A hybrid medical decision making system based on principles component analysis, k-NN based weighted pre-processing and adaptive neuro-fuzzy inference system

2006 ◽  
Vol 16 (6) ◽  
pp. 913-921 ◽  
Author(s):  
Kemal Polat ◽  
Salih Güneş
Keyword(s):  
Decision Making ◽  
Fuzzy Inference System ◽  
Medical Decision Making ◽  
Fuzzy Inference ◽  
Component Analysis ◽  
Medical Decision ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Decision Making System

scholarly journals Robustness of Adaptive Neuro- Fuzzy Inference System for Optimal Prediction using Roulette Wheel Method

2020 ◽  
Vol 8 (5) ◽  
pp. 380-384
Keyword(s):  
Decision Making ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Optimal Prediction ◽  
Data Set ◽  
Inference System ◽  
Roulette Wheel ◽  
Neuro Fuzzy ◽  
Support Decision Making

An information system that supports automatic decision making with help of intelligent system by computerized manner. The proposed work has been developed and deployed a robust method is contributed to decision making in medical system and the diagnosis the major risk of the patients in earlier. The main goal of the proposed research is to develop data mining techniques to support decision making and to control the controllable risk factors and also overcome the other parts of organs highly affected by diabetes, kidney disease, heart condition and which in turn reduces the risk of the patients. Robustness of Adaptive Neuro-Fuzzy Inference System (RANFIS) designed a fuzzy inference system (FIS) to enrich the knowledge about the data set whose membership function parameters can be altered randomly by the process of mutation.

Neuro-Fuzzy-Based Frame Pre-Emption Using Time-Sensitive Networking for Industrial Ethernet

2021 ◽  
Vol 20 (Supp01) ◽  
pp. 2140008
Author(s):  
R. Kannamma ◽  
K. S. Umadevi
Keyword(s):  
Decision Making ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Data Traffic ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Fuzzy Techniques ◽  
Critical Traffic ◽  
Time Critical

IEEE802.1 Time-Sensitive Networking (TSN) makes it conceivable to convey the data traffic of time as well as critical applications using Ethernet shared by different applications having diversified Quality of Service (QoS) requirements for both TSN and non-TSN. TSN assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data for time-critical traffic. By holding networking resources for basic traffic, and applying different queuing and traffic shaping strategies, TSN accomplishes zero congestion loss for basic time-critical traffic. In proposed system, backpropagation algorithm is used to train the training set and fuzzy inference system methodologies such as Mamdani fuzzy inference system which has fuzzy inputs and fuzzy outputs, Sugeno FIS which has fuzzy inputs and a crisp output and adaptive-network-based fuzzy inference system has obtained from the neural network and fuzzy logic. The proposed system uses neuro-fuzzy techniques to handle frame pre-emption and reduces the time taken for decision making. It presents a decision making process using the traffic class.

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 Identifikasi Individu Berdasarkan Pola Citra Rugae Palatina Menggunakan Adaptive Neuro Fuzzy Inference System (ANFIS)

2021 ◽  
Vol 12 (2) ◽  
pp. 156
Author(s):  
Farah Hana Kusumaputri ◽  
Suryo Adhi Wibowo ◽  
Yuti Malinda
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Logic ◽  
Natural Disasters ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Principal Component ◽  
Component Analysis ◽  
Inference System ◽  
Identification Process ◽  
Neuro Fuzzy

Abstract Indonesia is a country that is in an area prone to natural disasters, such as volcanic eruptions, earthquakes, tsunamis, and others. These natural disasters often cause many victims to die. To identify the victims' identities, an identification process is needed. The identification method most commonly used today is using fingerprints, teeth, and DNA. However, this identification method still has some shortcomings. At present a more effective alternative method is offered by utilizing the palatine rugae pattern. Rugae palatina has individual characteristics and is resistant to all kinds of damage. So that Rugae palatina has the potential to be used in the process of individual identification. In this research, application of palatine rugae image processing application will be developed with data recording, image registration, feature extraction using Principal Component Analysis (PCA) method, and palatine rugae pattern classification using Adaptive Neuro Fuzzy Inference System (ANFIS) method. The expected output from this final project is a system that is able to identify individuals by utilizing the palatine rugae pattern. To get good and effective parameters for system performance, periodic testing is carried out. The sampling procedure uses original photographs directly taken from the palatine rugae, so that it will facilitate the identification process. Keyword: ANFIS, ANN, Fuzzy Logic, PCA, rugae palatina Abstrak Negara Indonesia merupakan negara yang berada di daerah rawan bencana alam, seperti erupsi gunung merapi, gempa bumi, tsunami, dan lain-lain. Bencana alam tersebut seringkali menyebabkan korban meninggal dalam jumlah yang banyak. Untuk mengenali identitas para korban tersebut diperlukannya proses identifikasi. Metode identifikasi yang paling sering digunakan saat ini yaitu menggunakan sidik jari, gigi, dan DNA. Namun, metode identifikasi tersebut masih mempunyai beberapa kekurangan. Saat ini ditawarkan metode alternatif yang lebih efektif yaitu dengan memanfaatkan pola rugae palatina. Rugae palatina memiliki sifat yang individual dan tahan terhadap segala macam kerusakan. Sehingga Rugae palatina memiliki potensi untuk digunakan dalam proses identifikasi individu. Dalam penelitian ini akan dikembangkan aplikasi pengolahan sampel citra rugae palatina dengan proses perekaman data, registrasi citra, ekstrasi ciri menggunakan metode Principal Component Analysis (PCA), dan klasifikasi pola rugae palatina menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS). Keluaran yang diharapkan dari penelitian ini adalah sebuah sistem yang mampu mengidentifikasi individu dengan memanfaatkan pola rugae palatina. Untuk mendapatkan parameter yang baik dan efektif terhadap performansi sistem, maka dilakukan pengujian secara berkala. Prosedur pegangambilan sampel menggunakan foto asli yang secara langsung diambil dari rugae palatina, sehingga akan mempermudah proses identifikasi. Kata kunci: ANFIS, ANN, Fuzzy Logic, PCA, rugae palatina 

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