Engineering Applications of Artificial Intelligence最新文献

{"title":"A novel framework for segmenting open-pit mining road","authors":"Shuo Fan ,&nbsp;Yachun Mao ,&nbsp;Shuai Zhen ,&nbsp;Jing Liu ,&nbsp;Liming He ,&nbsp;Xinqi Mao","doi":"10.1016/j.engappai.2025.112811","DOIUrl":"10.1016/j.engappai.2025.112811","url":null,"abstract":"<div><div>Accurate segmentation of open-pit mine road networks presents a critical challenge for mine digitization and autonomous driving applications. These roads are prone to mechanical compaction, geological erosion, and coverage by gravel dust, resulting in segmentation outcomes characterized by blurred boundaries, holes, fractures, and geometric deformations, which severely compromise measurement accuracy. To address these challenges, this paper proposes the Mining Road Segmentation Network (MRS-Net), which integrates local features with global semantics. First, a Residual Network Version 2 (ResNetV2)-Transformer cascaded encoder is constructed, employing residual connections to preserve sub-pixel-level edge details and multi-head self-attention to establish long-range dependencies, thereby enhancing the representation of weak texture features. Second, the Road Multi-scale Features Fusion Module (RMFF) was designed to extract local geometric features and global continuity features through progressive hollow convolution, enabling the model to extract multi-scale features and effectively suppress interference from gravel dust. Finally, a progressive decoding architecture incorporating bilinear interpolation is adopted to improve edge smoothness. MRS-Net is evaluated on an Unmanned Aerial Vehicle (UAV)-acquired road dataset from the Anshan open-pit iron mine in Liaoning Province, China. Results demonstrate that MRS-Net achieves superior segmentation performance compared to models such as DeepLabV3+ and TransUNet across three distinct scenarios: main roads, temporary roads, and abandoned roads. Specifically, it achieves Intersection over Union (IoU), Dice coefficient(Dice), and Kappa coefficient (Kappa) values of 89.4 % / 94.1 % / 87.2 %, 75.7 % / 83.3 % / 75.1 %, and 83.8 % / 90.0 % / 84.85 % respectively for these scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"163 ","pages":"Article 112811"},"PeriodicalIF":8.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inverse compensation and adaptive fuzzy integral sliding-mode control for the underactuated soft massage physiotherapy robot","authors":"Zixin Huang ,&nbsp;Chengsong Yu ,&nbsp;Junjie Lu ,&nbsp;Hao Liu ,&nbsp;Peng Huang","doi":"10.1016/j.engappai.2025.112792","DOIUrl":"10.1016/j.engappai.2025.112792","url":null,"abstract":"<div><div>Acupoint massage physiotherapy is a kind of effective method to prevent and remedy diseases. Soft robotics technology is thriving, which has potential applications in the field of acupoint massage physiotherapy. Soft massage physiotherapy robot (SMPR) uses the soft robotics technology to realize the acupoint massage physiotherapy function. In this paper, an SMPR consisting of a wearable armor and several pneumatic physiotherapy actuators (PPAs) is design and fabricated. In order to describe complex hysteresis behavior of SMPR, the dynamic model of its PPA is established and identified, which includes two parts: a linear model and an asymmetric Prandtl–Ishlinskii hysteresis (APIH) model. An inverse compensator is then designed to compensate for the hysteresis behavior of the SMPR based on the APIH model, and an approximately linearized system is obtained. Then, by dint of the artificial intelligence method, a fuzzy approximator is designed to approximate the control system’s lumped uncertainty, which includes external disturbances, modeling errors and parameter perturbations. Further, an adaptive fuzzy integral sliding-mode control (AFISMC) is employed to handle the lump uncertainty. Moreover, based on the back-stepping control method, a nominal controller is designed to realize the control of the approximately linearized system. By combining the inverse compensator, fuzzy approximator, AFISMC and nominal controller, the control of the SMPR is realized and the acupoint massage physiotherapy can be controlled accurately. The stabilization to a control systems is theoretically demonstrated. Finally, the experimental results from multiple test scenarios conclusively demonstrate the efficacy and trajectory tracking capability of the developed control strategy.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"163 ","pages":"Article 112792"},"PeriodicalIF":8.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate multi-step wind and solar power forecasting based on multi-scale convolutional Kolmogorov-Arnold network and improved Lemming-optimized attention fusion","authors":"Siyuan Chen ,&nbsp;Hang Wan ,&nbsp;Botao Peng ,&nbsp;Rui Quan ,&nbsp;Yufang Chang ,&nbsp;William Derigent","doi":"10.1016/j.engappai.2025.112832","DOIUrl":"10.1016/j.engappai.2025.112832","url":null,"abstract":"<div><div>With the deepening of power market reform, the increasing share of wind and solar energy introduces significant challenges for power system stability due to the high volatility and uncertainty of weather-dependent generation. Accurate multi-step ultra-short-term forecasting is therefore essential for ensuring power balance and effective dispatch coordination in smart grids. To address this issue, we propose a novel hybrid deep learning framework