期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到15,967篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Understanding Agentic AI:Algorithms and Infrastructure 认领 引用
1
作者 Wanlun Ma Yongjian Guo +5 位作者 Qing-Long Han Wei Zhou Xiaogang Zhu Junwu Xiong Sheng Wen Yang Xiang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期776-795,共20页
The rapid evolution of large language models(LLMs)towards autonomous Agentic artificial intelligence(AI)necessitates a systemic overhaul across algorithms,infrastructure,and architectures.This paper presents a unified... The rapid evolution of large language models(LLMs)towards autonomous Agentic artificial intelligence(AI)necessitates a systemic overhaul across algorithms,infrastructure,and architectures.This paper presents a unified view of the“Agentic AI Infrastructure,”connecting research threads often studied in isolation.First,post-training algorithms are reviewed,contrasting traditional reinforcement learning(RL)with emerging reasoning-centric methods and test-time scaling strategies.Next,the transition of RL training frameworks is analyzed from monolithic,colocated designs to disaggregated,asynchronous architectures tailored for the extreme variance of agentic rollouts.Furthermore,progress in agent construction is synthesized,covering reflection,planning,tool use,and multi-agent collaboration.By integrating these layers,the paper elucidates how agentic AI systems impose unique demands on underlying training systems.Finally,open challenges are outlined by covering capability scaling,efficiency,safety,privacy,and governance for reliable realworld agentic AI deployment. 展开更多
关键词 Agentic artificial intelligence (AI) AI Infrastructure post-training reinforcement learning (RL)
暂未订购 下载PDF
AI Empowers Supply Chain Intelligence:A Three-Chain Four-Intelligence Framework 认领 引用
2
作者 Zuo-Jun Max Shen Shaochong Lin 《Engineering》 SCIE EI CSCD 2026年第6期430-443,共14页
This study introduces a novel conceptual framework to understand the transformative impact of artificial intelligence(AI)on global supply chains.We propose a Three-Chain Four-Intelligence framework that systematically... This study introduces a novel conceptual framework to understand the transformative impact of artificial intelligence(AI)on global supply chains.We propose a Three-Chain Four-Intelligence framework that systematically analyzes how AI reconfigures supply chain architecture and capabilities through enhanced contextual awareness.The Three-Chain perspective examines how AI transforms the logistics chain(physical flow),information chain(data flow),and value chain(value creation)from fragmented opera-tions to synchronized intelligent ecosystems.The Four-Intelligence pathway maps the evolutionary pro-gression from digital connectivity to operational optimization,collaborative ecosystems,and ultimately self-evolving intelligent systems.AI serves as an orchestrating force that processes rich contextual infor-mation spanning product attributes,market dynamics,environmental conditions,and operational reali-ties.We demonstrated the practical application of the framework through a comprehensive case study of JD.com,where AI implementation across all dimensions yielded quantifiable improvements.Our analysis reveals that the most transformative supply chain advancements emerge at the intersection of multiple chains with increasingly sophisticated contextual awareness.The paper concludes by identifying six emerging research frontiers,such as generative AI integration with decision optimization. 展开更多
关键词 Artificial intelligence Supply chain intelligence Machine learning Intelligent decision-making Generative AI
暂未订购 下载PDF
Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用
3
作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
暂未订购 下载PDF
Painted Wolf Optimization:A Novel Nature-Inspired Metaheuristic Algorithm for Real-World Optimization Problems 认领 引用
4
作者 Saeid Sheikhi 《Computers, Materials & Continua》 SCIE EI 2026年第5期243-271,共29页
Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems,yet existingmethods often struggle with balancing exploration and exploitation across diverse problem landscapes.T... Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems,yet existingmethods often struggle with balancing exploration and exploitation across diverse problem landscapes.This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization(PWO)algorithm.The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves,also known as African wild dogs in the wild,particularly their unique consensus-based voting rally mechanism,a behavior fundamentally distinct fromthe social dynamics of grey wolves.In this innovative process,pack members explore different areas to find prey;then,they hold a pre-hunting voting rally based on the alpha member to determine who will begin the hunt and attack the prey.The efficiency of the proposed PWO algorithm is evaluated by a comparison study with other well-known optimization algorithms on 33 test functions,including the Congress on Evolutionary Computation(CEC)2017 suite and different real-world engineering design cases.Furthermore,the algorithm’s performance is further tested across a spectrum of optimization problems with extensive unknown search spaces.This includes its application within the field of cybersecurity,specifically in the context of training a machine learning-based intrusion detection system(ML-IDS),achieving an accuracy of 0.90 and an F-measure of 0.9290.Statistical analyses using the Wilcoxon signed-rank test(all p<0.05)indicate that the PWO algorithm outperforms existing state-of-the-art algorithms,providing superior solutions in diverse and unpredictable optimization landscapes.This demonstrates its potential as a robust method for tackling complex optimization problems in various fields.The source code for thePWOalgorithmis publicly available at http://gffzz188fe103f8f1460asucww9nv5wunw66x6.ffgz.tsg.suse.edu.cn/saeidsheikhi/Painted-Wolf-Optimization. 展开更多
关键词 Optimization painted wolf optimization algorithm metaheuristic algorithm nature-inspired computing swarm intelligence
暂未订购 下载PDF
从AI for Science到AI for Intelligence:情报研究范式的转型及其在事实核查中的实现 认领 引用 被引量：4
5
作者 刘严 杨建林 王忠军 《中国图书馆学报》 CSSCI 北大核心 2026年第2期51-71,共21页
本文以AI for Science(AI4S)在情报场景中的适配张力为研究起点,基于库恩范式理论,揭示其在本体预设、目标导向与方法路径上的局部不可通约,据此提出以情报核实为核心任务的AI for Intelligence(AI4I)范式,强调认知建模、因果核实与对... 本文以AI for Science(AI4S)在情报场景中的适配张力为研究起点,基于库恩范式理论,揭示其在本体预设、目标导向与方法路径上的局部不可通约,据此提出以情报核实为核心任务的AI for Intelligence(AI4I)范式,强调认知建模、因果核实与对抗适应三类关键能力。这些能力在AI4S任务逻辑中长期处于边缘地位,而在当前情报相关研究中逐步显现为结构性诉求。为厘清AI4I的学理意义,梳理该范式对情报学经典理论的守正与创新。进一步,以事实核查任务为典型场景,阐明三类能力在舆论监测、主张筛选、验证采证、结论裁定与结果叙述等环节中的机制映射与协同关系,探讨在图书馆情境下的技术落地可行性保障,以呈现AI4I对情报实践的指导价值。在此基础上,提出“生成—核实”双环协同框架,设计对象层、模型层与流程层三类接口与切换策略,探讨效率与可信性并重的智能情报生态构建路径,以期为我国构建AI赋能的科技安全情报体系提供理论支撑。图3。表1。参考文献45。 展开更多
关键词 AI for Science AI for Intelligence 事实核查 情报研究范式
暂未订购 下载PDF
The Transparency Revolution in Geohazard Science:A Systematic Review and Research Roadmap for Explainable Artificial Intelligence 认领 引用
6
作者 Moein Tosan Vahid Nourani +5 位作者 Ozgur Kisi Yongqiang Zhang Sameh A.Kantoush Mekonnen Gebremichael Ruhollah Taghizadeh-Mehrjardi Jinhui Jeanne Huang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期77-117,共41页
The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unatt... The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics. 展开更多
关键词 Explainable artificial intelligence(XAI) geohazard assessment machine learning SHAP trustworthy AI model interpretability
暂未订购 下载PDF
Narrative review of artificial intelligence(AI)in neuro-ophthalmic education:approaches for AI literacy 认领 引用
7
作者 Ritu Sampige Tony Succar +3 位作者 Yena Jang Zain Moin Ahmed Abdou Eduardo Mayorga 《Annals of Eye Science》 2026年第1期40-50,共11页
