Network Transmission Flags Data Affinity-based  Classification by K-Nearest Neighbor

Aljojo, Nahla

doi:10.14500/aro.10880

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…To formulate the kNN classification algorithm, assume that the pair (x i , f(x i )) contains the feature vector x i and its corresponding label f(x i ) where f(x i ) ∈ {1,2,…, n} and i = 1,2,…, N (n and N is the number of classes and the number of train feature vectors, respectively). The principal idea behind kNN is to measure the distance between feature vectors such that the nearest neighbor for the tested sample makes a decision about the label of the features (Aljojo, 2022). The majority voting strategy among the k-nearest samples is basically adopted in this classifier.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Heartbeat Classification using Convolutional Neural Network-k Nearest Neighbor

Abdul,

Al‑Talabani,

Rahman

et al. 2024

ARO

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Electrocardiogram (ECG) analysis is widely used by cardiologists and medical practitioners for monitoring cardiac health. A high-performance automatic ECG classification system is challenging because there is difficulty in detecting and categorizing different waveforms in the signal, especially in manual analysis of ECG signals, which means, a better classification system is needed in terms of performance and accuracy. Hence, in this paper, the authors propose an accurate ECG classification and monitoring system called convolutional neural network-k nearest neighbor (CNN-kNN). The proposed method utilizes 1D-CNN and kNN. Unlike the existing techniques, the examined technique does not need training during classifying the ECG signals. The CNN-kNN is evaluated against the PhysioNet’s MIT-BIH and PTB diagnostics datasets. The CNN is fed using the ECG beat raw signal directly. In addition, the learned features are extracted from the 1D-CNN model and its dimensions are reduced using two fully connected layers and then fed to the k-NN classifier. The CNN-kNN model achieved average accuracies of 98% and 97.4% on arrhythmia and myocardial infarction classifications, respectively. These results are evidence of the great ability of the proposed model compared to the mentioned models in this article.

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Section: Introductionmentioning

confidence: 99%