Implementation of fuzzy inference system in children skin disease diagnosis application

2015 ◽  
Author(s):  
Aditya Agung Putra ◽  
Rinaldi Munir
Keyword(s):  
Fuzzy Inference System ◽  
Skin Disease ◽  
Fuzzy Inference ◽  
Disease Diagnosis ◽  
Inference System

scholarly journals The development of Adaptive Neuro-Fuzzy Inference System model to diagnosis diabetes disease data set

MATEMATIKA ◽  
2017 ◽  
Vol 33 (1) ◽  
pp. 11
Author(s):  
Mamman Mamuda ◽  
Saratha Sathasivan
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Disease Diagnosis ◽  
Learning Ability ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Linguistic Rules ◽  
Gradient Descent Algorithm

Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.

scholarly journals Multi-disease big data analysis using beetle swarm optimization and an adaptive neuro-fuzzy inference system

2021 ◽  
Author(s):  
Parminder Singh ◽  
Avinash Kaur ◽  
Ranbir Singh Batth ◽  
Sukhpreet Kaur ◽  
Gabriele Gianini
Keyword(s):  
Heart Disease ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Medical Decision ◽  
Complex Data ◽  
Swarm Optimization ◽  
Inference System ◽  
Neuro Fuzzy

AbstractHealthcare organizations and Health Monitoring Systems generate large volumes of complex data, which offer the opportunity for innovative investigations in medical decision making. In this paper, we propose a beetle swarm optimization and adaptive neuro-fuzzy inference system (BSO-ANFIS) model for heart disease and multi-disease diagnosis. The main components of our analytics pipeline are the modified crow search algorithm, used for feature extraction, and an ANFIS classification model whose parameters are optimized by means of a BSO algorithm. The accuracy achieved in heart disease detection is$$99.1\%$$99.1%with$$99.37\%$$99.37%precision. In multi-disease classification, the accuracy achieved is$$96.08\%$$96.08%with$$98.63\%$$98.63%precision. The results from both tasks prove the comparative advantage of the proposed BSO-ANFIS algorithm over the competitor models.

scholarly journals ANALISIS DIAGNOSA PENYAKIT TANAMAN PADI DENGAN METODE FUZZY INFERENCE SYSTEM DAN CERTAINTY FACTOR

2018 ◽  
Vol 5 (1) ◽  
pp. 110
Author(s):  
Muliadi Muliadi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Disease Diagnosis ◽  
Rice Cultivation ◽  
Certainty Factor ◽  
Inference System ◽  
Rice Plants ◽  
Rice Disease ◽  
Value Measure

<p><em>Rice plants are now many been developed in the swamp land. One of the problems is disease of rice is a risk that must be faced and counted in rice cultivation to increase production as expected. The purpose of this research is an analysis of rice disease diagnosis that grows in wetlands by applying the method of Fuzzy Inference System and Certainty Factor. Fuzzy Inference System used in this system is fuzzy Tsukamoto used to obtain the value measure of belief (MB) and a measure of disbelief (MD) symptoms of the disease. And the certainty factor (CF) for the assessment of each disease. The result that obtained is the analysis succeeded in giving a diagnosis of disease in rice of symptoms that attack the rice.</em></p><p><em><strong>Keywords</strong>:<strong> </strong>Rice Plants, Diagnosis, Fuzzy Inference System Tsukamoto, Certainty Factor.</em></p><p><em>Tanaman padi sekarang banyak dikembangkan di lahan rawa. Salah satu masalahnya adalah penyakit padi, yang merupakan resiko yang harus dihadapi dan diperhitungkan dalam budidaya padi untuk meningkatkan produksi yang sesuai dengan harapan. Tujuan penelitian ini adalah membuat analisis diagnosa penyakit padi yang tumbuh di lahan rawa dengan menerapkan metode Fuzzy Inference System dan Certainty Factor. Fuzzy Inference System yang digunakan dalam sistem ini adalah Fuzzy Tsukamoto yang digunakan untuk memperoleh nilai measure of belief  (MB) dan measure of disbelief (MD) gejala terhadap suatu penyakit. Sedangkan certainty factor (CF) untuk pemberian nilai masing-masing penyakit. Hasil yang didapatkan yaitu analisis ini berhasil memberikan diagnosa penyakit yang padi dari gejala-gejala yang menyerang padi tersebut.</em></p><em><strong>Kata kunci</strong>:<strong> </strong>Tanaman Padi, Diagnosa, Fuzzy Inference System Tsukamoto, Certainty Factor.</em>

scholarly journals Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

2022 ◽  
Vol 70 (3) ◽  
pp. 5305-5319
Author(s):  
Talha Mahboob Alam ◽  
Kamran Shaukat ◽  
Adel Khelifi ◽  
Wasim Ahmad Khan ◽  
Hafiz Muhammad Ehtisham Raza ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Disease Diagnosis ◽  
Inference System ◽  
Diagnosis System

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.

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