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Ping Dong, Kuo Li, Ming Wang, Feitao Li, Wei Guo and Haiping Si
In addition to the conventional situation of detecting a single disease on a single leaf in corn leaves, there is a complex phenomenon of multiple diseases overlapping on a single leaf (compound diseases). Current research on corn leaf disease detection ...
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Navid Khalili Dizaji and Mustafa Dogan
Brain tumors are one of the deadliest types of cancer. Rapid and accurate identification of brain tumors, followed by appropriate surgical intervention or chemotherapy, increases the probability of survival. Accurate determination of brain tumors in MRI ...
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Hexin Lu, Xiaodong Zhu, Jingwei Cui and Haifeng Jiang
The process of iris recognition can result in a decline in recognition performance when the resolution of the iris images is insufficient. In this study, a super-resolution model for iris images, namely SwinGIris, which combines the Swin Transformer and ...
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Zhe Yang, Yi Huang, Yaqin Chen, Xiaoting Wu, Junlan Feng and Chao Deng
Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user?s dissatisfactio...
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Zhichao Chen, Guoqiang Wang, Tao Lv and Xu Zhang
Diseases of tomato leaves can seriously damage crop yield and financial rewards. The timely and accurate detection of tomato diseases is a major challenge in agriculture. Hence, the early and accurate diagnosis of tomato diseases is crucial. The emergenc...
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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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Sara Rajaram and Cassie S. Mitchell
The ability to translate Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) into different modalities and data types is essential to improve Deep Learning (DL) for predictive medicine. This work presents DACMVA, a novel framework ...
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Wenkuan Huang, Hongbin Chen and Qiyang Zhao
The main research focus of this paper is to explore the use of the cycle-generative adversarial network (GAN) method to address the inter-turn fault issue in permanent magnet-synchronous motors (PMSMs). Specifically, this study aims to overcome the chall...
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Wenhao Sun, Yidong Zou, Yunhe Wang, Boyi Xiao, Haichuan Zhang and Zhihuai Xiao
In the practical production environment, the complexity and variability of hydroelectric units often result in a need for more fault data, leading to inadequate accuracy in fault identification for data-driven intelligent diagnostic models. To address th...
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Yong Liu, Jialin Zhou, Dong Zhang, Shaoyu Wei, Mingshun Yang and Xinqin Gao
To solve the problem of low diagnostic accuracy caused by the scarcity of fault samples and class imbalance in the fault diagnosis task of box-type substations, a fault diagnosis method based on self-attention improvement of conditional tabular generativ...
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