Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Shiyun Wa"'
Publikováno v:
Information, Vol 14, Iss 9, p 499 (2023)
This research primarily explores the application of Natural Language Processing (NLP) technology in precision financial fraud detection, with a particular focus on the implementation and optimization of the FinChain-BERT model. Firstly, the FinChain-
Externí odkaz:
https://doaj.org/article/2a597bfcd7e2458db39f30436e1bd7fe
Autor:
Lexin Zhang, Ruihan Wang, Zhuoyuan Li, Jiaxun Li, Yichen Ge, Shiyun Wa, Sirui Huang, Chunli Lv
Publikováno v:
Information, Vol 14, Iss 9, p 500 (2023)
This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data,
Externí odkaz:
https://doaj.org/article/693c33d197de40dba4af8c3a972fbfc8
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
The detection of plant disease is of vital importance in practical agricultural production. It scrutinizes the plant's growth and health condition and guarantees the regular operation and harvest of the agricultural planting to proceed successfully.
Externí odkaz:
https://doaj.org/article/59cca8e9456943d9b4c8de78516bf752
A Dilated Segmentation Network with the Morphological Correction Method in Farming Area Image Series
Publikováno v:
Remote Sensing, Vol 14, Iss 8, p 1771 (2022)
Farming areas are made up of diverse land use types, such as arable lands, grasslands, woodlands, water bodies, and other surrounding agricultural architectures. They possess imperative economic value, and are considerably valued in terms of farmers
Externí odkaz:
https://doaj.org/article/9da6bf8c98eb4984aea0c14b7159b08b
Publikováno v:
Remote Sensing, Vol 14, Iss 4, p 923 (2022)
There has been substantial progress in small object detection in aerial images in recent years, due to the extensive applications and improved performances of convolutional neural networks (CNNs). Typically, traditional machine learning algorithms te
Externí odkaz:
https://doaj.org/article/834dfebf2b7c43db9990d986fe3add16
Autor:
Yan Zhang, Shupeng He, Shiyun Wa, Zhiqi Zong, Jingxian Lin, Dongchen Fan, Junqi Fu, Chunli Lv
Publikováno v:
Symmetry, Vol 14, Iss 2, p 234 (2022)
Computed tomography (CT) is the first modern slice-imaging modality. Recent years have witnessed its widespread application and improvement in detecting and diagnosing related lesions. Nonetheless, there are several difficulties in detecting lesions
Externí odkaz:
https://doaj.org/article/057d53f560474c7fa3028571dea3c0ec
Publikováno v:
Symmetry, Vol 13, Iss 12, p 2395 (2021)
Automatic segmentation of intracranial brain tumors in three-dimensional (3D) image series is critical in screening and diagnosing related diseases. However, there are various challenges in intracranial brain tumor images: (1) Multiple brain tumor ca
Externí odkaz:
https://doaj.org/article/96662c4b9b034c9c90fca30ec6151443
Publikováno v:
Information, Vol 12, Iss 12, p 495 (2021)
Apple flower detection is an important project in the apple planting stage. This paper proposes an optimized detection network model based on a generative module and pruning inference. Due to the problems of instability, non-convergence, and overfitt
Externí odkaz:
https://doaj.org/article/ea63e75dde60458fb9d0b9e39ec4ea23
High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module
Publikováno v:
Remote Sensing, Vol 13, Iss 21, p 4218 (2021)
Maize leaf disease detection is an essential project in the maize planting stage. This paper proposes the convolutional neural network optimized by a Multi-Activation Function (MAF) module to detect maize leaf disease, aiming to increase the accuracy
Externí odkaz:
https://doaj.org/article/d9dc4bcade9441d6a7bf487342f623a7
Publikováno v:
Information, Vol 12, Iss 10, p 397 (2021)
To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation proc
Externí odkaz:
https://doaj.org/article/8f3b0c4cb7614520933d3ef82237ff1b