Artificial Intelligence Review最新文献

{"title":"Transformers for 3D medical image analysis: a systematic review of architectural innovations, performance, and clinical applications","authors":"Wandile Nhlapho,&nbsp;Marcellin Atemkeng,&nbsp;Yusuf Brima,&nbsp;Blaise Ravelo,&nbsp;Thierry Bouwmans,&nbsp;Sébastien Lalléchère,&nbsp;Jean-Claude Ndogmo","doi":"10.1007/s10462-026-11600-y","DOIUrl":"10.1007/s10462-026-11600-y","url":null,"abstract":"<div><p>The growing integration of Transformer-based architectures into 3D medical image analysis has driven significant advances across segmentation, classification, detection, registration, and reconstruction tasks. However, existing reviews remain fragmented, often focusing on 2D medical image analysis or specific modalities or tasks without providing a comprehensive, structured synthesis of architectural innovations, benchmark performance, and clinical applicability. This systematic review addresses these gaps by following the PRISMA 2020 framework to systematically evaluate 138 peer-reviewed studies published between January 2017 and December 2025, identified across Scopus, Web of Science, and PubMed. We evaluate Transformer-based and hybrid CNN–Transformer architectures across major 3D imaging modalities; MRI, CT, PET, and ultrasound using a structured five-question research framework addressing architectural evolution, benchmark performance, modality-specific trends, methodological rigour, and reproducibility. To quantify methodological progress beyond performance metrics, we introduce an Architectural Innovation Score grounded in a component-level innovation encoding scheme. Our analysis reveals a clear dominance of hybrid CNN–Transformer architectures, with MRI representing the most extensively studied modality. Multimodal imaging achieves the highest normalized mean performance, followed by MRI, CT, and ultrasound. Emerging paradigms, including State Space Models and diffusion-based Transformers, show promise but remain underexplored. Despite strong benchmark results, critical limitations persist, including insufficient external validation, limited code availability, and inconsistent reporting practices.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 11","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11600-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mitigating cyberattacks on autonomous vehicles: a comprehensive review of Generative Artificial Intelligence defense techniques","authors":"May Phyu Phyu Thaw,&nbsp;Doreen Sebastian Sarwatt,&nbsp;Huansheng Ning,&nbsp;Jianguo Ding","doi":"10.1007/s10462-026-11636-0","DOIUrl":"10.1007/s10462-026-11636-0","url":null,"abstract":"<div><p>Autonomous vehicles (AVs) are rapidly becoming foundational components of intelligent transportation systems (ITS), yet their complex cyber-physical architectures expose them to a broad and continuously evolving threat landscape. Existing cybersecurity solutions struggle to keep pace with the dynamic, data-intensive nature of AV ecosystems, leaving critical vulnerabilities unaddressed across perception, communication, and decision-making subsystems. Generative Artificial Intelligence (GAI), encompassing Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), has emerged as a powerful paradigm for both offensive simulation and defensive reinforcement, enabling synthetic data generation, adversarial attack emulation, and enhanced anomaly and intrusion detection. Yet despite growing interest in GAI for general cybersecurity, its systematic application to AV-specific security remains fragmented and underexplored. This paper addresses that gap through a PRISMA-guided systematic review of GAI-driven defense mechanisms for AV cybersecurity, synthesizing 216 peer-reviewed studies drawn from major scientific databases and published between January 2020 and February 2026. Three principal contributions are made. First, we introduce an AV-centric, three-dimensional taxonomy that classifies defenses along generative architecture, defensive function, and AV-relevant attack surface, explicitly anchoring each study to AV subsystems and operational contexts. Second, we provide a disciplined synthesis that separates study-specific performance findings from broader design insights, exposing fundamental gaps between conventional and GAI-based approaches in scalability, adaptability, and resilience. Third, we identify critical open challenges—including training instability, the absence of standardized AV security benchmarks, real-time deployment constraints, and limited explainability—and propose targeted research directions for safety-critical environments. By grounding GAI defenses within AV system layers and cyber-physical threat models, this review serves as a practitioner- and researcher-oriented reference for building robust, scalable, and trustworthy cybersecurity solutions for next-generation autonomous vehicles.