# _Separtial system innovation_
## 9 Results
### Research Questions
The analysis reveals a little prevalence for DL, 56 papers out of 100 (56%): this may be explained considering the recent impulse given by technological evolution favoring the experimentation of popular solutions from other application fields but with a resistance opposed by consolidated solutions that require fewer resources and fewer investments as in the case of ML. A few papers report both ML and DL methods aimed to compare their results. The exploratory nature of the current phase that emerged from this SLR justifies this tension between the two approaches. A deeper analysis of the situation is presented in Tables 3 and 4 for the two approaches, showing the types of algorithms the authors implement. For the sake of brevity, a full description of all methodologies in Tables 3 and 4 is not reported here, but references have been provided for each of them. Regarding the fourth research question (RQ.4), Figure 8 presents an analysis of the selected papers from the perspective of the nature of the datasets employed for the application of AI. This dataset characteristic serves as a critical proxy for assessing the current level of effort and maturity in this domain. The results can be further categorized into three main types: data derived from real-world industrial datasets, which originate from direct investigations and investments in operational plants; lab-generated data, produced under controlled conditions with limited investments, including both experimental and simulated data; and data sourced from third-party entities, such as literature or aggregated datasets, often reflecting early-stage studies with low investment levels, potentially indicative of skepticism, limited interest in AI-based solutions, or lack of data. New models for data generation are emerging that also use GANs and could help solve data scarcity problems, such as the new technique described in [173]. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Algorithm** & **Count** & **Reference** \\ \hline Convolutional Neural Networks (CNN) & 25 & [170] \\ \hline Artificial Neural Networks (ANN) & 24 & [171] \\ \hline Recurrent Neural Networks (RNN) & 6 & [172] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RQ.3.1 Deep Learning Algorithms. \begin{table}
\begin{tabular}{l c} \hline \hline
**Algorithm** & **Count** & **Reference** \\ \hline XG Boost & 14 & [157] \\ \hline Random Forest & 6 & [158] \\ \hline Decision Tree & 4 & [159] \\ \hline LightGBM & 4 & [160] \\ \hline Fuzzy Logic & 3 & [161] \\ \hline Support Vector Machine (SVM) & 3 & [162] \\ \hline k-Nearest Neighbors (k-NN) & 3 & [163] \\ \hline CatBoost & 2 & [164] \\ \hline Gaussian Bases Process Regression & 2 & [165] \\ \hline Bayesian Network & 1 & [166] \\ \hline Logistic Regression & 1 & [167] \\ \hline Rough set theory (RST) & 1 & [168] \\ \hline Explainable Boosting Machine (EBM) & 1 & [169] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RQ.3.1 Machine Learning Algorithms. Approximately 40% of the papers analyzed rely on industrial data, while the remaining papers are distributed among the other dataset categories. When the percentages for literature and aggregated data are combined, their prevalence corresponds to the dominance of methodological papers observed in Figure 7, aligning with the interpretation that the adoption of AI remains in its early stages. This is again a sign of a low maturity level rather than of cautiousness, but, in our opinion, the significant percentage of datasets consisting of real industrial data (and some literature data may be assimilated, as potential industrial data not produced in autonomy) confirms the effective efforts and interest rather than an attempt to ride the wave of public funding. In fact, a very interesting result in support of this interpretation also emerges from RQ.5, as, in most cases, AI plays a major role in the context presented in the papers (95 out of 100 papers), while a minority of papers report a support role in processes.