that integrates a multi-scale convolutional Kolmogorov-Arnold network (MCKAN) to improve forecasting performance. This network is specifically designed to capture high-dimensional spatial and temporal features across multiple levels of abstraction. To improve feature selection and scale-specific weight allocation, we integrate an Efficient Additive Attention (EAA) mechanism, which is applied for the first time in the context of renewable energy forecasting. In addition, a Chaotic Quasi-Reverse Artificial Lemming Algorithm (CQALA) is proposed to automatically optimize the complex multivariate hyperparameters, enabling optimal hyperparameter selection and improving the model's overall predictive performance. Extensive experiments on a two-year wind and photovoltaic power dataset from the State Grid of China demonstrate that the proposed method outperforms several state-of-the-art models. For multi-step forecasting, the mean absolute error is reduced by up to 27.6 percent for photovoltaic power and 33.4 percent for wind power, highlighting the practical value of the proposed approach in real-world renewable energy management.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"163 ","pages":"Article 112832"},"PeriodicalIF":8.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A metaheuristic-driven categorical boosting framework with interpretability for high-precision prediction of mechanical properties in corroded reinforced concrete beams","authors":"Yuzhuo Zhang ,&nbsp;Zheng Wang ,&nbsp;Jinlong Liu ,&nbsp;Yalin Li ,&nbsp;Zhenqin Huang ,&nbsp;Xiaohu Yu","doi":"10.1016/j.engappai.2025.112804","DOIUrl":"10.1016/j.engappai.2025.112804","url":null,"abstract":"<div><div>The degradation of mechanical properties in corroded reinforced concrete (RC) beams presents a major challenge for assessing structural durability. To address this issue, this study proposes an integrated machine learning (ML) framework to predict the mechanical properties of such beams. First, a database of 464 samples was established, including 12 input parameters and 2 output parameters, followed by correlation analysis of the inputs. On this basis, the applicability of existing design codes and empirical models was evaluated. Subsequently, eight ML models were trained, with their hyperparameters optimized via Bayesian optimization (BO) to enhance prediction accuracy. The Categorical Boosting (CatBoost) model was identified as the most accurate, and its hyperparameters were further optimized using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for improved performance. Results show the PSO-optimized CatBoost model achieves the highest prediction accuracy to date: for the flexural strength test set, the coefficient of determination (<em>R</em>^<em>2</em>) is 0.984 and root mean square error (<em>RMSE</em>) is 3.1602; for the deflection test set, <em>R</em>^<em>2</em> is 0.975 and <em>RMSE</em> is 0.6259. Compared with design codes, flexural strength test set <em>R</em>^<em>2</em> increases by 27.3 % and <em>RMSE</em> decreases by 72.8 %; versus traditional models like Support Vector Regression (SVR), <em>R</em>^<em>2</em> rises by 5.4 % and <em>RMSE</em> drops by 43.5 %. Additionally, SHapley Additive exPlanations (SHAP) analysis reveals geometric parameters (beam height, beam width) dominate flexural strength, while elastic stiffness and beam length drive deflection. Finally, a user-friendly graphical user interface (GUI) was developed for rapid mechanical property assessment of corroded RC beams.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"163 ","pages":"Article 112804"},"PeriodicalIF":8.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based on-site identification and volume measurement of bulk material in construction industry","authors":"Zhen Cai ,&nbsp;Ghaith Allah Chebil ,&nbsp;Yuan Tan ,&nbsp;Stephan Kessler ,&nbsp;Johannes Fottner","doi":"10.1016/j.engappai.2025.112797","DOIUrl":"10.1016/j.engappai.2025.112797","url":null,"abstract":"<div><div>Bulk materials are important raw construction materials, the adequate and precise supply of which enables a smooth construction process. Two conventional techniques for controlling the quantity of bulk materials on-site along the supply chain are: 1) estimation based on the cone-shape of the material pile, but the accuracy is low; 2) calculation by using bulk density and weighing stations, which are not available in all facilities. To address this issue, we propose a novel hybrid camera-based method combining a red-green-blue (RGB) camera and a light detection and ranging (LiDAR) sensor for automatic material type identification and volume measurement. The data from two-dimensional pictures and three-dimensional point cloud were segmented and extracted with two Deep Learning models: “You Only Look Once“ (YOLO) v5 and PointNet++for the identification of material type and volume measurement. This novel hybrid camera-based method was developed as an Industry 4.0 solution to enable the automatic and accurate evaluation of the volume of bulk materials. With a precision of 81.6 % in object recognition and a volume estimation deviation of less than 8 %, it provides a reliable and efficient alternative to conventional, labour-intensive measurement techniques.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"163 ","pages":"Article 112797"},"PeriodicalIF":8.