Artificial Intelligence Review最新文献

{"title":"Automatic brain MRI tumors segmentation based on deep fusion of weak edge and context features","authors":"Leyi Xiao,&nbsp;Baoxian Zhou,&nbsp;Chaodong Fan","doi":"10.1007/s10462-025-11151-8","DOIUrl":"10.1007/s10462-025-11151-8","url":null,"abstract":"<div><p>Brain tumors pose a significant health risk to humans. The edge boundaries in brain magnetic resonance imaging (MRI) are often blurred and poorly defined, which can easily result in inaccurate segmentation of lesion areas. To address these challenges, we proposed an Automatic Brain MRI Tumor Segmentation based on deep fusion of Weak Edge and Context features (AS-WEC). First, AS-WEC introduces the Otsu Double Threshold Weak Edges Adaptive Detection (Otsu-WD), which focuses on tumor edge information and differentiates between lesion edges and normal cerebral sulci and gyri. Second, an edge branching network based on the Gated Recurrent Unit (GRU) is constructed to fully preserve the edge context information of the lesion region. Finally, a maximum index fusion mechanism has been designed to incorporate a multilayer feature map, preventing the loss of edge details during the deep feature fusion process. The experimental results demonstrate that the Otsu-WD method outperforms the Canny and TEED algorithms in detecting brain MRI tumor edges. In brain MRI tumor segmentation, AS-WEC delivers a clearer visual segmentation effect compared to the classical UNet++ network and recent models like PVT-Former. On both datasets, AS-WEC demonstrated improvements across multiple metrics. The Dice averaged 92.96%, and the mIoU reached 93.12%, effectively validating the method’s efficacy in brain MRI tumor segmentation. Code and pre-trained models are available at https://github.com/DL-Segment/AS-WEC.git.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11151-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529992","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":"Multi-attribute decision-making using q-rung orthopair fuzzy Zagreb index","authors":"Yongsheng Rao,&nbsp;Saeed Kosari,&nbsp;Saira Hameed,&nbsp;Zulqarnain Yousaf","doi":"10.1007/s10462-025-11149-2","DOIUrl":"10.1007/s10462-025-11149-2","url":null,"abstract":"<div><p>The <i>q</i>-rung orthopair fuzzy set (<i>q</i>-<i>ROFS</i>), an extension of intuitionistic and Pythagorean fuzzy sets, offers greater flexibility in representing vague information with two possible outcomes, yes or no. The fuzzy Zagreb index is an important graph parameter, widely used in fields such as network theory, spectral graph theory, mathematics, and molecular chemistry. In this paper, the first and second Zagreb indices for q-rung orthopair fuzzy graphs (q-ROFGs) are introduced, and bounds for these indices are established, including their behavior in regular q-ROFGs. Additionally, it is explored, how various graph operations such as union, Cartesian product, direct product, and lexicographical product affect the first Zagreb index. Furthermore, a new approach is presented that combines Multiple-Attribute Decision-Making (MADM) with graph-based models to improve decision-making, particularly in vaccine selection. The methodology constructs a bipartite graph for each attribute, where virologists assign membership and non-membership values to vaccines. The Zagreb index is used to measure the importance of each vaccine, and a weighted aggregation technique normalizes the scores. The final ranking is derived from a computed score function. The results demonstrate the effectiveness of the approach in providing a systematic and mathematically rigorous framework for multi-attribute decision-making, with rank correlation analysis confirming its robustness compared to existing methods such as <i>q</i>-ROF PROMETHEE, <i>q</i>-ROF VICOR, <i>q</i>-ROF TOPSIS, <i>q</i>-ROFWG, and <i>q</i>-ROFWA.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11149-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521619","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":"Csan: cross-coupled semantic adversarial network for cross-modal retrieval","authors":"Zhuoyi Li,&nbsp;Huibin Lu,&nbsp;Hao Fu,&nbsp;Fanzhen Meng,&nbsp;Guanghua Gu","doi":"10.1007/s10462-025-11152-7","DOIUrl":"10.1007/s10462-025-11152-7","url":null,"abstract":"<div><p>Cross-modal retrieval aims to correlate multimedia data by bridging the heterogeneity gap. Most cross-modal retrieval approaches learn a common subspace to project the multimedia data into the subspace for directly measuring the similarity. However, the existing cross-modal retrieval frameworks cannot fully capture the semantic consistency in the limited supervision information. In this paper, we propose a Cross-coupled Semantic Adversarial Network (CSAN) for cross-modal retrieval. The main structure of this approach is mainly composed of the generative adversarial network, i.e., each modality branch is equipped with a generator and a discriminator. Besides, a cross-coupled semantic architecture is designed to fully explore the correlation of paired heterogeneous samples. To be specific, we couple a forward branch with an inverse mapping and implement a weight-sharing strategy of the inverse mapping branch to the branch of another modality. Furthermore, a cross-coupled consistency loss is introduced to minimize the semantic gap between the representations of the inverse mapping branch and the forward branch. Extensive qualitative and quantitative experiments are conducted to evaluate the performance of the proposed approach. By comparing against the previous works, the experiment results demonstrate our approach outperforms state-of-the-art works.