scholarly journals Prediction of the effects of environmental factors towards COVID-19 outbreak using AI-based models

2021 ◽  
Vol 10 (1) ◽  
pp. 35
Author(s):  
Khalid Mahmoud ◽  
Hatice Bebiş ◽  
A. G. Usman ◽  
A. N. Salihu ◽  
M. S. Gaya ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Fuzzy Inference ◽  
Classical Model ◽  
The Novel ◽  
Inference System ◽  
Performance Skills ◽  
Determination Coefficient ◽  
Test Models ◽  
Neuro Fuzzy

<p>The need for elucidating the effects of environmental factors in the determination of the novel corona virus (COVID-19) is very vital. This study is a methodological study to compare three different test models (1. Artificial neural networks (ANN), 2. Adaptive neuro fuzzy inference system (ANFIS), 3. A linear classical model (MLR)) used to determine the relationship between COVID-19 spread and environmental factors (temperature, humidity and wind). These data were obtained from the studies (Pirouz, Haghshenas, Haghshenas, &amp; Piro, 2020) with confirmed COVID-19 patients in Wuhan, China, using temperature, humidity and wind as the independent variables. The measured and the predicted results were checked based on three different performance indices; Root mean square error (RMSE), determination coefficient (R2) and correlation coefficient (R). The results showed that ANFIS and ANN are more promising over the classical MLR models having an average R-values of 0.90 in both calibration and verification stages. The findings indicated that ANFIS outperformed MLR and ANN. In addition, their performance skills boosted up to 25% and 9% respectively based on the determination coefficient for the prediction of confirmed COVID-19 cases in Wuhan city of China. Overall, the results depict the reliability and ability of AI-based models (ANFIS and ANN) for the simulation of COVID-19 using the effects of various environmental variables. </p>

scholarly journals Predictive Modeling of Henry’s Law Constant in Chemical Structures Using LSSVM and ANFIS Algorithms

2020 ◽  
Author(s):  
Qikai Wang ◽  
Aiqin Yao ◽  
Manouchehr Shokri ◽  
Adrienn A. Dineva
Keyword(s):  
Chemical Structure ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Support Vector ◽  
Inference System ◽  
Chemical Structures ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Accuracy Of Measurement

Henry&rsquo;s constants for different existing compounds in water have great importance in transfer calculations. Measurement of these constants face different difficulties including high costs of experiment and low accuracy of measurement apparatus. Due to these facts, proposing a low cost and accurate approach becomes highlighted. To this end, adaptive neuro-fuzzy inference system (ANFIS) and least squares support vector machine (LSSVM) have been used as Henry&rsquo;s constant predictor tools. The molecular structure of compounds has been used as inputs of models. After training the models, the visual and mathematical studies of outputs have been done. The coefficients of determination of LSSVM and ANFIS algorithms are 0.999 and 0.990 respectively. According to the comprehensiveness of databank and accurate prediction of algorithms, it can be concluded that LSSVM and ANFIS algorithms are accurate methods for prediction of Henry&rsquo;s constant in wide range of chemical structure of compounds in water.

Determination of Cutting Disc Deformation Using Axial Forces Produced during the Cutting Process

2014 ◽  
Vol 598 ◽  
pp. 124-128 ◽  
Author(s):  
Emre Akarslan ◽  
Fatih Onur Hocaoğlu ◽  
Ismail Ucun
Keyword(s):  
Fuzzy Inference ◽  
Three Dimensional ◽  
Experimental Setup ◽  
Inference System ◽  
Axial Forces ◽  
Neuro Fuzzy ◽  
Cutting Processes ◽  
Sawing Process ◽  
Innovative Techniques

The reliability of the cutting disc in a sawing process is of vital importance in industry. There exist a lot of reported accidents due to damaged disc usage. In most cases the damage on the disc is not visible. Therefore innovative techniques are required to determine the damages. For this aim an experimental setup is built in Afyon Kocatepe University. Different experiments are performed. While experiments different parameters are measured and calculated. In this paper axial forces produced while the cutting processes are studied. Each experiment is represented by a vector of three dimensional axial forces (Fx, Fy, Fz). Experiments are repeated using four different class of cutting disc (solid, less damaged, much damaged and broken). An Adaptive Neuro Fuzzy Inference System (ANFIS) structure is proposed to classify the deformations that occur on a cutting disc in sawing processes. The results indicate that proposed ANFIS structure is very effective on classification.

