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Critical Patient Image Data Acquisition Strategy by Exploiting Edge Intelligence and Dynamic-Static Synergy in Smart Healthcare 认领 引用
1
作者 Kiran Deep Singh Prabh Deep Singh +3 位作者 Narinder Kaur Jawad Khan Dildar Hussain Yeong Hyeon Gu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1276-1304,共29页
In smart healthcare systems,Image data of critical patients is essential in controlling and diagnosing the disease development.To acquire the medical images,traditional methods encountered the difficulty of generating... In smart healthcare systems,Image data of critical patients is essential in controlling and diagnosing the disease development.To acquire the medical images,traditional methods encountered the difficulty of generating costeffective data.This research work introduces a novel and innovative approach to collect high-quality image data from individuals with atypical clinical presentations.Initially,a new Internet of Medical Things(IoMT)image collection architecture is introduced.This design uses edge intelligence and motion-static synergy to make it easier to record both coarse-grained and fine-grained patient images.This study introduces an image acquisition technique that leverages edge intelligence and collaborative static-dynamic monitoring,exemplified in intensive care units,to improve the efficiency and data value of image acquisition in healthcare IoMT settings.This approach revolves around the three distinct steps.To begin with,an advanced YOLO-based clinical abnormality detection is implemented by the edge server to identify patients affected by abnormal physiological conditions.The images from affected patients are captured by static monitoring nodes.In the next phase,coordinate calculation methods for the localization of abnormal patients and quantification techniques for severity assessment are introduced.The final step involves the intervention of a path optimization algorithm for mobile medical assistive robots using severity metrics and principles of ant colony optimization.Ultimately,algorithmic performance evaluations at every phase indicate that acquisition efficiency and image data value surpass traditional methodologies. 展开更多
关键词 Edge intelligence image acquisition motion collaboration path optimization medical IoT patient monitoring
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CB-D3QN:Malware propagation defense for edge intelligence based industrial Internet of Things via a Colonel Blotto game-enhanced dueling double deep Q-network approach 认领 引用
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作者 Shigen Shen Siyu Wei Xiao-Zhi Gao 《Intelligent and Converged Networks》 EI 2026年第2期111-128,共18页
Malware spread in edge intelligence based Industrial Internet of Things(IIoT)systems is a serious challenge.Resources are unequal—attackers can put all their resources on one target,but defenders have to protect ever... Malware spread in edge intelligence based Industrial Internet of Things(IIoT)systems is a serious challenge.Resources are unequal—attackers can put all their resources on one target,but defenders have to protect everything at once.To solve this challenge,we build CB-D3QN,which is a defense method using asymmetric Colonel Blotto game theory and Dueling Double Deep Q-Network(D3QN).We treat the fight between attackers and defenders as a Colonel Blotto game where both sides have different amounts of resources.This matches what happens in real IIoT malware attacks.CB-D3QN brings together Colonel Blotto games and D3QN,and uses deep reinforcement learning to find the right balance and make defense strategies better.The system considers malware behavior history,how we split resources,and which side has the advantage in each area.We evaluate CB-D3QN against other state-of-the-art methods and experimental results show that it achieves higher malware mitigation success rate,longer system resilience,and lower false intervention rate. 展开更多
关键词 edge intelligence Industrial Internet of Things(IIoT) malware defense Colonel Blotto game deep reinforcement learning
Artificial intelligence dimensions and design features for the knowledge-based work of the future:a socio-technical perspective 认领 引用
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作者 Jungwoo Lee Jaehyun Park Noha A.Mostafa 《Data Science and Management》 EI CSCD 2026年第1期15-25,共11页
