Artificial Intelligence Review最新文献

{"title":"Efficient maximum iterations for swarm intelligence algorithms: a comparative study","authors":"Shen Si-Ma,&nbsp;Han-Ming Liu,&nbsp;Hong-Xiang Zhan,&nbsp;Zhao-Fa Liu,&nbsp;Gang Guo,&nbsp;Cong Yu,&nbsp;Peng-Cheng Hu","doi":"10.1007/s10462-024-11104-7","DOIUrl":"10.1007/s10462-024-11104-7","url":null,"abstract":"<div><p>A swarm intelligence algorithm usually iterates many times to approximate the optimum to obtain the solution of a problem. The maximum iteration is influenced by many factors such as the algorithm itself, problem types, as well as dimensions and search space sizes of decision variables. There are few existing studies on efficient maximum iterations, especially a large-scale study on comparison for different problem types. By dividing three CEC benchmark sets into several problem types, this study made a large-scale performance comparison of 123 common swarm intelligence algorithms from several views. The experimental results show that for low-dimensionality, wide search space, and/or simple- and medium-complex problems, about a quarter of the algorithms are concentrated in iterations of about 30 ~ 80, while most algorithms for other types of problems tend to have as many iterations as possible. By and large, for the Classical set, large iterations are beneficial for improving the performance of most algorithms, while less than half of the algorithms for CEC 2019 and CEC 2022 do so. And, the efficient iterations of excellent algorithms are about 300 on low dimensionality, wide search space and simple-complexity problems, while other types are as large as possible. In terms of algorithm speed, LSO, DE and RSA are the fastest on all the three benchmark sets, and the runtime of all algorithms is almost linearly related to the maximum iterations. Although the conclusions largely depend on the problem types, we believe that an efficient iteration is necessary to optimize algorithm performance.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11104-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938867","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":"AncientGlyphNet: an advanced deep learning framework for detecting ancient Chinese characters in complex scene","authors":"Hengnian Qi,&nbsp;Hao Yang,&nbsp;Zhaojiang Wang,&nbsp;Jiabin Ye,&nbsp;Qiuyi Xin,&nbsp;Chu Zhang,&nbsp;Qing Lang","doi":"10.1007/s10462-024-11095-5","DOIUrl":"10.1007/s10462-024-11095-5","url":null,"abstract":"<div><p>Detecting ancient Chinese characters in various media, including stone inscriptions, calligraphy, and couplets, is challenging due to the complex backgrounds and diverse styles. This study proposes an advanced deep-learning framework for detecting ancient Chinese characters in complex scenes to improve detection accuracy. First, the framework introduces an Ancient Character Haar Wavelet Transform downsampling block (ACHaar), effectively reducing feature maps’ spatial resolution while preserving key ancient character features. Second, a Glyph Focus Module (GFM) is introduced, utilizing attention mechanisms to enhance the processing of deep semantic information and generating ancient character feature maps that emphasize horizontal and vertical features through a four-path parallel strategy. Third, a Character Contour Refinement Layer (CCRL) is incorporated to sharpen the edges of characters. Additionally, to train and validate the model, a dedicated dataset was constructed, named Huzhou University-Ancient Chinese Character Dataset for Complex Scenes (HUSAM-SinoCDCS), comprising images of stone inscriptions, calligraphy, and couplets. Experimental results demonstrated that the proposed method outperforms previous text detection methods on the HUSAM-SinoCDCS dataset, with accuracy improved by 1.36–92.84%, recall improved by 2.24–85.61%, and F1 score improved by 1.84–89.08%. This research contributes to digitizing ancient Chinese character artifacts and literature, promoting the inheritance and dissemination of traditional Chinese character culture. The source code and the HUSAM-SinoCDCS dataset can be accessed at https://github.com/youngbbi/AncientGlyphNet and https://github.com/youngbbi/HUSAM-SinoCDCS.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11095-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938866","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 survey on human pose estimation: upstream and downstream tasks, approaches, lightweight models, and prospects","authors":"Zheyan Gao,&nbsp;Jinyan Chen,&nbsp;Yuxin Liu,&nbsp;Yucheng Jin,&nbsp;Dingxiaofei Tian","doi":"10.1007/s10462-024-11060-2","DOIUrl":"10.1007/s10462-024-11060-2","url":null,"abstract":"<div><p>In recent years, human pose estimation has been widely studied as a branch task of computer vision. Human pose estimation plays an important role in the development of medicine, fitness, virtual reality, and other fields. Early human pose estimation technology used traditional manual modeling methods. Recently, human pose estimation technology has developed rapidly using deep learning. This study not only reviews the basic research of human pose estimation but also summarizes the latest cutting-edge technologies. In addition to systematically summarizing the human pose estimation technology, this article also extends to the upstream and downstream tasks of human pose estimation, which shows the positioning of human pose estimation technology more intuitively. In particular, considering the issues regarding computer resources and challenges concerning model performance faced by human pose estimation, the lightweight human pose estimation models and the transformer-based human pose estimation models are summarized in this paper. In general, this article classifies human pose estimation technology around types of methods, 2D or 3D representation of outputs, the number of people, views, and temporal information. Meanwhile, classic datasets and targeted datasets are mentioned in this paper, as well as metrics applied to these datasets. Finally, we generalize the current challenges and possible development of human pose estimation technology in the future.