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Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband and Shatirah Akib
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow...
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Zhiqiang Jiang, Yongyan Ma and Weijia Li
Accurate forecasting of ship motion is of great significance for ensuring maritime operational safety and working efficiency. A data-driven ship motion forecast method is proposed in this paper, aiming at the problems of low generalization of a single fo...
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Jiahao Chen, Jiaxin Li, Deqian Zheng, Qianru Zheng, Jiayi Zhang, Meimei Wu and Chaosai Liu
The multi-field coupling of grain piles in grain silos is a focal point of research in the field of grain storage. The porosity of grain piles is a critical parameter that affects the heat and moisture transfer in grain piles. To investigate the distribu...
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Zhou Fang, Xiaoyong Wang, Liang Zhang and Bo Jiang
Currently, deep learning is extensively utilized for ship target detection; however, achieving accurate and real-time detection of multi-scale targets remains a significant challenge. Considering the diverse scenes, varied scales, and complex backgrounds...
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Mazen A. Al-Sinan, Abdulaziz A. Bubshait and Zainab Aljaroudi
Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated aut...
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Wei Wang, Huanhuan Feng, Yanzong Li, Quanwei You and Xu Zhou
At present, the determination of tunnel parameters mainly rely on engineering experience and human judgment, which leads to the subjective decision of parameters and an increased construction risk. Machine learning algorithms could provide an objective t...
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Binita Kusum Dhamala, Babu R. Dawadi, Pietro Manzoni and Baikuntha Kumar Acharya
Graph representation is recognized as an efficient method for modeling networks, precisely illustrating intricate, dynamic interactions within various entities of networks by representing entities as nodes and their relationships as edges. Leveraging the...
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Peipei Chen, Jianguo Dai, Guoshun Zhang, Wenqing Hou, Zhengyang Mu and Yujuan Cao
Nitrogen plays a crucial role in cotton growth, making the precise diagnosis of its nutrition levels vital for the scientific and rational application of fertilizers. Addressing this need, our study introduced an EMRDFC-based diagnosis model specifically...
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Weiwei Yuan, Wanxia Yang, Liang He, Tingwei Zhang, Yan Hao, Jing Lu and Wenbo Yan
The extraction of entities and relationships is a crucial task in the field of natural language processing (NLP). However, existing models for this task often rely heavily on a substantial amount of labeled data, which not only consumes time and labor bu...
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Yu Zhang, Jiajun Niu, Zezhong Huang, Chunlei Pan, Yueju Xue and Fengxiao Tan
An algorithm model based on computer vision is one of the critical technologies that are imperative for agriculture and forestry planting. In this paper, a vision algorithm model based on StyleGAN and improved YOLOv5s is proposed to detect sandalwood tre...
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