Expert Systems最新文献

{"title":"From AI to the Era of Explainable AI in Healthcare 5.0: Current State and Future Outlook","authors":"Anichur Rahman,&nbsp;Dipanjali Kundu,&nbsp;Tanoy Debnath,&nbsp;Muaz Rahman,&nbsp;Utpol Kanti Das,&nbsp;Abu Saleh Musa Miah,&nbsp;Ghulam Muhammad","doi":"10.1111/exsy.70060","DOIUrl":"https://doi.org/10.1111/exsy.70060","url":null,"abstract":"<div>\u0000 \u0000 <p>Artificial intelligence (AI) and explainable artificial intelligence (XAI) are advancing rapidly, with the potential to deliver significant benefits to modern society. The healthcare sector, in particular, has experienced transformative changes; overall, these technologies are helping to address numerous challenges, such as cancer cell detection, tumour zone identification in animal bodies, predictions of major and minor diseases, diagnosis, and more. This article provides an in-depth and detailed overview of AI and XAI, focusing on recent trends and their implications for advancing Healthcare 5.0 applications. Initially, the study examines the key concepts and exceptional features of AI, XAI, and Healthcare 5.0. Additional emphasis is placed on state-of-the-art practices currently being implemented in healthcare, particularly those involving AI and XAI. Subsequently, it establishes a coherent link between AI and XAI in Healthcare 5.0, grounded in contemporary advancements. Based on the findings, algorithms are recommended to address initial obstacles to integrating AI into the Healthcare 5.0 framework. Proposals for further enhancing Healthcare 5.0 performance through the integration of XAI and its unique features are discussed in detail. The work also provides in-depth implementation strategies and highlights model-specific trends within AI and XAI frameworks in Healthcare 5.0. Particular attention is given to AI model predictions in healthcare settings, emphasising their contributions to improved patient feedback and the delivery of more sophisticated care. Most importantly, this research highlights the potential for AI and XAI to support sustainable advancements in Healthcare 5.0 applications. Finally, significant issues are analysed, and an open discussion is presented on future guidelines for the blending of AI with XAI, and Healthcare 5.0 applications.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Parameter Control Strategy for Parallel Island-Based Metaheuristics","authors":"Roberto Prado-Rodríguez,&nbsp;Patricia González,&nbsp;Julio R. Banga","doi":"10.1111/exsy.70061","DOIUrl":"https://doi.org/10.1111/exsy.70061","url":null,"abstract":"<p>In the field of optimisation, the accurate configuration of parameters in metaheuristic algorithms is a critical yet often arduous task that significantly impacts the efficiency and efficacy of the search process. This study was motivated by the need to address the inefficiencies and limitations associated with conventional methods of parameter configuration, which typically involve manual, trial-and-error approaches. These traditional methods can lead to suboptimal performance and increased computational overhead. To tackle these challenges, this study introduces a novel adaptive parameter control strategy for parallel island-based metaheuristics, with a particular emphasis on the ant colony optimisation (ACO) algorithm. Our research process involved extensive experimentation to evaluate the effectiveness of this adaptive strategy. We conducted a series of tests to enable real-time adjustment of key parameters based on the performance of ACO colonies, thereby enhancing both exploration and exploitation capabilities. The results indicate that the adaptive strategy consistently outperforms offline manual and automated tuning configurations, particularly in larger and more complex problem instances, providing a more efficient solution for parameter optimisation in metaheuristics. These findings highlight the potential of dynamic parameter control to reduce dependency on expert knowledge and manual tuning while improving algorithmic performance.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Lightweight Plant Disease Detection With Adaptive Multi-Scale Model and Relationship-Based Knowledge Distillation","authors":"Wei Li,&nbsp;Xu Xu,&nbsp;Wei Wang,&nbsp;Junxin Chen","doi":"10.1111/exsy.70059","DOIUrl":"https://doi.org/10.1111/exsy.70059","url":null,"abstract":"<div>\u0000 \u0000 <p>Plant disease detection is able to control disease spread and help prevent significant food production losses. However, existing detection methods are still limited to different target scales and high model parameters. To this end, we develop a novel framework, that is, FPDD-Net, for lightweight plant disease detection. It is based on YOLOv8 with an adaptive multi-scale model (AMSM) and relationship-based knowledge distillation (RKD). More specifically, the original cross stage partial (CSP) bottleneck is replaced by an AMSM to effectively fuse the multi-scale features. Next, an Alpha-IoU loss optimization is adopted for aligning predicted boxes more precisely with ground truth, leading to fewer localization errors. Finally, RKD is introduced to assist the training and further improve the performance of target detection. To evaluate our network, the FPDD-Net is trained and tested on two typical datasets, that is, the plant village dataset and the plant-doc dataset. Experimental results indicated that our FPDD-Net is lightweight and has advantages over peer methods.