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Author: Admin | 2025-04-28
Min–max scaling is defined as follows: X_norm = (X − Xmin)/(Xmax − Xmin) (6) where X is the original data point, Xmin is the minimum value in the dataset, and Xmax is the maximum value. AI can optimize this process by considering data distributions across different scales and by applying adaptive normalization techniques that are better suited to heterogeneous datasets.Ultimately, error correction methods, such as mean imputation, are essential for preserving data accuracy. The mean imputation can be expressed as follows: Value = (∑〖Non-Missing Values〗)/Number of Non-Missing Values (7) However, AI-imputation methods—e.g., K-nearest neighbors (KNN) or deep learning-based techniques—would provide superior estimations by taking intricate data patterns and relationships into account and, consequently, producing more robust datasets. Additionally, the use of AI to prove veracity is not confined to those simple methods. It can also read and continuously monitor data streams on the fly faster and correct any errors or anomalies. However, machine learning models have the capability to correct themselves and learn from past corrections to the data environment.Figure 10 presents AI integration in the data veracity component of the model.As can be seen from Figure 10, AI enhancement methodologies ensure that Big Data analysis is reliable, reducing the risk of error and increasing the credibility of the results. As data veracity is crucial for informed decision-making, the use of AI to maintain data integrity represents a significant advancement in Big Data analytics. 4.3. Proposed Components 4.3.1. VolatilityIn Big Data, volatility is a significant obstacle characterized by continuous and
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