Intelligent Systems with Applications最新文献

{"title":"Federated learning using quality-based aggregation method for brain tumour segmentation on multimodality medical images","authors":"Rim El Badaoui ,&nbsp;Ester Bonmati ,&nbsp;Vasileios Argyriou ,&nbsp;Barbara Villarini","doi":"10.1016/j.iswa.2025.200601","DOIUrl":"10.1016/j.iswa.2025.200601","url":null,"abstract":"<div><div>Deep learning for medical imaging has shown great potential in improving patient outcomes due to its high accuracy in disease diagnosis. However, a major challenge preventing the widespread adoption of such models in clinical settings is data accessibility, which conflicts with the General Data Protection Regulation (GDPR) in a traditional centralised training environment. Hence, to address this issue, Federated Learning (FL) was introduced as a decentralised alternative that enables collaborative model training among data owners without sharing any private data. Despite its significance in healthcare, limited research has explored FL for medical imaging, particularly in multimodal brain tumour segmentation, due to challenges such as data heterogeneity.</div><div>In this study, we present Federated E-CATBraTS, an advanced federated deep learning model derived from the existing E-CATBraTS framework. This model is designed to segment brain tumours from multimodal magnetic resonance imaging (MRI) while preserving data privacy. Our framework introduces a novel aggregation method, DaQAvg, which optimally combines model weights based on data size and quality, demonstrating resilience against corrupted medical images.</div><div>We evaluated the performance of Federated E-CATBraTS using two publicly available datasets: UPenn-GBM and UCSF-PDGM, including a degraded version of the latter to assess the efficacy of our aggregation method. The results indicate a 6% overall improvement over traditional centralised approaches. Furthermore, we conducted a comprehensive comparison against state-of-the-art FL aggregation algorithms, including FedAVG, FedProx and FedNova. While FedNova demonstrated the highest overall DSC, DaQAvg demonstrated superior robustness to noisy conditions, showcasing its specific advantage in maintaining performance with variable data quality, a critical aspect in medical imaging.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200601"},"PeriodicalIF":4.3,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520079","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":"Beyond algorithms: Artificial intelligence driven talent identification with human insight","authors":"Tiago Jacob Fernandes França ,&nbsp;José Henrique Pereira São Mamede ,&nbsp;João Manuel Pereira Barroso ,&nbsp;Vítor Manuel Pereira Duarte dos Santos","doi":"10.1016/j.iswa.2025.200604","DOIUrl":"10.1016/j.iswa.2025.200604","url":null,"abstract":"<div><div>The rapid evolution of Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), with growing interest in its role in talent identification. While AI has demonstrated effectiveness in analysing structured data, its limitations in assessing qualitative attributes such as creativity, adaptability, and emotional intelligence remain underexplored. This study addresses these gaps through an exploratory mixed-methods design, combining a global survey (<em>n</em> = 240) with semi-structured interviews of HR professionals. Quantitative analysis highlights patterns of association between key competencies, while qualitative findings provide contextual insights into perceptions of fairness, bias, and cultural resistance. The results suggest that AI can complement, but not replace, human judgement, supporting a Hybrid Evaluative Model that integrates algorithmic efficiency with human interpretation. The study contributes rare empirical evidence to a nascent field, highlights the ethical imperatives of bias mitigation and transparency, and underscores the importance of cultural context (collectivist versus individualist orientations) in shaping the acceptance and effectiveness of AI-enabled HR practices. These findings offer practical guidance for organisations and advance theory-building at the intersection of AI and HRM.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200604"},"PeriodicalIF":4.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571742","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":"Alert correlation for intelligent threat detection and response","authors":"Bronagh Lanigan ,&nbsp;Zeinab Rezaeifar ,&nbsp;Federico Cruciani ,&nbsp;Michael Milliken ,&nbsp;Jordan Vincent ,&nbsp;Samuel Moore ,&nbsp;Muhammad Aaqib ,&nbsp;Alan Mills ,&nbsp;Pushpinder K. Chouhan ,&nbsp;Alfie Beard ,&nbsp;Chris D. Nugent ,&nbsp;Luke Chen ,&nbsp;Alex Healing","doi":"10.1016/j.iswa.2025.200606","DOIUrl":"10.1016/j.iswa.2025.200606","url":null,"abstract":"<div><div>With the increasing diversity of IoT devices, keeping IT systems secure is becoming increasingly difficult. Attackers exploit vulnerabilities within the system in order to access sensitive information, typically reaching their objective through several steps. Current Intrusion Detection Systems (IDSs) focus on low-level alerts, and tend to produce a high rate of false positives. This type of information alone is insufficient for the detection of sophisticated attack scenarios such Advanced Persistent Threats (APTs). Consequently, correlation techniques have recently been introduced to correlate alerts and reconstruct attack scenarios, however, various attack scenarios exist, with diverse characteristics. Also, different steps of the APTs scenarios may have their