Transactions on Emerging Telecommunications Technologies最新文献

{"title":"An Automated Object Detection and Tracking System in Surveillance Aerial Images Using 3D Adaptive Multi-Dilated Yolov8 With Attention Mechanism","authors":"A. R. Shravya,&nbsp;S. Srividhya","doi":"10.1002/ett.70392","DOIUrl":"10.1002/ett.70392","url":null,"abstract":"<div>\u0000 \u0000 <p>Nowadays, the need of detecting targets and performing tracking in aerial images using drones along with power sensor devices has increased. Both drone technology and object detection models provide a major contribution to agricultural irrigation, battlefield reconnaissance, traffic management, and forest patrolling. These kinds of tasks are accomplished with superior accuracy by drone technology. But, the prior techniques faces several challenging problem due to wider image perspective, and enormous tiny objects in the image. Various prior deep learning mechanisms show poor resolution in handling the tiny samples. These kinds of outcomes contribute to poor localization accuracy in the network architecture and also the image fractions are settled with the objects. In order to mitigate these issues, the research leverages a new deep learning-based object detection and tracking approach using surveillance aerial images. Initially, the required surveillance aerial images are gathered by benchmark resources. Further, the garnered images are transferred into the 3D Adaptive Multi-Dilated Yolov8 with Attention Mechanism (3DAMDY-AM) for detecting and tracking the objects in surveillance aerial images. The proposed Improved Lyrebird Optimization Algorithm (ILOA) is employed for optimizing the parameters. At last, the numerical experiments are conducted for the developed model, contrasted with the existing related techniques. The accuracy outcomes of the recommended technique show 95.14%. To ensure better statistical analysis, the best measure of the developed model shows 1.74%, 1.44%, 4.58%, and 1.73% better performance than MTBO-3DAMDY-AM, OOA-3DAMDY-AM, POA-3DAMDY-AM, and LOA-3DAMDY-AM. The mean Average Precision (mAP) measure of the developed approach shows 74.19%, 36.11%, 77.35%, and 80.92% better performance than CNN, N-FINDR, Yolov8, and 3DAMDY-AM, respectively, at the batch size of 8. The significant advancement in the recommended technique guarantees to enhance the system's efficacy and reliability. In summary, the developed model shows a promising outcome, but the work needs to expand on validating real-time data. Moreover, it needs to validate dynamic scalability conditions with diverse surveillance applications in overcrowded scenarios. Further, the multi-data source is also required to improve the model performance on diverse factors like scene times, weather conditions, and locations, respectively.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683973","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":"Cloud Computing With Image Processing Framework for Lung Cancer Diagnosis Using Improved Heuristic-Aided Hybrid Residual Attention Network","authors":"Jayapal Medida,&nbsp;A. B. Pradeep Kumar,&nbsp;Santoshachandra Rao Karanam,&nbsp;Srinivasa Rao Thamanam","doi":"10.1002/ett.70395","DOIUrl":"https://doi.org/10.1002/ett.70395","url":null,"abstract":"<div>\u0000 \u0000 <p>Lung cancer is a primary origin of cancer-based mortality. In the early phases, the recognition of pulmonary cancer can greatly enhance the rate of survival. The manual delineation of lung nodules by medical experts is a tiresome operation. In previous years, the high growth of cloud computing has revolutionized the medical sector to another level. Since lung cancer's causes stay unclear, the prevention process becomes relatively impossible, hence the lung tumor's timely identification is the primary and only way to treat lung cancer. Nowadays, in developed countries, this situation is highly enhanced by employing conventional data sources about lung cancer. Nevertheless, because of the poor data collection approach's synchronization, the gathered data is heterogeneous, and immediately cannot be employed. Therefore, in this developed framework, the cloud-based paradigm for detecting lung cancer disorder utilizing the adaptive deep learning technique is introduced. First, the structural representation of cloud computing is explained and used in the medical application. Further, the necessary images are gathered from the standard data sources. In order to diagnose lung cancer, the novel model is designed and named as Adaptive Hybrid Residual Attention Network (AHRANet), in which the “Convolutional Neural Network” (CNN) is associated with the Densenet. For improving the efficiency, the Random-integer Revamped Hippopotamus Optimization (RRHO) is recommended for optimizing the parameters present in the model. Finally, the efficacy of the system is validated with multiple metrics. In contrast with other techniques, the designed system provides promising results to evince a superior diagnosis of lung cancer ailments.