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11152-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521625","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 artificial intelligence - based algorithm towards fetal facial anomalies detection (2013–2024)","authors":"Natarajan Sriraam,&nbsp;Babu Chinta,&nbsp;Suresh Seshadri,&nbsp;Sudarshan Suresh","doi":"10.1007/s10462-025-11160-7","DOIUrl":"10.1007/s10462-025-11160-7","url":null,"abstract":"<div><p>This review explores the growing need for AI-based algorithms in diagnosing fetal facial anomalies, which are often difficult to detect due to limitations in current imaging techniques like ultrasound and MRI. These challenges include low resolution, motion artifacts, and insufficient annotated data, which hinder early and accurate diagnosis. AI algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs) and U-Net, offers significant potential to overcome these challenges by analyzing large datasets and improving image analysis. Early diagnosis of these anomalies is crucial for enabling timely interventions, personalized treatment plans, and better prenatal care. This study adopts a systematic review approach, to assess existing research on AI-based approaches for fetal facial anomaly detection. The review includes peer-reviewed studies from key biomedical databases like PubMed, IEEE Xplore, and ScienceDirect, focusing on the last 15 years. Studies that implemented AI techniques and manual techniques for detecting anomalies in prenatal images were considered. Among all models reviewed, CNNs and U-Net architectures were found to be the most effective. CNNs excel at classifying medical images, while U-Net is particularly powerful for image segmentation. These models have demonstrated high accuracy in identifying conditions such as cleft lip, palate, and micrognathia. The use of AI in clinical settings can greatly enhance the precision and efficiency of fetal anomaly detection, addressing current limitations in medical imaging. By integrating AI, particularly deep learning models, into clinical workflows, prenatal care can be transformed, allowing for earlier and more accurate diagnosis. This can lead to more personalized care, timely interventions, and ultimately improved health outcomes for affected individuals and their families.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11160-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521626","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":"Applications of deep learning algorithms in ischemic stroke detection, segmentation, and classification","authors":"Tanzeela Kousar,&nbsp;Mohd Shafry Mohd Rahim,&nbsp;Sajid Iqbal,&nbsp;Fatima Yousaf,&nbsp;Muhammad Sanaullah","doi":"10.1007/s10462-025-11119-8","DOIUrl":"10.1007/s10462-025-11119-8","url":null,"abstract":"<div><p>Ischemic, one of the fatal diseases characterized by insufficient blood supply to tissues poses a significant global health burden, necessitating the development of robust diagnostic and classification methodologies. Timely identification, intervention, and treatment are essential to reduce associated risk factors. Modern machine learning methods like deep learning and neural networks are being successfully employed on medical images to detect and segment the region of interest for various diseases where the performance of these computational methods is improving daily and for various tasks has surpassed natural intelligence. This success has convinced medical practitioners to trust computational methods and incorporate computer-based solutions into their clinical practices. It is, therefore, essential to examine the available solutions critically by considering their strengths and weaknesses to establish their trust and clinical applicability.</p><p>In the context of the above-mentioned task, this work focuses on two aspects: first, a broad review has been done for Ischemic stroke prognostication using various brain-imaging biomarkers via diverse deep learning frameworks, and second, the reviewed works are categorized based on their computational approach employed for Ischemic stroke detection, segmentation, and classification.</p><p>Finally, this work presents recent advances and future research directions to invent high-performance methods.</p><p>It was concluded that recent advancements in ischemic stroke detection have achieved 85–98% accuracy using CNNs and transformer-based models with separate imaging, clinical, and molecular data, though combined analysis remains largely underexplored. Integrating vascular imaging, clinical signs, and proteomic data can enhance real-time monitoring. However, challenges persist in unifying diverse parameters, necessitating advanced methodologies such as transfer learning, multi-task learning, advanced transformers, federated learning, and standardized protocols. These findings pave the way for improved diagnostics, treatment, and outcomes in stroke management.