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Atherosclerotic plaque classification in carotid ultrasound images using machine learning and explainable deep learning

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Intelligent Medicine 2024年第2期4卷 83-95页

作者： Soni Singh Pankaj K.Jain Neeraj Sharma Mausumi Pohit Sudipta Roy School of Vocational Studies and Applied Sciences Gautam Buddha UniversityGreater NoidaUttar PradeshIndia Artificial intelligence and data Science Jio Institute Navi MumbaiMaharashtraIndia School of Biomedical engineering Indian Institute of TechnologyVaranasiUttar PradeshIndia

Objective The incidence of cardiovascular diseases(CVD)is rising rapidly *** forms of CVD,such as stroke and heart attack,are more common among patients with certain *** development is a major factor underlying cardiovascular events,such as heart attack and stroke,and its early detection may prevent such *** imaging of carotid arteries is a useful method for diagnosis of atherosclerotic plaques;however,an automated method to classify atherosclerotic plaques for evaluation of early-stage CVD is ***,we propose an automated method for classification of high-risk atherosclerotic plaque ultrasound *** Five deep learning(DL)models(VGG16,ResNet-50,GoogLeNet,XceptionNet,and SqueezeNet)were used for automated classification and the results compared with those of a machine learning(ML)-based technique,involving extraction of 23 texture features from ultrasound images and classification using a Support Vector Machine *** enhance model interpretability,output gradient-weighted convolutional activation maps(GradCAMs)were generated and overlayed on original *** A series of indices,including accuracy,sensitivity,specificity,F1-score,Cohen-kappa index,and area under the curve values,were calculated to evaluate model *** output images allowed visualization of the most significant ultrasound image *** GoogLeNet model yielded the highest accuracy(98.20%).Conclusion ML models may be also suitable for applications requiring low computational ***,DL models could be more completely automated than ML models.

关键词： Explainable deep learning Carotid artery Classification VGG 16 ResNet-50 GoogLeNet XceptionNet SqueezeNet

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Sparse convolutional model with semantic expression for waste electrical appliances recognition

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Science China Technological Sciences 2024年第9期67卷 2881-2893页

作者： HAN HongGui LIU YiMing LI FangYu DU YongPing Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational intelligence and Intelligent System engineering research center of Digital Community Ministry of Education Beijing Artificial intelligence Institute and Beijing Laboratory for Intelligent Environmental Protection

Deep neural networks play an important role in the recognition of waste electrical appliances. However, deep neural network components still lack reliability in decision-making features. To address this problem, a sparse convolutional model with semantic expression(SCMSE) is proposed. First, a low-rank sparse semantic expression component, combining the benefits of residual networks and sparse representation, is adapted to enhance sparse feature extraction and semantic expression. Second, a reliable network architecture is obtained by iterating the optimal sparse solution, enhancing semantic expression. Finally, the results of visualization experiments on the waste electrical appliances dataset demonstrate that the proposed SCMSE can obtain excellent semantic performance.

关键词： sparse convolutional model deep neural network semantic expression visualization computer vision

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Dynamic motion of quadrupedal robots on challenging terrain:a kinodynamic optimization approach

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Frontiers of Mechanical engineering 2024年第3期19卷 23-40页

作者： Qi LI Lei DING Xin LUO engineering research center of Hubei Province for Clothing Information Wuhan 430200China School of Computer Science and Artificial intelligence Wuhan Textile UniversityWuhan 430200China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China

The dynamic motion of quadrupedal robots on challenging terrain generally requires elaborate spatial–temporal kinodynamic motion planning and accurate control at higher refresh rate in comparison with regular ***,conventional quadrupedal robots usually generate relatively coarse planning and employ motion replanning or reactive strategies to handle terrain *** resultant complex and computation-intensive controller may lead to nonoptimal motions or the breaking of locomotion *** this paper,a kinodynamic optimization approach is *** generate long-horizon optimal predictions of the kinematic and dynamic behavior of the quadruped robot on challenging terrain,we formulate motion planning as an optimization problem;jointly treat the foot’s locations,contact forces,and torso motions as decision variables;combine smooth motion and minimal energy consumption as the objective function;and explicitly represent feasible foothold region and friction constraints based on terrain *** track the generated motions accurately and stably,we employ a whole-body controller to compute reference position and velocity commands,which are fed forward to joint controllers of the robot’s *** verify the effectiveness of the developed approach through simulation and on a physical quadruped robot *** show that the quadruped robot can successfully traverse a 30°slope and 43% of nominal leg length high step while maintaining the rhythm of dynamic trot gait.