Background and Objective:Artificial intelligence(AI)-based tools in graduate medical education are increasingly utilized to support and augment learning due to their ability to provide tailored feedback to learners.Wi... Background and Objective:Artificial intelligence(AI)-based tools in graduate medical education are increasingly utilized to support and augment learning due to their ability to provide tailored feedback to learners.With the growing use of AI in educational settings,there is a need to further understand the implications and utility of AI-based tools,particularly in the field of neuro-ophthalmology.With the increasing use of AI tools in neuro-ophthalmology,this narrative review provides an overview of the current and future use of AI for neuro-ophthalmic education.Methods:Two databases,PubMed and Google Scholar,were searched with the following keywords:Neuro-ophthalmology,Ophthalmology,Artificial Intelligence,Large Language Models,Deep Learning Models,Education,Medical Education,Teaching,Curriculum,Training,Diagnostics,and Diagnosis from May to October 2025.Inclusion criteria included peer-reviewed sources that were specific to AI and neuroophthalmology in the English language through 2025.Key Content and Findings:AI tools,including large language models(LLMs),deep learning models,and virtual reality(VR)devices,may be utilized to teach learners about creating a differential diagnosis,identifying diseases through imaging and diagnostics[e.g.,fundus photography,optical coherence tomography(OCT),visual field patterns,radiology images,and understanding exam preparation questions].Learners across various levels(i.e.,medical students,residents,fellows)may interact with custom generative pre-training transformers(GPTs)in real-time to delve deeper into ophthalmic conditions and receive personalized feedback based on each learner’s knowledge gaps.Additionally,AI tools may harness the power of active learning methods through case generation and Socratic questioning.However,the caveat is that such tools vary in their knowledge base of neuro-ophthalmology concepts.Nonetheless,learners need to acknowledge the current limitations in the knowledge base of AI data sets and the need for human surveillance of AI-generated neuro-ophthalmic output,particularly through hybrid AI educational models.Conclusions:With the current shortage of neuro-ophthalmologists and the projected shortage of ophthalmologists overall,AI tools may serve as accessible,efficient,individualized,and accurate educational resources for learners across training levels,including in resource-limited regions. 展开更多
关键词 Artificial intelligence(AI) large language models(LLMs) neuro-ophthalmology
暂未订购 下载PDF
Artificial intelligence-based prediction of shear modulus and damping ratio of recycled tire rubber-soil mixtures for sustainable engineering applications 认领 引用
8
作者 Ahmed Yar Akhtar Hing-Ho Tsang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期2158-2176,共19页
Frugal innovation stands as an imperative cog in the wheel of sustainable development.In the pursuit of simplicity,cost-effectiveness,and environmental compatibility,waste tire rubber and polyurethane-coated rubber(PU... Frugal innovation stands as an imperative cog in the wheel of sustainable development.In the pursuit of simplicity,cost-effectiveness,and environmental compatibility,waste tire rubber and polyurethane-coated rubber(PUcR)emerge as pivotal components in sustainable practices.These materials are advocated for various purposes,including protecting utility tunnels,serving as railway subgrades,and enhancing structural resilience through geotechnical seismic isolation(GSI).Their inherent characteristics,such as modest shear modulus(G)and robust damping ratio(D),make them well-suited for such endeavors,contributing to sustainability goals by repurposing substantial quantities of non-biodegradable waste.For practicality,leveraging artificial intelligence(AI)-based modern computing techniques for recycled material applications is imperative.In this regard,gene expression programming(GEP)was utilized to develop models for predicting the G and D of rubber–soil mixtures(RSMs)and polyurethane-coated RSMs(PUcRSMs).Employing laboratory testing data from 63 samples across three soil types,the newly proposed models demonstrated exceptional accuracy,with correlation coefficient(R2)values of 0.91 and 0.97 for G-prediction of RSM and PUcRSM,and 0.9 and 0.86 for D-prediction,respectively.Using AI-based methods,such as GEP to predict mixtures’dynamic response can cut laboratory costs and optimize mix designs,thereby advancing sustainable material applications. 展开更多
关键词 Frugal innovation Artificial intelligence(AI) Sustainability Gene expression programming(GEP) Recycled waste tire rubber Geotechnical seismic isolation(GSI)
