Artificial Intelligence (AI): The broad field of research and development of systems and machines capable of performing tasks that usually require human intelligence. These tasks include learning from data, recognizing patterns, making decisions, and solving problems.

AOI (Area of Interest): A specific geographic area selected for focused analysis or data collection, often used in mapping, GIS, and remote sensing applications. The designated area in which the prediction model is going to be ran.

Assays: Tests or analyses conducted to determine the composition and concentration of minerals within a sample, particularly used in mining to assess ore quality.

Epochs: In machine learning, epochs refer to the number of complete passes through the entire dataset during the training process of a model. Each epoch allows the model to adjust its internal parameters to better predict outcomes.

False Negative: The model incorrectly predicts a negative result when it was actually a positive. (Type II error)

False Positive: The model incorrectly predicts a positive result when it was actually a negative (Type I error)

Feature(s): In machine learning, a feature is an individual measurable property or characteristic used by a model to make predictions. In GIS, features refer to spatial objects like points, lines, and polygons that represent real-world entities.

Histogram: A graphical representation of the distribution of data. In the context of geospatial data or images, a histogram shows how pixel values or data points are distributed across different intensity levels.

Learning Point: Same as Validation Point. Refers to an individual data instance that the model uses during training to learn patterns and relationships

Lithology: The study and description of rocks, including their physical characteristics such as color, texture, and composition, which is crucial in understanding the geology of an area.

Loss Function: A mathematical function used in machine learning to measure how well a model's predictions match the actual outcomes. It helps the model learn by minimizing the difference between predictions and true values during training.

Machine Learning (ML): A subset of AI that enables systems to learn from data without explicit programming. Over time, these systems improve as they process more data.

Overfitting: Overfitting happens when a model is too complex, capturing noise along with patterns, resulting in high accuracy on training data but poor performance on new data.

Patch Size: In the context of Vision Transformers, patch size refers to the dimensions of the smaller sections (patches) into which an input image is divided for processing by the model.

Principal Component Analysis (PCA): A dimensionality reduction and machine learning method used to simplify a large data set into a smaller set.

Plutons: Large, intrusive igneous rock bodies that form deep underground from slowly cooled magma. Plutons are important in the study of geology and mineral exploration.

Prediction Map: In VRIFY AI, creating a Prediction Map is what you are doing when you apply specific settings and parameters in VRIFY AI that lead to the generation of Verified Prospectivity Scores (VPS) for targeted mineralization identification within your area of interest.

Random Forest: A machine learning method that combines multiple decision trees to make predictions. It works by analyzing random parts of the data and averaging the results, making it reliable for handling large or complex datasets.

Raster: A grid of pixels or cells used to represent spatial data, where each cell holds a value that represents information, such as color or elevation, often used in mapping and geospatial analysis.

ROC Curve (Receiver Operating Characteristic Curve): A graphical representation of a model's diagnostic ability, plotting the true positive rate against the false positive rate. It's commonly used to evaluate the performance of binary classification models.

.ers File (Raster File): A file format used to store raster data, particularly in GIS and remote sensing. It includes information about pixel values and georeferencing.

.shp File (Shapefile): A widely used geospatial vector data format for geographic information system (GIS) software, storing location, shape, and attributes of geographic features.

Transformer Model: A machine learning model architecture that uses self-attention mechanisms to process data, originally designed for natural language processing but now applied to other domains like image analysis (e.g., Vision Transformers).

True Negative: The model correctly predicts a negative (below threshold) learning point.

True Positive: The model correctly predicts a positive outcome (above threshold) the validation point.

Underfitting: Underfitting occurs when a model is too simple, missing key patterns, leading to poor performance on both training and test data.

Validation Point: Same as Learning Point. Refers to an individual data instance that the model uses during training to learn patterns and relationships

Vision Transformer Model (ViT): A type of machine learning model based on the transformer architecture, designed specifically for processing and analyzing image data, particularly by breaking images into patches and analyzing them with attention mechanisms.

VRIFY Prospectivity Score (VPS): A quantified score assigned to predicted exploration targets identified by VRIFY AI, indicating the likelihood of mineralization based on AI analysis of geological data and patterns. The score is represented as a probability (0 - 1.0) and indicates to what extent your desired commodities (above your indicated grade) may exist at a targeted x, y, z coordinate.

Reach out to your dedicated VRIFY AI Contact or email Support@VRIFY.com for more information.