## 1 Introduction  
The sun is the largest source of carbon-free energy available throughout human history. A lot of research has been done to find out how to use and apply solar energy as a primary energy source [1]. Generally, solar energy application is divided into two basic groups. The first is electricity generation using photovoltaic cells, which convert direct solar energy into electricity, and the other is the thermal application category, which involves solar drying [2]. Drying is defined as a process of removing water from a product and can be applied in two steps. In the first stage, the moisture inside the product is removed to the surface and dried as a water vapor in a constant air. The second stage involves a slow drying rate and the drying process varies according to the type of material to be dried [3]. In sun drying, the product is dried at a high temperature in a closed area or a drying cabinet with the aid of hot air generated in a device known as solar energy and air heater. This drying is an efficient drying process compared to direct sun drying. The product is dried in sun dryer, high temperature and low relative humidity, in comparison with outdoor sun drying. For most agricultural products, a more suitable drying air temperature range is between 45 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, and between these values the products can be dried by solar and air heating collector systems at varying drying air temperatures [4]. The convective heat transfer coefficient (\(h_{c}\)) is an important parameter in drying rate simulation, since the temperature difference between the air and product varies with this coefficient. [PERSON] has made to evaluate the convective heat transfer coefficient during drying of various crops and to investigate influence of drying air velocity and temperature on the convective heat transfer coefficient. She has observed that the convective heat transfer coefficient increased in large amount with the increase of the drying air velocity, but increased in small amount with the rise of the drying air temperature [5]. [PERSON] and [PERSON] have studied heat and mass transfer in product drying systems. They have reported the values of convective heat transfer coefficient for wheat and gram as 12.68 and 9.62 W/m\({}^{2}\)\({}^{\circ}\)C. They have used simple regression and multiple regression technique for predicted the convective heat transfer coefficient [6]. Various predictive models for \(h_{c}\) values were established in various systems in the literature. Artificial neural networks are usually used to construct these models [7; 8; 9; 10]. Predictive models of \(h_{c}\) values were created by using SVM regression in different topics [11; 12]. The use of SVM regression for predicting \(h_{c}\) values in food drying systems has not been found in literature. So the SVM regression was chosen for the predictive model. Recently, SVM regression studies were carried out in various fields. [PERSON] et al. have focused on the estimation of yearly mean daily horizontal global solar radiation by using an approach that utilizes fuzzy regression functions with support vector machine (FRF-SVM). To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, they have conducted an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions [13]. [PERSON] at all have investigated the total volatile basic nitrogen (TVB-N) in a total of 210 pork meat pieces (tenderloin) with an average weight of approximately 200-400 g. They have developed Multivariate calibration models using partial least-squares regression (PLSR) and least-squares support vector machines (LS-SVM) in the full spectral range [14]. [PERSON] et al. have utilized conventional back propagation neural network (BPNN), radial basis function neural network (RBFNN) general regression neural network (GRNN), and support vector machine (SVM) modeling techniquesfor estimating the hourly cooling load in a building.