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting tourism demand using data based on a two-stage feature selection: A hybrid deep learning approach incorporating Time2Vec","authors":"Jinghui Wei ,&nbsp;Sheng Wu ,&nbsp;Qiangwen Zheng","doi":"10.1016/j.engappai.2025.112768","DOIUrl":"10.1016/j.engappai.2025.112768","url":null,"abstract":"<div><div>Accurate tourism demand forecasting is important for regional tourism planning, management, and industry development. However, existing models often struggle with the complexity of external variables or fail to capture essential temporal patterns and multi-scale temporal correlations, directly limiting their accuracy and robustness. Therefore, we propose a predictor with Two-Stage Feature Selection and Time2Vec-enhanced Extraction Mechanisms (TFS-T2VEM). The model employs a two-stage feature selection strategy to refine predictive variables and integrates a Time2Vec-driven temporal pattern extraction module to effectively capture key temporal patterns across multiple scales. By leveraging multi-scale features from intermediate layers of Convolutional Neural Networks (CNN), it captures both mid-short-term fluctuations and long-term trends. Time2Vec further serves as an implicit temporal decomposition module, replacing traditional methods by embedding temporal information directly into the network. This enables dynamic attention adjustment based on intrinsic periodicity and external disturbances, enhancing the temporal attention mechanism by focusing on critical time points and reducing noise from irrelevant features. These improvements ultimately contribute to higher predictive accuracy and robustness. Extensive experiments on three datasets show that our model consistently outperforms baseline methods, confirming its effectiveness in tourism demand forecasting.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"163 ","pages":"Article 112768"},"PeriodicalIF":8.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning solutions with deep multilayer exogenous networks for distributed denial of service attacks model on networked resources in critical infrastructure","authors":"Rana Abdullah Zaeem ,&nbsp;Chuan-Yu Chang ,&nbsp;Maryam Pervaiz Khan ,&nbsp;Muhammad Shoaib ,&nbsp;Chi-Min Shu ,&nbsp;Muhammad Asif Zahoor Raja","doi":"10.1016/j.engappai.2025.112872","DOIUrl":"10.1016/j.engappai.2025.112872","url":null,"abstract":"<div><div>The increasing dependency on critical infrastructure and the vulnerability to cyber-attacks, particularly Distributed Denial of Service attacks, pose significant challenges and threats in this cold warfare era. This paper explores an epidemic model based distributed denial of service attacks system to analyze the impact of seclusion strategies on protecting critical infrastructure against cyber-attacks by leveraging machine learning knowledge with non-linear exogenous networks supported with Levenberg-Marquardt backpropagation. The proposed information security model presents the critical infrastructure nodes into susceptible, infected, quarantined and recovered differential compartments for the targeted population to portray the attack's dynamics and quarantine measures effectively. To analyze the rates for infection, efficiency in the quarantine and the recovery state, the synthetic data is acquired to carry out processes on various scenarios with Adams numerical solver and the said information is fed to intelligent supervised nonlinear autoregressive exogenous neural networks to decipher the attack patterns. The efficacy of the proposed stochastic computing paradigm is established on mean squared error-based convergence trends, error in time series illustrations, error-histogram, and error distribution in histograms, statistics on correlation and autocorrelation metrics based on an exhaustive simulation study for an information security model. The validation of the performance of the design nonlinear networks is further endorsed from counterpart's backpropagation schemes of Bayesian regularization and scaled conjugate gradient, based on the results of statistics in terms of mean, standard deviation, worst, and best of the convergence arcs, error distribution on heat map, inference on median with box plots, plot-matrix analysis, violin plots dynamics and computational time analysis, on exhaustive autonomous executions for solving cyber-attack model in information security.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"163 ","pages":"Article 112872"},"PeriodicalIF":8.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cosmetic packaging design method based on online reviews","authors":"Zhan Gao,&nbsp;Zhenyu Li","doi":"10.1016/j.engappai.2025.112865","DOIUrl":"10.1016/j.engappai.2025.112865","url":null,"abstract":"<div><div>To address the transformation of user experience and packaging iteration in cosmetics due to the diversification of usage scenarios and demands, this study capitalizes on the advancements in artificial intelligence across user analysis, data analysis, and generative design domains, and proposes a cosmetic packaging design approach centered around online reviews. In this study, 124,879 pieces of user review data were collected from JingDong (JD), a Chinese e-commerce platform, using Python programming technology. Five topics are clustered through the application of the Latent Dirichlet Allocation (LDA) topic model. By integrating the coding of Grounded Theory, 18 demand elements within six core categories are summarized. The