With recent advances in transformative technologies,such as the Internet of Things,cloud computing,and mobile devices,jobs have become more knowledge-based.To implement successful technologies in any society,user perc... With recent advances in transformative technologies,such as the Internet of Things,cloud computing,and mobile devices,jobs have become more knowledge-based.To implement successful technologies in any society,user perception and acceptance are critical.This study aims to identify a range of artificial intelligence(AI)design features applicable to future-oriented,knowledge-based work from a socio-technical perspective.Industrial reports on AI design artifacts appearing from 2015 to 2025 were reviewed to understand how AI can revolutionize business process management by integrating data analytics,automation,and real-time insights.Subsequently,a typological framework for AI design features was proposed,focusing on automation and intelligence as the two primary dimensions.Using this framework,nine AI design features were derived and discussed.A design feature matrix concerning the changing nature of work was constructed using nine AI design features,and the characteristics of each design feature were described in detail for emerging industry cases.The implications of this framework are discussed in terms of future knowledge,socio-materiality,and expected AI-design affordances.The primary contribution of this study is the proposal of a framework of AI design features for knowledge-based future work from a socio-technical perspective,paving the way for Industry 5.0. 展开更多
关键词 Artificial intelligence Automation Knowledge-based work Design features Socio-technical perspective
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Exploratory Research on Knowledge Graph Combined with Artificial Intelligence Teaching Assistant in the Fundamentals of Nursing Course 认领 引用
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作者 Jianwei Hu Lijuan Wang +1 位作者 Li Yang Yu Cai 《Journal of Contemporary Educational Research》 2026年第3期72-77,共6页
Higher education is undergoing digital construction,and traditional education can no longer meet the training needs of nursing talents in the new era.The teaching of Fundamentals of Nursing faces several dilemmas,incl... Higher education is undergoing digital construction,and traditional education can no longer meet the training needs of nursing talents in the new era.The teaching of Fundamentals of Nursing faces several dilemmas,including cumbersome course content and scattered knowledge points.With the iterative update of modern digital technologies,the knowledge graph-structured,semantic,scalable,and multi-source integrated-provides a technical path to solve these problems.This study proposes a planned teaching application scheme based on the Xuexitong platform,which integrates the knowledge graph,artificial intelligence teaching assistant,and task engine.This scheme not only provides practical reference for the teaching innovation of Fundamentals of Nursing but also offers new ideas for the in-depth integration of educational technology and nursing teaching. 展开更多
关键词 Knowledge graph Artificial intelligence Teaching assistant Fundamentals of Nursing Undergraduate nursing education
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Parallel Medical Devices and Instruments:Integrating Edge and Cloud Intelligence for Smart Treatment and Health Systems 认领 引用 被引量：5
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作者 Fei Lin Tommy Gao +5 位作者 Dali Sun Qinghua Ni Xianting Ding Jing Wang David Wenzhong Gao Fei-Yue Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第4期651-654,共4页
WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enh... WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs.In this wave of technological innovation,parallel intelligence,along with Artificial systems,Computational experiments,and Parallel execution(ACP)approach[2]will play a crucial role.Through parallel interactions between virtual and real systems,this approach optimizes the functionality of medical devices and instruments,enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems. 展开更多
关键词 artificial intelligence ai edge computingand parallel intelligence artificial systemscomputational experimentsand cloud intelligence medical systems edge computing process complex data cloud intelligencethe
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EdgeAIGC:Model caching and resource allocation for edge artificial intelligence generated content 认领 引用
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作者 Wu Wen Yibin Huang +3 位作者 Xinxin Zhao Peiying Zhang Kai Liu Guowei Shi 《Digital Communications and Networks》 SCIE EI CSCD 2025年第6期1941-1950,共10页
