Non stationary signal analysis and classification using FTT transform and Naive Bayes classifier

Khan, Rizwan Ahmed; Padhi, S. K.; Sahu, Badrinarayan; Behera, Suresh Kumar

doi:10.1109/pcitc.2015.7438136

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Naive Bayes termasuk salah satu algoritma dalam penggolongan probabilitas sederhana yang berdasarkan pada penerapan teori Bayes dengan asumsi indepedensi yang kuat (naive) pada fitur-fitur yang ada serta salah satu teknik sederhana untuk membangun klasifikasi model dengan menetapkan kelas untuk suatu masalah [9] [10].…”

Section: Naïve Bayesunclassified

Comparison of Accuracy in Naïve Bayes and Random Forests in Classification of Liver Disease

Lubis¹,

Erdiansyah²,

Siregar³

2021

Com, Engine, Sys, Sci

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Pada penelitian ini bertujuan untuk melakukan komparasi terhadap metode Naïve Bayes dan Random Forest dalam klasifikasi data pasien penyakit liver. Adapun data pengujian yang digunakan yaitu Indian Liver Patient Dataset (ILPD) yang diperoleh dari UCI Machine Learning Repository. Dataset tersebut memiliki 583 record data, 10 kriteria, dan 1 variable kelas serta dengan jumlah kelas sebanyak 2 kelas atribut, serta data set tersebut berjenis multivariate. Terdapat beberapa tahapan preprocessing yang dilakukan, antara lain normalisasi data yang diujikan, selanjutnya dilakukan analisis klasifikasi menggunakan metode naïvebayes dan random forest. Berdasarkan hasil pengujian yang dilakukan dalam memperoleh nilai akurasi perhitungan klasifikasi menggunakan Confusion Matrix, maka metode Random Forest memperoleh hasil yang terbaik yaitu dengan peroleh akurasi sebesar 70.60 % bila dibandingkan dengan Naïve Bayes yang hanya memperoleh akurasi sebesar 55.80 %. Sehingga Random Forest memiliki performa kinerja yang lebih unggul dalam perolehan akurasi yang dihasilkan dalam klasifikasi penyakit liver.

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Section: Naïve Bayesunclassified

Comparison of Accuracy in Naïve Bayes and Random Forests in Classification of Liver Disease

Lubis¹,

Erdiansyah²,

Siregar³

2021

Com, Engine, Sys, Sci

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“…The combination of BC and SVM for hardware explanation of PQ disturbances is discussed in [195]. Although the probability density function of single and multiple PQ events must be identified in advance in Naive-BC, it is beneficial for the identification of signal patterns applied to classify different PQ disturbances in [196]. Application of BC networks in renewable energy sources, such as solar thermal, geothermal, hydroelectric energies, SPV, WE and biomass is explained in [197].…”

Section: E: Bayesian Classifier Based Pqds Classificationmentioning

confidence: 99%

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

et al. 2020

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The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This paper presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

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“…In a Bayesian Classifier, the learning agent builds a probabilistic model of the features and uses that model to predict the classification of a new example [24], [25]. Naïve Bayesian Classifier is the simplest case which assumes that the input features are independent of each other for a given condition of classification features.…”

Section: Naive Bayesian Classifier (Nbc)mentioning

confidence: 99%

Design and Implementation of a Speller based on EMG Signal

Prasad¹,

Swarnkar²,

Hashmi³

et al. 2017

IJCIS

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A speller is a communication device designed for those suffering from neuromuscular disorders having difficulty to speak. An EMG based design is proposed which uses eye blinks for character selection that offers high accuracy and more comfort to the user. The eye blink signals are feature extracted using Fast Walsh Hadamard Transform (FWHT) and classified using Naive Bayes Classifier. The proposed design has achieved an accuracy of 100% for all users. The average values achieved for spelling rate was 12.12 characters/minute and ITR was 71.39 bits/minute.

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Non stationary signal analysis and classification using FTT transform and Naive Bayes classifier

Cited by 6 publications

References 13 publications

Comparison of Accuracy in Naïve Bayes and Random Forests in Classification of Liver Disease

Comparison of Accuracy in Naïve Bayes and Random Forests in Classification of Liver Disease

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

Design and Implementation of a Speller based on EMG Signal

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