International Journal of Cognitive Computing in Engineering最新文献

{"title":"Bibliometric analysis of author count, funding, and citations in AI research","authors":"Wei-Chao Lin ,&nbsp;Huei-Hua Tsao ,&nbsp;You-Shyang Chen ,&nbsp;Chien-Lung Hsu","doi":"10.1016/j.ijcce.2025.09.004","DOIUrl":"10.1016/j.ijcce.2025.09.004","url":null,"abstract":"<div><div>The academic community places significant emphasis on publishing research in SCI and SSCI journals, which are known for their credibility, high quality, and strong reputations. Most research requires significant investment in human resources and equipment, making the acquisition of research funding crucial. Understanding the motivation behind authorship and its association with citation patterns in SCI and SSCI journals represents a significant research concern in bibliometric studies. This study identifies the relationship among author number, research funding, and citation count using content analysis techniques, including the chi-square and analysis of variance tests. Investment in AI research, development, and applications is increasing; thus, this study examines 4488 articles published in the field of artificial intelligence (AI) from Springer in 2018. The empirical results indicate that (1) the average number of authors is highest in Q1 journals, with non-single-author papers being more common than single-author papers and concentrated in higher rankings; (2) papers with research funding are more common than those without; (3) papers with citations are more frequent than those without; (4) the ranking of papers with research funding and citations is higher than that of other papers without funding; and (5) the average citation count of papers with research funding leads in Q1 and is higher than in other rankings. This study is the first attempt at highlighting papers in the field of AI from Springer. The results and important findings provide useful references for researchers, reviewers, publishers, and interested parties with different purposes for academic and technical publications with sustained success. This study uniquely integrates four dimensions—author count, research funding, journal ranking, and citation count—to offer novel insights into academic publishing performance in the AI field.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 104-117"},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MpoxNet: Leveraging hybrid deep learning for enhanced monkeypox diagnosis and risk identification","authors":"Tushar Deb Nath,&nbsp;Md. Golam Moazzam","doi":"10.1016/j.ijcce.2025.09.001","DOIUrl":"10.1016/j.ijcce.2025.09.001","url":null,"abstract":"<div><div>Monkeypox is a rare viral disease historically documented in Central and West Africa, though its recent emergence in global outbreaks has raised significant public health concerns. Accurate and timely diagnosis is crucial for effective containment. While existing research predominantly focuses on image-based diagnostics, the potential of tabular data—which is vital for symptom-driven screening and epidemiological analysis—remains largely underexplored. Furthermore, developing robust diagnostic models from tabular data is challenged by limited sample sizes and high data heterogeneity. To address this, we propose MpoxNet, a novel hybrid deep learning model that integrates Long Short-Term Memory (LSTM) networks with Multi-Layer Perceptrons (MLPs) to classify monkeypox cases and identify associated risk factors, particularly among HIV-positive individuals. We preprocessed publicly available Kaggle datasets by applying resampling techniques to mitigate class imbalance and conducted symptom correlation analysis to improve feature representation. Our experimental results demonstrate that MpoxNet achieved an accuracy of 65.35%, a precision of 65.04%, and a recall of 65.68% on Dataset D1, and an accuracy of 87.50%, a precision of 73.33%, and a recall of 100% on Dataset D2. For comparison, we evaluated traditional ensemble models, including AdaBoost, XGBoost, and Random Forest, which consistently outperformed baseline classifiers. These findings highlight the significant diagnostic value of tabular data and establish a foundation for deploying AI-driven, symptom-based tools to augment clinical decision-making and enhance public health surveillance strategies for monkeypox.