With the rapid development of generative artificial intelligence technology,the traditional cloud-based centralized model training and inference face significant limitations due to high transmission latency and costs,... With the rapid development of generative artificial intelligence technology,the traditional cloud-based centralized model training and inference face significant limitations due to high transmission latency and costs,which restrict user-side in-situ Artificial Intelligence Generated Content(AIGC)service requests.To this end,we propose the Edge Artificial Intelligence Generated Content(Edge AIGC)framework,which can effectively address the challenges of cloud computing by implementing in-situ processing of services close to the data source through edge computing.However,AIGC models usually have a large parameter scale and complex computing requirements,which poses a huge challenge to the storage and computing resources of edge devices.This paper focuses on the edge intelligence model caching and resource allocation problems in the Edge AIGC framework,aiming to improve the cache hit rate and resource utilization of edge devices for models by optimizing the model caching strategy and resource allocation scheme,and realize in-situ AIGC service processing.With the optimization objectives of minimizing service request response time and execution cost in resource-constrained environments,we employ the Twin Delayed Deep Deterministic Policy Gradient algorithm for optimization.Experimental results show that,compared with other methods,our model caching and resource allocation strategies can effectively improve the cache hit rate by at least 41.06%and reduce the response cost as well. 展开更多
关键词 Generative AI Edge model caching Resource allocation Edge intelligence
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Wireless Environmental Information Theory:A New Paradigm Toward 6G Online and Proactive Environment Intelligence Communication 认领 引用 被引量：4
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作者 Jianhua Zhang Li Yu +4 位作者 Shaoyi Liu Yichen Cai Yuxiang Zhang Hongbo Xing Tao Jiang 《Engineering》 SCIE EI CSCD 2026年第1期186-200,共15页
Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the d... Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the design of mobile communication systems from first generation(1G)to fifth generation(5G).However,this approach relies on offline channel measurements in specific environments,and thus,the system passively adapts to new environments,resulting in deviation from the optimal performance.As sixth generation(6G)expands into ubiquitous environments and pursues higher capacity,numerous sensing and artificial intelligence(AI)-based methods have emerged to combat random channel fading.However,there remains an urgent need for a proactive and online system design paradigm.From a system perspective,we propose an environment intelligence communication(EIC)based on wireless environmental information theory(WEIT)for 6G.The proposed EIC architecture operates in three steps.First,wireless environmental information(WEI)is acquired using sensing techniques.Then,leveraging WEI and channel data,AI techniques are employed to predict channel fading,thereby mitigating channel uncertainty.Finally,the communication system autonomously determines the optimal air-interface transmission strategy based on real-time channel predictions,enabling intelligent interaction with the physical environment.To make this attractive paradigm shift from theory to practice,we establish WEIT for the first time by answering three key problems:How should WEI be defined?Can it be quantified?Does it hold the same properties as statistical communication information?Subsequently,EIC aided by WEI(EIC-WEI)is validated across multiple air-interface tasks,including channel state information prediction,beam prediction,and radio resource management.Simulation results demonstrate that the proposed EIC-WEI significantly outperforms the statistical paradigm in decreasing overhead and performance optimization.Finally,several open problems and challenges,including regarding its accuracy,complexity,and generalization,are discussed.This work explores a novel and promising way for integrating communication,sensing,and AI capability in 6G. 展开更多
关键词 Sixth generation Intelligent communication Environment intelligence Wireless environmental information theory Environment sensing and reconstruction Channel prediction Digital twin channel ChannelGPT
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Multi-model applications and cutting-edge advancements of artificial intelligence in hepatology in the era of precision medicine 认领 引用 被引量：1
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作者 Ying Zheng Han Li +2 位作者 Ru Wang Cong-Shan Jiang Yi-Tong Zhao 《World Journal of Gastroenterology》 SCIE CAS 2025年第39期94-103,共10页
