IoT assisted fuzzy inference systems for intelligent 3D art design in movie animation scene design.

Animation scene design is assisted by intelligent computing programs and Internet of Things (IoT) services with technological advancements. Animation scene creation, modeling, and rendering require extensive computational and conditional resources. To improve the design selection and 3D art modeling, this article proposes an amalgamated design model using IoT resource exploitation and fuzzy interference computation. The proposed method involves understanding the animation scene for which IoT-aided designs are generated, along with a corresponding timeline. The fuzzy interference process interprets the generated design in relation to the Scene that best suits the scenario. Based on a suitable design, the scenario is verified with the animation sequence between different timelines. The maximum Likelihood design is selected and updated in the IoT platform to achieve the best scene match. This scene matching is referenced for Further art design recommendations from the IoT platform. The proposed IoT-assisted Fuzzy inference system achieves approximately an 18% improvement in matching rate, a 22% enhancement in precision level, a 20% increase in design recommendation accuracy, and a 12% reduction in design error rate. These improvements reflect the system's significant performance gains over traditional manual or heuristic methods, demonstrating the effectiveness in improving scene matching, modeling precision, recommendation accuracy, and reducing errors in animated 3D scene generation. The inference from the last known best design is used to Further improve 3D art modeling with the IoT reference and maximum matching criteria. This enhances the precision of 3D art selection and modeling for animated scenes, thereby improving the overall quality of the animation.