fuzzy inference system
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2022 ◽  
Vol 24 (3) ◽  
pp. 1-19
Author(s):  
Sunita Tiwari ◽  
Sushil Kumar ◽  
Vikas Jethwani ◽  
Deepak Kumar ◽  
Vyoma Dadhich

A news recommendation system not only must recommend the latest, trending and personalized news to the users but also give opportunity to know about the people’s opinion on trending news. Most of the existing news recommendation systems focus on recommending news articles based on user-specific tweets. In contrast to these recommendation systems, the proposed Personalized News and Tweet Recommendation System (PNTRS) recommends tweets based on the recommended article. It firstly generates news recommendation based on user’s interest and twitter profile using the Multinomial Naïve Bayes (MNB) classifier. Further, the system uses these recommended articles to recommend various trending tweets using fuzzy inference system. Additionally, feedback-based learning is applied to improve the efficiency of the proposed recommendation system. The user feedback rating is taken to evaluate the satisfaction level and it is 7.9 on the scale of 10.


2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Nasrin Taherkhani ◽  
Mohammad Mehdi Sepehri ◽  
Roghaye Khasha ◽  
Shadi Shafaghi

Abstract Background Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed to rank the patients based on kidney allocation factors. The main objective was to develop an expert system, which would mimic the expert intuitive thinking and decision-making process in the face of the complexity of kidney allocation. Methods In the first stage, kidney allocation factors were identified. Next, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to weigh them. The purpose of this stage is to develop a point scoring system for kidney allocation. Fuzzy if-then rules were extracted from the United Network for Organ Sharing (UNOS) dataset by constructing the decision tree, in the second stage. Then, a Multi-Input Single-Output (MISO) Mamdani fuzzy inference system was developed for ranking the patients on the waiting list. Results To evaluate the performance of the developed Fuzzy Inference System for Kidney Allocation (FISKA), it was compared with a point scoring system and a filtering system as two common approaches for kidney allocation. The results indicated that FISKA is more acceptable to the experts than the mentioned common methods. Conclusion Given the scarcity of donated kidneys and the importance of optimal use of existing kidneys, FISKA can be very useful for improving kidney allocation systems. Countries that decide to change or improve the kidney allocation system can simply use the proposed model. Furthermore, this model is applicable to other organs, including lung, liver, and heart.


2022 ◽  
Author(s):  
M.Uma Maheswar Rao ◽  
Kanhu Charan Patra ◽  
Suvendu Kumar Sasmal

Abstract Floods disrupt human activities, resulting in the loss of lives and property of a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because of its complexity, understanding and forecasting rainfall is incredibly a challenge. This study investigates the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting rainfall using several surface weather parameters as predictors. An ANFIS model is developed for forecasting rainfall over the Upper Brahmani Basin by using 30 years of climate data. A hybrid model with six membership functions gives the best forecast for an area. The suggested method blends neural network learning capabilities with language representations of fuzzy systems that are transparent. The application of ANFIS is to the upper Brahmani river basin is tried for the first time. The ANFIS model with various input structures and membership functions has been built, trained, and tested to evaluate the capability of the model. Statistical performance indices are used to evaluate the performance. Using the developed model, forecast is done for year 2021 – 2030.


2022 ◽  
Author(s):  
Asghar Dabiri ◽  
Nader Jafarnia Dabanloo ◽  
Fereidoun Nooshiravan ◽  
Keivan Maghooli

Abstract This paper presents design and simulation of an Interval type-2 fuzzy system (IT2FS) based, Adaptive neuro-fuzzy inference system(ANFIS) pacemaker controller in MATLAB. After designing the type-1 fuzzy logic model, the stability of the designed system has been verified in the time-domain (unit step response). In previous works, type-1 (IT1FS) model step response was analyzed and compared with the other PID and Fuzzy models that only least-square-estimation and the backpropagation algorithms are used for tuning membership functions and generation of type-1 fis (fuzzy inference system) file, but at current work Fuzzy C Means (FCM) method that shows better results has been used. The pacemaker controller determines the pacing rate and adjusts the heart rate of the patient with respect to the reference input signal. The rise-time, overshoot and settling-time have been improved significantly.


Author(s):  
Haripriyan Uthayakumar ◽  
Perarasu Thangavelu ◽  
Saravanathamizhan Ramanujam

Introduction: The estimation of air pollution level is well indicated by Air Quality Index (AQI), which tells how unhealthy the ambient air is and how polluted it can become in near future. Hence, the predictions or modeling of AQI is always of greater concern among researchers and this present study aims to develop such a model for forecasting the AQI. Materials and methods: A combination of Artificial Neural Network (ANN) and Fuzzy logic (FL) system, called Adaptive Neuro-Fuzzy Inference System (ANFIS) have been considered for model development. Daily air quality data (PM2.5 and PM10) and meteorological data (temperature and humidity) over a period of March 2020 to March 2021 were used as the input data and AQI as the output variable for the ANFIS model. The performances of models were evaluated based on Root Mean Square Error (RMSE), Regression coefficient (R2) and Average Absolute Relative Deviation (AARD). Results: A total of 100 datasets is split into training (70), testing (15) and simulation (15). Gaussian and Constant membership functions were employed for classifications and the final index consisted of 81 inference (IF/THEN) rules. The ANFIS Simulation result shows an R2 and RMSE value of 0.9872 and 0.0287 respectively. Conclusion: According to the results from this study, ANFIS based AQI is a comprehensive tool for classification of air quality and it is inclined to produce accurate results. Therefore, local authorities in air quality assessment and management schemes can apply these reliable and suitable results.


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