Kano model and the Analytic Hierarchy Process (AHP) are employed to classify and rank these demands. Notably, aspects such as strong brand recognition (M1, 0.2182), strong brand value perception (M5, 0.1129), and visually appealing and refined aesthetics (A5, 0.0983) exhibit relatively high weights. Subsequently, six lipstick packaging design schemes are developed by combining traditional software with the MidJourney generative artificial intelligence tool. Through comprehensive evaluation using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, the optimal Scheme c is identified and further optimized. This study constructs a comprehensive design strategy with user online reviews at its core, encompassing data collection, analysis, scheme design, artificial intelligence (AI)-assisted design, and evaluation. It is recommended that the application of artificial intelligence (AI)-assisted design be significantly enhanced throughout the entire design process, enabling precise and rapid generation of design schemes, streamlining the process, and shortening the development cycle.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"163 ","pages":"Article 112865"},"PeriodicalIF":8.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid framework of penetration resistance analysis by machine learning and finite element simulation","authors":"Xu Long,&nbsp;Irfan Ali,&nbsp;Khawaja Haseeb Maqbool,&nbsp;Muhammad Muaz Khan","doi":"10.1016/j.engappai.2025.112868","DOIUrl":"10.1016/j.engappai.2025.112868","url":null,"abstract":"<div><div>A comprehensive understanding of projectile penetration in reinforced concrete (RC) structures is essential for developing resilient defense and infrastructure systems. Such investigations provide valuable insights into the behavior of structural components under extreme loading conditions. However, accurately modeling penetration resistance remains challenging due to the complex interaction among projectile velocity, geometry, and the nonlinear behavior of concrete. To address this challenge, this study applies artificial intelligence (AI) techniques in combination with finite element (FE) simulations to enhance predictive modeling. The AI framework incorporates deep neural networks (DNN), support vector machines (SVM), and random forests (RF) for prediction and classification tasks, while Bayesian neural networks (BNN) are employed for uncertainty quantification, providing statistically reliable confidence bounds for the depth of penetration (DoP). Damage categorization is further optimized through K-means clustering, enabling clear differentiation between minor and severe damage states. The analysis is based on 540 data samples generated from a validated FE model calibrated with experimental results. The hybrid DNN–RF model achieved an R² of 0.994 for DoP prediction, while the SVM attained 99.08 % precision in damage classification and the RF achieved 98.16 % accuracy in ballistic limit prediction. The BNN yielded a 95 % confidence interval, confirming the reliability of the AI-based predictions. Among various clustering algorithms, including Density-Based Spatial Clustering of Applications with Noise, Gaussian Mixture Models, and hierarchical clustering, K-means demonstrated the best performance. The proposed AI-driven framework provides a reliable and efficient tool for rapid RC design assessment and optimization, contributing to advancements in defense, infrastructure resilience, and high-performance structural engineering.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"163 ","pages":"Article 112868"},"PeriodicalIF":8.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An innovative optimization strategy based on Mamba and generative adversarial networks for efficient and high-performance multimodal image fusion","authors":"Yichen Sun ,&nbsp;Mingli Dong ,&nbsp;Lianqing Zhu","doi":"10.1016/j.engappai.2025.112788","DOIUrl":"10.1016/j.engappai.2025.112788","url":null,"abstract":"<div><div>Multimodal image fusion (MIF) integrates multisource data into a single high-quality image with minimal redundancy. While deep learning has advanced MIF by improving fusion quality, convolutional neural networks (CNNs) struggle with long-range dependencies, and Transformers incur high computational costs. Additionally, preserving fine textures, suppressing noise, and achieving high efficiency remain challenges, particularly for infrared and visible image fusion (IVIF). This paper proposes MMGFuse, a novel MIF framework based on a Multi-Parallel Vision Mamba Generative Adversarial Network. MMGFuse leverages the Mamba model's efficiency and generative adversarial networks' realism, introducing a residual parallel vision Mamba (ResPViM4) module to enhance texture and detail preservation and a multi-parallel vision Mamba (MPViM) module to capture both global and local features across scales. A dual-modality image discriminator further optimizes visual quality. Experiments show that MMGFuse outperforms state-of-the-art methods in subjective visual quality and objective metrics for IVIF and medical image fusion, demonstrating its effectiveness, efficiency, and broad applicability in advancing image fusion. The codes are available at <span><span>https://github.com/sunyichen1994/MMGFuse</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"163 ","pages":"Article 112788"},"PeriodicalIF":8.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}