Optimized Art Design Model With Statistical Model with Digital Media

Art design is a form of creative expression that encompasses the visual aesthetics and conceptual elements of various mediums. Art design has undergone a transformative evolution with the integration of digital media, reshaping the landscape of creative expression. In contemporary art, artists leverage digital tools and technologies to explore innovative ways of crafting visual narratives. Hence, to improve the quality of the art design this paper constructed a framework of Weighted Genetic Optimization (WGO). The proposed WGO model incorporates the statistical modeling of digital media technology. The statistical technique comprises the estimation of the features in the art design model. Through the integration of WGO with the statistical model features related to the art design with the incorporation of digital media are evaluated. The statistical features in the art design are observed as the digital information such as geometric, GLCM and HUE are the essential features in the integrated WGO with statistical techniques. The estimated features are applied over the deep learning model with the LSTM network for the automated classification of art design that uses digital media for improvement. Simulation results demonstrated that the proposed WGO integrated statistical model achieves the HUE value ranges from 0 -360 which is effective for art design modeling. Also, the proposed model achieves a significant classification rate of 0.98 accuracy with a loss value of 0.2 which is ~9% less loss than the conventional techniques.