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11119-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496892","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":"An assessment framework for explainable AI with applications to cybersecurity","authors":"Maria Carla Calzarossa,&nbsp;Paolo Giudici,&nbsp;Rasha Zieni","doi":"10.1007/s10462-025-11141-w","DOIUrl":"10.1007/s10462-025-11141-w","url":null,"abstract":"<div><p>Several explainable AI methods are available, but there is a lack of a systematic comparison of such methods. This paper contributes in this direction, by providing a framework for comparing alternative explanations in terms of complexity and robustness. We exemplify our proposal on a real case study in the cybersecurity domain, namely, phishing website detection. In fact, in this domain explainability is a compelling issue because of its potential benefits for the detection of fraudulent attacks and for the design of efficient security defense mechanisms. For this purpose, we apply our methodology to the machine learning models obtained by analyzing a publicly available dataset containing features extracted from malicious and legitimate web pages. The experiments show that our methodology is quite effective in selecting the explainability method which is, at the same time, less complex and more robust.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11141-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496881","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":"Reconstructing dance movements using a mathematical model based on optimized nature-inspired machine learning","authors":"Jing Song,&nbsp;Li Ding","doi":"10.1007/s10462-025-11142-9","DOIUrl":"10.1007/s10462-025-11142-9","url":null,"abstract":"<div><p>Recording dance movements nowadays becomes problematic due to complex recording procedures and unavoidable data loss caused by some resource elements, like bodily or clothing material composition. The task of filling in the missing data for the performed motion and retrieving the sequence as a whole becomes difficult due to the characteristics of physical motion, which include cinematographic perspectives that render the movements themselves non-linear. Previous works have indicated some level of success in loss motion recovery, but only for a short span. The first two-dimensional matrix computation paradigm lacks theoretical justification for the recovery of the non-linear motion information, which is a limitation. This issue has been addressed by developing a new enhanced model called the Machine Learning 2-Dimensional Matrix-Calculation (ML-2DMC), which is presumably designed to achieve the rehabilitation and recovery of human movement and dance. The proposed procedure takes advantage of the effectiveness of the machine learning algorithms and applies 2D matrix computation methods, permitting good results across a variety of experiments. A new method called fractal-chaotic map grey wolf optimizer (FCM-GWO) is introduced to optimize the parameters of ML-2DMC. This optimization itself increases the efficiency of the ML-2DMC model when it comes to the retrieval of complex movements of the processes involving dance. The paper gives experimental results validating the efficiency of the proposed approach against other methods, such as recurrent convolutional neural networks and other more sophisticated models and approaches incorporating multi-paradigm sensors and devices such as Kinect sensors along with low-rank matrix completion methods. The study shows that the ML-2DMC-FCM-GWO method effectively tackles the complexities of non-linear human motion and dance recovery, making a significant addition to the field of motion analysis and restoration.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11142-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496827","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 critical review of artificial intelligence based techniques for automatic prediction of cephalometric landmarks","authors":"R. Neeraja,&nbsp;L. Jani Anbarasi","doi":"10.1007/s10462-025-11135-8","DOIUrl":"10.1007/s10462-025-11135-8","url":null,"abstract":"<div><p>Automatic cephalometric landmark detection has emerged as a pivotal area of research that combines medical imaging, computer vision, and orthodontics. The identification of cephalometric landmarks is of utmost importance in the field of orthodontics, as it contributes significantly to the process of diagnosing and planning treatments, as well as conducting research on craniofacial aspects. This practice holds the potential to improve clinical decision-making and ultimately increase the outcomes for patients. This work explores a wide range of strategies, encompassing both traditional edge-based methods and advanced deep learning approaches. The study leveraged various academic publication databases like IEEEXplore, ScienceDirect, arXiv, Springer and PubMed to thoroughly search for articles related to automatic cephalometric landmark detection. Additionally, other pertinent publications were acquired from credible sources like Google Scholar and Wiley databases. Screening the articles relied on three selection criteria: (a) publication titles, abstracts, literature reviews, (b) cephalometric radiograph datasets suitable for 2D landmarking, and (c) studies conducted over different time periods were employed to gain a comprehensive understanding of the evolution of methodologies