# PRISMA on Machine Learning Techniques in Smart City Development
## 2 Methodology
We further excluded articles that did not entail \"machine learning\" or \"smart cities\" as part of their research or context. To establish validity of the inclusion and exclusion criteria, Robey & Dalebout used relevance and acceptability as criteria for establishing those aspects [23]. In accordance with PRISMA guidelines, we employed specific phrases to locate and sift through academic articles in the databases we used: (1) \"machine learning\", (2) \"smart cities\", (3) \"Machine Learning Techniques in Smart Cities Development\", and (4) \"Machine Learning in Smart Cities Development\". These keywords were selected to conduct a comprehensive yet targeted search, effectively capturing pertinent studies on the integration of machine learning within the framework of smart city development. We augmented these queries with synonyms related to each concept, conducting targeted searches within these databases to encompass any relevant papers. We also repeated these search terms in the reference search function to identify papers potentially off-topic. The titles and abstracts of the articles were manually scrutinized to select appropriate papers based on our inclusion and exclusion criteria, culminating in a comprehensive reading of the remaining articles to evaluate their suitability. The PRISMA methodology is depicted in Figure 1, with the detailed procedure outlined as follows. The initial database search yielded 4808 article titles, which were imported into Zoreto 7 software. Through this process, we removed 456 duplicates, 334 articles flagged as irrelevant by automated tools, and 514 articles lacking the keywords \"machine learning\", \"smart cities\", \"Machine Learning Techniques in Smart Cities Development\", or \"Machine Learning in Smart Cities Development\" in their titles (identified using Zoreto's search function). This resulted in a pool of 3504 articles. A list of these articles was compiled in MS Excel for manual screening. We assessed these titles against our research questions and criteria, discarding those not relevant to our research topic. This process left us with 401 papers, but only 241 were accessible in full text (PDF). To summarize these 241 articles,we employed two AI tools, Wordtune Read and ChatGPT, to assist in summarizing the articles for a more efficient review process. Wordtune Read condensed the articles into approximately 2000-word versions, and ChatGPT further distilled these summaries into single-page overviews, using the command 'create a one-page summary of this text':  
This approach was carefully chosen to maintain consistency and comprehensiveness in the review. While AI tools can introduce biases, we mitigated this risk by cross-verifying AI-generated summaries with the original texts and ensuring that the core content and context were preserved. This methodology allowed us to efficiently handle a large volume  
Figure 1: PRISMA flow diagram chart for search for academic articles on machine learning techniques in smart city development, created based on the guidelines and tool provided by [24]. of literature without compromising on the depth and accuracy of the review, enabling us to swiftly go through the 241 papers, pinpointing those pertinent to our study by examining their overall content. The Artificial Intelligence (AI)-generated summaries were more comprehensive than the abstracts, providing a more in-depth examination of the articles' contents. Subsequent to reviewing these summaries and applying our exclusion criteria to further narrow down the selection, we pinpointed 42 papers (originating from the database searches) for full analysis. These papers are catalogued in Table 1.  
The findings from this conclusive analysis will be detailed in Section 3 of the paper. As indicated in Figure 1, the 199 articles that were omitted were excluded for the following reasons: (1) Articles were excluded if they focused exclusively on machine learning applications or techniques without contextual relevance to smart cities, resulting in the exclusion of 59 papers.