K-Nearest Neighbors Algorithm (KNN)

2021 ◽  
pp. 161-178
Author(s):  
Abdelaziz Elbaghdadi ◽  
Soufiane Mezroui ◽  
Ahmed El Oualkadi
Keyword(s):  
Business Process ◽  
Scientific Research ◽  
Nearest Neighbors ◽  
Local Information ◽  
K Nearest Neighbors ◽  
Blockchain Technology ◽  
Use Of Data ◽  
Proposed Model

The cryptocurrency is the first implementation of blockchain technology. This technology provides a set of tracks and innovation in scientific research, such as use of data either to detect anomalies either to predict price in the Bitcoin and the Ethereum. Furthermore, the blockchain technology provide a set of technique to automate the business process. This chapter presents a review of some research works related to cryptocurrency. A model with a KNN algorithm is proposed to detect illicit transaction. The proposed model uses both the elliptic dataset and KNN algorithm to detect illicit transaction. Furthermore, the elliptic dataset contains 203,769 nodes and 234,355 edges; it allows to classify the data into three classes: illicit, licit, or unknown. Each node has associated 166 features. The first 94 features represent local information about the transaction. The remaining 72 features are called aggregated features. The accuracy exceeded 90% with k=2 and k=4, the recall reaches 56% with k=3, and the precision reaches 78% with k=4.

scholarly journals An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zongying Liu ◽  
Shaoxi Li ◽  
Jiangling Hao ◽  
Jingfeng Hu ◽  
Mingyang Pan
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Processing Time ◽  
Kernel Method ◽  
Nearest Neighbors ◽  
Human Activity Recognition ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Proposed Model

With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. However, these algorithms have their own limitations and their prediction accuracy still has space to improve. In this study, we focus on K-nearest neighbors (KNN) and solve its limitations. Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. The proposed model KNN with kernel (K-KNN) improves the accuracy of classification. Secondly, a novel reduced kernel method is proposed and used in model K-KNN, which is named as Reduced Kernel KNN (RK-KNN). It reduces the processing time and enhances the classification performance. Moreover, this study proposes an approach of defining number of K neighbors, which reduces the parameter dependency problem. Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models. It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively. Averagely, compared with the conventional KNN, the proposed model RK-KNN increases the accuracy by 1.82% and decreases standard deviation by 0.27. The small gap of processing time between KNN and RK-KNN in all datasets is only 1.26 seconds.

scholarly journals On Secured Blockchain Technology For K-Nearest Neighbors Algorithm

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1418-1425
Keyword(s):  
Knowledge Sharing ◽  
Nearest Neighbors ◽  
Private Cloud ◽  
Injection Mould ◽  
K Nearest Neighbors ◽  
Blockchain Technology ◽  
Manufacturing Knowledge

This paper proposes a cloud-based manufacturing knowledge sharing system for injection mould redesign (IMR) in perspective on square chain development. In our proposed structure, private cloud is connected to spare the IMR becoming more acquainted with, and square chain offers measures and shows to completing the system similarly as making certain the wellbeing in a trustless space. k-Nearest colleagues is connected for convalescing the square chain-essentially based report becoming more acquainted with. The proposed system not solely can energize imbuement structure supplant, yet further give a component to data proprietors to share their own special favorable circumstances adequately.

scholarly journals A hybrid model for predicting missile impact damages based on k-nearest neighbors and Bayesian optimization

2020 ◽  
Vol 14 (3) ◽  
pp. 1-14
Author(s):  
Quoc Hoan Doan ◽  
Duc-Kien Thai ◽  
Ngoc Long Tran
Keyword(s):  
Reinforced Concrete ◽  
Power Plants ◽  
Sampling Technique ◽  
Nearest Neighbors ◽  
Bayesian Optimization ◽  
Design Stage ◽  
K Nearest Neighbors ◽  
Rc Structures ◽  
Proposed Model ◽  
Four Levels

Due to the increase of missile performance, the safety design requirements of military and industrial reinforced concrete (RC) structures (i.e., bunkers, nuclear power plants, etc.) also increase. Estimating damage levels in the design stage becomes a crucial task and requires more accuracy. Thus, this study proposed a hybrid machine learning model which is based on k-nearest neighbors (KNN) and Bayesian optimization (BO), named as BO-KNN, for predicting the local damages of reinforced concrete (RC) panels under missile impact loading. In the proposed BO-KNN, the hyperparameters of the KNN were optimized by using the BO which is a well-established optimization algorithm. Accordingly, the KNN was trained on an experimental dataset that consists of 254 impact tests to predict four levels (or classes) of damages including perforation, scabbing, penetration, and no damage. Due to the unbalance of the number of tests in each damage class, an over-sampling technique called BorderlineSMOTE was employed as a balancing solution. The predictability of the proposed model was investigated by comparing with the benchmark models including non-optimized KNN, multilayer perceptron (MLP), and decision tree (DT). Accuracy, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) were utilized to evaluate the performance of these models. The implementation results showed that the proposed BO-KNN model outperformed the other benchmark models with the average class accuracy of 68.05%, F1-score = 0.641, and AUC = 85.8%. Thus, the proposed model can be introduced as a foundation for developing a tool for predicting the local damage of RC panels under the missile impact in the future. Keywords: impact damage; k-nearest neighbors; Bayesian optimization; oversampling; imbalanced data; RC panel.

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

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