own characteristics. Therefore, finding a proper method that covers all cases remains a challenge. Moreover, after detecting APTs, how the system should respond to these attacks to avoid sabotage to the system remains a challenge. Thus, in this paper, first for detection of the attacks, we classify different cases, and then, a method based on different characteristics of attack patterns is proposed to detect APT scenarios. The proposed method consists of two main phases: APT detection and the intelligent hybrid response framework. In APT detection phase, similar alerts are aggregated and attack graphs are generated based on a similarity matrix. These graphs, combined with third party API data enable alert correlation and APT scenario detection. Entity graphs are then created to visualise host behaviour, and alert graphs are analysed to detect APT scenarios. In the response phase, attack graphs produced from the correlation inform the hybrid response framework, integrating knowledge and data-driven components that facilitate automated or recommended mitigation. The approach was evaluated on the ZeekData24 dataset. Obtained precision and recall on the malicious traffic was observed to be 96.65% and 87.04% respectively. The results show that our approach can effectively filter false positive alerts with a reduction of the data going from 10,063 alerts daily to 586 meta-alerts, pruned to 48 attack graphs and finally reduced to 20 suspicious attack graphs.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200606"},"PeriodicalIF":4.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520065","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":"Sentiment analysis: From rule-based lexicons to large language models","authors":"Maikel Leon","doi":"10.1016/j.iswa.2025.200599","DOIUrl":"10.1016/j.iswa.2025.200599","url":null,"abstract":"<div><div>This study provides a comprehensive review of two decades of research in opinion mining and sentiment analysis, addressing the fragmentation of prior work across methodologies, application domains, and data sources. The evolution of the field is traced from pre-1990 rule-based systems to lexicon heuristics, statistical learning, machine learning, deep learning, and the current wave of transformer-driven, multimodal, and generative models. Applications are examined across marketing, finance, politics, and social media, with emphasis on how methodological innovations have improved accuracy and enabled broader adoption. Best practices – including transformer fine-tuning, prompt engineering, zero-shot and few-shot learning, multimodal fusion, and domain adaptation – are analyzed to distill evidence-based guidelines for researchers and practitioners. The synthesis shows how sentiment analysis has shaped critical areas, including brand management, investor decision-making, political discourse, and online user engagement. Findings highlight the effectiveness of transformer-based approaches, particularly when combined with domain adaptation and prompt engineering, in delivering state-of-the-art performance. Beyond methodological and applied insights, the study identifies promising directions for future research, including real-time customer journey analytics, explainability in generative AI, robustness across multiple languages, ethical implications, and sustainability considerations. By consolidating dispersed knowledge into a unified account, this review provides both historical grounding and a structured roadmap that advances theoretical understanding and informs managerial practice.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200599"},"PeriodicalIF":4.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465827","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":"Comprehensive analysis on laser spots adversarial attacks using genetic algorithm","authors":"Youssef Mansour ,&nbsp;Ayad Turky ,&nbsp;Ibrahim Abaker Hashem ,&nbsp;Imad Afyouni ,&nbsp;Ali Bou Nassif ,&nbsp;Ismail Shahin ,&nbsp;Ashraf Elnagar","doi":"10.1016/j.iswa.2025.200598","DOIUrl":"10.1016/j.iswa.2025.200598","url":null,"abstract":"<div><div>Deep Neural Networks (DNNs) are highly vulnerable to disruptions caused by minimal noise, yet research on physical attacks leveraging light-based methods remains scarce. Light-based physical attacks are exceptionally stealthy, posing substantial security threats to vision-dependent applications such as autonomous driving. This paper enhances a state-of-the-art light-based physical attack that employs a genetic algorithm to optimize laser spot placement for maximum effectiveness. We expand the algorithm by introducing additional hyperparameters and systematically optimizing them to establish the most efficient workflow for this problem. To our knowledge, this is the first light-based attack capable of reliably performing physical attacks during daylight conditions, making it the most effective and robust approach of its kind. Extensive experiments conducted in a digital environment demonstrate the superiority of the genetic algorithm over random-location methods. By identifying optimal hyperparameter values, we achieve significant improvements in both performance and efficiency. Specifically, we managed to achieve an Attack Success Rate (ASR) of 89.7%, with an Average Query (AQ) of only 109.4, demonstrating a highly efficient and effective approach. The results reveal that laser spots can severely interfere with advanced DNNs, highlighting the critical security risks associated with this technique.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200598"},"PeriodicalIF":4.