This paper presents a digital twin-based river management and flood prediction system designed for hydrological environments, including volcanic geology. To address the problems of rapid runoff and complex terrain, a deep learning-based hybrid model is proposed that integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) units for temporal sequence modeling. The performance evaluation results show that the proposed CNN-RNN hybrid model outperforms individual CNN and RNN baselines. The hybrid model achieves a macro-average precision of 0.97, a recall of 0.99, and an F1 score of 0.98, significantly outperforming existing methods. The system is also integrated with a 3D digital twin visualization platform to enable real-time monitoring and data-driven decision-making.
The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industr…
The rapid development of artificial intelligence (AI) and 5G paradigm brings infinite possibilities for data annotation for new applications in the industrial Internet of Things (IIoT). However, the problem of data annotation consistency under distributed architectures and growin…
High-precision environmental perception is essential for deep-sea exploration and autonomous underwater vehicle operations. However, physical factors such as light scattering and selective absorption, along with high target–background similarity, cause existing detection methods…
This study addresses the challenges of data imbalance and missing values in credit card transaction datasets by employing mode-based imputation and various machine learning models. We analyzed two distinct datasets: one consisting of European cardholders and the other from Americ…
Autonomous wheelchair navigation requires both reliable global guidance and safe local interaction with the environment, typically addressed using separate perception and control strategies. This paper presents a unified vision-based control framework that combines learning-based…
Due to the rapid proliferation of social media and online reviews, the accurate identification and classification of controversial texts has emerged as a significant challenge in the field of natural language processing. However, traditional text-classification methodologies freq…