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AbstractThis paper introduces two more definitions of the conditional average entropy. Some properties of the three definitions are studied and some mistakes in the preceding literature are corrected.
PublisherTaylor and Francis
Journal titleCommunications in Statistics - Theory and Methods
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Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropiesShoeibi, Afshin; Ghassemi, Navid; Khodatars, Marjane; Moridian, Parisa; Alizadehsani, Roohallah; Zare, Assef; Khosravi, Abbas; Subasi, Abdulhamit; Acharya, U Rajendra; External Collaboration; et al. (Elsevier, 2022-03-01)pileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74% in classifying into two classes and an accuracy of 99.46% in ternary classification on the Bonn dataset and 99.28% on the Freiburg dataset, reaching state-of-the-art performances on both of them.
Comparison of two-lifetime models of solid-state lighting based on sup-entropyKittaneh, Omar; Majid, M.A.; College Collaboration; Electrical and Computer Engineering (Elsevier B.V., 2019)On the basis of the efficiency function introduced by Kittaneh and Beltagy , we compare the performance of censored samples from lognormal and Weibull distributions as two possible fitting models of solid-state lighting (SSL) luminaire lifetime. The validity of the efficiency function is demonstrated through several correlations with the accuracy in estimating the mean lifetime to fail.
Deriving the Efficiency Function for Type I Censored Sample from Exponential Distribution Using Sup-EntropyKittaneh, Omar; College Collaboration; Electrical and Computer Engineering (2012)This paper utilizes information theory to quantify Efficiency of Type I Censored sample drawn from Exponential Distribution and the consequent information loss due to Censoring. Based on Awad Sup-Entropy, an Efficiency Function for Censored sample is derived explicitly. The properties of the derived Efficiency Function are explained as a function of the Exponential Parameter and the termination time of the experiment. The estimation for the termination time of the experiment for a given Efficiency is discussed. Furthermore, under certain Efficiency, the Maximum Likelihood and Interval Estimation for the Exponential Parameter are also introduced.