Hepatology encompasses various aspects,such as metabolic-associated fatty liver disease,viral hepatitis,alcoholic liver disease,liver cirrhosis,liver failure,liver tumors,and liver transplantation.The global epidemiol... Hepatology encompasses various aspects,such as metabolic-associated fatty liver disease,viral hepatitis,alcoholic liver disease,liver cirrhosis,liver failure,liver tumors,and liver transplantation.The global epidemiological situation of liver diseases is grave,posing a substantial threat to human health and quality of life.Characterized by high incidence and mortality rates,liver diseases have emerged as a prominent global public health concern.In recent years,the rapid advan-cement of artificial intelligence(AI),deep learning,and radiomics has transfor-med medical research and clinical practice,demonstrating considerable potential in hepatology.AI is capable of automatically detecting abnormal cells in liver tissue sections,enhancing the accu-racy and efficiency of pathological diagnosis.Deep learning models are able to extract features from computed tomography and magnetic resonance imaging images to facilitate liver disease classification.Machine learning models are capable of integrating clinical data to forecast disease progression and treatment responses,thus supporting clinical decision-making for personalized medicine.Through the analysis of imaging data,laboratory results,and genomic information,AI can assist in diagnosis,forecast disease progression,and optimize treatment plans,thereby improving clinical outcomes for liver disease patients.This minireview intends to comprehensively summarize the state-of-the-art theories and applications of AI in hepatology,explore the opportunities and challenges it presents in clinical practice,basic research,and translational medicine,and propose future research directions to guide the advancement of hepatology and ultimately improve patient outcomes. 展开更多
关键词 Hepatology Artificial intelligence Deep learning Convolutional neural network Natural language processing Support vector machine Graph neural network Transformer model Recurrent neural network
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Artificial intelligence enhanced edge server placement for workload balancing and energy efficiency in B5G networks 认领 引用
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作者 Vaibhav Tiwari Chandrasen Pandey +4 位作者 Shamila J.Francis Ishan Budhiraja Pronaya Bhattacharya Zhu Zhu Thippa Reddy Gadekallu 《Digital Communications and Networks》 SCIE EI CSCD 2025年第6期1951-1960,共10页
The Internet of Things(IoT)and allied applications have made real-time responsiveness for massive devices over the Internet essential.Cloud-edge/fog ensembles handle such applications'computations.For Beyond 5 th ... The Internet of Things(IoT)and allied applications have made real-time responsiveness for massive devices over the Internet essential.Cloud-edge/fog ensembles handle such applications'computations.For Beyond 5 th Generation(B5G)communication paradigms,Edge Servers(ESs)must be placed within Information Communication Technology infrastructures to meet Quality of Service requirements like response time and resource utilisation.Due to the large number of Base Stations(BSs)and ESs and the possibility of significant variations in placing the ESs within the IoTs geographical expanse for optimising multiple objectives,the Edge Server Placement Problem(ESPP)is NP-hard.Thus,stochastic evolutionary metaheuristics are natural.This work addresses the ESPP using a Particle Swarm Optimization that initialises particles as BS positions within the geography to maintain the workload while scanning through all feasible sets of BSs as an encoded sequence.The Workload-Threshold Aware Sequence Encoding(WTASE)Scheme for ESPP provides the number of ESs to be deployed,similar to existing methodologies and exact locations for their placements without the overhead of maintaining a prohibitively large distance matrix.Simulation tests using open-source datasets show that the suggested technique improves ESs utilisation rate,workload balance,and average energy consumption by 36%,17%,and 32%,respectively,compared to prior works. 展开更多
关键词 Mobile edge computing Evolutionary optimization 6G Edge server placement Load balancing Performance evaluation
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AI-driven design of powder-based nanomaterials for smart textiles: from data intelligence to system integration 认领 引用 被引量：3
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作者 Zihui Liang Yun Deng +12 位作者 Zhicheng Shi Xiaohong Liao Huiyi Zong Lizhi Ren Xiangzhe Li Xinyao Zeng Peiying Hu Wei Ke Bing Wu Kai Wang Jin Qian Weilin Xu Fengxiang Chen 《Advanced Powder Materials》 EI CAS CSCD 2026年第1期39-63,共25页