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465829","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":"CTR-Net: Scalable safe reinforcement learning via neural approximations of control theoretic regulators","authors":"Ramen Ghosh","doi":"10.1016/j.iswa.2025.200597","DOIUrl":"10.1016/j.iswa.2025.200597","url":null,"abstract":"<div><div>Ensuring hard constraint satisfaction during both training and deployment is central to safety-critical reinforcement learning (RL). Control-theoretic regularization (CTR) enforces safety by filtering actions through viability- or barrier-certified safe sets, but evaluating the state-dependent regulator <span><math><mrow><mi>R</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> online is often prohibitive in high dimensions. We propose a scalable CTR framework based on <em>neural regulator approximators</em> <span><math><mrow><msub><mrow><mover><mrow><mi>R</mi></mrow><mrow><mo>ˆ</mo></mrow></mover></mrow><mrow><mi>θ</mi></mrow></msub><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span>—differentiable surrogates of <span><math><mrow><mi>R</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> that enable fast projection or rejection-sampling filters within standard RL loops. We formalize a learning-theoretic analysis for approximate safety filtering and prove probably approximately correct (PAC)-style guarantees: if the set approximation error is bounded by <span><math><mi>ɛ</mi></math></span> with confidence <span><math><mrow><mn>1</mn><mo>−</mo><mi>δ</mi></mrow></math></span>, then the probability of constraint violation along a length–<span><math><mi>T</mi></math></span> rollout is bounded by a term that scales linearly in <span><math><mi>T</mi></math></span> and <span><math><mi>ɛ</mi></math></span> (plus <span><math><mi>δ</mi></math></span>). We further show that the performance suboptimality of the filtered policy is controlled analytically by the same approximation envelope, yielding an explicit, provably quantified safety-versus-optimality tradeoff (PAC bounds linear in <span><math><mi>T</mi></math></span> and the envelope), complemented by empirical ablations; see also the calculus-of-variations view of constrained tradeoffs (Younis, 2023). The resulting method, <strong>CTR-Net</strong>, is architecture-agnostic and supports real-time execution via fast, differentiable safety layers. Empirical evaluations on high-dimensional continuous-control benchmarks — including safe locomotion and constrained multi-joint manipulation — demonstrate reliable constraint satisfaction during learning and deployment, robustness under modeling uncertainty and substantial computational gains relative to exact viability/barrier baselines. By coupling operator-free neural safety sets with CTR guarantees, CTR-Net bridges theoretical safety certificates and scalable implementation, advancing practical, real-time safe RL for complex intelligent systems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200597"},"PeriodicalIF":4.3,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416658","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":"Indirect visual odometry with a light-field camera","authors":"Mohamad Al Assaad,&nbsp;Stéphane Bazeille,&nbsp;Christophe Cudel","doi":"10.1016/j.iswa.2025.200600","DOIUrl":"10.1016/j.iswa.2025.200600","url":null,"abstract":"<div><div>Visual odometry is the technique of determining a robot’s pose by analyzing images of its surroundings as it moves. Visual odometry can be categorized into monocular when using a single camera, or stereo when using two cameras or more. In this study, we investigate the use of light-field camera for visual odometry. Capitalizing on the distinctive capability of a light-field camera to record both the intensity and the direction of light, we propose an indirect visual odometry method able to estimate the scale of the translation similarly to stereo visual odometry, but using a single camera sensor. Our visual odometry framework combines light-field imaging with conventional odometry techniques to track the camera movements, using the depth insights provided by a light-field depth estimation approach. Additionally, this method differs from state-of-the-art methods by using a simplified calibration process and a new keypoints extraction method, which makes the use of the light-field cameras easier for robotics perception.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200600"},"PeriodicalIF":4.3,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465828","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":"End-to-end semantically aware tactile generation","authors":"Mohammad Mahdi Heydari Dastjerdi,&nbsp;Abbas Akkasi,&nbsp;Hilaire Djani,&nbsp;Aatreyi Pranavbhai Mehta,&nbsp;Majid Komeili","doi":"10.1016/j.iswa.2025.200594","DOIUrl":"10.1016/j.iswa.2025.200594","url":null,"abstract":"<div><div>Tactile graphics are an essential tool for conveying visual information to visually impaired individuals. However, translating 2D plots, such as B’ezier curves, polygons, and bar charts, into an effective tactile format remains a challenge. This paper presents a novel, two-stage deep learning pipeline for automating this conversion process. Our method leverages a Pix2Pix architecture, employing a U-Net++ generator network for robust image generation. To improve the perceptual quality of the tactile representations, we incorporate an adversarial perceptual loss function alongside a gradient penalty. The pipeline operates in a sequential manner: firstly, converting the source plot into a grayscale tactile representation, followed by a transformation into a channel-wise equivalent. We evaluate the performance of our model on a comprehensive synthetic dataset consisting of 20,000 source-target pairs encompassing various 2D plot types. To quantify performance, we utilize fuzzy versions of established metrics like pixel accuracy, Dice coefficient, and Jaccard index. Additionally, a human study is conducted to assess the visual quality of the generated tactile graphics. The proposed approach demonstrates promising results, significantly streamlining the conversion of 2D plots into tactile graphics. This paves the way for the development of fully automated systems, enhancing accessibility of visual information for visually impaired individuals.