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Privacy Preserving Multimodal Biometrics Recognition for Cross-Silo Datasets","authors":"Isha Kansal,&nbsp;Vikas Khuallar,&nbsp;Gifty Gupta,&nbsp;Deepali Gupta,&nbsp;Sapna Juneja,&nbsp;Ali Nauman,&nbsp;Ghulam Muhammad","doi":"10.1111/exsy.70053","DOIUrl":"https://doi.org/10.1111/exsy.70053","url":null,"abstract":"<div>\u0000 \u0000 <p>Different biometric modalities, such as fingerprints and left and right eye irises, contain physiological characteristics that offer high accuracy in identification processes. These modalities complement each other; for example, fingerprints provide intricate ridge patterns, while irises exhibit stable, precise features that perform well in challenging environments. A new proposed framework based on federated learning with optimised features, pre-trained deep learning models, linear discriminant analysis and dense neural networks ensures privacy protection for multi-modal biometric recognition across diverse biometric datasets. The system obtains better accuracy levels alongside increased robustness through the combination of fingerprint and iris scan technology that functions across independent and identically distributed (IID) and non-independent and non-identically distributed (non-IID) conditions. Privacy protection functions as a key asset of federated learning because it allows distributed training operations through non-raw data sharing, supporting high classification results. The system's performance is enhanced by implementing feature fusion alongside dimensionality reduction methods, which enhance both the efficiency and resistance to noise and variabilities. The system establishes an essential reference point for distributed and heterogeneous real-world biometric recognition because it implements accurate computation with enhanced efficiency together with privacy protection. The IID data experiments demonstrated 98.86% training accuracy while achieving precision and recall at precise levels of 98.86% and 96.59%. All metrics achieved 100% on the validation data set while keeping loss at zero. The system's performance slightly decreased under non-IID training data conditions, which resulted in 95.01% training accuracy and 0.18 training loss. The reported precision levels matched recall values since both measurements reached 97.99% and 95.01%. The system maintained perfect validation results through all metrics, which demonstrated a strong ability to generalise beyond data distribution impediments. The integration of multimodal biometric systems with federated learning enables the optimisation of large-scale solutions because it establishes efficient but accurate and secure applications across domains that include surveillance and security together with healthcare.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Towards a No Code Deployment of Social Robotics Use Cases","authors":"Alba Gragera,&nbsp;Carmen Díaz-de-Mera,&nbsp;Juan Pedro Bandera,&nbsp;Ángel García-Olaya,&nbsp;Fernando Fernández","doi":"10.1111/exsy.70038","DOIUrl":"https://doi.org/10.1111/exsy.70038","url":null,"abstract":"<p>Social Autonomous Robotics aims to deploy robots in scenarios that involve intensive and continuous interaction with humans. To control the behaviour of robotic platforms in such environments, the use of automated planning (AP) within a control architecture has been proposed as an effective mechanism. However, the design of AP models is time-consuming and typically carried out by domain experts and engineers. A significant amount of knowledge must be acquired in order to properly define the use case description by specifying the different tasks performed by the robot. In this paper, we present <span>DeVPlan</span>, a framework for graphically designing robotic use cases and configuring the platform for the desired execution. <span>DeVPlan</span> provides an interface that allows domain experts, in collaboration with knowledge engineers, to use state transition diagrams to specify the tasks a robot can perform and define recovery strategies for exogenous events that disrupt normal execution. This graphical design is automatically translated into the standard Planning Domain Definition Language (PDDL). Additionally, to facilitate the integration of the AP model with the robot's control architecture, <span>DeVPlan</span> includes a module for generating the configuration files required to set up the control system. The proposed framework has been successfully used to design and deploy two different use cases in a real environment in a retirement home.