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Generative Semantic Communication:Architectures,Technologies,and Applications 认领 引用 被引量：4
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作者 Jinke Ren Yaping Sun +7 位作者 Hongyang Du Weiwen Yuan Chongjie Wang Xianda Wang Yingbin Zhou Ziwei Zhu Fangxin Wang Shuguang Cui 《Engineering》 SCIE EI CSCD 2026年第1期45-61,共17页
Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the so... Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the source data rather than the complete set of bits(symbols).However,traditional deep-learning-based SemCom systems present challenges such as limited generalization,low robustness,and inadequate reasoning capabilities,primarily due to the inherently discriminative nature of deep neural networks.To address these limitations,generative artificial intelligence(GAI)is seen as a promising solution,offering notable advantages in learning complex data distributions,transforming data between high-and low-dimensional spaces,and generating high-quality content.This paper explores the applications of GAI in SemCom and presents a comprehensive study.It begins by introducing three widely used SemCom systems enabled by classical GAI models:variational autoencoders,generative adversarial networks,and diffusion models.For each system,the fundamental concept of the GAI model,the corresponding SemCom architecture,and a literature review of recent developments are provided.Subsequently,a novel generative SemCom system is proposed,incorporating cutting-edge GAI technology—large language models(LLMs).This system features LLM-based artificial intelligence(AI)agents at both the transmitter and receiver,which act as“brains”to enable advanced information understanding and content regeneration capabilities,respectively.Unlike traditional systems that focus on bitstream recovery,this design allows the receiver to directly generate the desired content from the coded semantic information sent by the transmitter.As a result,the communication paradigm shifts from“information recovery”to“information regeneration,”marking a new era in generative SemCom.A case study on point-to-point video retrieval is presented to demonstrate the effectiveness of the proposed system,showing a 99.98%reduction in communication overhead and a 53%improvement in average retrieval accuracy compared to traditional communication systems.Furthermore,four typical application scenarios for generative SemCom are described,followed by a discussion of three open issues for future research.In summary,this paper provides a comprehensive set of guidelines for applying GAI in SemCom,laying the groundwork for the efficient deployment of generative SemCom in future wireless networks. 展开更多
关键词 Semantic communication Generative artificial intelligence Large language model Variational autoencoder Generative adversarial network Diffusion model
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An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling 认领 引用 被引量：1
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作者 Lei Liu Wei Li +7 位作者 Jian Gao Da-Li Yue De-Gang Wu Wu-Rong Wang Jin Lin Zhi-Bo Li Qian Zhong Jia-Gen Hou 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1754-1772,共19页
Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we... Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we innovatively propose an interpretable attention-guided generative adversarial network framework with dual-domain learning,which achieves precise sedimentary facies modeling under the constraints of well facies and soft probability data.Specifically,we first effectively extract and preserve prior information of sedimentary facies models from both spatial and frequency domain perspectives.Then,during simulation,to enhance the capability of the network model for finely characterizing complex heterogeneous models,cross-spatial attention mechanisms are designed to effectively capture short-range and long-range dependencies between multi-scale pattern features.Additionally,through systematic feature map visualization analysis,we elucidate the processes of conditional fitting and complex sedimentary facies model reconstruction,intuitively demonstrating the functional mechanisms of each module.Finally,systematic experiments are conducted on multiple datasets to validate the effectiveness of the proposed method.The results demonstrate that the generated sedimentary facies models exhibit high consistency with training datasets in terms of visual realism and statistical indicators.Quantitative comparisons reveal remarkable performance of the method,achieving low Wasserstein distance(0.09),Kernel Inception Distance(0.0017)and Kernel Maximum Mean Discrepancy(0.21).These findings further confirm the high realism of the generated realizations regarding pattern features.This study offers a reliable and practical method for geological reservoir modeling,thereby advancing quantitative,precise geological research with broad application prospects. 展开更多