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 86-94"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel channel estimation of MIMO-OFDM using hybrid bionic binary spotted hyena optimization","authors":"C. Premila Rosy ,&nbsp;S. Yazhinian ,&nbsp;M. Therasa ,&nbsp;K.R. Surendra ,&nbsp;Anand Karuppannan ,&nbsp;A. Manikandan","doi":"10.1016/j.ijcce.2025.09.003","DOIUrl":"10.1016/j.ijcce.2025.09.003","url":null,"abstract":"<div><div>A promising generalized inverse discrete Fourier transform non-orthogonal frequency division multiplexing (GIDFT-OFDM) system can satisfy the requirement of supporting higher data rates in fifth-generation (5G) technology. However, this system has a high peak-to-average power ratio (PAPR) because many subcarrier signals are transmitted. The inverse discrete Fourier transform (IDFT) is used in an orthogonal frequency-division multiplexing (OFDM) modulator to convert symbols from the frequency domain to the time domain and add a cyclic prefix before sending them through the channel. In pilot-based channel estimation, pilots are inserted into the transmitter and detected at the receiver, along with the OFDM symbols. In this study, we searched for local and global optimal solutions of the Bionic Binary Spotted Hyena Optimization (BBSHO) algorithm with position coordinate vectors (PCVs) of social behavior. It also introduces the BBSHO algorithm to improve the local search capability within the search space. Optimized pilots provided better performance than orthogonal and randomly placed pilots. The stochastic, quadrature, and whale swarm algorithms detect the position of the pilot. To improve the data quality and reduce the BER, MSE, and SER, we introduced several optimization algorithms on the channels of MIMO-OFDM devices. The performance of the two optimization algorithms proposed above contrasts with that of the current simple algorithms and shows improved results in MIMO-OFDM networks. The proposed optimization algorithm was implemented using the MATLAB 2021(a) software. For channel optimization, metaheuristic algorithms such as the Whale Swarm Algorithm (WSA) and the Hybrid Bionic Binary Spotted Hyena Optimization (BBSHO) algorithm are used.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 95-103"},"PeriodicalIF":0.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OcclusionNetPlusPlus: a multi-scale similarity network with adaptive occlusion detection for robust iris recognition","authors":"Rahul Tanna ,&nbsp;Tanish Patel ,&nbsp;Faisal Mohammed Alotaibi ,&nbsp;Rutvij H. Jhaveri ,&nbsp;Thippa Reddy Gadekallu","doi":"10.1016/j.ijcce.2025.09.002","DOIUrl":"10.1016/j.ijcce.2025.09.002","url":null,"abstract":"<div><div>A significant challenge in iris recognition systems is the presence of occlusions affecting the iris, face, and periocular regions. To address this issue, this study proposes an OcclusionNetPlusPlus framework which employs carefully designed bank of Gabor filters to capture iris texture patterns at different scales and orientations. We then inject 2D positional encodings into these filter responses to embed explicit (x,y) location information, enabling downstream modules to reason about where each feature came from. The innovation in our approach is the introduction of an occlusion detection mechanism that generates probability maps based on local variance analysis, effectively identifying occluded regions in the iris image. These probability maps are used to dynamically weight the extracted features, reducing the influence of unreliable regions during similarity computation. The framework incorporates a custom loss function that optimizes feature similarity while maintaining discriminative power across different iris patterns. Training and evaluation were conducted on publicly available iris recognition datasets, ensuring a diverse test bed for assessing performance across different occlusion scenarios. We evaluated OcclusionNetPlusPlus on CASIA-Iris-Thousand and IIT Delhi V1.0. In controlled tests, it achieves an EER of 0.51 %, an FRR of 0.54 % at FAR = 1 % (0.61 % at FAR = 0.1 %), and a d-prime of 7.04. Even under simulated unconstrained conditions—adding noise, blur, and random occlusions—EER stays around 2 %.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 74-85"},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing ML classifiers for superior intrusion detection in resource-constrained smart homes","authors":"Rong Xu","doi":"10.1016/j.ijcce.2025.08.003","DOIUrl":"10.1016/j.ijcce.2025.08.003","url":null,"abstract":"<div><div>Machine learning (ML) has indeed become essential to the enhancement of intrusion detection systems in different scenarios. It has become a critical barrier against various sophisticated cyber threats. Security vulnerabilities pose special challenges to smart homes, given that devices, sensors, and network connections make the ecosystem highly connected. Such systems improve convenience and efficiency but are generally based on hardware with limited processing power and storage capacity. Therefore, these are prone to a variety of potential attacks. To do so, an effective IDS would have to identify known and evolving threats at all the various vulnerable points, starting from network interfaces down to