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Zengyu Cai, Chunchen Tan, Jianwei Zhang, Liang Zhu and Yuan Feng
As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and ...
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Ku Muhammad Naim Ku Khalif, Woo Chaw Seng, Alexander Gegov, Ahmad Syafadhli Abu Bakar and Nur Adibah Shahrul
Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of ...
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Zhiqing Guo, Xiaohui Chen, Ming Li, Yucheng Chi and Dongyuan Shi
Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction me...
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Yuan Luo, Changbo Wu and Caiyun Lv
The proposed method can improve emotion recognition accuracy in human?computer interactions.
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Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo and Francesco Camastra
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep lea...
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Petros Brimos, Areti Karamanou, Evangelos Kalampokis and Konstantinos Tarabanis
Traffic forecasting has been an important area of research for several decades, with significant implications for urban traffic planning, management, and control. In recent years, deep-learning models, such as graph neural networks (GNN), have shown grea...
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Anqi Jin and Xiangyang Zeng
Long-range underwater targets must be accurately and quickly identified for both defense and civil purposes. However, the performance of an underwater acoustic target recognition (UATR) system can be significantly affected by factors such as lack of data...
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Qianjing Li, Jia Tian and Qingjiu Tian
The combination of multi-temporal images and deep learning is an efficient way to obtain accurate crop distributions and so has drawn increasing attention. However, few studies have compared deep learning models with different architectures, so it remain...
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Jingjing Liu, Xinli Yang, Denghui Zhang, Ping Xu, Zhuolin Li and Fengjun Hu
Multi-node wind speed forecasting is greatly important for offshore wind power. It is a challenging task due to unknown complex spatial dependencies. Recently, graph neural networks (GNN) have been applied to wind forecasting because of their capability ...
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Hui Sheng, Min Liu, Jiyong Hu, Ping Li, Yali Peng and Yugen Yi
Time-series data is an appealing study topic in data mining and has a broad range of applications. Many approaches have been employed to handle time series classification (TSC) challenges with promising results, among which deep neural network methods ha...
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