used in landmark prediction to identify the most relevant papers for this review. The initial electronic database search identified 268 papers on landmark detection. A total of 118 publications were selected and incorporated in the present study after a meticulous screening process. Performance analysis was conducted on studies that reported Successful Detection Rates (SDRs) within different clinically accepted precision ranges, Mean Radial Error (MRE) with Standard Deviation (SD) between manually annotated and automated landmarks as outcomes. Bar graphs and custom combination plots were utilized to analyse the correlations among different methodologies employed and their evaluation metrics outcomes. The performance comparison results indicate that Deep Learning techniques showed superior accuracy in automating 2D cephalometric landmarks compared to other conventional and Machine Learning approaches. Recently, more advanced Deep Learning algorithms have been developed to improve the accuracy of automatic landmark prediction.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11135-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496826","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":"Leveraging generative AI synthetic and social media data for content generalizability to overcome data constraints in vision deep learning","authors":"Panteha Alipour,&nbsp;Erika Gallegos","doi":"10.1007/s10462-025-11137-6","DOIUrl":"10.1007/s10462-025-11137-6","url":null,"abstract":"<div><p>Generalizing deep learning models across diverse content types is a persistent challenge in domains like facial emotion recognition (FER), where datasets often fail to reflect the wide range of emotional responses triggered by different stimuli. This study addresses the issue of content generalizability by comparing FER model performance between models trained on video data collected in a controlled laboratory environment, data extracted from a social media platform (YouTube), and synthetic data generated using Generative Adversarial Networks. The videos focus on facial reactions to advertisements, and the integration of these different data sources seeks to address underrepresented advertisement genres, emotional reactions, and individual diversity. Our FER models leverage Convolutional Neural Networks Xception architecture, which is fine-tuned using category based sampling. This ensures training and validation data represent diverse advertisement categories, while testing data includes novel content to evaluate generalizability rigorously. Precision–recall curves and ROC-AUC metrics are used to assess performance. Results indicate a 7% improvement in accuracy and a 12% increase in precision–recall AUC when combining real-world social media and synthetic data, demonstrating reduced overfitting and enhanced content generalizability. These findings highlight the effectiveness of integrating synthetic and real-world data to build FER systems that perform reliably across more diverse and representative content.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11137-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475225","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":"Image-based deep learning for smart digital twins: a review","authors":"Md Ruman Islam,&nbsp;Mahadevan Subramaniam,&nbsp;Pei-Chi Huang","doi":"10.1007/s10462-024-11002-y","DOIUrl":"10.1007/s10462-024-11002-y","url":null,"abstract":"<div><p>Smart Digital Twins (<i>SDTs</i>) are being increasingly used to virtually replicate and predict the behaviors of complex physical systems through continual data assimilation, enabling the optimization of the performance of these systems by controlling the actions of systems. Recently, the Deep Learning (<i>DL</i>) models have significantly enhanced the capabilities of <i>SDTs</i>, particularly for tasks such as predictive maintenance, anomaly detection, and optimization. In many domains, including medicine, engineering, and education, <i>SDTs</i> use image data (image-based <i>SDTs</i>) to observe, learn, and control system behaviors. This paper focuses on various approaches and associated challenges in developing image-based <i>SDTs</i> by continually assimilating image data from physical systems. The paper also discusses the challenges in designing and implementing <i>DL</i> models for <i>SDTs</i>, including data acquisition, processing, and interpretation. In addition, insights into the future directions and opportunities for developing new image-based <i>DL</i> approaches to develop robust <i>SDTs</i> are provided. This includes the potential for using generative models for data augmentation, developing multi-modal <i>DL</i> models, and exploring the integration of <i>DL</i> models with other technologies, including Fifth Generation (<i>5 G</i>), edge computing, and the Internet of Things (<i>IoT</i>). In this paper, we describe the image-based <i>SDTs</i>, which enable broader adoption of the Digital Twins (<i>DTs</i>) paradigms across a broad spectrum of areas and the development of new methods to improve the abilities of <i>SDTs</i> in replicating, predicting, and optimizing the behavior of complex systems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11002-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475224","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}