Artificial intelligence(AI)is emerging as a transformative enabler in the development of smart textile systems,particularly those integrating powder-based functional materials.This review highlights recent progress in... Artificial intelligence(AI)is emerging as a transformative enabler in the development of smart textile systems,particularly those integrating powder-based functional materials.This review highlights recent progress in AIguided design of carbon nanomaterials,metallic nanoparticles,and framework-based powders for applications in energy harvesting,intelligent sensing,and robotic actuation.Machine learning techniques,including supervised learning,transfer learning,and Bayesian optimization are discussed for accelerating materials discovery,enhancing integration strategies,and enabling real-time adaptive control.Emphasis is placed on how AI enables multifunctional,wearable platforms that sense,process,and respond to environmental and physiological cues with high accuracy and autonomy.Representative breakthroughs in soft robotics,haptic interfaces,and assistive devices are presented,demonstrating the synergy of AI and responsive textiles.Finally,the review outlines key challenges related to data scarcity,model generalizability,manufacturing scalability,and sustainability,while proposing future directions involving multimodal learning,autonomous experimentation,and ethics-aware design.This work offers a comprehensive outlook on next-generation AI-driven textile systems that seamlessly integrate intelligence,functionality,and wearability. 展开更多
关键词 Smart textiles Artificial intelligence Powder-based functional materials Machine learning AI-driven textile system
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Artificial intelligence-enabled Bioprinting 5.0 认领 引用 被引量：1
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作者 Long Bai Yi Zhang +3 位作者 Sicheng Wang Jinlong Liu Yuanyuan Liu Jiacan Su 《Bio-Design and Manufacturing》 SCIE EI CAS CSCD 2026年第1期32-62,I0002,共31页
With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has prog... With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has progressed from the fabrication of simple models(1.0)to the fabrication of permanent implants(2.0),tissue engineering scaffolds(3.0),and complex biostructures utilizing living cells(4.0).Nevertheless,significant challenges remain,particularly in accurately replicating the structure and function of host tissues,selecting appropriate materials,and optimizing printing parameters.The integration of artificial intelligence(AI),especially machine learning,provides promising novel opportunities in bioprinting(5.0).This review systematically summarizes the current applications of AI in bioprinting,discussing both construction strategies and application scenarios.It also explores the potential of AI to improve bioprinting in the preparation of complex functional tissues and in situ tissue repair.Overall,the synergy between AI and bioprinting is poised to drive the development of personalized medicine,facilitate high-throughput preparation of in vitro models,and provide robust tools for regenerative medicine and precision healthcare. 展开更多
关键词 Artificial intelligence Bioprinting Tissue engineering Machine learning
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Space-Ground Fluid AI for 6G Edge Intelligence 认领 引用
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作者 Qian Chen Zhanwei Wang +5 位作者 Xianhao Chen Juan Wen Di Zhou Sijing Ji Min Sheng Kaibin Huang 《Engineering》 SCIE EI CSCD 2025年第11期14-19,共6页
1.Introduction The commercialization of sixth-generation(6G)mobile networks is expected by 2030.According to the International Telecommunication Union(ITU),two of three new usage scenarios in 6G mobile networks—as co... 1.Introduction The commercialization of sixth-generation(6G)mobile networks is expected by 2030.According to the International Telecommunication Union(ITU),two of three new usage scenarios in 6G mobile networks—as compared with fifth-generation(5G)mobile networks—are“integrated artificial intelligence(AI)and communication”and“ubiquitous connectivity.” 展开更多
关键词 sixg ai artificial intelligence ai ubiquitousconnectivity communication
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A comprehensive survey of artificial intelligence applications in UAV-enabled wireless networks 认领 引用 被引量：2
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作者 Li Zhou Hao Yin +3 位作者 Haitao Zhao Jibo Wei Dewen Hu Victor C.M.Leung 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期561-583,共23页
This comprehensive survey paper examines the applications of Artificial Intelligence(AI)in Unmanned Aerial Vehicle(UAV)-enabled wireless networks.With the increasing demand for efficient and adaptive communication sys... This comprehensive survey paper examines the applications of Artificial Intelligence(AI)in Unmanned Aerial Vehicle(UAV)-enabled wireless networks.With the increasing demand for efficient and adaptive communication systems,the integration of AI with UAV networks promises to revolutionize various aspects of wireless communication.The paper first outlines the background and motivation behind AI integration,highlighting the potential for enhanced network performance,autonomy,and adaptability.It then delves into the key AI applications across different network layers,including data sensing and collection,placement and trajectory optimization,radio resource management,routing and topology control,edge computing and caching,as well as security and privacy enhancement.For each application,the paper discusses relevant AI techniques,main findings,optimization objects,and the potential benefits and challenges.The survey also identifies open issues,such as the practical implementation gap,standardization issues,and real-world application barriers,and proposes future directions to address these challenges and further advance the field.In conclusion,the integration of AI with UAV-enabled Wireless Networks(UWNs)holds tremendous potential for transforming wireless communication,enabling new applications and services with unprecedented capabilities. 展开更多
