Results 11 to 20 of about 495,115 (315)
An Enhanced Quantum K-Nearest Neighbor Classification Algorithm Based on Polar Distance
Entropy, 2023 The K-nearest neighbor (KNN) algorithm is one of the most extensively used classification algorithms, while its high time complexity limits its performance in the era of big data.
Congcong Feng +4 more
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Ilkom Jurnal Ilmiah, 2020 K-Nearest Neighbor is a data mining algorithm that can be used to classify data. K-Nearest Neighbor works based on the closest distance. This research using the Euclidean and Manhattan distances to calculate the distance of Lhokseumawe-Medan bus ...
Rozzi Kesuma Dinata +2 more
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$N$ -Dimensional Approximation of Euclidean Distance [PDF]
IEEE Transactions on Circuits and Systems II: Express Briefs, 2020 Several applications in different engineering areas require the computation of the Euclidean distance, a quite complex operation based on squaring and square root. In some applications, the Euclidean distance can be replaced by the Manhattan distance. However, the approximation error introduced by the Manhattan distance may be rather large, especially ...
Gian Carlo Cardarilli +5 more
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Hardware Trojan Detection Based on Improved Euclidean Distance [PDF]
Jisuanji gongcheng, 2017 The accuracy of the hardware Trojan detection method based on the traditional Euclidean distance discrimination is too low to meet the demand.For this reason,the side-channel power consumption information is collected and analized in this paper,and an ...
WANG Jianxin,WANG Boren,QU Ming,ZHANG Lei
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Euclidean Distance Matrix-Based Rapid Fault Detection and Exclusion
Navigation, 2023 Faulty signals from global navigation satellite systems (GNSSs) often lead to erroneous position estimates. A variety of fault detection and exclusion (FDE) methods have been proposed in prior research to both detect and exclude faulty measurements. This
Derek Knowles, Grace Gao
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Generalized Euclidean distance matrices [PDF]
Linear and Multilinear Algebra, 2021 Euclidean distance matrices (EDM) are symmetric nonnegative matrices with several interesting properties. In this article, we introduce a wider class of matrices called generalized Euclidean distance matrices (GDMs) that include EDMs. Each GDM is an entry-wise nonnegative matrix. A GDM is not symmetric unless it is an EDM.
R. Balaji, R.B. Bapat, Shivani Goel
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Analisis Metode Euclidean Distance dalam Menentukan Koordinat Peta pada Alamat Rumah
Jurnal Teknologi dan Manajemen Informatika, 2022 A map is drawn on a flat plane and reduced or scaled. The use of scale on a map is a comparison between the image plane and the actual surface of the earth.
Abdi Pandu Kusuma, Ananda Dwi Oktavianto
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JuTISI (Jurnal Teknik Informatika dan Sistem Informasi), 2020 Sign Language use to communicate to people with dissabilities. American Sign Language (ASL) one of popular sign language. Histogram of Oriented Gradient (HOG) can be use as feature extraction. Then feature stored in database.
Muhammad Ezar Al Rivan +3 more
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SCENE CLASSIFICATION BASED ON THE INTRINSIC MEAN OF LIE GROUP [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Remote Sensing scene classification aims to identify semantic objects with similar characteristics from high resolution images. Even though existing methods have achieved satisfactory performance, the features used for classification modeling are still ...
C. Xu, G. Zhu, K. Yang
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Indonesian Journal of Statistics and Its Applications, 2021 There are several algorithms to solve many problems in grouping data. Grouping data is also known as clusterization, clustering takes advantage to solve some problems especially in business. In this note, we will modify the clustering algorithm based on distance principle which background of K-means algorithm (Euclidean distance).
Said Al Afghani +1 more
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