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568180","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 Reinforcement Learning–Driven Payoff-Adaptive Game-Theoretic Framework for Secure and Reliable Operation of Mobile Ad Hoc Networks","authors":"Shaik Khader Zelani,&nbsp;K. V. S. S. Ramakrishna,&nbsp;Fatima Asiri,&nbsp;Ahmed Alyahya,&nbsp;Shakila Basheer","doi":"10.1002/ett.70394","DOIUrl":"https://doi.org/10.1002/ett.70394","url":null,"abstract":"<div>\u0000 \u0000 <p>Mobile ad hoc networks (MANETs) are inherently vulnerable to selfish forwarding, collusion, stealthy oscillations, and resource-draining attacks. Due to their decentralized, dynamic, and adversarial nature, existing anomaly-based and deep-learning intrusion detection systems rely on static training distributions and fixed payoff structures. This limits their adaptability under dynamic network conditions. This paper presents a secure and reliable operation of MANETs under adversarial environments. The model integrates a reinforcement learning–driven (RL-driven), payoff-adaptive, three-action game-theoretic (GT) framework. This combination helps learning both the utility structure and the optimal defense strategies for MANET nodes. The model employs online payoff learning, Boltzmann-guided Q-learning, and feature-aware utility estimation to dynamically update security, throughput, energy, and latency trade-offs. Experiments on a large-scale NS-3 mobility dataset (<span></span><math></math> trace snapshots) over 80-node MANETs, 500 rounds, and 10 random seeds demonstrate a strong stability and resilience. The system achieves near-optimal reliability (packet delivery ratio, <span></span><math></math>, low latency <span></span><math></math>, efficient energy usage controlled energy consumption <span></span><math></math> and high average utility <span></span><math></math>. Under seven adversarial threats, the framework consistently restores optimal operation. misreporting impact is reduced, with utility improving from <span></span><math></math>, collusion inflation suppression remains <span></span><math></math>, and regret normalization goes from <span></span><math></math> to <span></span><math></math>. Overall, the proposed GT–RL model provides adaptive equilibrium formation, multi-metric flexibility, and theoretical convergence guarantees for secure MANET operation.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568181","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":"Blockchain-Powered IoT Healthcare Framework With Dandelion Depthwise Separable Convolutional Neural Network for Enhanced Security","authors":"Harish Kumar Taluja,&nbsp;Anuradha Taluja,&nbsp;D. Bhuvana Suganthi,&nbsp;L. Guganathan","doi":"10.1002/ett.70396","DOIUrl":"https://doi.org/10.1002/ett.70396","url":null,"abstract":"<div>\u0000 \u0000 <p>Technological trends are evolving fairly quickly within healthcare systems with blockchain and Deep Learning Hybrid technologies seen as at the pinnacle of making patient care, scalability and security better. Indeed, challenges such as Interoperability, privacy, and data integrity are some of the challenges that normal healthcare systems face, especially in the IoT paradigm. Worse still, secure and decentralized management of data is what is needed. Using a combination of deep learning architectures for IoT security and scalability, as well as data protection through blockchain technology, this study aims at designing a new secure healthcare system that can be implemented using the IoT network. In the present work, a Triple Attention Depthwise Separable Convolutional Neural Network (TADSCNN) is designed with the help of Dandelion Optimizer (DO) incorporated with blockchain. Thus, the proposed system achieved F1-score of 0.993, accuracy of 0.999, precision of 0.992, and recall of 0.994, which means that the system's performance is quite high. Furthermore, it supports 220 kbps of throughput and 85 s latency, and 2.9 s processing time for the protocol while providing data secrecy of 99.12%. This framework offers a viable, scalable, and secure means of addressing Internet of Things health care system.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568192","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":"Spatiotemporal Graph Neural Network-Driven Anomaly Detection for Cooperative Vehicle Messaging in Dense VANET Corridors","authors":"Ahmed Zohair Ibrahim","doi":"10.1002/ett.70405","DOIUrl":"https://doi.org/10.1002/ett.70405","url":null,"abstract":"<div>\u0000 \u0000 <p>Cooperative messaging in vehicular ad-hoc networks (VANETs) enables safety-critical functions such as collision avoidance, platooning, and traffic optimization. However, in dense urban traffic environments, rapidly changing topology and high-volume broadcast messaging introduce vulnerabilities to spoofing, flooding, and falsified data injection. Traditional anomaly detection approaches, including basic machine learning or static rule-driven models, are insufficient to capture the evolving spatial–temporal dependencies and message correlations inherent to mobile vehicular ecosystems. To overcome these limitations, this paper introduces graph neural network–enabled adaptive resilient intelligence for spatiotemporal event detection (GNN-ARISE). A novel anomaly detection architecture integrating GNN-based reasoning with traffic-aware message intelligence. GNN-ARISE comprises: (1) a