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 9","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11636-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fairness in federated medical imaging: a systematic review through the dual fairness lens","authors":"Pengyang Yu,&nbsp;Zhongping Dong,&nbsp;Sahraoui Dhelim,&nbsp;Chun-Mei Feng,&nbsp;M. Tahar Kechadi","doi":"10.1007/s10462-026-11632-4","DOIUrl":"10.1007/s10462-026-11632-4","url":null,"abstract":"<div><p>Federated learning (FL) enables multi-institutional collaboration in medical imaging while preserving patient privacy, yet its fairness landscape remains fragmented: existing methods predominantly address either <i>collaboration fairness</i> (equitable performance across institutions) or <i>group fairness</i> (equitable outcomes across demographic subgroups), but rarely both. In this systematic review, we adopt <i>dual fairness</i>—the joint satisfaction of both dimensions—as the analytical lens for organizing and critically evaluating this landscape. Following the PRISMA 2020 guidelines, we analyze 132 publications and classify fairness-aware FL methods through a three-dimensional taxonomy: client-side, server-side, and communication-based approaches. Among the 20 fairness-aware or fairness-adapted FL methods catalogued, only three partially address both dimensions, and none provides provable joint guarantees under clinically realistic conditions. Our critical analysis identifies three fundamental challenges: the Local–Global Pareto Frontier Conflict, in which collaboration and group fairness gradients in the accuracy space can exceed <span>(150^{circ })</span> under sufficiently asymmetric demographic imbalance (e.g., <span>(pi _1 ge 0.8)</span>); the Privacy–Fairness Compounding Effect, through which differential privacy mechanisms disproportionately suppress minority gradient signals; and the risk of pseudo-fairness, whereby equipment–demographic confounding masks genuine algorithmic discrimination. We further outline a seven-direction research roadmap. To the best of our knowledge, this constitutes the first systematic review to formally analyze the gradient-level conflict between collaboration fairness and group fairness in federated medical imaging, while also providing a structured causal analysis of equipment–demographic confounding, offering both a critical synthesis and actionable directions toward equitable AI-assisted healthcare.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 11","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11632-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for heart disease anomaly detection: performance factors and algorithms","authors":"Hong Zhou,&nbsp;Duc-Anh Nguyen,&nbsp;Nhien-An Le-Khac","doi":"10.1007/s10462-026-11633-3","DOIUrl":"10.1007/s10462-026-11633-3","url":null,"abstract":"<div><p>Heart disease is a prevalent concern for individuals in every age group, as it significantly impacts their health and remains a leading cause of mortality today. An effective heart disease detection method is essential for people to assess their heart conditions accurately. Over the past decades, heart disease detection techniques, whether based on machine learning or deep learning, have evolved considerably—from relying on handcrafted features to automatically learned features, from using single models to hybrid models and from applications confined to clinical settings to deployment in daily life through wearable and remote sensing technologies. This paper provides a comprehensive review of various types of heart-related data and popular deep learning algorithms employed for heart disease detection. In addition, we conduct a comparative experiment that showcases the existing gaps between clinical and wearable signals and highlights the potential for further research on wearable devices. This paper also identifies the key challenges, emphasises the primary factors affecting detection performance and discusses potential solutions.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 11","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11633-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of recent advancements in hyperspectral object tracking","authors":"Mohammad Aminul Islam,&nbsp;Fengchao Xiong,&nbsp;Jun Zhou,&nbsp;Wangzhi Xing,&nbsp;Dong Bao,&nbsp;Yongsheng Gao","doi":"10.1007/s10462-026-11625-3","DOIUrl":"10.1007/s10462-026-11625-3","url":null,"abstract":"<div><p>Visual object tracking is a fundamental problem in computer vision. Traditional tracking methods, which primarily rely on RGB imagery, often face difficulties in complex scenarios such as low resolution and background clutter. Hyperspectral imaging, which captures both spatial and spectral information across multiple narrow spectral bands, has emerged as a promising solution. However, hyperspectral tracking suffers from challenges including the complexity of spatial-spectral-temporal modeling, the high dimensionality of spectral bands, low spatial resolution, and limited training samples. In response, researchers have developed a variety of hyperspectral object tracking (HOT) methods, integrating innovations in band selection and advanced architectures such as Siamese networks, Transformers, and multimodal fusion-based approaches. Despite this progress, existing reviews primarily focus on specific sub-domains and provide limited coverage of recent developments in hyperspectral video camera technology and the associated data processing pipelines, including calibration and correction techniques. Therefore, this review aims to fill that gap by providing an in-depth and systematic overview of HOT. It examines the motivations driving this research, highlights recent technological and methodological developments, surveys publicly available datasets, and evaluates performance metrics. Finally, we discuss future research directions and prospects, offering a valuable resource for researchers and practitioners. Moreover, to support ongoing innovation, we have established a dedicated GitHub repository to document and continuously update relevant works: https://github.com/aamin0102/HOT-Review.