Comparative Analysis of Machine Learning and Deep Learning Approaches in IoT-Enabled Enterprise Resource Planning Systems

The integration of Internet of Things (IoT) technologies into Enterprise Resource Planning (ERP) systems has revolutionized real-time decision-making, process automation, and operational efficiency. Machine Learning (ML) and Deep Learning (DL) provide two distinct approaches to leveraging IoT data within ERP systems, each with its own strengths and limitations. ML is well-suited for structured and semi-structured data, making it effective for tasks where the data format is consistent and manageable. However, it struggles with large-scale unstructured data typical of IoT environments, such as images and videos, and often requires manual, domain-specific feature extraction. In contrast, DL excels in processing unstructured and high-dimensional data streams, such as those from IoT sensors, by automatically extracting relevant features without human intervention. While ML is advantageous for simpler datasets and straightforward IoT applications, DL offers superior performance for complex, high-dimensional, and unstructured data, making it particularly beneficial in advanced IoTenabled ERP systems