Dynamic GNN Graph Constructor, continuously reshaping communication graphs using mobility signatures and message trust indices. (2) a Temporal Evolution Encoder, leveraging gated recurrent attention to model message propagation patterns over time. (3) a Resilient Anomaly Classifier, fusing graph embeddings and vehicle trust scoring for robust detection under real-time constraints. Evaluation using dense VANET simulation datasets demonstrates that GNN-ARISE improves detection accuracy by 21.2%, reduces false-positive rates by 23.5%, and maintains processing latencies under 22 ms, outperforming baseline GNN and spatiotemporal learning models. The results highlight the value of integrating GNN-based adaptive reasoning for securing next-generation intelligent transportation communication infrastructures. The proposed method achieves the anomaly detection accuracy (86%–96%), FPR (6%–11%), detection latency of 45% and 125 ms, robustness under high node (82%–90%), attack type generalization capability (83%–87%).</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568182","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 Trust-Centric Federated Edge Learning Paradigm in Healthcare for Decentralized Threat Intelligence Sharing","authors":"Syed Thouheed Ahmed,&nbsp;Afifa Salsabil Fathima,&nbsp;Marwah A. Halwani,&nbsp;Ahlam Almusharraf,&nbsp;Abdullah Albuali","doi":"10.1002/ett.70401","DOIUrl":"https://doi.org/10.1002/ett.70401","url":null,"abstract":"<div>\u0000 \u0000 <p>Growing adoption of consumer applications powered by edge intelligence and federated learning (FL) enables real-time personalization and rapid cyber response. The conventional FL remains vulnerable to adversarial attacks and lacks secure mechanism for collaborative intelligence sharing. To address this, the blockchain-enhanced FL framework that integrates decentralized model training with blockchain based threat intelligence exchange is proposed. Edge (IoT) devices collaboratively train lightweight FL models for localized detection, while blockchain ensure secure validation and dissemination on updates. A dynamic trust scoring system identifies and penalizes the malicious participants, thereby improving model reliability and resilience. Experimental results demonstrate that the proposed system has secured an accuracy of 92.8%, reduces communication overhead by 35% and maintain stable convergence across heterogeneous edge-IoT environments.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567528","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":"Federated Security Framework: A Heterogeneous Federated Learning Architecture for Privacy-Preserving Intrusion Detection in IoT Networks","authors":"Umar Islam,&nbsp;Fakhrud Din,&nbsp;Anwar Ul Haq,&nbsp;Inayat Ali","doi":"10.1002/ett.70402","DOIUrl":"https://doi.org/10.1002/ett.70402","url":null,"abstract":"<div>\u0000 \u0000 <p>Internet of things (IoT) applications are growing at an accelerating pace in the various infrastructures of critical systems, with cyber threats growing as well, making intrusion detection systems (IDS) significant. Some reflections of the traditional centralized IDS systems include privacy of data, lag time and low scalability of distributed networks. In this paper, a new framework federated security framework (FSF), a fresh model of federated learning, is introduced, which is an intrusion detector that integrates local ensemble learning and global feature extraction to improve the security of the network. The FSF uses the models of the random forest and XGBoost to issue local decisions concerning the IoT devices, whereas global 1D-CNN carries out the extracting of the features centrally without jeopardizing the privacy of the information. Much of the literature on studying three benchmark datasets underpins the fact that XGBoost has an immensely high performance with an accuracy of 98.09% on NSL-KDD, 97.94% on TON-IoT, and 96.15% on CICEVSE2024. The architecture saved the communication overhead that was 67.3% of centralized methods and enhanced the computing performance by 89.2%. The performance of model distillation methods proved the accuracy of 95.65% and complexity reduction of 78%; this means that the paradigm has potential applications in supporting resource-limited IoT applications. It is a federated solution which offers privative-aware intrusion diagnosis solutions which are required in the contemporary distributed network infrastructure.