## 2 Methodology
### Sateg 1--Planning the Review
#### 2.1.2 Step 2 Developing and Validating the Review Protocol  
The review of the literature was structured into three key components: search strategy, inclusion criteria, and exclusion criteria. The search strategy defined the search string used and the databases selected, ensuring the identification of high-quality and relevant studies. The inclusion criteria established the essential characteristics that the selected studies needed to meet, while the exclusion criteria delineated those that were not part of the analysis. These components ensured methodological rigor in addressing the RQs.  
The search string used included the following terms: ((\"deep\" AND \"learning\") AND (\"violence\" OR \"crime\") AND (\"smart\" AND \"city\") AND (\"internet\" AND \"of\" AND \"things\" OR \"iot\")). This query was adapted and applied to the Scopus (1) and  
Figure 1: Literature review process based on [21].  
Web of Science (2) databases, both recognized for their extensive coverage of high-impact scientific publications.  
\[\begin{split}\text{TITLE-ABS-KEY(deep AND learning) AND TITLE-ABS-KEY(violence OR}\\ \text{crime) AND TITLE-ABS-KEY(smart AND city) AND TITLE-ABS-KEY}\\ \text{(internet AND of AND things OR iot)}\end{split} \tag{1}\]  
\[\begin{split}\text{deep learning (Topic) and violence OR crime (Topic) and smart city (Topic)}\\ \text{and internet of things OR lot (Topic)}\end{split} \tag{2}\]  
The selection of Scopus and Web of Science as the primary databases was based on their widespread recognition as two of the most comprehensive multidisciplinary research sources. Both platforms index a vast number of high-impact, peer-reviewed journals and international conference proceedings, ensuring broad coverage of relevant research. Moreover, their integration with digital libraries such as IEEE Xplore, Springer, Elsevier, ACM, Wiley, and other repositories in engineering and Computer Science ensures access to rigorous and scientifically validated studies, thereby enhancing the reliability and comprehensiveness of the research findings.  
Regarding inclusion criteria, studies published between 2010 and 2024, written in English, and presenting practical applications or prototypes related to deep learning and IoT in the context of public security management were considered, provided they offered full-text access. Among the selected studies, those proposing real-world smart city applications and studies with laboratory simulations for monitoring security patterns in both physical environments and IoT-based cyberspace were included. In the latter case, special attention was given to ensuring that the datasets used to train machine learning models were not artificially generated but derived from real-world contexts.  
The following exclusion criteria were used to rule out studies:  
* Published outside the defined timeframe (2010-2024).
* Written in languages other than English.
* Not directly related to deep learning, IoT, and public security.
* Proposing purely theoretical models without empirical validation.
* Describing only conceptual frameworks for smart city security.  
This study aims to quantitatively analyze the effectiveness of deep learning models in monitoring and detecting security patterns in both physical and cyberspace environments where IoT technologies are employed. Therefore, it was crucial for the included studies to provide effectiveness evaluation metrics and the accuracy of the trained neural network model.