Journal of Applied Science and Engineering

Published by Tamkang University Press

ESCI jase impact factor scopus logo open access rate of Scopus journal

Leveraging a Natural Language Processing-Based AI System to Facilitate Narrative Externalization for University Students’ Mental Well-being

 2026

 Volume 33

Wei Du1, Junqian Hao2, and Xue Han3

1Psychological Health Education and Counseling Center, Jiaozuo Normal College, Jiaozuo, 454000, Henan Province, China

2Department of Primary Education, Jiaozuo Normal College, Jiaozuo, 454000, Henan Province, China

3Institute of Marxism, Jiaozuo Normal College, Jiaozuo, 454000, Henan Province, China

Received: April 17, 2026
Accepted: May 29, 2026
Publication Date: July 10, 2026

上傳圖片

Process workflow diagram of the proposed model 

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation:  BibTeX | http://dx.doi.org/10.6180/jase.202610_33.035  

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Traffic flow (TF) forecasting plays a crucial role in modern urban transportation systems by enabling congestion mitigation and efficient traffic management. With the rapid growth of intelligent transportation systems, robust prediction models are required to process large-scale traffic data in real time. This study presents an enhanced urban traffic forecasting model based on Deep Recurrent Networks (DRN) integrated with Bidirectional Long
ShortTerm Memory (BiLSTM) networks to effectively capture temporal dependencies and spatial correlations in traffic data. Traffic datasets collected from sensors at major intersections include vehicle counts, speed, and congestion information across peak and off-peak periods. Noise reduction is performed using median filtering, followed by Min-Max normalization. Wavelet transform-based feature extraction captures multi-resolution traffic patterns. The proposed Malleable Aquila Optimized DRN-BiLSTM model achieves superior performance, attaining an R2 of 0.97 , RMSE of 12.14, and MAE of 10.04, demonstrating its effectiveness for shortterm urban traffic forecasting and planning.

Keywords: Urban Traffic Flow, Intelligent Transportation Systems, Malleable Aquila Optimized-DRN with BiLSTM, Traffic Management, Predictive Model

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