## 3 Results and Discussion
### Other Algorithms in Artificial Intelligence vs. Drying  
The most well-known neural model has become the MLP-type network. The choice of the model is identified by a simple structure with which any computational operation can be effectively implemented. This has also translated into the use of this topology, most often in convection spray drying. At present, there is little work using other artificial intelligence learning methods in spray and convection drying. Table 3 shows other methods of learning that appeared in the last few years. In 2020, [PERSON] et al. [54] conducted a study to predict the moisture ratio in persimmon fruit. Persimmon fruit is characterized by a high content of bioactive components; it contains fiber, phenolic compounds and minerals, among others. The above features make persimmon fruit a preferred choice for healthy eating [106; 107]. It is worth noticing that apart from the MLP network, the support vectors method (SVM) and k-nearest neighbors (k-NN) were also applied. The SVM tool, as a method of computational intelligence, gave better results compared to ANN and k-NN. SVM helped give a view of a wider range of experimental data. [PERSON] et al. (2020) [54] claimed that ANN was limited to determined experimental conditions in most cases. Application of SVM, for which the aforementioned determination reached the ideal level of R\({}^{2}\) = 1.00, presented an alternative modeling method of behavior during the process of drying the slices of persimmon fruit [54]. For comparison, the application of the k-NN method, for which the coefficient of determination was R\({}^{2}\) = 0.9591, served as one of the regression models, which were created for the needs of predicting moisture ratio in date fruits. Date fruits are one of the most popular fruits, especially in Iran, both in fresh and dried forms [108]. Date fruits are rich in fiber, vitamins, minerals and antioxidants [109; 110; 111]. Date consumption can prevent atherosclerosis development, which translates into a lower risk of other cardiovascular diseases. A dried date has similar nutritional values, which is why the process of date fruit drying turned out to be so important [108]. In the above case, the researchers used both methods using the k-NN random forest algorithm, for which it was possible to obtain coefficient R at the level of R\({}^{2}\) = 0.959. Three RF models were trained with different sets of parameters. In order to optimize the above method, on account of the bigger number of parameters of the RF method than in the k-NN model, [PERSON] et al. (2021) [108] proposed the combination of calculations obtaining approximately 4000 possibilities. Simulations of the drying process were also based on RBF neural network topology. [PERSON] (2022) [65] compared the learning results of MLP with RBF. In RBF, as in MLP, the input variables were air temperature, air velocity and drying time, and the output variable was the moisture concentration of the apple slice. The number of neurons is based on the input and output parameters. The coefficient of determination using RBF to evaluate apple slice moisture reached a value of R\({}^{2}\) = 0.98. On the other hand, carrot is a type of vegetable that is well-known for their high content of \(\beta\)-carotene. B-carotene plays an important role in the human diet due to its high content of vitamin A [112]. It is also known for its antioxidant activity, which involves the physical and chemical scavenging of free radicles [73; 113]. In the study, different types of drying and different drying conditions were used, as a result of which six groups of samples were obtained, among others, in the process of convective drying. It is worth adding that in this case MobileNet architecture was applied in the process of learning, which was based on separate convolutions [114]. On the basis of the experiment that was carried out, it can be concluded that effective discrimination of the objects contained in digital images, i.e., dried carrots samples, is possible using CNN.