关键词： quadrupedal robot kinodynamic planning nonlinear optimization challenging terrain whole-body control

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Assistive techniques and their added value for tremor classification in multiple sclerosis

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Neural Regeneration research 2024年第5期19卷 977-978页

作者： Nabin Koirala Abdulnasir Hossen Ioannis U.Isaias Jens Volkmann Muthuraman Muthuraman Child Study center School of MedicineYale UniversityNew HavenCTUSA Department of Electrical&Computer engineering Sultan Qaboos UniversityAl-KhodMuscatOman Neural engineering with Signal analytics and Artificial intelligence Department of NeurologyUniversity Hospital of WürzburgWürzburgGermany Centro Parkinson e Parkinsonism Azienda Socio Sanitaria Territoriale G.Pini-CTO20126 MilanItaly

Tremor occurs in about half of multiple sclerosis(MS)*** tremor has a broad frequency range of 2.5-7 Hz,with a higher prevalence of postural tremor(44%)compared to intentional tremor(6%)(Alusi et al.,2001).Tremor may affect the upper and lower extremities,head,and trunk,and may even affect the vocal cords in isolated cases of palatal *** tremor is classically attributed to lesions of the brain stem,cerebellum,or cerebellar peduncles,and tremor intensity has been shown to correlate with the number of lesions or their functional ***,recent work has demonstrated that inflammatory damage to the cerebello-thalamic and cortico-thalamic pathways might also play an important role in causing tremor,as it co-occurs with other signs and symptoms of MS such as dysarthria,dysmetria,dysdiadochokinesia,and dystonia(Alusi et al.,2001).

关键词： tremor sclerosis

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Privacy-Preserving Average Consensus Algorithm Under Round-Robin Scheduling Protocol

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1705-1707页

作者： Yingjiang Guo Wenying Xu Haodong Wang Jianquan Lu Shengli Du the School of Mathematics Southeast UniversityNanjing 21189China the School of Electronic Information and Electrical engineering Chengdu UniversityChengdu 610106Chengdu 610106China the School of Artificial intelligence and Automation Beijing University of TechnologyBeijing 100124 the engineering research center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China

Dear Editor,Over the past decades,cooperative control and distributed optimization have gained significant research attention due to their broad applications such as signal processing,robotics,and social networks[1],[2].As a fundamental component of distributed control and optimization,the issue of average consensus has become a recurring topic of interest[3],[4].

关键词： optimization Round Robin

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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of big data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and Advanced Innovation center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and engineering research center of Financial Computing and Digital engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems engineering Macao University of Science and TechnologyMacao 999078China School of Artificial intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and engineering Northeastern UniversityShenyang 110819China center of research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy research and Innovation center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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Association of DNA methylation/demethylation with the functional outcome of stroke in a hyperinflammatory state

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Neural Regeneration research 2024年第10期19卷 2229-2239页

作者： Yubo Wang Ling Zhang Tianjie Lyu Lu Cui Shunying Zhao Xuechun Wang Meng Wang Yongjun Wang Zixiao Li Vascular Neurology Department of NeurologyBeijing Tiantan HospitalCapital Medical UniversityBeijingChina China National Clinical research center for Neurological Diseases Beijing Tiantan HospitalCapital Medical UniversityBeijingChina National center for Healthcare Quality Management in Neurological Diseases Beijing Tiantan HospitalCapital Medical UniversityBeijingChina Chinese Institute for Brain research BeijingChina research Unit of Artificial intelligence in Cerebrovascular Disease Chinese Academy of Medical SciencesBeijingChina Beijing engineering research center of Digital Healthcare for Neurological Diseases BeijingChina

Inflammation is closely related to stroke prognosis, and high inflammation status leads to poor functional outcome in stroke. DNA methylation is involved in the pathogenesis and prognosis of stroke. However, the effect of DNA methylation on stroke at high levels of inflammation is unclear. In this study, we constructed a hyperinflammatory cerebral ischemia mouse model and investigated the effect of hypomethylation and hypermethylation on the functional outcome. We constructed a mouse model of transient middle cerebral artery occlusion and treated the mice with lipopolysaccharide to induce a hyperinflammatory state. To investigate the effect of DNA methylation on stroke, we used small molecule inhibitors to restrain the function of key DNA methylation and demethylation enzymes. 2,3,5-Triphenyltetrazolium chloride staining, neurological function scores, neurobehavioral tests, enzyme-linked immunosorbent assay, quantitative reverse transcription PCR and western blot assay were used to evaluate the effects after stroke in mice. We assessed changes in the global methylation status by measuring DNA 5-mc and DNA 5-hmc levels in peripheral blood after the use of the inhibitor. In the group treated with the DNA methylation inhibitor, brain tissue 2,3,5-triphenyltetrazolium chloride staining showed an increase in infarct volume, which was accompanied by a decrease in neurological scores and worsening of neurobehavioral performance. The levels of inflammatory factors interleukin 6 and interleukin-1 beta in ischemic brain tissue and plasma were elevated, indicating increased inflammation. Related inflammatory pathway exploration showed significant overactivation of nuclear factor kappa B. These results suggested that inhibiting DNA methylation led to poor functional outcome in mice with high inflammation following stroke. Further, the effects were reversed by inhibition of DNA demethylation. Our findings suggest that DNA methylation regulates the inflammatory response in stroke

关键词： DNA demethylation DNA methylation DNMT3A functional outcome hyperinflammatory state interleukin neuroinflammation stroke TET2

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication engineering University of Science and Technology BeijingBeijing100083China Beijing engineering research center for Cyberspace data Analysis and Applications Beijing100083China Faculty of Informatics engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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