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11060-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925655","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":"Systematic review on neural architecture search","authors":"Sasan Salmani Pour Avval,&nbsp;Nathan D. Eskue,&nbsp;Roger M. Groves,&nbsp;Vahid Yaghoubi","doi":"10.1007/s10462-024-11058-w","DOIUrl":"10.1007/s10462-024-11058-w","url":null,"abstract":"<div><p>Machine Learning (ML) has revolutionized various fields, enabling the development of intelligent systems capable of solving complex problems. However, the process of manually designing and optimizing ML models is often time-consuming, labor-intensive, and requires specialized expertise. To address these challenges, Automatic Machine Learning (AutoML) has emerged as a promising approach that automates the process of selecting and optimizing ML models. Within the realm of AutoML, Neural Architecture Search (NAS) has emerged as a powerful technique that automates the design of neural network architectures, the core components of ML models. It has recently gained significant attraction due to its capability to discover novel and efficient architectures that surpass human-designed counterparts. This manuscript aims to present a systematic review of the literature on this topic published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms developed for NAS. The methodology follows the guidelines of Systematic Literature Review (SLR) methods. Consequently, this study identified 160 articles that provide a comprehensive overview of the field of NAS, encompassing discussion on current works, their purposes, conclusions, and predictions of the direction of this science branch in its main core pillars: Search Space (SSp), Search Strategy (SSt), and Validation Strategy (VSt). Subsequently, the key milestones and advancements that have shaped the field are highlighted. Moreover, we discuss the challenges and open issues that remain in the field. We envision that NAS will continue to play a pivotal role in the advancement of ML, enabling the development of more intelligent and efficient ML models for a wide range of applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11058-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925653","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":"Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems","authors":"Yaning Xiao,&nbsp;Hao Cui,&nbsp;Ruba Abu Khurma,&nbsp;Pedro A. Castillo","doi":"10.1007/s10462-024-11023-7","DOIUrl":"10.1007/s10462-024-11023-7","url":null,"abstract":"<div><p>The advent of the intelligent information era has witnessed a proliferation of complex optimization problems across various disciplines. Although existing meta-heuristic algorithms have demonstrated efficacy in many scenarios, they still struggle with certain challenges such as premature convergence, insufficient exploration, and lack of robustness in high-dimensional, nonconvex search spaces. These limitations underscore the need for novel optimization techniques that can better balance exploration and exploitation while maintaining computational efficiency. In response to this need, we propose the Artificial Lemming Algorithm (ALA), a bio-inspired metaheuristic that mathematically models four distinct behaviors of lemmings in nature: long-distance migration, digging holes, foraging, and evading predators. Specifically, the long-distance migration and burrow digging behaviors are dedicated to highly exploring the search domain, whereas the foraging and evading predators behaviors provide exploitation during the optimization process. In addition, ALA incorporates an energy-decreasing mechanism that enables dynamic adjustments to the balance between exploration and exploitation, thereby enhancing its ability to evade local optima and converge to global solutions more robustly. To thoroughly verify the effectiveness of the proposed method, ALA is compared with 17 other state-of-the-art meta-heuristic algorithms on the IEEE CEC2017 benchmark test suite and the IEEE CEC2022 benchmark test suite. The experimental results indicate that ALA has reliable comprehensive optimization performance and can achieve superior solution accuracy, convergence speed, and stability in most test cases. For the 29 10-, 30-, 50-, and 100-dimensional CEC2017 functions, ALA obtains the lowest Friedman average ranking values among all competitor methods, which are 1.7241, 2.1034, 2.7241, and 2.9310, respectively, and for the 12 CEC2022 functions, ALA again wins the optimal Friedman average ranking of 2.1667. Finally, to further evaluate its applicability, ALA is implemented to address a series of optimization cases, including constrained engineering design, photovoltaic (PV) model parameter identification, and fractional-order proportional-differential-integral (FOPID) controller gain tuning. Our findings highlight the competitive edge and potential of ALA for real-world engineering applications. The source code of ALA is publicly available at https://github.com/StevenShaw98/Artificial-Lemming-Algorithm.