Mathematical analysis of Dynamic Neural Network Fusion for Intelligent Transportation Systems (DNNF-ITS) on Internet of Things-enabled scalable data synchronization technique

Abstract

A comparative analysis of three important techniques in Intelligent Transportation Systems (ITS) is presented in this research. These approaches are the Dynamic Neural Network Fusion for ITS (DNNF-ITS), the Internet of Things-enabled scalable data synchronization methodology (IoT-SDSM), and the Intelligent Automated Software System (IASS). A growing need for optimal solutions in enhancing the efficiency and intelligence of transportation systems is addressed in this comparative study, which highlights the significance of the study and emphasizes its importance. The paper provides an overview of the difficulties that are inherent in each methodology and suggests a methodical approach to evaluate the effectiveness of these methodologies. These approaches are investigated for their potential applications in real-world scenarios, which include anything from the forecast of traffic to the synchronization of data and intelligent automation. A simulation analysis is incorporated into the study, which offers a more nuanced comprehension of the practical consequences and effectiveness of the techniques. When taking into consideration the distinct qualities and capabilities of each methodology, the purpose of this research is to provide decision-makers and practitioners with a roadmap that can assist them in picking the most appropriate way for progressing ITS.