Zobrazeno 1 - 10
of 323
pro vyhledávání: '"Negative sample"'
Publikováno v:
Ecological Indicators, Vol 169, Iss , Pp 112948- (2024)
Landslide susceptibility assessment (LSA) aims to determine the spatial probability of landslides, reducing the loss caused by future landslides. In order to assess the impact of various negative sample collection strategies on the prediction accurac
Externí odkaz:
https://doaj.org/article/503ff4794cad43bbb8404f5fdb572e75
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Code search aims to search for code snippets from large codebase that are semantically related to natural query statements. Deep learning is a valuable method for solving code search tasks in which the quality of training data directly impac
Externí odkaz:
https://doaj.org/article/2a79db37f45341a0afa58d56063dfbdc
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 6, Pp 1590-1599 (2024)
On the one hand, the existing knowledge graph entity prediction methods only use the neighborhood and graph structure information to enhance the node information, and ignore the multi-modal information outside the knowledge graph to enhance the knowl
Externí odkaz:
https://doaj.org/article/cef40bb53cc84431bcb3c90a64bcd03f
Publikováno v:
地质科技通报, Vol 43, Iss 3, Pp 192-199 (2024)
Objective Landslide susceptibility evaluation is an important means for landslide disaster prevention and control. Unreasonable negative landslide samples will affect landslide susceptibility evaluation, thereby affecting landslide disaster preventio
Externí odkaz:
https://doaj.org/article/6f92e6f2935a4b23ace3dec459258e53
Publikováno v:
Applied Sciences, Vol 14, Iss 20, p 9240 (2024)
The lack of reliable negative samples is an important factor limiting the quality of machine learning-based debris flow susceptibility mapping (DFSM). The purpose of this paper is to propose multiple negative-sample acquisition strategies for DFSM co
Externí odkaz:
https://doaj.org/article/0b04274989264269b0182f7ec3e570f5
Publikováno v:
Frontiers in Cellular and Infection Microbiology, Vol 14 (2024)
ObjectiveTo evaluate the diagnostic value of metagenomic sequencing technology based on Illumina and Nanopore sequencing platforms for patients with suspected lower respiratory tract infection whose pathogen could not be identified by conventional mi
Externí odkaz:
https://doaj.org/article/d7bb3b9917274873814a064b15e04994
Autor:
Sara Giordana Rimoldi, Alessandro Tamoni, Alberto Rizzo, Concetta Longobardi, Cristina Pagani, Federica Salari, Caterina Matinato, Chiara Vismara, Gloria Gagliardi, Miriam Cutrera, Maria Rita Gismondo
Publikováno v:
Pathogens, Vol 13, Iss 9, p 743 (2024)
Bacterial infections pose significant global health challenges, often underestimated due to difficulties in accurate diagnosis, especially when culture-based diagnostics fail. This study assesses the effectiveness of 16S-based metagenomic next genera
Externí odkaz:
https://doaj.org/article/fdd5c2b6f27d41b891253060ec5f85c5
Construction and Optimization of Landslide Susceptibility Assessment Model Based on Machine Learning
Publikováno v:
Applied Sciences, Vol 14, Iss 14, p 6040 (2024)
The appropriate selection of machine learning samples forms the foundation for utilizing machine learning models. However, in landslide susceptibility evaluation, discrepancies arise when non-landslide samples are positioned within areas prone to lan
Externí odkaz:
https://doaj.org/article/4d351ab4e56b4415921c3706cea71ed7
Autor:
Shuaiwen Sun, Zhijing Xu
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 8, Pp 15018-15043 (2023)
At present, ship detectors have many problems, such as too many hyperparameter, poor recognition accuracy and imprecise regression boundary. In this article, we designed a large kernel convolutional YOLO (Lk-YOLO) detection model based on Anchor free
Externí odkaz:
https://doaj.org/article/58eafdae19f34fc19815c6e048c4d525
Publikováno v:
Frontiers in Genetics, Vol 14 (2024)
Increasing evidence indicates that mutations and dysregulation of long non-coding RNA (lncRNA) play a crucial role in the pathogenesis and prognosis of complex human diseases. Computational methods for predicting the association between lncRNAs and d
Externí odkaz:
https://doaj.org/article/2670295c40c940abb856cd424022f9c8