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11023-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925732","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":"Beyond one-shot explanations: a systematic literature review of dialogue-based xAI approaches","authors":"Dimitry Mindlin,&nbsp;Fabian Beer,&nbsp;Leonie Nora Sieger,&nbsp;Stefan Heindorf,&nbsp;Elena Esposito,&nbsp;Axel-Cyrille Ngonga Ngomo,&nbsp;Philipp Cimiano","doi":"10.1007/s10462-024-11007-7","DOIUrl":"10.1007/s10462-024-11007-7","url":null,"abstract":"<div><p>In the last decade, there has been increasing interest in allowing users to understand how the predictions of machine-learned models come about, thus increasing transparency and empowering users to understand and potentially contest those decisions. Dialogue-based approaches, in contrast to traditional one-shot eXplainable Artificial Intelligence (xAI) methods, facilitate interactive, in-depth exploration through multi-turn dialogues, simulating human-like interactions, allowing for a dynamic exchange where users can ask questions and receive tailored, relevant explanations in real-time. This paper reviews the current state of dialogue-based xAI, presenting a systematic review of 1339 publications, narrowed down to 15 based on inclusion criteria. We explore theoretical foundations of the systems, propose key dimensions along which different solutions to dialogue-based xAI differ, and identify key use cases, target audiences, system components, and the types of supported queries and responses. Furthermore, we investigate the current paradigms by which systems are evaluated and highlight their key limitations. Key findings include identifying the main use cases, objectives, and audiences targeted by dialogue-based xAI methods, in addition to an overview of the main types of questions and information needs. Beyond discussing avenues for future work, we present a meta-architecture for these systems from existing literature and outlined prevalent theoretical frameworks.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11007-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925630","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":"Advancing paleontology: a survey on deep learning methodologies in fossil image analysis","authors":"Mohammed Yaqoob,&nbsp;Mohammed Ishaq,&nbsp;Mohammed Yusuf Ansari,&nbsp;Yemna Qaiser,&nbsp;Rehaan Hussain,&nbsp;Harris Sajjad Rabbani,&nbsp;Russell J. Garwood,&nbsp;Thomas D. Seers","doi":"10.1007/s10462-024-11080-y","DOIUrl":"10.1007/s10462-024-11080-y","url":null,"abstract":"<div><p>Understanding ancient organisms and their interactions with paleoenvironments through the study of body fossils is a central tenet of paleontology. Advances in digital image capture now allow for efficient and accurate documentation, curation, and interrogation of fossil forms and structures in two and three dimensions, extending from microfossils to larger specimens. Despite these developments, key fossil image processing and analysis tasks, such as segmentation and classification, still require significant user intervention, which can be labor-intensive and subject to human bias. Recent advances in deep learning offer the potential to automate fossil image analysis, improving throughput and limiting operator bias. Despite the emergence of deep learning within paleontology in the last decade, challenges such as the scarcity of diverse, high quality image datasets and the complexity of fossil morphology necessitate further advancement which will be aided by the adoption of concepts from other scientific domains. Here, we comprehensively review state-of-the-art deep learning based methodologies applied to fossil analysis, grouping the studies based on the fossil type and nature of the task. Furthermore, we analyze existing literature to tabulate dataset information, neural network architecture type, and key results, and provide textual summaries. Finally, we discuss novel techniques for fossil data augmentation and fossil image enhancements, which can be combined with advanced neural network architectures, such as diffusion models, generative hybrid networks, transformers, and graph neural networks, to improve body fossil image analysis.