Forthcoming and Online First Articles

International Journal of System Control and Information Processing

International Journal of System Control and Information Processing (IJSCIP)

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International Journal of System Control and Information Processing (4 papers in press)

Regular Issues

  • A Prediction Model for Successful Vaginal Birth after Cesarean Delivery in Chinese mothers by using Machine Learning   Order a copy of this article
    by Lin Rao, Wei Gao, Kaixin Fu, Yingying Zhang, Yu Huang, Xuan Zhou, Hong Li 
    Abstract: Background:This study aimed to build a personalized prediction algorithm for successful vaginal birth after cesarean delivery in Chinese mothers . Methods: This study used data from electronic medical records of 406 admitted pregnant women between January 2010 and October 2020. Descriptive analyses, chi-square test, and multivariate logistic regression were undertaken. By using Spearman correlation coefficient, a prediction model for successful vaginal birth after cesarean delivery was derived. Results:The identified predictors included degree of cervical dilatation, fetal exposure, cervical canal regression, uterine orifice location, height, the thickness of lower uterine segment, gestational age, BMI before delivery, and estimated birth weight. The prediction model performed well with an area under the receiver operating characteristics curve of 0.954 (95% CI, 0.87-0.94) . Conclusion: The results show that the prediction model can better predict VBAC. The new prediction model may be used in clinical consultations to decide the preferred delivery mode.
    Keywords: Prediction Model,cesarean delivery; obstetrics; trial of labor; vaginal delivery.
    DOI: 10.1504/IJSCIP.2023.10059669
  • Automatic Handover Parameter Optimization for Ultra-Dense Small Cell Networks   Order a copy of this article
    by Qianyu LIU, Lianwei Ma, Chiew Foong Kwong, Feifan Shen, Chenhao Shi 
    Abstract: In recent years, the exponential growth in global mobile traffic, driven by advanced applications, services, and devices, has led to the deployment of numerous small cell base stations to meet future network needs. Ensuring stable connectivity is crucial for seamless mobile user experiences. This paper introduces an adaptive fuzzy logic-based handover parameter adjustment approach aimed at enhancing UE mobility robustness by reducing handover failure (HOF) and ping-pong handover (PPHO) occurrences, all while maintaining high throughput. Simulation results demonstrate the methods effectiveness in adjusting handover parameters based on system performance indicators across different speed ranges. Compared to traditional fuzzy logic, clustering-based fuzzy logic, and A3 event-based optimisation methods, our proposed approach consistently outperforms in terms of reducing HOPP and HOF while sustaining high throughput levels.
    Keywords: fuzzy logic; handover; neural networks; ultra-dense networks.
    DOI: 10.1504/IJSCIP.2023.10059740
  • Optimization of Task Offloading Scheduling Strategy for Vehicular Edge Computing Network   Order a copy of this article
    by Tianqi Gao, Hongfeng Tao, Yuanzhi Ni 
    Abstract: In the last decade, the service of intelligent transportation system has benefited from the rapid development of advanced computing and communication technologies. However, it is difficult to satisfy the increasing user demand and strict service requirements with the current local or cloud computing paradigm only. In this paper, we propose an operator-based edge service network composed of multi-layer RSUs for various scenarios. An optimized task offloading scheduling strategy considering the service demand, computing delay and energy consumption, is designed for both stochastic and concurrent tasks. Furthermore, a Genetic Algorithm (GA) is proposed to solve the bandwidth allocation problem after the computation execution. Finally, the simulation results verify the service efficiency and operation effectiveness of the proposed strategy in terms of the task execution delay, operation cost and attainment rate.
    Keywords: edge computing; vehicular network; task offloading.
    DOI: 10.1504/IJSCIP.2023.10059829
  • Prescribed-time bipartite synchronization of switched coupled neural networks via switching controllers   Order a copy of this article
    by Meng Tao, Xiaoyang Liu 
    Abstract: This paper is concerned with the prescribed-time bipartite synchronization of switched coupled neural networks in signed graphs. A novel switching control method is proposed to force the coupled system into a prespecified attraction domain within a predefined time. Then, a twist controller is designed to further drive the system to the origin in another prescribed time. The dwell time of the prescribed-time control is allowed to be flexibly set based on specific task requirements, which adaptability enhances the applicability of the approach across various scenarios. The switched network topology encompasses both competitive and cooperative relationships. Finally, a simulation example is constructed to justify the theoretical results.
    Keywords: bipartite synchronization; coupled neural networks; prescribed-time control; switching controllers; signed graphs.
    DOI: 10.1504/IJSCIP.2023.10060145