## 1 Introduction
Many products have been dried using IR-HA drying, including shititake mushroom [14], mint leaves [15], white mulberry [13], Kiwi and Turnip [16; 17], sweet potato [12], and savory leaves [18]. Drying is a very complex and uncertain phenomenon and there are unknown factors for them. This phenomenon has been modeled with different levels of complexity [19]. Predicting the drying kinetics of agricultural products under different conditions is very important and basic for equipment and process design, quality control, energy and fuel management, and selection of appropriate storage [20]. In order to model food processing processes such as drying and predicting the desired parameters in the design and development of systems, various methods and equations have been used. In situations where the relationships between independent and dependent parameters are complex, mathematical modeling methods will face limitations such as selecting parameters, the application of assumptions to solve equations, and the complexity of solving equations. Therefore, the best choice is to use intelligent optimization methods [21]. These methods include artificial neural networks (ANNs), adaptive fuzzy-neural inference system (ANFIS),and support vector regression (SVR), which allow adequate and accurate prediction of the drying process in industrial applications. Many authors have successfully used ANN, ANFIS, and SVR to describe the drying properties of many agricultural and food products. [PERSON] et al. [22] conducted a study to predict the moisture ratio (_MR_), energy, and exergy of onions during drying with a semi-industrial type of continuous dryer by using two different approaches ANNs and ANFIS. The results showed that the ANFIS model provided better results for all predicted parameters. In another study, the _MR_, energy, and exergy analysis of drying of blackberry in an IR-HA dryer with ultrasonic pretreatment were predicted by ANNs and ANFIS [23]. [PERSON] et al. [24] performed comparisons between mathematical and inference models to predict the _MR_ of quintec in the IR drying. According to the results, the ANFIS model with (_R_\({}^{2}\) = 0.998) had better performance than the mathematical models. SVR has also been used to predict the moisture content (_MC_) of wood during the drying process [25]. Several studies have employed ANNs to estimate the _MR_ of different products in different dryers, including green peas [26], apricot [27], stevia [28], basil seed [29], and yam slices [30]. Therefore, in this study, ANN, ANFIS, and SVR are evaluated to predict the moisture drying ratio of rice in an IR-HA dryer under different operating conditions (two levels of infrared power and three levels of inlet air temperature). Previous studies have generally focused on neural networks and semi-empirical models. These methods are powerful tools for modeling complex phenomena such as the drying process, but these methods provide district information based on drying behavior, and therefore it is inevitable to consider different models for precise simulation of the drying process. The number of studies comparing ANN, ANFIS, and SVR methods based on drying characteristics is limited in the literature. Therefore, the purposes of the present study are (a) investigation of the effect of drying temperature (40, 50, and 60 \({}^{\circ}\)C) at two levels of IR power (0.32 and 0.49 W/cm\({}^{2}\)) on parboiled hulls moisture ratio (b) the evaluation of the different topologies of ANN models as shaped by the selection of the networks, activation functions, training algorithm, the neuron and the hidden layer number, (c) the evaluation of different first-order Takagi-Sugeno type ANFIS models with different number and types of membership function for each input and output, training algorithm, number of output membership functions and number of fuzzy rules for predicting the drying characteristics of parboiled hulls, (d) the comparison of the various learning algorithms of the support vector regression method for estimating the _MR_ of drying of parboiled hulls.