关键词 Sedimentary facies models Attention-guided generative adversarial network Interpretable framework Sedimentary patterns Multi-condition modeling
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A Generative Steganography Based on Attraction-Matrix-Driven Gomoku Games 认领 引用
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作者 Yi Cao Kuo Zhang +2 位作者 Chengsheng Yuan Linglong Zhu Wentao Ge 《Computers, Materials & Continua》 SCIE EI 2026年第2期939-962,共24页
Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distr... Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distribution of training data,often neglecting the sequential continuity between moves in the game.This oversight can result in unnatural patterns that deviate from real user behavior,thereby reducing the security of the hidden communication.To address this issue,we design a Gomoku agent based on the AlphaZero algorithm.The model engages in self-play to generate a sequence of plausible moves.These moves formthe basis of the stego images.We then apply an attractionmatrix at each step.It guides themove selection so that themoves appearmore natural.Thismethod helps maintain logical flow between moves.It also extends the game length,which increases the embedding capacity.Next,we filter and prioritize the generated moves.The selected moves are embedded into a move pool.Secret message is mapped to thesemoves.It is then embedded step by step as the game progresses.The finalmove sequence constitutes a complete steganographic game record.The receiver can extract the secret message using this record and a predefined mapping rule.Experiments show that our method reaches a maximum embedding capacity of 223 bits per carrier.Detection accuracy is 0.500 under XuNet and 0.498 under YeNet.These results are equal to random guessing,showing strong imperceptibility.The proposed method demonstrates superior concealment,higher embedding capacity,and greater robustness against common image distortions and steganalysis attacks. 展开更多
关键词 Generative steganography information hiding steganography steganalsis attraction matrix
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Generative AI for Efficient and Secure Authentication in UAV-Enabled Smart City Transportation Systems 认领 引用
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作者 Akmalbek Abdusalomov Kudratjon Zohirov +6 位作者 Sojida Ochilova Jakhongir Oramov Zafar Ruziyev Malika Rustamova Gulrukh Sherboboyeva Komil Tashev Young Im Cho 《Computers, Materials & Continua》 SCIE EI 2026年第8期1164-1180,共17页
Unmanned aerial vehicles(UAVs)are also increasingly becoming more often in the transportation infrastructure of smart cities,so that they can successfully achieve real-time observation of traffic,emergency coordinatio... Unmanned aerial vehicles(UAVs)are also increasingly becoming more often in the transportation infrastructure of smart cities,so that they can successfully achieve real-time observation of traffic,emergency coordination,and two-way communication relaying.However,the security and privacy risks arising in open,highly mobile intelligent transportation systems(ITS)enabled by UAVs are critical,as they pose threats of impersonation,replay,Sybil,and tracking attacks.Secondly,standard static authentication mechanisms are unable to support dynamic risk environments and excessive resource consumption on UAV platforms with limited capacity.To address these challenges,this study introduces a Generative-AI-assisted Risk-Adaptive Authentication(GRAA)system that modulates the intensity of the authentication process based on risk levels identified by mobility,contextual awareness,and the environment.The framework contains unlinkable pseudonymous credentials and,unlike the accumulator-based revocation scheme and AI-based trust evaluation,it is impossible to correlate sessions.The coherence with the majority of attacks is demonstrated under the formal analysis model,which is also based on the real-or-random(ROR)session key,alongside the justifications of forward secrecy and unlinkability.The performance analysis shows that GRAA can achieve up to 87.9%reduction in computation cost and 56.7%reduction in communication overhead compared to pairing-and-group signature schemes,while lowering the latency and energy consumption of the UAVs in a congested urban setting.Generally,the suggested architecture provides a scalable,convenient,and privacy-friendly authentication system for next-generation smart transportation systems that use UAVs. 展开更多
关键词 Generative AI UAV-enabled intelligent transportation systems risk-adaptive authentication privacy-preserving security unlinkability scalable revocation
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Negative-One-Day Malware Detection with Generative AI:A Stable Diffusion-Based Proactive Defense Framework 认领 引用
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作者 Sohail Khan Toqeer Ali Syed +3 位作者 Mohammad Nauman Salman Jan It Ee Lee Qamar Wali 《Computers, Materials & Continua》 SCIE EI 2026年第7期1505-1522,共18页
