**3. Results**

We tested the study on the example of the evaluation of experts for smart city and green transportation and mobility as members of the Transport and Construction Commission of the city of Košice, using a neural network. For the evaluation, we had five candidate experts for the position of member of the Transport and Construction Commission [46]. The input data were the answers of the applicants to the questions given in the form of the term set of linguistic variables, L = {H; HC; C; B}, proposed in the study, and estimates of the "coefficient of confidence" *d*; see Table 1.



We next show the process of obtaining an aggregate assessment of the competencies of the specialists using the proposed four-layer neural network. To fuzzify the input data, we defined the membership function in the numerical interval [0; 10], where H ∈ [0; 2], HC ∈ [2; 5], C ∈ [5; 8] and B ∈ [8; <sup>10</sup>].

The value of *μ Olji* was calculated using formula (11); Figure 4.

Next, to aggregate the values of the activation conditions, we calculated the value of the level of excitation of neurons using formula (12). To aggregate the values of the weights of the groups of criteria, the manager of the municipality had his own considerations about the synaptic weights according to the groups of criteria: *K*1—the most important effect (*<sup>α</sup>*1 = 10); *<sup>K</sup>*2—significant effect (*<sup>α</sup>*3 = 8); *K*3—average effect (*<sup>α</sup>*2 = 6). Then, we calculated the functions of the postsynaptic potential of the neurons of the third layer using formula (13). The results of the calculations are shown in Figure 5.

Next, we defuzzified the data and compared the levels of decision-making on the linguistic interpretation of the competencies of specialists using the activation function (14): *<sup>Z</sup>*(*<sup>e</sup>*1) = 0.5424; *<sup>Z</sup>*(*<sup>e</sup>*2) = 0.5048; *<sup>Z</sup>*(*<sup>e</sup>*3) = 0.6524; *<sup>Z</sup>*(*<sup>e</sup>*4) = 0.687; *<sup>Z</sup>*(*<sup>e</sup>*5) = 0.7371. We built a ranking of the specialists using the obtained values: *e*5;*e*4;*e*3;*e*1;*e*2. We compared the quantitative result with the original variable *Y* and found that the specialists *e*5 and *e*4 had a "specialist rating above average" and all the others had a "specialist rating—average".

**Figure 4.** Fuzzification of input data.

**Figure 5.** Values of neuronal excitation level (*Z*1; *Z*2; *Z*3) and postsynaptic potential function (*W*1; *W*2; *W*3).

As part of the study, an innovative web platform named Smart City Concept Personnel Selection [48] was developed on the basis of the proposed technology for the evaluation and selection of specialists, as shown in Figure 6.


**Figure 6.** Head screen of the Smart City Concept Personnel Selection web platform.

All of the important components on which the technology of decision support for assessing the competencies of specialists and their selection is based were placed in the settings (Figure 7).


**Figure 7.** Smart City Concept Personnel Selection web platform setup screen.

In addition, in the setup, it is easy for system analysts to build information evaluation models for various experts, such as members of other commissions. Thus, using the identifier (ID), it is possible to store and protect the adjusted information models on the server for the assessment of various experts and various users (Figure 8).


**Figure 8.** ID for the evaluation information model members of the Transport and Construction Commission.

> After selecting the model for assessing the competencies of smart city specialists and entering the input data, we proceeded to the calculations. Based on the initial assessment of the competencies of specialists and the level of rating, further decisions could be made.

> The developed technology and web platform is a useful innovative tool for managers in the concept of a smart city when assessing the competencies of specialists and choosing them to solve special or innovative tasks, which reveals the vagueness of input assessments, increases the validity of further managemen<sup>t</sup> solutions, and uses the analysis of reasoning, experience, and knowledge of managers.