Evapotranspiration Prediction Using Adaptive Neuro-Fuzzy Inference System and Penman FAO 56 Equation for St. Johns, FL, USA

2017 ◽  
Author(s):  
Mustafa Mamak ◽  
Fatih Üneş ◽  
Yunus Ziya Kaya ◽  
Mustafa Demirci
Keyword(s):  
Fuzzy Inference System ◽  
Empirical Formula ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Anfis Model ◽  
Inference System ◽  
Determination Coefficient ◽  
Neuro Fuzzy ◽  
Hydraulic Design ◽  
Design Calculations

Evapotranspiration (ET) estimation is a primary problem for irrigation engineers and hydraulic designers because it is an important part of hydrologic cycle. Even it is non-negligible in hydraulic design calculations, it is not clear enough to estimate or calculate ET. There are some meteorological parameters which effect ET directly or indirectly such as Relative Humidity (RH), Solar Radiation (SR), Air Temperature (AT) and Wind Speed (U). In this study authors used Adaptive Neuro-Fuzzy Inference System (ANFIS) for prediction of ET and results are compared with Penman FAO 56 empirical formula. 1158 daily AT, SR, RH and U values are used to train ANFIS model and 385 daily values are used to test it. ANFIS model determination coefficient with daily observed ET values found as 0.909. Also test set values are used to calculate Penman FAO 56 formula and the determination coefficient of Penman FAO 56 with daily observed ET values found as 0.857. For the comparison of the ANFIS model and Penman FAO 56 formula results Mean Square Error (MSE) and Mean Absolute Error (MAE) are computed. According to the comparison it is understood that ANFIS model has better performance than Penman FAO 56 empirical formula for the prediction of daily ET.

scholarly journals PENYUSUNAN DAN PENERAPAN METODE REGRESSION ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (RANFIS) UNTUK ANALISIS DATA KURS IDR/USD

2020 ◽  
Vol 9 (2) ◽  
pp. 182-192
Author(s):  
Lamik Nabil Mu'affa ◽  
Tarno Tarno ◽  
Suparti Suparti
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Interest Rates ◽  
Price Index ◽  
Fuzzy Inference ◽  
Predictor Variable ◽  
Inference System ◽  
Neuro Fuzzy ◽  
The Stability

The exchange rate of rupiah is one of the important prices in an open economy because the exchange rate can be used as a tool to measure the economic condition of a country. The movement of the rupiah exchange rate affected the Indonesian economy, maintaining the stability of the rupiah exchange rate became an important thing to do. In an effort to maintain the stability of the rupiah exchange rate, the factors that influence it must first be identified. Several factors affect the IDR / USD exchange rate, namely the large trade price index, foreign exchange reserves, money supply and interest rates. In this study, the Regression Adaptive Neuro Fuzzy Inference System (RANFIS) method was used to analyze the effect of predictor variables on IDR / USD exchange rates. The optimal RANFIS model is strongly influenced by three things, namely the determination of input predictor variable, membership functions, and number of clusters. Determination of the optimal RANFIS model is measured based on the smallest MAPE in-sample. Based on empirical studies applied to predictor variables on IDR / USD exchange rates, it was found that the RANFIS model was optimal, namely with 3 predictor variable inputs consisting of large trade price index variables, money supply and interest rates; with the gauss membership function; 2 clusters and rules produce an MAPE in-sample of 1.93% and an MAPE out-sample of 2.68%, so the performance of the RANFIS model has a very good level of accuracy.

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