The detection of zero-day malware represents one of the most significant challenges in contemporary cybersecurity.In this paper,we introduce a novel concept called“Negative-One-Day Malware Detection”,which aims to i... The detection of zero-day malware represents one of the most significant challenges in contemporary cybersecurity.In this paper,we introduce a novel concept called“Negative-One-Day Malware Detection”,which aims to identify potentially malicious software before it is actually created by threat actors.Our approach leverages recent advancements in generative AI,specifically diffusion-based generative models,to generate and analyze potential future malware variants.By doing so,we can train detection systems to recognize these variants before they emerge in the wild,thereby closing the critical protection gap that currently exists between malware creation and detection.We demonstrate the effectiveness of our approach through extensive experimentation,showing that our framework can generate executable malware samples that combine characteristics from different families while exhibiting novel behaviors.These synthetically generated samples significantly improve the detection capabilities of security systems when incorporated into training data,providing a proactive rather than reactive approach to cybersecurity. 展开更多
关键词 Adversarial machine learning Generative AI stable diffusion models zero-day malware detection negative-one-day malware detection proactive cyber defense
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A Super-Resolution Generative Adversarial Network for Remote Sensing Images Based on Improved Residual Module and Attention Mechanism 认领 引用
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作者 Yifan Zhang Yong Gan +1 位作者 Mengke Tang Xinxin Gan 《Computers, Materials & Continua》 SCIE EI 2026年第2期689-707,共19页
High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleim... High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft. 展开更多
关键词 Remote sensing imagery generative adversarial networks super-resolution enhanced residual unit selfattention mechanism
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A Power System Preventive Control Method Based on Generative Adversarial Proximal Policy Optimization 认领 引用
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作者 Yun Yu Li Lin +3 位作者 Ximing Zhang Yang Yu Wei Zhang Kai Cheng 《Energy Engineering》 EI 2026年第7期304-322,共19页
Traditional transient stability preventive control calculation methods suffer from low computational efficiency,struggling to meet the real-time decision demands of increasingly large-scale power systems.Meanwhile,rei... Traditional transient stability preventive control calculation methods suffer from low computational efficiency,struggling to meet the real-time decision demands of increasingly large-scale power systems.Meanwhile,reinforcement learning-based preventive control approaches,which adopt an"offline training,online application'framework,show greater promise in preventive control.However,they still face challenges such as low computational efficiency in electromechanical transient simulation and insufficient decision robustness.Therefore,this paper proposes a power system predictive control strategy based on Generative Adversarial Proximal Policy Optimization(GA-PPO).Firstly,considering multiple constraints in transient stability operation,a power system preventive control model is constructed with the objective of minimizing the total amount of adjustments,along with its Markov Decision Process(MDP)formulation.Then,the discriminator of Generative Adversarial Network(GAN)measures the gap between the expert demonstration distribution and the generated trajectory distribution,providing correction parameters for the advantage function of the Proximal Policy Optimization(PPO)algorithm,enhancing the agent's exploration efficiency.Finally,the discriminator's update mechanism is enhanced by Wasserstein distance,ensuring more stable training while enabling continuous adversarial interaction between discriminator and generator to explore higher convergent rewards.Case studies demonstrate that the proposed GA-PPO algorithm significantly reduces training time and achieves higher convergent rewards compared to PPO and Soft Actor-Critic(SAC)algorithms. 展开更多
关键词 Generative adversarial proximal policy optimization transient stability preventive control
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Electric-Field-Driven Generative Nanoimprinting for Tilted Metasurface Nanostructures 认领 引用 被引量：1
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作者 Yu Fan Chunhui Wang +6 位作者 Hongmiao Tian Xiaoming Chen Ben QLi Zhaomin Wang Xiangming Li Xiaoliang Chen Jinyou Shao 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第1期290-305,共16页
Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is p... Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality. 展开更多