暂未订购 下载PDF
Convergence of 6G-Empowered Edge Intelligence and Generative AI:Theories,Algorithms,and Applications 认领 引用
9
作者 Wu Yuan Dusit Niyato +5 位作者 Cui Shuguang Zhao Lian Tony Q.S.Quek Zhang Yan Qian Liping Li Rongpeng 《China Communications》 SCIE EI CSCD 2025年第7期I0002-I0005,共4页
The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G en... The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G envisions a hyper-connected environment that supports ubiquitous intelligence through ultra-low latency,high throughput,massive device connectivity,and integrated sensing and communication.On the other hand,generative AI,powered by large foundation models,has emerged as a powerful paradigm capable of creating. 展开更多
关键词 G ubiquitous intelligence edge intelligence algorithms generative artificial intelligence ai theories large foundation modelshas intelligent computingon
暂未订购 下载PDF
Space-Ground Fluid AI for 6G Edge Intelligence 认领 引用
10
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
暂未订购 下载PDF
A comprehensive survey of artificial intelligence applications in UAV-enabled wireless networks 认领 引用 被引量：2
11
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
暂未订购 下载PDF
Advanced Design for High-Performance and AI Chips 认领 引用 被引量：1
12
作者 Ying Cao Yuejiao Chen +2 位作者 Xi Fan Hong Fu Bingang Xu 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第1期306-336,共31页
Recent years have witnessed transformative changes brought about by artificial intelligence(AI)techniques with billions of parameters for the realization of high accuracy,proposing high demand for the advanced and AI ... Recent years have witnessed transformative changes brought about by artificial intelligence(AI)techniques with billions of parameters for the realization of high accuracy,proposing high demand for the advanced and AI chip to solve these AI tasks efficiently and powerfully.Rapid progress has been made in the field of advanced chips recently,such as the development of photonic computing,the advancement of the quantum processors,the boost of the biomimetic chips,and so on.Designs tactics of the advanced chips can be conducted with elaborated consideration of materials,algorithms,models,architectures,and so on.Though a few reviews present the development of the chips from their unique aspects,reviews in the view of the latest design for advanced and AI chips are few.Here,the newest development is systematically reviewed in the field of advanced chips.First,background and mechanisms are summarized,and subsequently most important considerations for co-design of the software and hardware are illustrated.Next,strategies are summed up to obtain advanced and AI chips with high excellent performance by taking the important information processing steps into consideration,after which the design thought for the advanced chips in the future is proposed.Finally,some perspectives are put forward. 展开更多
关键词 Artificial intelligence Advanced chips AI chips Design tactics Review and perspective
暂未订购 下载PDF
A Deep Learning-Based Computational Algorithm for Identifying Damage Load Condition: An Artificial Intelligence Inverse Problem Solution for Failure Analysis 认领 引用 被引量：9
13
作者 Shaofei Ren Guorong Chen +2 位作者 Tiange Li Qijun Chen Shaofan Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2018年第12期287-307,共21页
In this work,we have developed a novel machine(deep)learning computational framework to determine and identify damage loading parameters(conditions)for structures and materials based on the permanent or residual plast... In this work,we have developed a novel machine(deep)learning computational framework to determine and identify damage loading parameters(conditions)for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure.We have shown that the developed machine learning algorithm can accurately and(practically)uniquely identify both prior static as well as impact loading conditions in an inverse manner,based on the residual plastic strain and plastic deformation as forensic signatures.The paper presents the detailed machine learning algorithm,data acquisition and learning processes,and validation/verification examples.This development may have significant impacts on forensic material analysis and structure failure analysis,and it provides a powerful tool for material and structure forensic diagnosis,determination,and identification of damage loading conditions in accidental failure events,such as car crashes and infrastructure or building structure collapses. 展开更多