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11080-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925631","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":"Enhanced crayfish optimization algorithm with differential evolution’s mutation and crossover strategies for global optimization and engineering applications","authors":"Binanda Maiti,&nbsp;Saptadeep Biswas,&nbsp;Absalom El-Shamir Ezugwu,&nbsp;Uttam Kumar Bera,&nbsp;Ahmed Ibrahim Alzahrani,&nbsp;Fahad Alblehai,&nbsp;Laith Abualigah","doi":"10.1007/s10462-024-11069-7","DOIUrl":"10.1007/s10462-024-11069-7","url":null,"abstract":"<div><p>Optimization algorithms play a crucial role in solving complex challenges across various fields, including engineering, finance, and data science. This study introduces a novel hybrid optimization algorithm, the Hybrid Crayfish Optimization Algorithm with Differential Evolution (HCOADE), which addresses the limitations of premature convergence and inadequate exploitation in the traditional Crayfish Optimization Algorithm (COA). By integrating COA with Differential Evolution (DE) strategies, HCOADE leverages DE’s mutation and crossover mechanisms to enhance global optimization performance. The COA, inspired by the foraging and social behaviors of crayfish, provides a flexible framework for exploring the solution space, while DE’s robust strategies effectively exploit this space. To evaluate HCOADE’s performance, extensive experiments are conducted using 34 benchmark functions from CEC 2014 and CEC 2017, as well as six engineering design problems. The results are compared with ten leading optimization algorithms, including classical COA, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Moth-flame Optimization (MFO), Salp Swarm Algorithm (SSA), Reptile Search Algorithm (RSA), Sine Cosine Algorithm (SCA), Constriction Coefficient-Based Particle Swarm Optimization Gravitational Search Algorithm (CPSOGSA), and Biogeography-based Optimization (BBO). The average rankings and results from the Wilcoxon Rank Sum Test provide a comprehensive comparison of HCOADE’s performance, clearly demonstrating its superiority. Furthermore, HCOADE’s performance is assessed on the CEC 2020 and CEC 2022 test suites, further confirming its effectiveness. A comparative analysis against notable winners from the CEC competitions, including LSHADEcnEpSin, LSHADESPACMA, and CMA-ES, using the CEC-2017 test suite, revealed superior results for HCOADE. This study underscores the advantages of integrating DE strategies with COA and offers valuable insights for addressing complex global optimization problems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11069-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925684","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":"Systematic review of recent years: machine learning-based interactive therapy for people suffering from dementia","authors":"Coralie Rohrer,&nbsp;Souhir Ben Souissi,&nbsp;Mascha Kurpicz-Briki","doi":"10.1007/s10462-024-11084-8","DOIUrl":"10.1007/s10462-024-11084-8","url":null,"abstract":"<div><p>Medical advances over the last century have significantly extended life expectancy. Today, the world’s population is quite old, and will become even older in the years to come. Diseases that particularly concern the elderly are therefore more frequent, and dementia is one of them. This condition mainly affects the elderly and cannot be cured today. However, people suffering from dementia do require care, and this entails significant costs for our society. Machine learning could be useful in a context where it is difficult to find medical staff and where cost reduction is a priority. In recent years, research has been conducted to find ways of treating dementia with machine learning-based therapies in which the patient can actively participate. In this paper, a systematic literature review of these therapies is conducted: (a) paper metadata is analysed, (b) dataset characteristics are examined, (c) therapy types are compared, (d) suggested architectures are considered, (e) therapy performance is reviewed, (f) usability is discussed, and g) ethical considerations are taken into account. Twenty-three papers were selected in which various types of therapy were suggested for use with cell phones, computers, robots, or virtual reality. The results of the usability tests were very positive, both in terms of cognitive faculties evolution and patient satisfaction.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11084-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925691","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 for transformer-based long-term series forecasting","authors":"Liyilei Su,&nbsp;Xumin Zuo,&nbsp;Rui Li,&nbsp;Xin Wang,&nbsp;Heng Zhao,&nbsp;Bingding Huang","doi":"10.1007/s10462-024-11044-2","DOIUrl":"10.1007/s10462-024-11044-2","url":null,"abstract":"<div><p>The emergence of deep learning has yielded noteworthy advancements in time series forecasting (TSF). Transformer architectures have witnessed broad utilization and adoption in TSF tasks. Transformers have proven to be the most successful solution to extract the semantic correlations among the elements within a long sequence. Various variants have enabled Transformer architecture to effectively handle long-term time series forecasting (LTSF) tasks. In this article, we first present a comprehensive overview of Transformer architectures and their subsequent enhancements developed to address various LTSF tasks. Then, we summarize the publicly available LTSF datasets and relevant evaluation metrics. Furthermore, we provide valuable insights into the best practices and techniques for effectively training Transformers in the context of time-series analysis. Lastly, we propose potential research directions in this rapidly evolving field.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11044-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925692","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}