Zobrazeno 1 - 10
of 26 017
pro vyhledávání: '"Nearest neighbors"'
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-55 (2024)
Abstract The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performa
Externí odkaz:
https://doaj.org/article/47afd56c6b464b078637a5b751e3bc57
Publikováno v:
Alexandria Engineering Journal, Vol 103, Iss , Pp 51-59 (2024)
Metamaterial absorbers (MMAs) have tremendous potential for controlling and modulating Terahertz electromagnetic (EM) waves. It is challenging to design MMAs for optimal performance using conventional methods due to their time-consuming and computati
Externí odkaz:
https://doaj.org/article/e4dbf00a4c4042098d2ce27760bd8c02
Publikováno v:
Genome Biology, Vol 25, Iss 1, Pp 1-21 (2024)
Abstract Typical clustering methods for single-cell and spatial transcriptomics struggle to identify rare cell types, while approaches tailored to detect rare cell types gain this ability at the cost of poorer performance for grouping abundant ones.
Externí odkaz:
https://doaj.org/article/72d330b981b141d68ba3fe09704b0048
Publikováno v:
Frontiers in Big Data, Vol 7 (2024)
The k-nearest neighbors (KNN) regression method, known for its nonparametric nature, is highly valued for its simplicity and its effectiveness in handling complex structured data, particularly in big data contexts. However, this method is susceptible
Externí odkaz:
https://doaj.org/article/7cf00e1736854eecac28a92ca26d159b
Autor:
Jianbo DAI, Zhongbin WANG, Yan ZHANG, Lei SI, Dong WEI, Wenbo ZHOU, Jinheng GU, Xiaoyu ZOU, Yuyu SONG
Publikováno v:
Meitan kexue jishu, Vol 52, Iss 7, Pp 209-221 (2024)
In the field of drilling in coal mine underground, drilling rate (DR) is one of the most effective indicators for assessing drilling operations. Accurate prediction of DR is a prerequisite for the realization of intelligent drilling in coal mines, wh
Externí odkaz:
https://doaj.org/article/8e8f469d8a2c449f8ae4511179bbda79
Publikováno v:
Jurnal Riset Informatika, Vol 6, Iss 3, Pp 175-184 (2024)
This research aims to develop an automated classification system to accurately identify and classify Ulos batik patterns using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) techniques. The method is based on computer vision technology a
Externí odkaz:
https://doaj.org/article/742b4788e3c74def9e546b59d529c57c
Autor:
Xiaodi LIANG, Yindong LIU
Publikováno v:
Zhongguo Jianchuan Yanjiu, Vol 19, Iss 3, Pp 150-157 (2024)
ObjectiveTo address the issue of assessing structural breach damage in ships under underwater explosion, a breach prediction method based on the PCA-BOA-KNN model is established. MethodsFirst, finite element models for five-compartment and seven-comp
Externí odkaz:
https://doaj.org/article/0277b17febd44179a5bed44e9b7bfb22
Publikováno v:
Jurnal Sisfokom, Vol 13, Iss 2, Pp 170-178 (2024)
The Digital Population Identity Application provides convenience for users to access and manage their population data digitally. Based on the increasing usage of the Digital Population Identity Application on the Google Play Store, various user revie
Externí odkaz:
https://doaj.org/article/555f46033ae145b9ac803c5acd225188
Publikováno v:
Jurnal Sisfokom, Vol 13, Iss 2, Pp 261-266 (2024)
Impulsivity is the tendency to act without considering consequences or without careful planning. It involves a quick response to a stimulus without sufficient consideration of the consequences. Impulsivity needs to be measured and detected because it
Externí odkaz:
https://doaj.org/article/9cf4a2573b864884a4bf1ff8b7cfca4f
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Deep learning has emerged as a highly effective and precise method for classifying images. The presence of plant diseases poses a significant threat to food security. However, accurately identifying these diseases in plants is challenging du
Externí odkaz:
https://doaj.org/article/33b9f22cdef44cb8913da30ba2b992f4