TY  - CONF
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781538672693
Y1  - 2018///
A1  - Adil, S.H.
A1  - Ebrahim, M.
A1  - Raza, K.
A1  - Azhar Ali, S.S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059762097&doi=10.1109%2fICIAS.2018.8540596&partnerID=40&md5=4fe7d33ee4f6544fb56a0228989a80b7
AV  - none
ID  - scholars9653
TI  - Prediction of Eye State Using KNN Algorithm
KW  - Complex networks; Deep neural networks; Electroencephalography; Electrophysiology; Forecasting; Learning systems; Neural networks; Support vector machines
KW  -  Eeg datum; Emotiv epoc; Eye State; K-nearest neighbors; k-NN algorithm; Prediction accuracy; Research papers; State prediction
KW  -  Nearest neighbor search
N1  - cited By 5; Conference of 7th International Conference on Intelligent and Advanced System, ICIAS 2018 ; Conference Date: 13 August 2018 Through 14 August 2018; Conference Code:143005
N2  - In this research paper, basic machine learning methodology for the classification of Eye State (i.e., Eyes Open or Closed) using Electroencephalography (EEG) Data is suggested. The idea is to compare and validate that basic Machine Learning (ML) approach (K-Nearest Neighbors KNN) can also provide better prediction accuracy in certain domains (in this case eye state prediction) than complex ML approaches (Support Vector Machine (SVM), Artificial Neural Network (ANN), or Deep Neural Network (DNN). The EEG data was collected using EMotiv EPOC headset and each record was labelled manually, containing 14 channels (columns of the record) using camera as open or closed eyes. The experimental results validate that stated methodology of using KNN provides better prediction accuracy in lesser time than other complex ML approaches. © 2018 IEEE.
ER  -