for Distributed Digital Twin-based Industrial IoT

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Journal of Communications and Information Networks 2025年第4期9卷 362-373页

作者： Xia, Yingjie Liu, Xuejiao Zhao, Yunxiao Wang, Yun Micro-Electronics Research Institute Hangzhou Dianzi University Hangzhou310005 China School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China School of Cyberspace Security Technology Hangzhou Dianzi University Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310012 China Hangzhou Yuantiao Technology Company Hangzhou310036 China

Digital twin (DT) technology is currently pervasive in industrial Internet of things (IoT) appli-cations, notably in predictive maintenance scenarios. Prevailing digital twin-based predictive maintenance methodologies are constrained by a narrow focus on singular physical modeling paradigms, impeding comprehensive analysis of diverse factory data at scale. This paper introduces an improved method, federated continual learning with authentication for distributed digital twin-based industrial IoT (FCLA-DT). This decentralized strategy ensures the continual learning capability vital for adaptive and real-time decision-making in complex industrial predictive maintenance systems. An authentication scheme based on group signature is introduced to enable the verification of digital twin identities during inter-twin collaborations, avoiding unauthorized access and potential model theft. security analysis shows that FCLA-DT can enable numerous nodes to collaborate learning without compromising individual twin privacy, thereby ensuring group authentication in the cooperative distributed industrial IoT. Performance analysis shows that FCLA-DT outperforms traditional federated learning methods with over 95% fault diagnosis accuracy and ensures the privacy and authentication of digital twins in multi-client task learning. © 2025, Posts and Telecom Press Co Ltd. All rights reserved.

关键词： Federated learning

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On Task and in sync: Examining the Relationship between Gaze synchrony and self-Reported Attention During Video Lecture learning

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Proceedings of the ACM on Human-computer Interaction 2024年第ETRA期8卷 1-18页

作者： Bühler, Babette Bozkir, Efe Deininger, Hannah Gerjets, Peter Trautwein, Ulrich Kasneci, Enkelejda Hector Research Institute of Education Sciences and Psychology University of Tübingen Tübingen Germany University of Tübingen & Technical University of Munich Tübingen Germany Leibniz-Institut für Wissensmedien Tübingen Germany Technical University of Munich Munich Germany

successful learning depends on learners’ ability to sustain attention, which is particularly challenging in online education due to limited teacher interaction. A potential indicator for attention is gaze synchrony, demonstrating predictive power for learning achievements in video-based learning in controlled experiments focusing on manipulating attention. This study (N=84) examines the relationship between gaze synchronization and self-reported attention of learners, using experience sampling, during realistic online video learning. Gaze synchrony was assessed through Kullback-Leibler Divergence of gaze density maps and MultiMatch algorithm scanpath comparisons. Results indicated significantly higher gaze synchronization in attentive participants for both measures and self-reported attention significantly predicted post-test scores. In contrast, synchrony measures did not correlate with learning outcomes. While supporting the hypothesis that attentive learners exhibit similar eye movements, the direct use of synchrony as an attention indicator poses challenges, requiring further research on the interplay of attention, gaze synchrony, and video content type. © 2024 Copyright held by the owner/author(s).

关键词： Eye tracking

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spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： sannasi Chakravarthy Bharanidharan Nagarajan surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a spatial Attention Layer with XGBoost(EsA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIs-DDsM,INbreast,and MIAs *** accuracy of 97.585%(CBIsDDsM),98.255%(INbreast),and 98.91%(MIAs)is obtained using the proposed EsA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed EsA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

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Trade-off Analysis between Knowledge Distillation and Federated learning in Distributed Edge system 24

Trade-off Analysis between Knowledge Distillation and Federa...

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33rd International symposium on High-Performance Parallel and Distributed Computing (HPDC)

作者： Joaquim, Molo Mbasa Univ Pisa Pisa Italy Natl Res Council Italy Pisa Italy

ISBN: (纸本)9798400704130

Recent years have witnessed significant advancements in Artificial Intelligence (AI), particularly with the rise of Deep Neural Networks fueled by large datasets and increased model complexity. However, the demand for substantial computational resources poses challenges in centralized data scenarios. Edge Intelligence (EI), combining Edge Computing and AI, emerges as a transformative solution for decentralized learning, crucial in the era of IoT proliferation. While Federated learning (FL) has been a prominent paradigm in decentralized learning, its limitations have prompted researchers to explore alternative solutions using Knowledge Distillation (KD) as a basis. The purpose of this Ph.D. research is to explore KD as a new paradigm for decentralized learning, contribute to enhancing its performance, and study the trade-off between FL and KD in terms of efficiency and effectiveness to identify best practices and insights in EI environments.

关键词： Knowledge Distillation computer Vision Object Recognition Deep learning Edge Intelligence

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Wavelet Transform Convolution and Transformer-based learning Approach for Wind Power Prediction in Extreme scenarios

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computer Modeling in Engineering & sciences 2025年第4期143卷 945-965页

作者： Jifeng Liang Qiang Wang Leibao Wang Ziwei Zhang Yonghui sun Hongzhu Tao Xiaofei Li Electric Power Research Institute State Grid Hebei Electric Power Co.Ltd.Shijiazhuang050021China State Grid Hebei Electric Power Co. Ltd.Shijiazhuang050021China College of Artificial Intelligence and Automation Hohai UniversityNanjing210098China China National Power Dispatching and Control Center State Grid Corporation of ChinaBeijing100031China China Electric Power Research Institute Co. Ltd.Beijing210037China

Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power *** wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage *** enhances the efficiency of wind power integration into the *** allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power ***,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance *** these benefits,accurate wind power prediction especially in extreme scenarios remains a significant *** address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and ***,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under ***,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input *** model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling *** analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global *** to other models,it achieves R-squared(R^(2))as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMsE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex w

关键词： Extreme scenarios conditional generative adversarial network wavelet transform Transformer wind power prediction

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