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Hannan Quinn Quantum Grasshopper Optimization and Attention Deep Intelligent Train Status Prediction

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Abstract

Providing accurate and timely traffic information such as arriving time of train plays a significant part in intelligent train status prediction. Maximum-speed train status forecast is a significant topic as far as railway dispatching. In this paper proposes a novel train status prediction method based on the Hannan Quinn Quantum Grasshopper Optimized Attention Deep Learning (HQQGO-ADL) method introduced for accurate train status prediction. The proposed HQQGO-ADL is designed with three different processes namely preprocessing, feature selection, and prediction. This method can well capture the influence of discrete and continuous features in addition to spatiotemporal characteristics, therefore modeling train status prediction. At first, Mid-hinge Inter-Quartile Interpolation is utilized to perform preprocessing for eradicating or discarding the outliers with less train status identification time. Second, HannanQuinn Quantum Grasshopper Optimization is employed to capture the influence of discrete and continuous features on spatiotemporal characteristics, therefore modeling train status prediction. Besides, the maximum log likelihood discrete feature fine tuned to obtain continuous features. Finally, the Attention Deep Neural Train Status Prediction model is utilized for improved latency train status prediction. The overall analysis of HQQGO-ADL has better performance of higher train status prediction accuracy by 32% and lower train status prediction time, overhead, and error rate by 32%, 32%, and 27%as compared to existing techniques.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Galgotias University, Greater Noida, India

    Ajay Singh & Santosh Kumar

  2. Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India

    Rajesh Kumar Dhanaraj

  3. Department School of Computer Science Engineering & Technology, Bennett University, Greater Noida, Uttar Pradesh, India

    Ajith Abraham

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  1. Ajay Singh
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  2. Rajesh Kumar Dhanaraj
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Correspondence to Rajesh Kumar Dhanaraj or Santosh Kumar.

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Singh, A., Dhanaraj, R.K., Kumar, S. et al. Hannan Quinn Quantum Grasshopper Optimization and Attention Deep Intelligent Train Status Prediction. Multimed Tools Appl 84, 29689–29714 (2025). https://doi.org/10.1007/s11042-024-20122-0

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