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 11","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11625-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review on empirical studies in explainable artificial intelligence","authors":"Tobias Jahn,&nbsp;Philipp Hühn,&nbsp;Manfred Reichert","doi":"10.1007/s10462-026-11595-6","DOIUrl":"10.1007/s10462-026-11595-6","url":null,"abstract":"<div><p>As artificial intelligence (AI) systems become more integrated into decision-making processes, the need for explainability has emerged to foster trust, understanding, and effective human-AI collaboration. With the variety of explainable AI (XAI) methods available, selecting the right one for a specific user group and a given use case remains challenging, especially given the limited empirical validation of existing theoretical guidance. This systematic literature review addresses this gap by synthesizing human-grounded evaluations of XAI methods to identify the influence of specific explanation properties on user outcomes across diverse settings. Moving beyond high-level taxonomies, we classify XAI methods along multiple dimensions, such as scope and output type. Based on this classification, we analyze how the properties of XAI methods affect different user groups, tasks, and domains. Our findings underscore the necessity of context-aware method selection, as the effectiveness of XAI methods varies significantly across use cases. Moreover, our analysis reveals imbalances in the existing empirical landscape, where certain methods and user groups are overrepresented while others are largely overlooked. By validating theoretical proposals with empirical evidence, this review provides actionable guidance for selecting XAI methods that are aligned with user needs and use case demands, paving the way for more targeted and effective human-AI interaction.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 8","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11595-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148380636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PEFT unlocked: methodologies, formulas, and applications in efficient LLM adaptation with a specific focus on LoRA","authors":"Maryam Najafi,&nbsp;Ehsan Tavan,&nbsp;Stefan Kuhn,&nbsp;Salaheddin Alakkari,&nbsp;Simon Colreavy","doi":"10.1007/s10462-026-11620-8","DOIUrl":"10.1007/s10462-026-11620-8","url":null,"abstract":"<div><p>The increasing intricacy and scale of deep learning (DL) models have heightened the necessity for effective weight and parameter optimization algorithms that sustain superior performance while reducing computational resource consumption. This paper thoroughly analyzes the evolution of parameter optimization strategies, ranging from initial methodologies to modern advancements, elucidating their principles and applications in natural language processing (NLP) and machine learning (ML). We pay special emphasis to parameter-efficient fine-tuning (PEFT) approaches, such as low-rank adaptation (LoRA) and its extensions, which facilitate the adaptation of large language models (LLMs) on resource-limited devices. These methods address challenges such as high computational demands, energy consumption, and deployment restrictions, thereby promoting more accessible and environmentally sustainable artificial intelligence (AI) solutions. By integrating methodological understandings with recent advancements, this survey underscores the essential role of parameter optimization in enabling scalable deep learning systems. It serves as an essential guide for researchers seeking to apply these techniques across diverse domains, emphasizing their impact on achieving efficient and robust model performance.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 11","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11620-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of toxicity in large language models: definitions, datasets, detectors, detoxification methods and challenges","authors":"Guillermo Villate-Castillo,&nbsp;Javier Del Ser,&nbsp;Borja Sanz Urquijo","doi":"10.1007/s10462-026-11548-z","DOIUrl":"10.1007/s10462-026-11548-z","url":null,"abstract":"<div><p>The emergence of the transformer architecture has ushered in a new era of possibilities, showcasing remarkable capabilities in generative tasks exemplified by models like GPT4o, Claude 3, and Llama 3. However, these advancements come with a caveat: predominantly trained on data gleaned from social media platforms, these systems inadvertently perpetuate societal biases and toxicity. Recognizing the paramount importance of AI Safety and Alignment, our study embarks on a thorough exploration through a comprehensive literature review focused on toxic language. Delving into various definitions, detection methodologies, and mitigation strategies, we aim to shed light on the complexities of this issue. Our study primarily focuses on transformer-based architectures, with deep learning methods serving as a crucial baseline for comparison in our exploration of toxic language detection and mitigation. Through our investigation, we uncover a multitude of challenges inherent in toxicity mitigation and detection models. These challenges range from inherent biases and generalization issues to the necessity for standardized definitions of toxic language and the quality assurance of dataset annotations. Furthermore, we emphasize the significance of transparent annotation processes, resolution of annotation disagreements, and the enhancement of Large Language Models (LLMs) robustness. Additionally, we advocate for the creation of standardized benchmarks to gauge the effectiveness of toxicity mitigation and detection methods. Addressing these challenges is not just imperative, but pivotal in advancing the development of safer and more ethically aligned AI