关键词 Generative nanoimprinting Electric field assistance Tilted metasurface structures Large-area fabrication
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Advancing electric vehicle ecosystems:a survey of generative artificial intelligence and distributed machine learning applications 认领 引用
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作者 Seyed Mahmoud Sajjadi Mohammadabadi Aidin Karimi Moghaddam +4 位作者 Mahmoudreza Entezami Mirali Seyedrezaei Dorsa Charkhian Behzad Moghaddami Mohammad Sassani 《Global Energy Interconnection》 EI CSCD 2026年第2期315-336,共22页
The growing popularity of Electric Vehicles(EVs)necessitates advanced systems capable of managing the increasing complexity of EV-generated data.However,the exponential expansion of data streams poses significant chal... The growing popularity of Electric Vehicles(EVs)necessitates advanced systems capable of managing the increasing complexity of EV-generated data.However,the exponential expansion of data streams poses significant challenges to existing network infrastructure,potentially limiting EV performance and scalability.This survey investigates the synergistic potential of Generative Artificial Intelligence(GenAI)and Distributed Machine Learning(DML)to address key challenges and enhance EV efficiency across diverse domains.DML facilitates collaborative learning across decentralized devices,enabling optimized resource allocation,strengthened privacy,and improved EV operations without data centralization.Meanwhile,GenAI techniques,such as Generative Adversarial Networks(GANs)and Variational Autoencoders(VAEs),offer transformative capabilities,including synthetic data generation for energy forecasting,data compression for efficient transmission,and resource-efficient task offloading.This paper explores the applications of GenAI and DML in several key areas of the EV ecosystem.These include battery lifecycle management,energy optimization,fault detection,and workload balancing.Furthermore,it highlights the primary advantages and challenges of implementing these technologies,such as addressing computational demands,algorithmic complexity,and mitigating biases in generated content.By advancing the integration of GenAI and DML,this study lays a foundation for a more sustainable,intelligent,and efficient transportation future. 展开更多
关键词 Generative artificial intelligence Electric vehicles Distributed machine learning Resource optimization Energy forecasting Fault detection ChatGPT Optimization
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High-Q and Maximally Intrinsically Chiral Quasi-Bound States in the Continuum via Generative Adversarial Networks 认领 引用
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作者 Chen Zhong Yi-Lin Wang +4 位作者 Jun-Wu Guo E-Xian Liu Jun Yi Xin-Xing Zhou Jian-Jun Liu 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第6期27-32,I0010-I0024,共6页
Resonances combining high Q-factors with maximal intrinsic chirality offer substantial advantages for optical sensing and chiral emis-sion.Yet the conflicting symmetry requirements for achieving these two attributes h... Resonances combining high Q-factors with maximal intrinsic chirality offer substantial advantages for optical sensing and chiral emis-sion.Yet the conflicting symmetry requirements for achieving these two attributes have made it challenging for current metasurface designs to realize both simultaneously.In the low-loss limit with background transparency,we reveal that the resonant contribution with-in the polarization subspace inherently manifests as a rank-1 projector.The system completely decouples from the opposite polarization when the coupling vector is strictly parallel to a specific circularly polarized eigenstate.Crucially,this behavior is governed solely by the relative amplitude and phase of the coupling vector rather than the absolute radiation intensity.Thus,we demonstrate that resonant modes coupled to linearly polarized channels with identical coupling strengths and a phase difference ofπ/2 can simultaneously yield high Q-factors(reaching 2×105)and extreme circular dichroism(CD,up to 0.98).Additionally,we developed a generative adversarial network.Unlike traditional regression methods that merely predict the statistical mean of simple correspondences,the discriminator adversarially compels the generator to capture the complex one-to-many mapping between structures and chiral resonances.Benefiting from the coexistence of high Q-factors and strong CD,the metasurface also realizes excellent chiral refractive-index sensing,achieving a sensitivity of 105 nm/RIU and a figure of merit of 8030.This design establishes a new paradigm for high-performance chiral sensing and polarization-controlled devices. 展开更多
关键词 maximally intrinsic chirality high q resonances chiral emission metasurface designs generative adversarial networks optical sensing quasi bound states continuum
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A Survey of Generative Adversarial Networks for Medical Images 认领 引用
