# A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays
## 5 Conclusions  
The proposed UCP model uses two different ANN architectures and four different datasets, a sigmoid activation function, a fuzzification method, and a Taguchi method to estimate the effort and cost of software development. By monitoring the MMRE value and the convergence rate of each of these architectures, this model gives much better results compared to the previous two models. Based on the three performed parts of the experiment, it was concluded that the ANN-L16 architecture converges after the fourth iteration and gives an MMRE value of only 7.5%, which is 35.8% better than the first COCOMO2000 model. The error value of the UCP model is 21.3% lower than the other proposed COSMIC FP model. Following the prediction through all parts of the experiment, both ANN architectures of this model have a value of 100%, which means that the model is exact, accurate, and reliable. In addition to the MMRE value, the influence of the dependent variables UUCP and AUCP was monitored to check the influence on the change in the MMRE value. The resulting error is less than 0.5%, so it can be concluded that the ANN-L36 prim architecture and vice versa can replace the ANN-L16 architecture. Compared to the results obtained in previous studies, the best so far being 10% [22], our proposed approach gave a better result. The main advantages of this model are as follows: the number of iterations being in the interval from 4 to 6, which means reduced effort estimation time thanks to the exceptional convergence rate of both architectures; the simplicity of the two proposed ANN architectures; the high coverage of different real effort values; and the lowest MMRE value being 7.5%. A possible drawback is the finding of new methods that could further reduce the MMRE value. There are no specific limitations in applying this approach. This model can be used alone or in combination with the previous two for assessment depending on the company's historical data for which the software is implemented. Although not as standardized as the previous two, it is increasingly used  
Figure 10: MMRE value for the approaches used. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **COCOMO2000 and ANN** & **COSMIC FP and ANN** & **UCP and ANN** \\ \cline{2-5} MMRE (\%) & ANN-L36 & ANN-L36 prim & ANN-L16 & ANN-L36 prim \\  & 43.3\% & 28.8\% & 7.5\% & 7.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: COCOMO2000 and ANN vs. COSMIC FP and ANN vs. UCP and ANN. by software companies, software engineers, and teams to assess the effort required to implement software projects effectively. Possible applications of the proposed model are as follows: signal processing; image and speech recognition; the recognition and processing of natural languages and different types of knowledge; the recognition of printed texts; and others. This model can also be successfully used in the medical sciences to construct various software solutions to diagnose many diseases. In addition, it is widely used in meteorology to forecast weather conditions. It can be relevant in nuclear science, robotics, automatic control, telecommunications, finance, and banking services. Numerous new applications of the proposed model of artificial intelligence are expected in the future. Future research will focus on constructing models to solve problems related to cybercrime. Conceptualization, N.R., D.R., M.I. and L.L. ; Data curation, N.R., D.R., M.I. and L.L. ; Formal analysis, N.R., D.R., M.I. and L.L. ; Funding acquisition, N.R., D.R., M.I. and L.L. ; Investigation, N.R., D.R., M.I. and L.L. ; Methodology, N.R., D.R., M.I. and L.L. ; Project administration, N.R., D.R., M.I. and L.L. ; Resources, N.R., D.R., M.I. and L.L. ; Software, N.R., D.R., M.I. and L.L. ; Supervision, N.R., D.R., M.I. and L.L. ; Validation, N.R., D.R., M.I. and L.L. ; Visualization, N.R., D.R., M.I. and L.L. ; Writing--original draft, N.R., D.R., M.I. and L.L. ; Writing--review & editing, N.R., D.R., M.I. and L.L. All authors have read and agreed to the published version of the manuscript.