Treffer: Research on the Integration and Innovation of Artificial Intelligence and Big Data in the Digital Transformation of the Economy.

The fusion of artificial intelligence (AI) and big data is one of the main drivers of digital transformation in the global economy, enabling informed decision-making, process automation, and creative business solutions. The combination of AI methods with big data analytics was essential for industries such as finance, healthcare, and manufacturing. The high-dimensional, multi-source dataset was utilized which includes sensor, transactional, and time-series data that went through substantial preprocessing. This involves data cleaning, normalization, imputation of missing values, and Wavelet Transform (WT) for feature extraction, which enables the extraction of both time and frequency domain features from the raw data. These features are critical for capturing subtle patterns in large datasets, particularly in dynamic environments. The hybrid blue monkey optimization algorithm tuned intelligent random forest (BMOA-IntRF) model efficiently determines the ideal parameters for prediction tasks to maximize the model’s performance. This combination improves the model’s capacity to accurately anticipate outcomes and categorize difficult data. The proposed method is implemented by using Python 3.10.1. The results demonstrated that the BMOA-IntRF model performs significantly and helps to attain the classification tasks with prediction accuracy and downtime reduction. It illustrates how BMOA-IntRF combined with AI and Big Data integration could improve process efficiency in areas, like supply chain management, market forecasting, and customized services. The suggested approach helps to address the challenges of algorithmic biases, data privacy concerns, and resource requirements while offering strategies for overcoming these obstacles and fostering continuous innovation in the digital economy. [ABSTRACT FROM AUTHOR]

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