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作者 Sameera V.Mohd Sagheer U.Nimitha +3 位作者 P.M.Ameer Muneer Parayangat MohamedAbbas Krishna Prakash Arunachalam 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期130-185,共56页
Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation... Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment. 展开更多
关键词 Generative adversarial networks medical images denoising segmentation translation
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Virtual Magnetic Resonance Elastography Using a Deep Generative Model for Liver Fibrosis Staging 认领 引用
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作者 Longyu Sun Yikun Wang +7 位作者 Yan Li Xumei Hu Wenyue Mao Mengting Sun Zian Wang Fuhua Yan Ruokun Li Chengyan Wang 《iRADIOLOGY》 CSCD 2026年第1期23-34,共12页
Background:Liver biopsy is invasive,which presents many limitations in clinical settings.Magnetic resonance elastography(MRE)has significant value in non-invasively diagnosing liver fibrosis.However,its use is current... Background:Liver biopsy is invasive,which presents many limitations in clinical settings.Magnetic resonance elastography(MRE)has significant value in non-invasively diagnosing liver fibrosis.However,its use is currently uncommon because it requires specialized equipment.This study aimed to propose and assess the reliability of virtual MRE(vMRE)using diffusion weighted imaging(DWI)and evaluate its effectiveness in diagnosing liver fibrosis.Methods:We proposed a Registration-based Generative Adversarial Network-convolutional Block Attention Model(RegGAN-CBAM)to synthesize stiffness(cMap)and viscosity(phiMap)using DWI data acquired from 128 patients diagnosed with liver fibrosis or cirrhosis.Correlation and agreement between native MRE(nMRE)-and vMRE-derived measurements were assessed using Spearman correlation coefficients and Bland-Altman analysis.Receiver operating characteristic curves were constructed to evaluate the diagnostic performance of these measures for cirrhosis.Results:The proposed RegGAN-CBAM model demonstrated favorable performance in image synthesis and estimation.vMRE measures had high consistency with nMRE for images;moreover,they correlated significantly with cMap(r=0.77)and phiMap(r=0.58)measurements.Staging based on predicted cMap and phiMap demonstrated excellent performance(p<0.01),with considerable accuracy for diagnosing cirrhosis(area under the curve:cMap=0.75 and phiMap=0.74)among the test set(n=40),which included 21 patients with histologically confirmed cirrhosis(52.5%).Conclusions:Our study highlights the reliability of our proposed model for liver fibrosis diagnosis.Furthermore,the non-invasive approach may serve as a practical alternative to conventional clinical MRE,particularly in healthcare facilities without access to MRE equipment. 展开更多
关键词 diffusion weighted imaging generative model liver fibrosis machine learning MR elastography
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A Systematic Literature Review on the Impact of Generative AI in Digital Marketing: Advancements, Opportunities, and Challenges 认领 引用
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作者 Arifur Rahman MD Azam Khan +4 位作者 Farhad Uddin Mahmud Kanchon Kumar Bishnu Ashifur Rahman M.F.Mridha Md.Jakir Hossen 《Computers, Materials & Continua》 SCIE EI 2026年第6期391-435,共45页
Generative Artificial Intelligence(AI)is reshaping digital marketing by creating automated content,personalizing campaigns,and offering new ways to engage consumers.This systematic review examines research on generati... Generative Artificial Intelligence(AI)is reshaping digital marketing by creating automated content,personalizing campaigns,and offering new ways to engage consumers.This systematic review examines research on generative AI,highlighting both its technological progress and the ethical,technical,and organizational hurdles that could limit its use.We used a PRISMA-based method to search major databases(ACM Digital Library,IEEE Xplore,and Scopus)for peer-reviewed studies published from 2018 to 2025.Our findings reveal major gains in text creation,image generation,and multimodal campaigns,which can lower costs and spark creative thinking.Still,data privacy,bias in models,and laws around compliance show the need for clear and responsible adoption.By bringing in ideas from Innovation Diffusion Theory and the Technology Acceptance Model,this review shows how organizational culture and perceived value interact with ethical frameworks to shape how generative AI tools take hold.We provide insights for marketers who want to apply generative AI in a responsible way and set a path for future research aimed at protecting consumer trust. 展开更多
关键词 Generative AI digital marketing content generation personalization ethical AI technology adoption innovation diffusion theory transformer models
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Generative World Modeling for Risk-Aware Autonomous UAV Navigation in Dynamic Traffic Networks 认领 引用
