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Mohammed Abaker, Hatim Dafaalla, Taiseer Abdalla Elfadil Eisa, Heba Abdelgader, Ahmed Mohammed, Mohammed Burhanur, Aiman Hasabelrsoul, Mohammed Ibrahim Alfakey and Mohammed Abdelghader Morsi
In recent years, several strategies have been introduced to enhance early warning systems and lower the risk of rock-falls. In this regard, this paper introduces a deep learning- and IoT-based framework for rock-fall early warning, devoted to reducing ro...
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Weijie Ding, Jin Huang, Guanyu Shang, Xuexuan Wang, Baoqiang Li, Yunfei Li and Hourong Liu
Highly accurate trajectory prediction models can achieve route optimisation and save airspace resources, which is a crucial technology and research focus for the new generation of intelligent air traffic control. Aiming at the problems of inadequate extr...
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Andrius Zuoza,Aurelijus Kazys Zuoza,Audrius Gargasas
Pág. 135 - 140
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Jiahui Zhao, Zhibin Li, Pan Liu, Mingye Zhang
Pág. 115 - 142
Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the so...
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Mengzhen Wu, Xianghong Xu, Haochen Zhang, Rui Zhou and Jianshan Wang
As a traditional numerical simulation method for pantograph?catenary interaction research, the pantograph?catenary finite element model cannot be applied to the real-time monitoring of pantograph?catenary contact force, and the computational cost require...
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Bingyu Li, Lei Wang, Qiaoyong Jiang, Wei Li and Rong Huang
In view of the limitations of traditional statistical methods in dealing with multifactor and nonlinear data and the inadequacy of classical machine learning algorithms in dealing with and predicting data with high dimensions and large sample sizes, this...
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Min Liu, Hua Hu, Liqian Zhang, Yongan Zhang and Jia Li
Air quality level has a complex nonlinear relationship with air pollutant and meteorological conditions, including multiple factors, overlapping information, and difficulty solving equations. In order to identify significant factors, remove correlations,...
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Guoyan Xu, Yuwei Lu, Zixu Jing, Chunyan Wu and Qirui Zhang
The accuracy of dam deformation prediction is a key issue that needs to be addressed due to the many factors that influence dam deformation. In this paper, a dam deformation prediction model based on IEALL (IGWO-EEMD-ARIMA-LSTM-LSTM) is proposed for a si...
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Caosen Xu, Jingyuan Li, Bing Feng and Baoli Lu
Financial time-series prediction has been an important topic in deep learning, and the prediction of financial time series is of great importance to investors, commercial banks and regulators. This paper proposes a model based on multiplexed attention me...
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Chunwei Hu, Xianfeng Liu, Sheng Wu, Fei Yu, Yongkun Song and Jin Zhang
Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic g...
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Daping Xi, Yuhao Feng, Wenping Jiang, Nai Yang, Xini Hu and Chuyuan Wang
The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic ...
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Sihan Song, Qiujing Zhou, Tao Zhang and Yintao Hu
Concrete dam deformation prediction is important for assessing the safety of dams. A TPE-STL-LSTM deformation prediction model for concrete dams is established by introducing the TPE algorithm based on the decomposition?prediction model. Taking the Wanji...
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Zongyou Xia, Gonghao Duan and Ting Xu
Since 2020, COVID-19 has repeatedly arisen around the world, which has had a significant impact on the global economy and culture. The prediction of the COVID-19 epidemic will help to deal with the current epidemic and similar risks that may arise in the...
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Qingyan Zhou, Hao Li, Youhua Zhang and Junhong Zheng
Traditional product evaluation research is to collect data through questionnaires or interviews to optimize product design, but the whole process takes a long time to deploy and cannot fully reflect the market situation. Aiming at this problem, we propos...
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Xianchang Wang, Siyu Dong and Rui Zhang
In the prediction of time series, Empirical Mode Decomposition (EMD) generates subsequences and separates short-term tendencies from long-term ones. However, a single prediction model, including attention mechanism, has varying effects on each subsequenc...
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Jian Yang and Jinhan Guan
In today?s world, heart disease is the leading cause of death globally. Researchers have proposed various methods aimed at improving the accuracy and efficiency of the clinical diagnosis of heart disease. Auxiliary diagnostic systems based on machine lea...
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Wei Huang, Chuankun Liu, Weiqiang Guo and Ya Wei
What are the main findings?
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Young-Rong Kim, Min Jung and Jun-Bum Park
As interest in eco-friendly ships increases, methods for status monitoring and forecasting using in-service data from ships are being developed. Models for predicting the energy efficiency of a ship in real time need to effectively process the operationa...
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Yue Zhang and Fangai Liu
A deep belief network (DBN) is a powerful generative model based on unlabeled data. However, it is difficult to quickly determine the best network structure and gradient dispersion in traditional DBN. This paper proposes an improved deep belief network (...
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Guojing Huang, Qingliang Chen and Congjian Deng
With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, th...
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