systems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 11","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11548-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engineering carbon credits with AI towards a responsible FinTech era: the practices, implications, and future","authors":"Qingwen Zeng,&nbsp;Hanlin Xu,&nbsp;Nanjun Xu,&nbsp;Zhenghao Zhao,&nbsp;Joakim Westerholm,&nbsp;Flora Salim,&nbsp;Junbin Gao,&nbsp;Huaming Chen","doi":"10.1007/s10462-026-11638-y","DOIUrl":"10.1007/s10462-026-11638-y","url":null,"abstract":"<div><p>Carbon emissions drive climate change, and carbon credits mitigate climate deterioration and environmental damage while assisting organizations in managing their carbon footprint. Fully utilizing carbon credits remains challenging. This study enhances understanding of the engineering practices for carbon credits to develop responsible fintech solutions and provide insights for carbon emission management. We review the negative impacts of organizations’ strategy of evading carbon management through non-disclosure of carbon emissions. Evidence shows that both non-disclosure of carbon emissions and high carbon emissions negatively affect an organization’s financial stability and market value, suggesting that organizations should manage carbon emissions and transparently share information to mitigate risks. We examine engineering methods for more cost-effective carbon management, focusing on data-driven computing solutions: factors influencing carbon prices, carbon price prediction algorithms for optimized carbon credit purchasing strategies, and corporate carbon emission prediction algorithms. These methods enable performance assessments for investors and governments when carbon emissions data is not disclosed and help organizations estimate future carbon credits needs for budget planning and strategy optimization. Finally, integrating carbon price and carbon emission predictions, we propose future research directions, including prediction of corporate-level carbon management costs, laying a foundation for quantitative research on how carbon management practices impact corporate market value and financial performance. This systematic review provides a comprehensive synthesis of carbon credits with a unique focus on computing solutions and engineering practices, highlighting AI’s role in enhancing transparency and fostering social accountability for an inclusive and trustworthy low-carbon transition.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 11","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11638-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of multisensor methods for open-carry and concealed knife detection","authors":"Ogonna Okafor,&nbsp;David Ada Adama,&nbsp;Doratha Vinkemeier","doi":"10.1007/s10462-026-11626-2","DOIUrl":"10.1007/s10462-026-11626-2","url":null,"abstract":"<div><p>Reliable detection of openly carried and concealed knives remains a challenging problem in artificial intelligence (AI) due to the small size, thin geometry, frequent occlusion, and material variability of blade objects. Although advances in deep learning have improved weapon detection performance, the literature remains fragmented across sensing modalities, datasets, and evaluation protocols, limiting reproducibility and systematic comparison. This paper presents a systematic and modality-aware review of AI methods for multisensor knife detection, covering visible-spectrum imaging, thermal infrared, X-ray imaging, millimetre-wave and terahertz sensing, and frequency-modulated continuous-wave radar. A PRISMA-informed literature selection process identified 39 primary studies published between 2018 and 2025. The reviewed works are analysed with respect to sensing modality, dataset characteristics, preprocessing strategies, detection architectures, multimodal fusion methods, and evaluation protocols. The review identifies recurring architectural patterns that improve blade detection, including multi-scale feature representations, small-object-aware training strategies, attention mechanisms, and modality-specific enhancement pipelines. It further provides a cross-modality synthesis and semi-quantitative summary showing that reported performance is strongly shaped by sensing modality, task formulation, dataset difficulty, and reporting practice, including inconsistencies in evaluation metrics, hardware platforms, and inference speed reporting, rather than detector architecture alone. True multimodal fusion remains underexplored, but available evidence suggests that it has potential to improve robustness under occlusion, concealment, and challenging environmental conditions. The review also highlights limitations in dataset diversity, evaluation consistency, and ethical considerations associated with deployment in real-world surveillance contexts. This survey makes three primary contributions. First, it introduces a modality-aware taxonomy that unifies open-carry and concealed-knife detection within a common AI framework. Second, it synthesises datasets, detector architectures, fusion strategies, and evaluation practices with emphasis on small-object performance and operational reliability. Third, it identifies key research gaps and outlines a roadmap toward standardised benchmarks, multimodal learning frameworks, physics-informed simulation pipelines, privacy-preserving deployment architectures, and robust real-world evaluation.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"59 11","pages":""},"PeriodicalIF":18.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-026-11626-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}