## 4 Data Analysis and Discussion
### Descriptive Analysis  
For this research, data have been sampled to perform a cluster and frequency analysis. On the one hand, we have considered the distribution of articles published in journals illustrated in Figure 2 and listed in Table 2 per year (as shown in Figure 3) over the sample period: from 2000 to 2020. In fact, Table 2 presents information on the number of articles published in different journals related to neural processing, as well as the total number of articles across all of the journals. Table 2 can be used to understand the relative importance and popularity of the different journals in the field of neural processing. In this study, we adopted 6 journals and 10,661 articles. Figure 3 shows the distribution of the total number of articles published in the six selected journals related to neural processing from 2000 to 2020. Figure 3 displays the distribution of articles per year from 2000 to 2020. It provides a visual representation of the number of articles published in the field of neural processing over time. On the other hand, Figure 2 illustrates the distribution of articles per journal. It shows the number of articles published in six different journals related to neural processing, providing insights into the relative importance and popularity of each journal in the field. Additionally, Table 2 complements Figure 2 by presenting information on the number of articles published in each journal, as well as the total number of articles across all the journals. The collected data include the title, authors, year of publishing, journal name, and volume and issue number of the research. The studies dealing with ANNs have increased in the last few years, which is expected due to their efficiency and effectiveness. Figure 1: Research Methodology. The sampled articles deal with ANN research fields and are distributed according to three time intervals (i.e., 2007-2011, 2012-2015, and 2016-2019) (Some of the old papers, especially those published before the mid-2000s, are useless due to the lack of one or more important key features (e.g., title, year of publishing, or empty keyword list)) as shown in Figure 4. They are distributed as follows: 21.4% of the references are in the time period (i.e., 2007-2011, 2012-2015, and 2016-2019), and 2016-2019). \begin{table}
\begin{tabular}{l l} \hline \hline
**Journal Name** & **Total Articles** \\ \hline Neural processing Letters & 1414 \\ Journal of Neural Transmission & 2429 \\ Neural Computing and Applications & 4747 \\ Artificial Intelligence Review & 782 \\ Optical Memory and Neural Networks & 440 \\ Journal of Computational Neuroscience & 799 \\
**Total** & **10,661** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of articles per journal. Figure 3: Distribution of articles per year. Figure 2: Distribution of articles per journal. from 2007 to 2011, 29.2% of the references are in the period 2012 and 2015, and 49.4% of the references are in the period 2016 and 2019. On the other hand, the analysis step extracts and ranks the relevant keywords that are related to neural networks on the base of their frequency (number of documents) in the dataset. Our starting point is 35,973 keywords associated with adopted articles is 35,973. They are extracted from a corpus of the publications from journals listed in Table 2 and distributed according to the time intervals mentioned above. Their distribution is performed according to the same time intervals. Figure 5 displays the distribution of keywords extracted from the publications in the corpus over the three time periods considered in this study: 2007-2011, 2012-2015, and 2016-2019. The total number of keywords extracted from the corpus is 35,973. It is observed that the highest proportion of keywords (49.5%) is found in the period 2016-2019, while the lowest proportion (21.6%) is found in the period 2007-2011. This indicates an increasing trend in the use of keywords related to neural processing over time.