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  1. 1141

    Urban intersection traffic flow prediction: A physics-guided stepwise framework utilizing spatio-temporal graph neural network algorithms by Yuyan Annie Pan, Fuliang Li, Anran Li, Zhiqiang Niu, Zhen Liu

    Published 2025-06-01
    “…This manuscript introduces a novel framework, the Physics-Guided Spatio-Temporal Graph Neural Network (PG-STGNN), specifically designed for traffic flow prediction. By integrating the principles of traffic flow physics with advanced spatio-temporal graph neural network algorithms, the framework captures complex spatio-temporal dependencies in traffic networks. …”
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  2. 1142

    Large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring by Zhenyu ZHU, Xiaorong ZHU, Yan CAI, Hongbo ZHU

    Published 2021-03-01
    “…In order to solve the problem of high collision rate and low timeliness of large-scale terminals access in the Internet of things, a large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring (ACB) was proposed.Firstly, the services were classified based on the data from each terminal by the volume of the services processed and the requirements for delay.For the services that were not time-sensitive and whose effective data portion was less than 1 000 bit, a slot-based ALOHA-based competitive access method was used.ACB-based random access was used for the services that were time-sensitive or whose data portion was greater than 1 000 bit.On this basis, a method was proposed for predicting the access application volume based on the quantitative estimation, and dynamically adjusting the ACB control parameters based on this predicted value.Simulation results show that compared with other existing access algorithms, the proposed algorithm reduces the collision rate and improves the system access success rate under the premise of ensuring the high priority service delay requirements.…”
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  3. 1143

    Advanced Control Algorithm for Shunt Active Power Filter: Enhancing Power Quality in Autonomous Grids by Agata Bielecka

    Published 2024-12-01
    “…This paper proposes an advanced predictive control algorithm with feedback from the supply current developed for a shunt active power filter. …”
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    Modeling and Discovering Direct Causes for Predictive Models by Yizuo Chen, Amit Bhatia

    Published 2025-05-01
    “… We introduce a causal modeling framework that captures the input-output behavior of predictive models (e.g., machine learning models). The framework enables us to identify features that directly cause the predictions, which has broad implications for data collection and model evaluation. …”
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    A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement by Bin Zhao, Hao Zheng, Xuesong Cheng

    Published 2024-12-01
    “…To enhance site selection and planning efficiency, we developed a predictive model integrating Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs). …”
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  10. 1150

    Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China by Baran Yılmaz, Rachel Samra

    Published 2024-09-01
    “…This work aims to provide a hybrid model using machine learning to accurately predict solar Direct normal irradiance with the least amount of error. …”
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    A Probability Integral Parameter Inversion Method Integrating a Selection-Weighted Iterative Robust Genetic Algorithm by Chuang Jiang, Wei Liu, Lei Wang, Xu Zhu, Hao Tan

    Published 2025-07-01
    “…However, the use of traditional genetic algorithms (GA) for inversion prediction has problems such as poor resistance to differences, and the accuracy of inversion parameters is affected when key monitoring points are missing. …”
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  16. 1156

    Predictive framework of vegetation resistance in channel flow by Fengcong Jia, Weijie Wang, Yu Han, Jiayu Du, Yue Zhang, Zihan Liu, Hairong Gao

    Published 2025-03-01
    “…To improve predictive performance, optimization algorithms such as PSO, WSO, and RIME were applied. …”
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    Comparing the Indices predictive of the thermal injury outcome by E A. Zhirkova, T. G. Spiridonova, A. V. Sachkov, A. O. Medvedev, E. I. Eliseenkova, I. G. Borisov, M. L. Rogal, S. S. Petrikov

    Published 2024-03-01
    “…While developing the algorithms for diagnosis and treatment of patients with thermal injury, an injury outcome prediction index with the best predictive properties should be used.Aim. …”
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