individual devices. This work tackles these challenges by designing and optimizing ML models that offer reliable intrusion detection tailored for resource-constrained smart home environments. This work argues for intrusion prediction in smart homes using the Extra Tree Classification (ETC) and Linear Discriminant Analysis Classification (LDAC). To strengthen these base models' predictive capability, this paper considered the use of 2 optimization algorithms: the Rider Optimization Algorithm (ROA) and the Aquila Optimizer (AO). The optimizers were integrated strategically with the base models for improved accuracy, thus giving rise to new hybrid models. The combination of ETC with AO provides the ETAO model, while ETC with ROA gives the ETRO model. In equal measure, the LDAC model combined with ROA gives the LDRO model, while that of the LDAC model combined with AO gives the LDAO model. Basically, these hybrid models aim to ensure better performance from a prediction perspective. In the test section, the ETAO model was head and shoulders above the others in this metric, with an excellent value of 0.984, while for the ETRO model, the second-best performing model achieved 0.975. Later on, looking at the entire section, the precision metric again scored highest with the ETAO model at 0.987, while the weakest performance was from the LDAO model, which had a value of 0.888.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 58-73"},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating artificial intelligence and quantum computing: A systematic literature review of features and applications","authors":"Vanessa García Pineda ,&nbsp;Alejandro Valencia-Arias ,&nbsp;Francisco Eugenio López Giraldo ,&nbsp;Edison Andrés Zapata-Ochoa","doi":"10.1016/j.ijcce.2025.08.002","DOIUrl":"10.1016/j.ijcce.2025.08.002","url":null,"abstract":"<div><div>Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 26-39"},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transforming legal texts into computational logic: Enhancing next generation public sector automation through explainable AI decision support","authors":"Markus Bertl ,&nbsp;Simon Price ,&nbsp;Dirk Draheim","doi":"10.1016/j.ijcce.2025.07.003","DOIUrl":"10.1016/j.ijcce.2025.07.003","url":null,"abstract":"<div><div>This research presents a novel approach for translating legal texts into machine-executable computational logic to support the automation of public sector processes. Recognizing the high-stakes implications of artificial intelligence (AI) in legal domains, the proposed method emphasizes explainability by integrating explainable AI (XAI) techniques with natural language processing (NLP), employing scope-restricted pattern matching and grammatical parsing. The methodology involves several key steps: document structure inference from raw legal text, semantically neutral pre-processing, identification and resolution of internal and external references, contextualization of legal paragraphs, and rule extraction. The extracted rules are formalized as Prolog predicates and visualized as structured textual lists and graphical decision trees to enhance interpretability. To demonstrate the automatic extraction of explainable rules from legal text, we develop a Law-as-Code prototype and validate it through a real-world case study at the Austrian Ministry of Finance. The system successfully extracts executable rules from the Austrian <em>Study Funding Act</em>, confirming the feasibility and effectiveness of the proposed approach. This validation not only underscores the practical applicability of our method, but also highlights promising avenues for future research, particularly the integration of Generative AI and Large Language Models (LLMs) into the rule extraction pipeline, while preserving traceability and explainability.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 40-57"},"PeriodicalIF":0.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a feature vector for accurate breast cancer detection in mammographic images","authors":"Aisulu Ismailova ,&nbsp;Gulzira Abdikerimova ,&nbsp;Nurgul Uzakkyzy ,&nbsp;Raikhan Muratkhan ,&nbsp;Murat Aitimov ,&nbsp;Aliya Tergeusizova ,&nbsp;Aliya Beissegul","doi":"10.1016/j.ijcce.2025.08.001","DOIUrl":"10.1016/j.ijcce.2025.08.001","url":null,"abstract":"<div><div>Breast cancer remains one of the leading causes of mortality among women, making early and accurate detection crucial for effective treatment. Despite the extensive use of deep learning models in mammographic image classification, existing approaches often lack interpretability. They are prone to diagnostic errors due to image heterogeneity, noise, and the limited availability of annotated datasets. This study addresses these challenges by proposing a novel hybrid model that integrates