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Accelerated Artificial Bee Colony Optimization for Cost-Sensitive Neural Networks in Multi-Class Problems","authors":"Hilal Hacilar,&nbsp;Bilge Kagan Dedeturk,&nbsp;Mihrimah Ozmen,&nbsp;Mehlika Eraslan Celik,&nbsp;Vehbi Cagri Gungor","doi":"10.1111/exsy.70045","DOIUrl":"https://doi.org/10.1111/exsy.70045","url":null,"abstract":"<p>Metaheuristics are advanced problem-solving techniques that develop efficient algorithms to address complex challenges, while neural networks are algorithms inspired by the structure and function of the human brain. Combining these approaches enables the resolution of complex optimization problems that traditional methods struggle to solve. This study presents a novel approach integrating the ABC algorithm with ANNs for weight optimization. The method is further enhanced by vectorization and parallelization techniques on both CPU and GPU to improve computational efficiency. Additionally, this study introduces a cost-sensitive fitness function tailored for multi-class classification to optimize results by considering relationships between target class levels. It validates these advancements in two critical applications: network intrusion detection and earthquake damage estimation. Notably, this study makes a significant contribution to earthquake damage assessment by leveraging machine learning algorithms and metaheuristics to enhance predictive models and decision-making in disaster response. By addressing the dynamic nature of earthquake damage, this research fills a critical gap in existing models and broadens the understanding of how machine learning and metaheuristics can improve disaster response strategies. In both domains, the ABC-ANN implementation yields promising results, particularly in earthquake damage estimation, where the cost-sensitive approach demonstrates satisfactory outcomes in macro-F1 and accuracy. The best results for macro-F1, weighted-F1, and overall accuracy provides best results with the UNSW-NB15 and earthquake datasets, showing values of 64%, 72%, 68%, and 60%, 80%, and 79%, respectively. Comparative performance evaluations reveal that the proposed parallel ABC-ANN model, incorporating the novel cost-sensitive fitness function and enhanced by vectorization and parallelization techniques, significantly reduces training time and outperforms state-of-the-art methods in terms of macro-F1 and accuracy in both network intrusion detection and earthquake damage estimation.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"HiSum: Hierarchical Topic-Driven Approach for Role-Oriented Dialogue Summarisation","authors":"Keyan Jin,&nbsp;Yapeng Wang,&nbsp;Xu Yang,&nbsp;Sio Kei Im","doi":"10.1111/exsy.70043","DOIUrl":"https://doi.org/10.1111/exsy.70043","url":null,"abstract":"<div>\u0000 \u0000 <p>As the volume of information on online communication platforms continues to grow, the task of dialogue summarisation becomes increasingly critical for understanding and extracting key information from diverse conversations. Traditional approaches often struggle to cope with the dynamic nature of dialogues, such as managing perspectives from multiple speakers and seamlessly transitioning between different topics. We propose a novel hierarchical topic-driven approach to generate role-oriented dialogue summarisation (HiSum) to address these challenges. First, we utilise VarGMM clustering technology for in-depth topic segmentation, which enables the model to capture the key topics in a dialogue. Second, we employ a LayerAttn hierarchical attention mechanism to dynamically adjust the focus of dialogue content based on participants' importance and the topics' relevance. Experimental results on three public dialogue summarisation data sets (CSDS, MC and SAMSUM) demonstrate that our method significantly outperforms most existing strong baseline methods across various evaluation metrics and surpasses the current state-of-the-art methods in certain metrics. Detailed analysis demonstrates that HiSum can perform more precise topic segmentation and effectively identify critical information. Our code is publicly available at: https://github.com/kjin0119/HiSum.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Novel Continual Learning and Adaptive Sensing State Response-Based Target Recognition and Long-Term Tracking Framework for Smart Industrial Applications","authors":"Lu Chen,&nbsp;Gun Li,&nbsp;Jie Tan,&nbsp;Yang Li,&nbsp;Shenbing Fu,&nbsp;Haoyuan Ma,&nbsp;Yu Liu,&nbsp;Yuhao Yang,&nbsp;Weizhong Qian,&nbsp;Qinsheng Zhu,&nbsp;Amir Hussain","doi":"10.1111/exsy.70037","DOIUrl":"https://doi.org/10.1111/exsy.70037","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>With the rapid development of artificial intelligence technology, highly intelligent and unmanned factories have become an important trend. In the complex environments of smart factories, the long-term tracking and inspection of specified targets, such as operators and special products, as well as comprehensive visual recognition and decision-making capabilities throughout the whole production process, are critical components of automated unmanned factories. However, challenges such as target occlusion and disappearance frequently occur, complicating long-term tracking. Currently, there is limited research specifically focused on developing robust and comprehensive long-term visual tracking frameworks for unmanned factories, particularly those designed to integrate with embedded platforms and overcome various challenges.