关键词 Artificial intelligence(AI) Machine learning(ML) Unmanned aerial vehicle(UAV) Wireless network
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Reflective thinking meets artificial intelligence:Synthesizing sustainability transition knowledge in left-behind mountain regions 认领 引用
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作者 Andrej Ficko Simo Sarkki +2 位作者 Yasar Selman Gultekin Antonia Egli Juha Hiedanpää 《Geography and Sustainability》 CSCD 2025年第1期159-169,共11页
We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered tex... We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered text summarization and text mining,we synthesize experts’narratives on sustainable development challenges and solutions in Kardüz Upland,Türkiye.We then analyze their alignment with the UN Sustainable Development Goals(SDGs)using document embedding.Investment in infrastructure,education,and resilient socio-ecological systems emerged as priority sectors to combat poor infrastructure,geographic isolation,climate change,poverty,depopulation,unemployment,low education levels,and inadequate social services.The narratives were closest in substance to SDG 1,3,and 11.Social dimensions of sustainability were more pronounced than environmental dimensions.The presented approach supports policymakers in organizing loosely structured sustainability tran sition knowledge and fragmented data corpora,while also advancing AI applications for designing and planning sustainable development policies at the regional level. 展开更多
关键词 Artificial intelligence Innovation Reflective thinking Scientific imagination Text mining Text summarization
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Edge-Intelligent Photovoltaic Fault Localization via NAS-Optimized Feature-Space Sub-Pixel Matching 认领 引用
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作者 Hongjiang Wang Jian Yu +3 位作者 Tian Zhang Na Ren Nan Zhang Zhenyu Liu 《Computers, Materials & Continua》 SCIE EI 2026年第6期1108-1135,共28页
The rapid deployment of Industrial Internet of Things(IIoT)systems,such as large-scale photovoltaic(PV)power stations in modern power grids,has created a strong demand for edge-intelligent fault localization methods t... The rapid deployment of Industrial Internet of Things(IIoT)systems,such as large-scale photovoltaic(PV)power stations in modern power grids,has created a strong demand for edge-intelligent fault localization methods that can operate reliably under strict computational and memory constraints.In this work,we propose an edge-intelligent photovoltaic fault localization framework that integrates intelligent computation with classical sub-pixel optimization.The framework adopts a modular,edge-oriented design in which a radial basis function(RBF)network is first employed as a lightweight screening module to enable conditional execution,thereby reducing unnecessary computation for non-faulty samples.For suspicious samples,a compact convolutional feature extractor is activated to generate discriminative representations.The architecture of this feature extractor is automatically optimized using neural architecture search(NAS)in an offline design stage,explicitly balancing localization accuracy and computational efficiency for industrial edge hardware.Sub-pixel displacement estimation and recursive partitioning are then performed in the learned feature space using a sum of squared differences-based,preserving the mathematical transparency of classical sub-pixel matching while significantly improving robustness to thermal noise and background interference.Unlike large end-to-end detection models,the proposed framework combines intelligent feature representation with interpretable localization mechanisms,resulting in a flexible and resource-efficient solution for edge deployment.Experimental results on a photovoltaic infrared fault image dataset demonstrate that the proposed NAS-optimized feature-space sub-pixel matching framework achieves more stable fault localization than other baselines,with only marginal additional computational overhead. 展开更多
关键词 Edge intelligence neural architecture search sub-pixel localization feature-based matching photovoltaic fault localization industrial internet of things
