The diagram in Figure 2 is based on binary and ternary diagrams, known as discrimination diagrams, which were introduced in the 1970s to discriminate geotectonic settings based on the trace element [5] and major element [6] geochemical data of basalts and sandstones, respectively. The main geotectonic settings are the boundaries of either converging or diverging lithospheric plates where volcanic activity is observed, resulting in the formation of basalts, the most common rock formed by magma pouring out from volcanoes originating from the Earth's mantle. Basalts can also be formed at hot spots far away from tectonic boundaries, which are known as Ocean Island Basalts (OIB). Given the small amount of data available and the limited areas under examination at the time, discrimination diagrams performed well and this is why they are still used and are continuously being evolved [7; 8; 9]. Since 1970, however, the amount of rock data acquired from all over the world has rapidly increased. Huge datasets containing information on the chemical analysis of rock samples have become openly available from databases such as GEOROCK [10] and PetDB [11]. These datasets have been used to prove that conventional discrimination diagrams are inaccurate in many cases [12; 13] because of the overlap of the sample points, as is the case in the diagram in Figure 2.  
Machine learning (ML) has been used in many fields of science to study the hidden patterns in large datasets [14]. After 2000, attempts have been made to use this technology to discriminate tectonic settings. The first successful attempt to discriminate tectonic settings using ML was made using Decision Trees (DT) in 2006 [15]. Ten years later, a Support Vector Machine (SVM) was tested on a dataset using the geochemical and isotopic data of basalts to discriminate tectonic settings [16]. One year later, SVM, Random Forest (RF), and Sparse Multinomial Regression (SMR) were compared using 20 elements and five isotopic ratios of basaltic rocks. The accuracy achieved was over 83% [17]. In 2019, chemical data derived from olivine, a major mineral found in basalts, were used to compare the accuracy of Logistic Regression (LR), Naive Bayes (NB), RF, and Multi-Layer Perceptron (MLP) for discriminating tectonic settings [18]. The same group introduced a new method named SONFIS incorporating fuzzy logic [19]. The latest published work used the Logistic Regression Analysis (LRA) algorithm with the geochemical data of monazite, a mineral found in basalt rock [20]. In 2017, SVM was used to discriminate volcanic centers in Italy [21]. In this research, the aforementioned methodology used for discriminating tectonic settings was applied in order to evaluate its suitability for distinguishing volcanic regions belonging to one tectonic setting. This was accomplished by comparing the performance of 10 MLAs using six variations of the initial dataset retrieved from the GEOROCK databases. Only major elements were used, as usable trace element data were only available for 10% of the samples of the dataset. Finally a cloud-based application (CBA) was implemented using the best performing model with the ability to correlate a volcanic rock sample from the HVA with one of the four volcanic regions of the South Aegean, based on the geochemical composition of 10 major elements (SiO2, TiO2, Al2O3, Fe2O3T, CaO, MgO, MnO, K2O, Na2O, and P2O5). This application is publicly accessible at www.geology.gr (accessed on 6 September 2021).