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566393","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":"Spatial–Temporal Graph Learning for Taxi Origin–Destination Demand Prediction","authors":"Mingxia Huang,&nbsp;Bingyan Zheng,&nbsp;Dan Peng","doi":"10.1002/ett.70393","DOIUrl":"https://doi.org/10.1002/ett.70393","url":null,"abstract":"<p>Accurate prediction of taxi origin–destination demand contributes to optimizing operational management, relieving traffic congestion, and improving the transportation capacity of urban road networks. To address the challenges of origin–destination semantic differentiation and data sparsity in taxi origin–destination demand prediction, we propose a multilevel continuous - time dynamic node - based attention network model (MCNAT). In the spatial–temporal graph construction unit, this model utilizes a dual-strategy meta-path random walk to achieve semantic differentiation and an attenuation function to update node representation. In the spatial–temporal correlation unit, this model employs multilevel spatiotemporal perception layers based on multi-head attention mechanisms to aggregate local spaces and share parameter space. To mitigate the effects of data sparsity, L-sparse is introduced in the prediction states to make the model more focused on edges with lower travel demand. We conducted simulations using two real datasets and compared MCNAT with other baselines. The results show that MCNAT outperforms the base model in all metrics, with MAE and RMSE improved by 11.11% and 11.87% compared to the optimal model, and the <i>F</i>1 score metrics are above 0.69, which is improved by 11.1%.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566037","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":"AI Driven VANET Assisted Prediction and Anomaly Detection in Active Suspension Systems: Modeling and Performance Analysis","authors":"Fengkai Guo,&nbsp;Caiyun Duan","doi":"10.1002/ett.70385","DOIUrl":"https://doi.org/10.1002/ett.70385","url":null,"abstract":"<div>\u0000 \u0000 <p>Active suspension systems provide ride comfort and vehicle stability under dynamically altering road conditions, but onboard-only control designs are sensitive to mechanical defects, anomalous vibrations, and external disturbances. This paper suggests an AI-driven, VANET-assisted architecture for real-time active suspension system prediction and anomaly detection. It uses deep learning–based anomaly detection and machine learning prediction models to monitor suspension dynamics and detect early departures from normal operational behavior. VANET-enabled cooperative vehicle-to-vehicle and vehicle-to-infrastructure communication improves situational awareness by exchanging suspension and road-state information. A dynamic simulation environment simulates road disruptions and communication situations during framework evaluation. Performance results show 95% anomaly detection accuracy, 26% false-positive/false-negative rate, 92% communication reliability, 0.2 m/s² RMS body acceleration for ride comfort, and 1 s suspension settling time, surpassing onboard-only systems. We found that cooperative AI-VANET integration enables predictive maintenance and intelligent suspension control for next-generation connected and autonomous automobiles. Detection accuracy, false-positive/false-negative rates, communication reliability, RMS body acceleration, and settling time are computed by comparing predicted suspension states and detected anomalies against ground-truth fault injections within the simulation. This comparative setup ensures that the reported improvements arise from the integration of AI-driven anomaly prediction and cooperative VANET communication, with all results reflecting repeatable simulation outcomes under consistent baseline conditions.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566192","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":"Lightweight Multi-Hop Clustering With Cross-Layer Intelligence for VANET Malicious Behavior Detection","authors":"Hana Almagrabi,&nbsp;Hend Khalid Alkahtani,&nbsp;Lyan Alwakeel,&nbsp;Mohammed Maray,&nbsp;Haidar Almubarak,&nbsp;Nadiyah Almutairi,&nbsp;Haitham Assiri,&nbsp;Shouki A. Ebad","doi":"10.1002/ett.70397","DOIUrl":"https://doi.org/10.1002/ett.70397","url":null,"abstract":"<div>\u0000 \u0000 <p>Future vehicular ad hoc networks (VANETs) require fast, secure communication to support road-safety applications. However, high vehicle congestion, sudden link failures, and malicious behaviors make it difficult to maintain stable communication. To accurately detect and mitigate such attacks and failures, several security and coordination strategies are established. Specifically, clustering groups of vehicles enhances coordination and attack detection. However, existing clustering models often fail to deliver the expected results in behavioral analysis due to sudden cluster breakdowns, high communication overhead, and poor coordination. To address these challenges, we propose a Lightweight Multi-Hop Clustering framework with Cross-Layer Intelligence (LMHC-CI) for real-time malicious detection in VANETs. The study aims to enhance cluster stability and detection accuracy while maintaining low computational and communication costs. The suggested research also leverages cross-layer intelligence to enable efficient clustering and detection decisions. The model is simulated using two publicly available datasets: The Vehicular Clustering Dataset and the VANET Malicious Node Dataset. Simulation results indicate that the proposed model achieves 99.6% detection accuracy. The adaptive H-node remains within 0.15–0.35 and reduces unnecessary role changes after convergence. Also, multi-hop aggregation reduces EPC packet overhead as hop count increases. This will reduce cluster reformation and routing complexity compared to existing methods.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566466","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}