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作者 Alaa M.Momani Deema Mohammed Alsekait +4 位作者 Mahmoud Ahmad Al-Khasawneh Siti Hajar Othman Ibraheem Al-Tarawneh Nikunj Sharma Wee How Khoh 《Computers, Materials & Continua》 SCIE EI 2026年第9期1599-1615,共17页
Unmanned Aerial Vehicles(UAVs)are finding more and more applications in logistics,surveillance,and other operations at a large scale.However,autonomous navigation in dynamic traffic situations is not an easy task due ... Unmanned Aerial Vehicles(UAVs)are finding more and more applications in logistics,surveillance,and other operations at a large scale.However,autonomous navigation in dynamic traffic situations is not an easy task due to limited energy,moving obstacles,and inter-agent interactions.The proposed paper can be discussed as a Generative World Modeling(GWM)framework of risk-focused UAV navigation in the dynamic traffic network.This paper proposes a GWM framework for risk-aware UAV navigation in dynamic traffic networks.The proposed design incorporates three key elements;a generative world model for predicting future environmental conditions,a diffusion-based trajectory-generation component that generates multiple possible paths,and a risk-aware decision-making component that selects trajectories based on energy use,collision avoidance,and mission criteria.The framework is also extended to the case of a multi-UAV swarm,where a coordinated swarm is facilitated by shared representations in the latent space to alleviate potential conflicts.The experimental analysis of real-world-inspired UAV trajectory data indicates that the proposed GWM framework outperforms the classical,reinforcement-based,and conflict-aware baseline approaches across a range of performance metrics,including mission success rate,delivery time,energy cost,safety,and path efficiency.The findings indicate that with risk-sensitive and generative prediction,autonomous UAV missions should be more robust,effective,and secure in uncertain,complex environments. 展开更多
关键词 Unmanned aerial vehicles generative AI autonomous navigation risk-aware decision trajectory planning swarm coordination
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Multi-Constraint Generative Adversarial Network-Driven Optimization Method for Super-Resolution Reconstruction of Remote Sensing Images 认领 引用
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作者 Binghong Zhang Jialing Zhou +3 位作者 Xinye Zhou Jia Zhao Jinchun Zhu Guangpeng Fan 《Computers, Materials & Continua》 SCIE EI 2026年第1期779-796,共18页
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex... Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures. 展开更多
关键词 Charbonnier loss function deep learning generative adversarial network perceptual loss remote sensing image super-resolution
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Conditional Generative Adversarial Network-Based Travel Route Recommendation 认领 引用
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作者 Sunbin Shin Luong Vuong Nguyen +3 位作者 Grzegorz J.Nalepa Paulo Novais Xuan Hau Pham Jason J.Jung 《Computers, Materials & Continua》 SCIE EI 2026年第1期1178-1217,共40页
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of... Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence. 展开更多
关键词 Travel route recommendation conditional generative adversarial network heterogeneous information network anchor-and-expand algorithm
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Predicting permeability coefficients of earth-rock material using an improved generative adversarial network and explainable ensemble learning under small sample conditions 认领 引用
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作者 Chengyu YU Hongling YU +4 位作者 Xiaofeng QU Baoxi LIU Liangsi XU Xinyu LIU Xiangyu CHEN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第3期215-230,共16页
Accurate prediction of the permeability coefficient is crucial for evaluating the compaction quality of earthworks.However,during the compaction process,on-site testing is often time-consuming and expensive,leading to... Accurate prediction of the permeability coefficient is crucial for evaluating the compaction quality of earthworks.However,during the compaction process,on-site testing is often time-consuming and expensive,leading to fewer samples,which affects prediction accuracy.Moreover,most current predictive models have limited capabilities and tend to be black-box models with poor explainability.To overcome these issues,in this study,we proposed a new method to predict the permeability coefficient of earth-rock material based on an improved generative adversarial network(GAN)and explainable osprey optimization algorithm–Huber loss–light gradient boosting machine(OOA–HL–LightGBM).Firstly,by introducing the Wasserstein distance as the loss function into the conditional generative adversarial network(CGAN),the Wasserstein conditional generative adversarial network(WCGAN)was proposed to generate high-quality data,addressing the issue of insufficient information caused by small samples.Furthermore,by incorporating material and compaction parameters as inputs,a high-accuracy permeability coefficient prediction model was developed using LightGBM with the Huber loss function and the OOA.Finally,the Shapley additive explanation(SHAP)method was introduced into OOA–HL–LightGBM to analyze the specific roles of different features within the dataset to enhance the credibility of the prediction results.The proposed method was applied to a large-scale high-core rockfill dam in southwestern China to thoroughly verify its effectiveness and superiority. 展开更多