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Design of Virtual Driving Test Environment for Collecting and Validating Bad Weather SiLS Data Based on Multi-Source Images Using DCU with V2X-Car Edge Cloud 认领 引用 被引量：1
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作者 Sun Park JongWon Kim 《Computers, Materials & Continua》 SCIE EI 2026年第3期448-467,共20页
In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to... In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis. 展开更多
关键词 Virtual driving test DCU bad weather SiLS autonomous environment V2X-Car edge cloud
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Task Offloading and Edge Computing in IoT-Gaps, Challenges and Future Directions 认领 引用 被引量：1
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作者 Hitesh Mohapatra 《Computers, Materials & Continua》 SCIE EI 2026年第6期268-296,共29页
This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge.Existing literat... This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge.Existing literature shows that edge-based distributed intelligence reduces cloud dependency.It addresses transmission latency,device energy use,and bandwidth limits.Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination.Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8%and energy efficiency gains of 27.4%compared to conventional cloud-centric models.However,critical gaps remain in current methodologies.Most studies focus on static network topologies and do not adequately address load balancing across multiple edge nodes.Security vulnerabilities during task transmission are underexplored,and privacy considerations for sensitive data remain insufficiently integrated into existing frameworks.Task caching strategies and fault tolerance mechanisms require further investigation in highly dynamic environments.The ability of existing approaches to handle large-scale deployments and complex edge-cloud collaborative scenarios has not been thoroughly validated.This review synthesizes current progress while identifying fundamental challenges that must be resolved for practical deployment in time-sensitive applications spanning smart manufacturing,autonomous systems,and healthcare monitoring.Future work should prioritize robust security integration,efficient load distribution,and scalability across heterogeneous edge infrastructures. 展开更多
关键词 Edge computing task offloading deep reinforcement learning mobile edge computing IoT performance optimization methodological rigor assessment
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Artificial Intelligence for Spleen-Stomach Disorders in Traditional Chinese Medicine:Integrating Knowledge Graphs with Intelligent Diagnosis and Treatment 认领 引用
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作者 Yu-yu Duan Si-feng Jia +4 位作者 Song Ye Lekhang Cheang Wahou Tai Li-zhi Xiang Zhe-wei Ye 《Current Medical Science》 SCIE CAS CSCD 2025年第6期1348-1357,共10页
Spleen-Stomach disorders are prevalent clinical conditions in Traditional Chinese Medicine(TCM).The complex diagnostic and treatment model used in TCM is based on a“symptom-pattern-disease-formula”framework that hea... Spleen-Stomach disorders are prevalent clinical conditions in Traditional Chinese Medicine(TCM).The complex diagnostic and treatment model used in TCM is based on a“symptom-pattern-disease-formula”framework that heavily relies on practitioners’experience.However,this model faces several challenges,including ambiguous knowledge representation,unstructured data,and difficulties with knowledge sharing.Recent advancements in artificial intelligence,natural language processing,and medical knowledge engineering have significantly improved research on knowledge graphs(KGs)and intelligent diagnosis and treatment systems for these disorders,making these technologies crucial for modernizing TCM.This article systematically reviews two core research pathways related to Spleen-Stomach disorders.The first pathway focuses on constructing knowledge graphs for“structured knowledge representation”.This includes ontology modeling,entity recognition,relation extraction,graph fusion,semantic reasoning,visualization services,and an ensemble model to predict treatment efficacy.The second pathway involves the development of intelligent diagnosis and treatment systems,with a focus on“clinical applications”.This pathway includes key technologies such as quantitative modeling of TCM,the four diagnostic methods(inspection,auscultation-olfaction,interrogation,and palpation),semantic analysis of classical texts,pattern differentiation algorithms,and multimodal consultation recommenders.Through the synthesis and analysis of current research,several ongoing challenges have been identified.These include inconsistent models and annotation of TCM clinical knowledge,limited semantic reasoning capabilities,insufficient integration between KGs and intelligent diagnostic models,and limited clinical adaptability of existing intelligent diagnostic systems.To address these challenges,this review suggests future research directions that include enhancing heterogeneous multisource knowledge integration techniques,deepening semantic reasoning through collaborative reasoning frameworks that incorporate large language models,and developing effective cross-disease transfer learning strategies.These directions aim to improve interpretability,reasoning accuracy,and clinical applicability of intelligent diagnosis and treatment systems for Spleen-Stomach disorders in TCM. 展开更多
