Innovation and Development of Scientific Research Management Mode Under the Background of the Internet of Things and Fuzzy Control

Scientific research carried out worldwide increases chances for innovative ideas and applications through digital transformations for real-time functional ease. The fundamental Internet of Things (IoT) based scientific solutions are precise over the different scientific approaches in diverse application areas. This article introduces a Fuzzy-derivative Mode Management Method (FDM3) for improving the performance of research-aided scientific systems. The aim of optimal and best-afford solution extraction for complex research environments is satisfied using 3 levels of fuzzy derivatives. The first two levels are precise in classifying the best and worst-fit solutions using variation derivatives. The fuzzification for existing and improved solutions using the research parameters is performed in identifying a less varying third derivative. This third derivative normalizes the maximum best solution as a part of the optimal solution; the solution is defuzzified using performance parameters. In this process, the performance is varied across multiple low-to-high or vice versa mappings. This low or high factor is estimated from the previous output (maximum) generated over the varying fuzzifier output. The proposed method’s performance is analyzed using the metrics efficiency, fuzzification rate, derivation analysis, and time complexity. ©2023 Old City Publishing, Inc.