## 4 Results and Discussion  
### Bibliographic Survey  
The results of the search in the various databases were wide-ranging. For the initial search, preference was given to papers in which the chosen terms appeared in the title, abstract, or keywords. As new terms were added to those initially selected, the results become narrower. This is common when broader terms are refined to specific applications within the studied field. It is noteworthy that despite the substantial volume of results, many of those works were duplicated across the defined databases. As both databases tend to organize their results pages in order of relevance, a brief selection was made based on the titles before exporting to the reference manager. Table 1 provides a summary of the search in the respective databases. A total of 3256 papers were obtained, 425 at _Science Direct_, 1654 at _Web of Science_, and 1177 at _Scopus_. From the preliminary survey conducted, a total of 271 works were selected for filtering in the respective reference managers. Out of these, 149 articles were identified as duplicates, and 35 were found to be unrelated to the theme. Consequently, the final selection comprised a total of 87 articles for which the title, author, and Pub.Y data were available. Table 2 illustrates the ranking after obtaining the respective InOrdinatio 2.0 indexes. When equal weight was given to the factors \(\Delta\), \(\lambda\), and \(\Omega\), older yet highly cited articles were obtained. In this case, the nCi and IF gained prominence in the InOrdinatio 2.0 formula. As there were more recent publications, the year factor and metric (IF) became more relevant. The article at the 11 th position exemplified this relationship. Table 2 shows that over a 10-year interval (2011-2021), recent and recognized works were prioritized. This balance of results in the RankIn spreadsheet was linked to the value initially assigned of 10 to the \(\Delta\), \(\lambda\), and \(\Omega\) factors. \begin{table}
\begin{tabular}{c c c c} \hline
**Keywords** & **Database** & **Period** & **Results** \\ \hline Artificial Neural Network; Dyes & & 2002–2023 & 233 \\ Artificial Neural Network; Dyes; Adsorption & _Science_ & 2006–2023 & 117 \\ Artificial Neural Network; Dyes; Activated Carbon & _Direct_ & 2007–2023 & 39 \\ Artificial Neural Network; Dyes; Activated Carbon; Adsorption & & 2007–2023 & 36 \\ \hline
**Accumulated at Science Direct** & & & **425** \\ \hline Artificial Neural Network; Dyes & & 1996–2023 & 568 \\ Artificial Neural Network; Dyes; Adsorption & & 2003–2023 & 520 \\ Artificial Neural Network; Dyes; Activated Carbon & & 2005–2023 & 298 \\ Artificial Neural Network; Dyes; Activated Carbon; Adsorption & & 2005–2023 & 268 \\ \hline
**Accumulated at Web of Science** & & & **1654** \\ \hline Artificial Neural Network; Dyes & & 2014–2023 & 740 \\ Artificial Neural Network; Dyes; Adsorption & _Scopus_ & 2014–2023 & 286 \\ Artificial Neural Network; Dyes; Activated Carbon & & 2014–2023 & 81 \\ Artificial Neural Network; Dyes; Activated Carbon; Adsorption & & 2014–2023 & 70 \\ \hline
**Accumulated at Scopus** & & & **1177** \\ \hline
**TOTAL** & & & **3256** \\ \hline \end{tabular}
\end{table} 
Table 1: Results of the search in _Science Direct, Scopus_, and _Web of Science_ databases according to the defined keywords. As depicted in Figure 4, an analysis of recurring keywords was carried out using VOSviewer 2023 to explore the relationships between the selected articles [48]. A clear clustering of keywords emerged, highlighting distinct research trends. The red cluster primarily emphasized the role of ANNs in optimizing the use of advanced materials such as chitosan and zinc oxide (ZnO) nanoparticles, which are widely used in adsorptive and photocatalytic processes for pollutant removal. The significant presence of ANNs in this cluster indicated its utility in predicting and enhancing the efficiency of these materials for environmental remediation, particularly in wastewater treatment.