# Algorithms in Low-Code-No-Code for Research Applications: A Practical Review
## 5 LCNC Used in Existing Research  
As seen from Section 3, there are several advantages offered by LCNC that could be useful in solving problems in all areas of research. Researchers in all areas perform critical analysis in their domain of expertise. To conduct critical analysis, scientific studies often need to apply a range of AI-based algorithms. The LCNC platforms enable scientists and researchers in all areas to easily apply AI-based algorithms on their data to find critical insight. To apply AI-based algorithms, the researchers and scientists do not need to be a programmer or data scientist. Using the drag-and-drop features of LCNC platforms, citizen developers can execute complex AI-based analysis that includes regression, clustering, CNN, decomposition analysis, and others. As seen from Table 3, existing studies in [15; 18; 19; 20; 21; 22; 25; 26; 27; 31] have recently used these analytical features of LCNC platforms to solve research problems in multiple disciplines (i.e., answers RQ 3).  
Similarly, researchers analyzing human behavior or perceptions of society might want to execute NLP algorithms, such as sentiment analysis, NER, category classification, language detection, and translations. Since sociologists, behavioral scientists, and political scientists are often not comfortable in hand-coding NLP algorithms, LCNC platforms allow them a quick and easy approach to analyze their data. For example, using the LCNC platform, a social scientist may analyze anti-vaccine sentiments without hand-coding NLP algorithms [23]. Likewise, a political scientist or researcher may obtain critical insights on the multidimensional geopolitical impact arising from COVID-19. as shown in [15; 24].  
Since data-driven multidisciplinary research has gained popularity in recent times, the studies in [20; 21; 22] automatically obtained data from several thousand sources (e.g., Twitter, CNN, the BBC, the New York Times, etc.) using AI-based data acquisition features of LCNC platforms.  
Social scientists and researchers working with strategic decision-makers are often unable to create mobile apps for obtaining AI-based insights. As demonstrated in [25; 26; 27], social scientists easily created mobile apps running in iOS, Android, and Windows for evidence-based policy making using the LCNC platform. Without LCNC platforms, the researchers would need to hire expert coders for developing mobile apps or AI-based algorithms. Table 3 summarizes different features of LCNC platforms currently used by researchers, explaining RQ 3. In terms of answering RQ 4 (i.e., LCNC platforms used in research), Microsoft Power Platform is the most popular, followed by Mendix, SetXRM, the vf-OS platform, Aurea BPM, CRISP_DM, Primary AI, and others, as shown in Table 4. According to Table 4, LCNC platforms supported multidisciplinary research in the area of manufacturing, supply chain management, software development, business process automation, education, global news analysis, COVID-19, social media analysis, and even  
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disaster management (e.g., landslides, tornadoes, etc.). Niche research topics in the area of smart cities can also benefit from adopting LCNC platforms [47]. This answers RQ 5 for the various areas of research supported by existing LCNC platforms.  
It becomes evident from Table 3 that there are five crucial aspects of LCNC platforms that appeal the most towards multidisciplinary researchers (i.e., the answer to RQ3), as follows:  
1. Applying AI/ML algorithms;
2. Applying NLP algorithms;
3. AI-based data collection and pre-processing;
4. Interactive data visualization;
5. Mobile and tablet deployment.  
It should be mentioned that the above five features were used in [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] for solving research problems. These critical features of LCNC platforms answer the research question \"RQ3: Which features of modern LCNC platforms were used in existing studies\". These features briefly described below.