Data-Driven AI-Based Parameters Tuning Using Grid Partition Algorithm for Predicting Climatic Effect on Epidemic Diseases

Abstract

Adaptive Neuro-fuzzy Inference System (ANFIS) remains one of the promising AI techniques to handle data over-fitting and as well, improves generalization. Presently, many ANFIS optimization techniques have been synergized and found effective at some points through trial and error procedures. In this work, we tune ANFIS using Grid partition algorithm to handle unseen data effectively with fast convergence. This model is initialized using a careful selection of effective parameters that discriminate climate conditions; minimum temperature, maximum temperature, average temperature, wind speed and relative humidity. These parameters are used as inputs for ANFIS, whereas confirmed cases of COVID-19 is chosen as dependent values for two consecutive months and first ten days of December for new COVID-19 confirmed cases according to the Department of disease control (DDC) Thailand. The proposed ANFIS model provides outstanding achievement to predict confirmed cases of COVID-19 with R 2 of 0.99. Furthermore, data set trend analysis is done to compare fluctuations of daily climatic parameters, to satisfy our proposition, and illustrates the serious effect of these parameters on COVID-19 epidemic virus spread.

Biometric Information Recognition Using Artificial Intelligence Algorithms: A Performance Comparison

“…Subtractive clustering (SC); where generated clusters are utilized to derive iterative-based optimization clustering to estimate ANFIS parameters. This method has good representation, if high dimensional case is utilized for moderate biometric hand features [26], [33]. Optimization of ANFIS parameters using SC achieved best performance [35].…”

“…ANFIS is an AI approach, composing Artificial Neural Network (ANN) and fuzzy inference networks (FIS) as a single ANFIS model, leveraging individual limitations of ANN and FIS methods [25], [26]. One major advantage of ANFIS is its ability to make good representation of complex and non-linear connections among features [25].…”

“…There exist other ANFIS parameter estimations according to clustering mechanism such as: Fuzzy C-mean (FCM); which optimize ANFIS parameters by minimizing FCM-based derived objective function. Its major limitation is inability to estimate large number of parameters [26], [34]. Grid partition algorithm (GP); effectively handle ANFIS parameters for small sample size, its flexibility is due to inherent large amount of rules and parameters generation.…”

“…Grid partition algorithm (GP); effectively handle ANFIS parameters for small sample size, its flexibility is due to inherent large amount of rules and parameters generation. However, its main challenge is inherent exponential growth of fuzzy rules due to increase in sample size [26]. Subtractive clustering (SC); where generated clusters are utilized to derive iterative-based optimization clustering to estimate ANFIS parameters.…”

Visual Exploration of Regional Factors of the Health of Urban Residents

“…Kelly et al summarized the harmful effects of air pollution on the respiratory system based on epidemiological and toxicological studies, ranging from the worsening and progression of asthma, allergic rhinitis and chronic obstructive pulmonary disease to lung infections, bronchiectasis and cystic fibrosis [9]. Abdullahi et al used an adaptive neuro-fuzzy inference system to predict the increase in confirmed cases of new crown pneumonia based on climate information [14].…”