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200594"},"PeriodicalIF":4.3,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416791","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":"Explainable Artificial Intelligence: A systematic Review of Progress and Challenges","authors":"Azza Mohamed ,&nbsp;Khaled Abdelqader ,&nbsp;Khaled Shaalan","doi":"10.1016/j.iswa.2025.200595","DOIUrl":"10.1016/j.iswa.2025.200595","url":null,"abstract":"<div><div>This work employs a multidisciplinary approach to identify research gaps in the existing literature by presenting a systematic review of systematic reviews on Explainable Artificial Intelligence (XAI). To the best of our knowledge, this is the first thorough meta-review that combines the findings of several excellent reviews to offer a more elevated viewpoint on the goals and difficulties facing the area. The review covers empirical studies published between 2021 and 2023, focusing on high-quality sources. An initial pool of 997 entries was screened across multiple databases, yielding 928 unique articles after duplicate removal. Ultimately, 14 studies met the inclusion criteria and were analyzed in depth. The quality assessment confirmed that all selected reviews adhered to established methodological standards. The key findings show XAI's broad uses, which range from increasing trust and transparency to assisting with financial and management decision-making. The prevalence of healthcare-focused studies emphasizes XAI's importance in enhancing interpretability, fairness, regulatory compliance, and personalized treatment options. Commonly used techniques include visual explanation tools, interpretable machine learning models, and model-agnostic approaches. While the review offers valuable insights, it acknowledges limitations such as its reliance on Q1 journals and the exclusion of broader sources, which may affect comprehensiveness. To advance the field, the study recommends expanding future research to underrepresented domains like autonomous vehicles, defense, and smart cities. It also calls for methodological innovation to enhance accessibility, fairness, privacy, and the development of intuitive explanation strategies. Addressing these gaps can significantly improve the trustworthiness and effectiveness of AI systems across sectors.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200595"},"PeriodicalIF":4.3,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465826","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":"Fusing explainable deep learning ensembles and LLM recommendations for real-time plant leaf disease diagnosis","authors":"Dip Kumar Saha ,&nbsp;Mohammad Rasel Ahmed ,&nbsp;Tushar Deb Nath ,&nbsp;Rounakul Islam Boby ,&nbsp;Md. Jakir Hossen ,&nbsp;M.F. Mridha","doi":"10.1016/j.iswa.2025.200596","DOIUrl":"10.1016/j.iswa.2025.200596","url":null,"abstract":"<div><div>Timely and accurate identification of plant leaf diseases plays a vital role in ensuring sustainable agriculture and universal food security. Accurate identification of plant leaf diseases ensures healthier plant cultivation, which is pivotal for sustainable agriculture operations. In this study, we present a plant leaf disease recognition mechanism that utilizes a stacking ensemble structure combined with a Large Language Model (LLM) and Explainable AI (XAI) mechanism to improve identification accuracy and comprehensibility. To capture high textural structure, we utilized the Gray Level Co-occurrence Matrix (GLCM), whereas the MobileNetV3 architecture was utilized to maintain low computational cost in feature extraction. GoogleNet was integrated to improve multi-scale feature extraction by employing inception blocks, which effectively obtain fine-grained details and universal spatial patterns. Our ensemble framework integrates improved versions of MobileNetV3, GoogleNet, and ConvNeXtSmall with CatBoost employed as a nonlinear meta-learner allowing the framework to effectively capture complex connections among the base models within the ensemble framework. Moreover, we utilized additional CNN models, including AlexNet and EfficientNetV2B0, to compare the result of our proposed stacking ensemble model and to evaluate its generalization ability over various architectural designs. In addition, we developed a real-time system integrating an LLM with the proposed ensemble model, ensuring automatic plant leaf disease recognition and delivering corresponding curing recommendations. Our findings contribute to plant-based agriculture by enabling early diagnosis of leaf diseases and providing real-time recommendations through DL and LLM technology.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200596"},"PeriodicalIF":4.3,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416657","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}