<p>Ant Colony Algorithm-Based Art Education Information Recommendation System for Higher Education Institutions</p>

<p>Dance Education (DE) is</p><p>examined for its intrinsic value as a form of arts education and for its usefulness</p><p>as a tool for promoting learning across several disciplines and curriculum areas,</p><p>including life force skills. While educators and art specialists frequently consider</p><p>them mutually exclusive, they are viewed as complementary and equally significant</p><p>in the learning method. Dance offers enhanced artistic experiences that all children</p><p>should have as part of their developmental and academic benefits. Due to its outstanding</p><p>performance, as well as the alluring properties of Learning Feature (LF) representations,</p><p>Machine Learning (ML) has recently garnered considerable interest in various research</p><p>domains, including Natural Language Processing (NLP) and Computer Vision (CV). The</p><p>impact of ML is also widely felt; recent research on Recommender Systems (RS) and</p><p>information retrieval has demonstrated its effectiveness. The knowledge representation</p><p>paradigm, innovative learning methodology, and reasoning mechanisms comprise the</p><p>Smart Recommender System (SRS). The research for the Dance Education System (DES)</p><p>described in this paper aims to apply the Background Images Based Rhythm Learning</p><p>(BIBRL) method, allowing dance students to develop the backgrounds for their performances</p><p>without the assistance of demonstration performers. Dancers are assumed to recommend</p><p>background images they are most likely to use, rather than having to search through</p><p>the vast number of online resources. This research proposes a revolutionary content-based</p><p>social computing system for image recommendations. In this research, a novel Secured</p><p>Smart Recommender System (SRS) has been introduced for online users, which considers</p><p>the behaviour of new online users and uses the Ant Colony Algorithm (Sec-SRS-ACA)</p><p>for college dance education information by RS. This research employs these methods</p><p>to develop an ACA based on a Secure Smart Recommender System (Sec-SRS), which is</p><p>significantly more efficient and secure than prior implementations of similar systems.</p><p>Additionally, an attempt has been made to integrate the Singular-Value Decomposition</p><p>(SVD) function into their recommendation engine to improve the recommendations.</p><p>In this paper, efforts have been made to briefly explain the operation of the algorithms</p><p>and provide general data on ML, and then apply it to test data.</p>