Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction

2007 ◽  
Vol 37 (2) ◽  
pp. 227-244 ◽  
Author(s):  
Abdulhamit Subasi
Keyword(s):  
Feature Extraction ◽  
Epileptic Seizure ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Seizure Detection ◽  
Inference System ◽  
Epileptic Seizure Detection ◽  
Neuro Fuzzy

scholarly journals Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 5 (1) ◽  
pp. 35-44
Author(s):  
Suwanto Suwanto ◽  
M. Hasan Bisri ◽  
Dian Candra Rini Novitasari ◽  
Ahmad Hanif Asyhar
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
The Brain

Epilepsy is a disease that attacks the brain and results in seizures due to neurological disorders. The electrical activity of the brain recorded by the EEG signal test, because EEG test can be used to diagnose brain and mental diseases such as epilepsy. This study aims to identify whether a person has epilepsy or not along with the result of accurate, sensitivity, and precision rate using Fast Fourier Transform (FFT) and Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The FFT is used to transform EEG signals from time-based into frequency-based and continued with feature extraction to take characteristics from each filtering signal using the median, mean, and standard deviations of each EEG signal. The results of the feature extraction used for input on the category process based on characteristics data (classification) using ANFIS. EEG signal data is obtained from epilepsy center online database of Bonn University, German. The results of the EEG signal classification system using ANFIS with two classes (Normal-Epilepsy) states accuracy, sensitivity, and precision of 100%. The classification systems with three class division (Normal-Not Seizure Epilepsy-Epilepsy) resulted in an accuracy of 89.33% sensitivity of 89.37% and precision of 89.33%.

Internet of Things and Cloud Enabled Hybrid Feature Extraction with Adaptive Neuro Fuzzy Inference System for Diabetic Retinopathy Diagnosis

2020 ◽  
Vol 17 (12) ◽  
pp. 5261-5269
Author(s):  
K. Parthiban ◽  
K. Venkatachalapathy
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Internet Of Things ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fundus Image ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Hybrid Feature Extraction

At present times, the diabetic retinopathy (DR) become high and it is required to design an Internet of Things (IoT) enabled DR diagnosis tool to assist the diagnosis process of remote patients. This study designs and develops IoT and cloud computing based Hybrid Feature Extraction (HFE) with Adaptive Neuro Fuzzy Inference System (ANFIS) for DR detection and classification model, abbreviated as HFE-ANFIS model. The proposed model initially captures the retinal fundus image of the patient using the IoT enabled head mounted camera and transmit the images to the cloud server, which executes the diagnosis process. The image preprocessing takes place using three stages namely color space conversion, filtering, and contrast enhancement. Next, segmentation process takes place using fuzzy c-means (FCM) model to identify the diseased portions in the fundus image. Then, HFE based feature extraction and ANFIS based classification processes are carried out to grade the different levels of DR. The performance validation of the HFE-ANFIS model takes place against MESSIDOR dataset and the results are investigated under different dimensions. The simulation outcome indicated that the HFE-ANFIS model has offered superior performance to other methods with the maximum average sensitivity of 94.55%, specificity of 96.41%, precision of 94.66% and accuracy of 95.97%.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

Sign in / Sign up

Export Citation Format

Share Document