关键词 Permeability coefficient prediction Light gradient boosting machine(LightGBM) Wasserstein conditional generative adversarial network(WCGAN) Shapley additive explanation(SHAP)
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Aerodynamic shape optimization of hypersonic aircraft using data-driven generative nonlinear parameterization 认领 引用
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作者 Yan CHEN Jichao LI Jinsheng CAI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期64-76,共13页
Aerodynamic shape optimization of hypersonic vehicles is critically important yet profoundly challenging.The difficulties arise from the need to manage multiple competing objectives,complex three-dimensional geometrie... Aerodynamic shape optimization of hypersonic vehicles is critically important yet profoundly challenging.The difficulties arise from the need to manage multiple competing objectives,complex three-dimensional geometries,and the extreme computational cost of high-fidelity aerodynamic simulations across subsonic,transonic,and hypersonic regimes.Despite recent advances,an effective global optimization strategy for hypersonic aircraft design remains limited,largely hindered by the curse of dimensionality.To remove this barrier,we propose a data-driven generative nonlinear shape parameterization framework for efficient aerodynamic design of hypersonic aircraft.This framework begins by constructing diverse hypersonic aircraft shapes that cover the feasible sub-domains of a high-dimensional design space.A linear dimension reduction method is used to transform the high-dimensional point-cloud database to a low-dimensional modal space.Subsequently,a nonlinear generative model is trained to learn the statistical distribution feature of the linear mode coefficients.The resulting generative latent space provides an efficient,lowdimensional,and expressive parameterization of aerodynamic shapes.The proposed method is validated in both single-point and multi-point optimization of hypersonic aircraft,demonstrating superior efficiency and effectiveness compared with conventional parameterization approaches.This study presents an efficient roadmap for aerodynamic shape parameterization and global optimization of next-generation aircraft. 展开更多
关键词 Aerodynamic shape optimization Deep-learning Dimension reduction Generative parameterization Hypersonic aircraft design
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EGAIN: Enhanced Generative Adversarial Networks for Imputing Missing Values 认领 引用
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作者 Abolfazl Saghafi Soodeh Moallemian +1 位作者 Miray Budak Rutvik Deshpande 《Computers, Materials & Continua》 SCIE EI 2026年第8期2241-2255,共15页
Missing data remain a persistent challenge in statistical analysis and machine learning because many predictive methods require complete observations.Generative Adversarial Imputation Networks(GAIN)offer a flexible de... Missing data remain a persistent challenge in statistical analysis and machine learning because many predictive methods require complete observations.Generative Adversarial Imputation Networks(GAIN)offer a flexible deep-learning approach for missing value imputation,but their practical use is limited by convergence instability,sensitivity to hyperparameter selection,and dependence on outdated software implementations.To address these limitations,we propose Enhanced Generative Adversarial Imputation Networks(EGAIN),a modernized extension of GAIN implemented in TensorFlow 2.x.EGAIN incorporates convolution-based generator and discriminator networks,a channel-stacked representation of the data and mask,and checkpoint-based training diagnostics to improve stability and usability.EGAIN was evaluated on five benchmark datasets under multiple Missing Completely At Random(MCAR)settings and compared with the original GAIN implementation and median imputation.Across most evaluated conditions,EGAIN achieved lower root mean squared error(RMSE)and showed greater robustness,particularly when missingness was concentrated in a subset of variables.These results indicate that EGAIN provides a more stable and reproducible framework for missing data imputation in tabular datasets. 展开更多
关键词 Missing value imputation generative adversarial network tabular data imputation missing completely at random convolutional architectures training stability
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