## 3 Data Collection  
The study area is the Southern Suoi Nhum titanium placer deposit, Binh Thuan province (Vietnam), as shown in Figure 4. The previous geological surveys indicated that the study area and surroundings have loess sediments of the Pleistocene to Holocene age. These Quaternary sediments are distributed into sandy strips running parallel to the coastline. Exploration results showed that the mineral components of the placer sand consist mainly of ilmenite, rutile, anatase, leucoxene, zircon, and monazite (Figure 5). Among these heavy minerals, ilmenite is a common mineral and accounts for a significant proportion. These minerals exist in the red marine sediment of the Pleistocene age (Phan Thiet formation) and a gray marine-eolian residue of the Holocene age. Deo Ca Complex can be found in the surrounding areas. The complex consist of whitish-gray grano-syenite, biotite granite, and biotite-hornblende granite [35]. For data collection, placer sand samples were dried and mixed thoroughly in the laboratory. The samples were then sieved using the 1.18 mm (American Society for Testing and Materials) ASTM sieve. The coning and quartering method was applied continuously to reduce the sample to 20-30 g. The ultrafine clays in the samples were removed using distilled water. After drying, the total heavy minerals (THM) were isolated by using bromoform heavy liquid. The magnetic/nonmagnetic heavy materials in THM were separated using hand magnets. Then, each type of heavy mineral (ilmenite, rutile, anatase, leucoxene, zircon, and monazite) was determined by manual grain counting using an optical microscope. Finally, the weight percentage of individual heavy minerals was calculated by multiplying their percentage with the respective specific gravity values. In this study, 405 samples were collected from different positions of the mine. Scanning electron microscope ((SEM) Quanta 450, FEI company) with energy-dispersive X-ray spectroscopy (EDS) was used to double-check the identified heavy minerals through their morphology and composition. In order to prepare the samples for SEM-EDS analysis, the heavy minerals were put onto the surface of carbon conductive tape that attached to the specimen stub. The samples were then coated by carbon to enhance the quality of SEM images. In this study area, ilmenite is a common mineral and accounts for a significant proportion. For AI calculation, the weight percent of each heavy mineral (i.e., rutile, anatase, leucxene, zircon, and monazite) were used as input data for predicting ilmenite content (not titanium content in each heavy mineral). The statistical properties of the heavy minerals are listed in Table 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification** & **Rutile** & **Anatase** & **Leucoxene** & **Zircon** & **Monazite** & **Ilmenite** \\ \hline Min.: & 0.000 & 0.000 & 0.000 & 0.013 & 0.001 & 0.188 \\
1 st Qu.: & 0.001 & 0.0001 & 0.003 & 0.030 & 0.001 & 0.306 \\ Median: & 0.001 & 0.0001 & 0.005 & 0.052 & 0.001 & 0.424 \\ Mean: & 0.001343 & 0.0001573 & 0.008 & 0.064 & 0.003 & 0.467 \\
3 rd Qu.: & 0.002 & 0.0002 & 0.012 & 0.080 & 0.002 & 0.538 \\ Max.: & 0.009 & 0.0007 & 0.032 & 0.306 & 0.058 & 2.246 \\ \hline \end{tabular}
\end{table} 
Table 1: Statistical attributes of the weight percent of each heavy mineral. Figure 4: A view of the Southern Suoi Nhum titanium placer mine (Vietnam). In Table 2, the correlation matrix of input and output variables is presented. In the cases of high positive/negative correlations (above approximately 0.75 or below \(-0.75\)), they may adversely affect the performance of the models [77]. As a positive sign, all the pairs of attributes in Table 2 are insignificant. Therefore, they have been designated as independent variables to estimate the dependent variable. The feasibility of the AI techniques was assessed by explaining the complicated relationship between predictors and response variable. Figure 5: Images of the placer sand in the study area.