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We first construct three new benchmark datasets in the complex workshop environment of a smart factory (referred to as SF-Complex3 data), which include challenging conditions such as complete occlusion and partial occlusion of targets. A brain memory-inspired approach is used to determine uncertainty estimation parameters, including confidence, peak-to-sidelobe ratio and average peak-to-correlation energy, to develop a continual learning-based adaptive model update method. Additionally, we design a lightweight target detection model to automatically detect and locate targets in the initial frame and during re-detection. Finally, we integrate the algorithm with ground mobile robots and unmanned aerial vehicles-based imaging and processing equipment to build a new visual detection and tracking framework, smart factory complex recognition and tracking.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We conducted extensive tests on the benchmark UAV20L and SF-Complex3 datasets. The proposed algorithm demonstrates an average performance improvement of 6% when addressing key challenging attributes, compared to state-of-the-art tracking methods. Additionally, the algorithm was capable of running efficiently on embedded platforms, including mobile robots and UAVs, at a real-time speed of 36.4 frames per second.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed SFC-RT framework effectively addresses the challenges of target loss and occlusion in long-term tracking within complex smart factory environments. The framework meets the requirements for real-time performance, robustness and lightweight design, making it well suited for practical deployment.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Improved Grid Clustering Algorithm for Geographic Data Mining","authors":"Honglei He","doi":"10.1111/exsy.70042","DOIUrl":"https://doi.org/10.1111/exsy.70042","url":null,"abstract":"<div>\u0000 \u0000 <p>Grid clustering is a classical clustering algorithm with the advantage of lower time complexity, which is suitable for the analysis of large geographic data. However, it is sensitive to the grid division parameter <i>M</i> and density threshold <i>R</i>, and the clustering accuracy is poor. The article proposes a hybrid clustering algorithm HCA-BGP based on grid and division. the algorithm first uses grid clustering to obtain the core part of the class family, and then uses the division-based method to obtain the edge part of the class family. Through experiments on simulated datasets and real geographic datasets, it is proved to have better results than the existing grid clustering as well as some other classical algorithms. In terms of clustering accuracy, compared with the classical grid clustering algorithm Clique, the clustering F-value of this paper's algorithm is improved by 20.3% on dataset S1, 81.8% on dataset R15, and 7.6% on average on the eight geographic datasets. In terms of the sensitivity of parameters <i>M</i> and <i>R</i>, compared with Clique, the variance of the clustered F-value of this paper's algorithm is reduced by 89.3% on dataset S1; the variance of the clustered ARI is reduced by 99.9% on the real geographic dataset Data8. Compared to another grid-based clustering algorithm, GDB, HCA-BGP also demonstrates significant advantages.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Dual Indicator Ranking Method for Complexly Constrained Multi-Objective Optimization","authors":"Qian Zeng,&nbsp;Hai-Lin Liu","doi":"10.1111/exsy.70046","DOIUrl":"https://doi.org/10.1111/exsy.70046","url":null,"abstract":"<div>\u0000 \u0000 <p>Addressing multi-objective optimization problems (MOPs) with complex constraints presents a significant challenge due to their diverse nature. While existing algorithms can effectively handle specific types of complex constraints, they often struggle with a variety of such constraints. To address this issue, we propose an innovative evolutionary algorithm for constrained multi-objective optimization. A key feature is the integration of a novel differential operator that generates offspring based on the presence of feasible solutions within the main population. This strategy is particularly effective for handling complex constraints characterised by small feasible spaces and deceptive infeasible regions. Additionally, the algorithm employs a dual-indicator ranking mechanism to evaluate and select individuals from the auxiliary population based on the quality and quantity of feasible solutions generated by the main population. Promising individuals are then migrated back to the main population, thereby enhancing the exploration of the solution space. This approach demonstrates significant superiority in solving MOPs with discontinuous feasible regions or extensive infeasible areas. Empirical comparisons across a range of benchmark problems show that the proposed algorithm outperforms current state-of-the-art methods in evolutionary constrained multi-objective optimization, underscoring its potential as a robust tool for handling MOPs with complex constraints.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}