Application of Multimedia Imaging Technology in Business Intelligence Big Data Optimization

Abstract

This study delves into the integration of multimedia imaging technology within business intelligence (BI) frameworks, aiming to optimize big data analytics and decision-making processes. Employing a systematic methodology, encompassing data collection, preprocessing, feature extraction, machine learning modelling, and predictive analytics, the study evaluates the effectiveness of the developed approach across various tasks. Statistical analysis reveals impressive results, with image classification achieving consistently high accuracy rates exceeding 95% using convolutional neural networks (CNNs). Object detection algorithms exhibit robust precision and recall rates, exceeding 90%, while predictive analytics models demonstrate low prediction errors below 5%. Moreover, the methodology showcases scalability and real-time processing capabilities, crucial for handling large volumes of visual data without compromising performance. These findings underscore the transformative potential of multimedia imaging technology in augmenting BI and big data optimization initiatives. By leveraging advanced algorithms and machine learning techniques, organizations can derive actionable insights from visual data, enhance decision-making processes, and gain a competitive edge in today's data-driven landscape.