RETRACTED ARTICLE: Support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel

Basha, A. Jameer; Poornima, S.; Prathilothamai, M.; Venkatachalam, K.

doi:10.1007/s12652-020-02188-4

Cited by 25 publications

(7 citation statements)

References 22 publications

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“…In case of a utility dropping short of generation, because of forced outage of generators sudden enhancement in demand, additional utility of the pool can come to its salvage by making its excess power accessible. The machine learning based model proposed for classification (Malar et al 2020;Basha et al 2020;Yasoda et al 2020).…”

Section: Supply Unit Modelmentioning

confidence: 99%

RETRACTED ARTICLE: Automatic generation control with fuzzy logic controller incorporating tandem and cross compound turbine

Moniya

Anand

2020

J Ambient Intell Human Comput

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This dissertation characterize the Automatic Generation Control (AGC) knit together with tandem reheat and cross reheat thermal system wielding Proportional Integral (PI) and Fuzzy Logic Controller (FLC). Multi area thermal system is designed with non-linearities such as governor dead band in addition with generation rate constraint. This study is come up with a knowledgeable application of Redox flow batteries (RFB) correlative along Static Synchronous Series Compensator. In order to enhance dynamic reaction of the load frequency control, redox flow battery has been appended in couple of the units as a result of being active rejoinder and nether time persistent. Ziegler Nichols method is wielded to regulate the PI controllers. During enormous load distraction within areas, conventional control methods alone would be ineffective in diminishing frequency deflection and power of tie-line swinging as a result of medium reaction by speed governor process. In order prevail this hindrance; fuzzy logic controller has been executed within the plant. Simulation studies affirm the efficacy of the fuzzy logic controller, depends upon the parameters like overshoots, under shoots and settling time, hence AGC functioning during abnormality in power system has been ameliorated. System is also studied for hydro thermal power system with simulation outcome.

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Section: Supply Unit Modelmentioning

confidence: 99%

RETRACTED ARTICLE: Automatic generation control with fuzzy logic controller incorporating tandem and cross compound turbine

Moniya

Anand

2020

J Ambient Intell Human Comput

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“…The standard frequency-domain features were calculated, i.e., delta (δ, 0.5–4 Hz), theta (θ, 4–8 Hz), alpha (α, 8–12 Hz) and beta (β, 12–30 Hz) bands. We also calculated the less frequently used frequency-domain features, i.e., gamma (γ, >30 Hz), sigma (σ, 12–14 Hz), low alpha (α1, 8–10 Hz) and high alpha (α2, 10–12 Hz) bands [ 44 ].…”

Section: Methodsmentioning

confidence: 99%

EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization

Stančin

Frid

Cifrek

et al. 2021

Sensors

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Drowsiness is a risk to human lives in many occupations and activities where full awareness is essential for the safe operation of systems and vehicles, such as driving a car or flying an airplane. Although it is one of the main causes of many road accidents, there is still no reliable definition of drowsiness or a system to reliably detect it. Many researchers have observed correlations between frequency-domain features of the EEG signal and drowsiness, such as an increase in the spectral power of the theta band or a decrease in the spectral power of the beta band. In addition, features calculated as ratio indices between these frequency-domain features show further improvements in detecting drowsiness compared to frequency-domain features alone. This work aims to develop novel multichannel ratio indices that take advantage of the diversity of frequency-domain features from different brain regions. In contrast to the state-of-the-art, we use an evolutionary metaheuristic algorithm to find the nearly optimal set of features and channels from which the indices are calculated. Our results show that drowsiness is best described by the powers in delta and alpha bands. Compared to seven existing single-channel ratio indices, our two novel six-channel indices show improvements in (1) statistically significant differences observed between wakefulness and drowsiness segments, (2) precision of drowsiness detection and classification accuracy of the XGBoost algorithm and (3) model performance by saving time and memory during classification. Our work suggests that a more precise definition of drowsiness is needed, and that accurate early detection of drowsiness should be based on multichannel frequency-domain features.

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“…Heyat et al [55] proposed a sleep study with the input of EEG signal and extracted power spectral density features and obtained features are forwarded into decision tree classifier. The reported accuracy from the proposed model is 81.25%.…”

Section: Sn Computer Sciencementioning

confidence: 99%

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

Satapathy

Loganathan

2021

SN COMPUT. SCI.

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Sleep staging is one of the important methods for the diagnosis of the different types of sleep-related diseases. Manual inspection of sleep scoring is a very time-consuming process, labor-intensive, and requires more human interpretations, which may produce biased results. Therefore, in this paper, we propose an efficient automated sleep staging system to improve sleep staging accuracy. In this work, we extracted both linear and non-linear properties from the input signal. Next to that, a set of optimal features was selected from the extracted feature vector by using a feature reduction technique based on the ReliefF weight algorithm. Finally, the selected features were classified through four machine learning techniques like support vector machine, K-nearest neighbor, decision tree, and random forest. The proposed methodology performed using dualchannel EEG signals from the ISRUC-Sleep dataset under the AASM sleep scoring rules. The performance of the proposed methodology compared with the existing similar methods. In this work, we considered the 10-Fold cross validation strategy; our proposed methods reported the highest classification accuracy of 91.67% with the C4-A1 channel, and 93.8% with the O2-A1 channel using the Random forest classification model. The result of the proposed methodology outperformed the earlier contribution for two-class sleep states classification. The proposed dual-channel sleep staging method can be helpful for the clinicians during the sleep scoring and treatment for the different sleep-related diseases.

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RETRACTED ARTICLE: Support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel

Cited by 25 publications

References 22 publications

RETRACTED ARTICLE: Automatic generation control with fuzzy logic controller incorporating tandem and cross compound turbine

RETRACTED ARTICLE: Automatic generation control with fuzzy logic controller incorporating tandem and cross compound turbine

EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

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