关键词 Knowledge graphs Intelligent diagnosis and treatment Spleen-Stomach disorders Natural language processing Large language models Syndrome differentiation Traditional Chinese medicine informatics
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Introduction to the Special Issue on Next-Generation Intelligent Networks and Systems:Advances in IoT,Edge Computing,and Secure Cyber-Physical Applications 认领 引用
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作者 Nishu Gupta Manuel J.C.S.Reis 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期25-28,共4页
The accelerating convergence of intelligent networking paradigms,data-driven modeling,and cyberphysical integration is reshaping the foundations of modern engineering systems.Within this context,this Special Issue of ... The accelerating convergence of intelligent networking paradigms,data-driven modeling,and cyberphysical integration is reshaping the foundations of modern engineering systems.Within this context,this Special Issue of Computer Modeling in Engineering&Sciences(CMES)is devoted to recent advances in next-generation intelligent networks and systems,with a particular emphasis on the synergistic roles of the Internet of Things(IoT),edge computing,and secure cyber-physical applications. 展开更多
关键词 intelligent networking paradigmsdata driven computer modeling internet things edge computing intelligent networking paradigms cyberphysical integration internet things iot edge computingand data driven modeling
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Cascading Failure Dynamics and Edge-Intelligent Defense in Space-Air-Ground Integrated Networks for Internet of Things 认领 引用
20
作者 Peiying Zhang Yihong Yu +3 位作者 Lizhuang Tan Shuqing He Jian Wang Ameer El-Sayed 《Computers, Materials & Continua》 SCIE EI 2026年第8期865-880,共16页
As a core information infrastructure in the 6G era,the Space-Air-Ground Integrated Network(SAGIN)integrates space-based,air-based,and ground-based network resources to achieve seamless communication across all domains... As a core information infrastructure in the 6G era,the Space-Air-Ground Integrated Network(SAGIN)integrates space-based,air-based,and ground-based network resources to achieve seamless communication across all domains.However,its characteristics such as heterogeneous node coupling and dynamic topology changes make it prone to cascading failures,severely threatening critical business continuity in Internet of Things(IoT)applications spanning smart cities,healthcare,transportation,and industrial automation.This paper conducts systematic research addressing challenges including modeling difficulties in SAGIN cascading failure propagation,insufficient coordination of defense strategies,and poor resource adaptability.First,a multi-factor coupled dynamic model of cascading failure propagation is established to quantify the synergistic effects of node heterogeneity,link dynamics,and load redistribution.Second,a closed-loop collaborative defense system integrating“early warning-isolation-self-healing”is designed.The system incorporates a lightweight greedy-based self-healing algorithm and uses multi-criteria decision-making(Analytic Hierarchy Process)for resource optimization.These approaches ensure real-time performance and energy efficiency on resource-constrained edge nodes.Third,a joint simulation platform combining NS-3 and MATLAB is built to validate the model and strategies across diverse IoT application scenarios.Experimental results show that the proposed propagation model maintains prediction error within 10%,the defense strategies increase failure recovery rates to 85%–90%,reduce communication interruption duration by over 60%,and lower resource overhead by 20%–25%,providing theoretical support and technical guarantees for stable SAGIN operation in security and resiliency-critical environments. 展开更多
关键词 Space-Air-Ground Integrated Network cascading failure defense strategy edge intelligence Internet of Things resource allocation and optimization real-time and energy efficiency security and resiliency network reliability
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