关键词 Artificial intelligence(AI) deep learning forensic materials engineering plastic deformation structural failure analysis.
暂未订购 下载PDF
EdgeAIGC:Model caching and resource allocation for edge artificial intelligence generated content 认领 引用
14
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
暂未订购 下载PDF
AI-Integrated Feature Selection of Intrusion Detection for Both SDN and Traditional Network Architectures Using an Improved Crayfish Optimization Algorithm 认领 引用
15
作者 Hui Xu Wei Huang Longtan Bai 《Computers, Materials & Continua》 SCIE EI 2025年第8期3053-3073,共21页
With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with ... With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures. 展开更多
关键词 Software-defined networking(SDN) intrusion detection artificial intelligence(AI) feature selection crayfish optimization algorithm(COA)
暂未订购 下载PDF
The Development of Artificial Intelligence:Toward Consistency in the Logical Structures of Datasets,AI Models,Model Building,and Hardware? 认领 引用
16
作者 Li Guo Jinghai Li 《Engineering》 SCIE EI CSCD 2025年第7期13-17,共5页
The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficu... The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration. 展开更多
关键词 consistency datasets model building ai models artificial intelligence ai explore potential directions hardware artificial intelligence
暂未订购 下载PDF
GAI-BPAD:基于生成式AI Agent的业务流程异常主动识别框架 认领 引用 被引量：3
17
作者 张帅鹏 王世鹏 +4 位作者 何伟 孔兰菊 鹿旭东 郑永清 崔立真 《计算机集成制造系统》 EI CSCD 北大核心 2026年第3期1049-1060,共12页
在业务流程信息系统运行过程中,由于软件故障、操作人员失误等因素引发的异常现象十分普遍,这些异常会显著影响服务系统运行状态,并给企业组织带来风险,因此异常识别是业务流程管理中的关键环节。然而,部分组织机构由于新业务开展频率较... 在业务流程信息系统运行过程中,由于软件故障、操作人员失误等因素引发的异常现象十分普遍,这些异常会显著影响服务系统运行状态,并给企业组织带来风险,因此异常识别是业务流程管理中的关键环节。然而,部分组织机构由于新业务开展频率较低,存在业务数据积累不足或者某些潜在异常尚未在历史数据中展现的问题,这些异常一旦发生往往难以应对。同时,现有的异常识别方法难以主动应对复杂时序依赖和高维数据中的异常检测问题。为了解决上述问题,本文提出了基于生成式AI Agent的业务流程异常主动识别框架(GAI-BPAD),该框架分为感知、决策和执行3个主要模块,通过生成对抗网络(GAN)增强业务流程行为样本的多样性,并结合基于注意力机制的双向GRU神经网络(Att-Bi-GRU)进行异常识别。在9个真实数据集上进行了评估,实验结果表明,该方法相较于传统的异常识别方法,在准确性和鲁棒性方面均表现出显著提升,能够有效识别业务流程中的异常行为。 展开更多
关键词 流程挖掘 异常识别 AI Agent 人工智能
暂未订购 下载PDF
探寻可解释人工智能(XAI)的“解释奇点”:基于AIGC信息采纳视角 认领 引用 被引量：3
18
作者 卢新元 徐安琪 张进澳 《数据分析与知识发现》 EI CSSCI CSCD 北大核心 2026年第1期76-87,共12页
【目的】探寻可解释性和解释精度与信息采纳行为的作用关系,优化多场景适用的可解释设计。【方法】通过两个不同情境的平行实验,分析人工智能可解释性、解释内容关联度对用户信息采纳的影响,探讨不同任务信息需求情境下人工智能“解释... 【目的】探寻可解释性和解释精度与信息采纳行为的作用关系,优化多场景适用的可解释设计。【方法】通过两个不同情境的平行实验,分析人工智能可解释性、解释内容关联度对用户信息采纳的影响,探讨不同任务信息需求情境下人工智能“解释奇点”的变化规律。【结果】人工智能的可解释性对用户信息采纳具有显著影响,其具体的影响关系受制于内容关联度的制约;“解释奇点”是可解释设计作用于信息采纳行为的转折点,不同任务情境下“解释奇点”存在一定变化。【局限】未考虑虚假信息的存在,对可解释人工智能与用户信息采纳“效果”的揭示较为有限。【结论】本研究在多情境视角下揭示人工智能生成内容(AIGC)解释信息采纳机制差异,挖掘了“解释奇点”的变化规律。该发现不仅揭示了解释关系与内容关联度在驱动用户信息采纳中的关键作用,更强调其对用户信息采纳行为的边际影响。 展开更多
关键词 可解释人工智能 人智交互 人工智能生成内容 信息采纳行为 生成式人工智能
Design and Test Verification of Energy Consumption Perception AI Algorithm for Terminal Access to Smart Grid 认领 引用
19
作者 Sheng Bi Jiayan Wang +2 位作者 Dong Su Hui Lu Yu Zhang 《Energy Engineering》 EI 2025年第10期4135-4151,共17页
By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help s... By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks. 展开更多
关键词 Energy consumption perception terminal access smart grid AI Model SHAP Q-learning algorithm
暂未订购 下载PDF
AI时代生成式搜索对情报检索范式的影响与对策 认领 引用 被引量：3
20
作者 金家琴 刘炜 +1 位作者 赵宇翔 单蓉蓉 《现代情报》 CSSCI 北大核心 2026年第5期16-27,共12页
[目的/意义]生成式人工智能技术快速发展,对传统情报检索范式提出了根本性挑战。本研究旨在探讨生成式搜索技术对情报检索范式的影响机制。[方法/过程]本研究采用理论分析与案例研究相结合的方法,以Perplexity公司的Deep Research产品... [目的/意义]生成式人工智能技术快速发展,对传统情报检索范式提出了根本性挑战。本研究旨在探讨生成式搜索技术对情报检索范式的影响机制。[方法/过程]本研究采用理论分析与案例研究相结合的方法,以Perplexity公司的Deep Research产品为典型案例,从信息发现机制、认知处理过程、工作流程模式3个维度分析生成式搜索的影响机理。[结果/结论]生成式搜索实现了从“信息发现”到“知识生成”的信息检索范式转变,重构了情报检索范式的理论基础和实践方法,推动情报分析师从信息处理专家向问题设计专家转变,建立以人机协同为核心的新型工作范式。针对生成式搜索带来的准确性和隐私等挑战,本文提出了技术融合、人才建设和风险管理等对策建议,为情报机构有效应用生成式人工智能技术提供参考。 展开更多
关键词 生成式人工智能 生成式搜索 情报检索范式 Deep Research 人机协同
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