摘要：In the current era of technology, artificial intelligence has grown rapidly in such a way that it has established its presence in all fields. The purpose of artificial intelligence is to reduce human intervention and complete tasks with an enhanced result. In this research, we are going to study the application of artificial intelligence technology in art teaching, taking architectural painting as an example. Architectural painting is a type of painting that focuses only on architecture, including indoor and outdoor views of the buildings. In earlier stages, architecture was shown only in the background of paintings that had different objects as the main subject. Later, architecture itself became a mainstream genre in the field of painting. As has been shown by other researchers, the latest technologies such as Internet technology, wireless sensor networks (WSNs), and artificial intelligence like deep learning technologies are deployed in art teaching. Artificial intelligence has made teaching easier. This proposed system makes use of Internet technology, WSNs, artificial intelligence, and lightweight deep learning models in the field of art teaching. The teaching method is enhanced by adapting to this new technology. For performing the analysis of the proposed system, the Limited Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) art algorithm is implemented. This L-BFGS algorithm focuses on finding the local minima in any given application. In this art teaching of architectural painting, the proposed algorithm will aid in explaining the minute works to be noted while doing the artwork. The proposed algorithm is then compared with the traditional Gradient Descent, Adam, and Adadelta algorithms. From the results, it can be observed that the proposed algorithm has achieved accuracy of 97% and 98% in the training and testing phases, respectively.