handcrafted texture and geometric features—such as entropy, eccentricity, mean intensity, and GLCM descriptors—directly into a modified Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture. The primary objective is to improve both diagnostic accuracy and transparency in mammogram classification. Experiments were conducted on the publicly available VinDr-Mammo dataset, which includes 2136 annotated DICOM images with BI-RADS labels. The hybrid model demonstrated superior performance, achieving a 30% reduction in Total Loss, higher sensitivity (0.96), specificity (0.97), and ROC-AUC (0.96), compared to the baseline model without additional features. The integration of clinically interpretable descriptors enhances not only detection accuracy but also the explainability of the results, offering valuable insights for radiologists. These findings contribute to the development of AI-assisted diagnostic tools that are both robust and transparent, particularly in low-resource clinical environments.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 12-25"},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of emotional tendencies and discourse patterns in VKontakte social comments based on Nvivo12 encoding","authors":"Jiaxing Han","doi":"10.1016/j.ijcce.2025.07.001","DOIUrl":"10.1016/j.ijcce.2025.07.001","url":null,"abstract":"<div><div>To study the emotional changes of the public during the COVID-19 epidemic, the experiment conducted an analysis of emotional tendencies and discourse patterns for comments on the VKontakte platform. The study first used Nvivo12 to classify comments on social platforms into topics, emotions, and relationship nodes. Then, a bidirectional long short-term memory network was introduced to comprehensively understand the context and classify positive and negative emotions. In addition, natural language processing toolkits were used to analyze the discourse structure of social comments, and support vector machines were used to discriminate the emotional tendencies of comments. According to the experimental analysis, during the period of rapid incidence rate increased, 27.6 % of the public exhibited positive emotional tendencies, while &lt;39.3 % exhibited negative emotional tendencies. In the following three stages, the proportion of negative emotions in the public was greater than that of positive emotions. In the fourth stage of the epidemic, comments mainly concerned the supply of medical drugs, masks, and the construction and opening of hospitals. The existing problems indicate that the epidemic has had a significant impact on public emotions, and effective measures need to be taken to alleviate the negative emotions of the public. The research results are helpful in revealing the dynamic changes of public emotions and their discourse patterns during the COVID-19 epidemic, and provide a new perspective for understanding public emotions.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 1-11"},"PeriodicalIF":0.0,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating environmental clustering to enhance epidemic forecasting with machine learning models","authors":"Yosra Didi ,&nbsp;Ahlam Walha ,&nbsp;Ali Wali","doi":"10.1016/j.ijcce.2025.06.001","DOIUrl":"10.1016/j.ijcce.2025.06.001","url":null,"abstract":"<div><div>The COVID-19 pandemic underscored the urgent need for more accurate and adaptive forecasting models to support public health decision-making and limit disease spread. However, many existing models overlook the influence of environmental and climatic factors that significantly affect transmission dynamics. This study addresses this gap with a novel forecasting framework that integrates environmental data into predictive modelling. Our key contributions are threefold: (1) we analyse the relationship between environmental variables (temperature, humidity, and air quality) and COVID-19 trends across countries; (2) we propose a two-stage approach combining K-means clustering to group countries based on environmental conditions, followed by region-specific machine learning models using Support Vector Regression (SVR), Prophet, and Long Short-Term Memory (LSTM) networks for both univariate and multivariate time series forecasting; and (3) we demonstrate that LSTM significantly outperforms other models, achieving superior accuracy for 30-day COVID-19 case predictions. Our results highlight the importance of incorporating environmental variables in epidemic modelling and offer a practical tool for more targeted and effective public health responses. This research provides actionable insights that can inform the design of climate-aware forecasting systems for future pandemic preparedness.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 628-642"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}