Assessing the best art design based on artificial intelligence and machine learning using GTMA

被引:12
|
作者
Xu Wenjing [1 ]
Cai, Zilu [2 ]
机构
[1] Xian Shiyou Univ, Coll Econ & Management, Dept Management Sci & Engn, Xian 710065, Shaanxi, Peoples R China
[2] Guangzhou Broadcasting Network, Guangzhou 51000, Guangdong, Peoples R China
关键词
Art design; Artificial intelligence; Emotions; Sentiments; Machine learning;
D O I
10.1007/s00500-022-07555-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Art, like language, is a method for expressing a person's thoughts and opinions to others. Art design is a way of conveying human sentiments and emotions, particularly via the use of visual structures such as paintings or drawings. Every element of our lives, including arts and crafts, has been favorably influenced by the introduction of innovative technologies such as artificial intelligence (AI) and deep learning (DL). In today's modern era, the technologies have revolutionized the way art is created, consumed, and distributed. Machine learning and human emotions are the two most important aspects of interactive and high-quality art design. In the teaching and learning of art-related courses, AI and machine learning are used to develop students' creative talents more successfully than traditional learning methods. With the use of information technology, designers build and design more productive artwork in very little time and with much less effort. In the evolution and creation of interactive and intelligent digital art, artificial intelligence and machine learning are playing a highly positive role. Art developed with the use of these approaches is capable of conveying human emotions and sentiments precisely and correctly. The proposed study presents a comprehensive overview of various AI-grounded smart and intelligent applications employed for the betterment and development of art design. The study provided a summary of the existing approaches used in the area of art design for the designing of high-quality and precise artwork. Then, based on the proposed overview, various features are extracted from the existing literature. From these extracted features, important features were selected for the decision-making procedure and ranking. To perform the decision-making process for the ranking of the accessible alternatives, the Graph Theory Matrix Approach was applied. The results of the study show effectiveness of the AI-based systems for art design.
引用
收藏
页码:149 / 156
页数:8
相关论文
共 50 条
  • [31] Art design and production based on artificial intelligence and improved neural network
    Liao, Zeming
    SOFT COMPUTING, 2023,
  • [32] Guidelines for Quality Assurance of Machine Learning-Based Artificial Intelligence
    Fujii, Gaku
    Hamada, Koichi
    Ishikawa, Fuyuki
    Masuda, Satoshi
    Matsuya, Mineo
    Myojin, Tomoyuki
    Nishi, Yasuharu
    Ogawa, Hideto
    Toku, Takahiro
    Tokumoto, Susumu
    Tsuchiya, Kazunori
    Ujita, Yasuhiro
    INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING, 2020, 30 (11-12) : 1589 - 1606
  • [33] Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence
    Yasir, Muhammad
    Niang, Abdoul Jelil
    Hossain, Md Sakaouth
    Islam, Qamar Ul
    Yang, Qian
    Yin, Yuhang
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2023, 11 (10)
  • [34] Applications of artificial intelligence and machine learning in orthodontics
    Asiri, Saeed N.
    Tadlock, Larry P.
    Schneiderman, Emet
    Buschang, Peter H.
    APOS TRENDS IN ORTHODONTICS, 2020, 10 (01) : 17 - 24
  • [35] Role of Artificial Intelligence and Machine Learning in Nanosafety
    Winkler, David A.
    SMALL, 2020, 16 (36)
  • [36] Artificial Intelligence and Machine Learning in Cardiac Electrophysiology
    John, Mathews M.
    Banta, Anton
    Post, Allison
    Buchan, Skylar
    Aazhang, Behnaam
    Razavi, Mehdi
    TEXAS HEART INSTITUTE JOURNAL, 2022, 49 (02)
  • [37] Artificial Intelligence and Machine Learning in Emergency Medicine
    Tang, Kenneth Jian Wei
    Ang, Candice Ke En
    Constantinides, Theodoros
    Rajinikanth, V
    Acharya, U. Rajendra
    Cheong, Kang Hao
    BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2021, 41 (01) : 156 - 172
  • [38] Artificial intelligence and machine learning for clinical pharmacology
    Ryan, David K.
    Maclean, Rory H.
    Balston, Alfred
    Scourfield, Andrew
    Shah, Anoop D.
    Ross, Jack
    BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, 2024, 90 (03) : 629 - 639
  • [39] Machine learning and artificial intelligence in research and healthcare
    Rubinger, Luc
    Gazendam, Aaron
    Ekhtiari, Seper
    Bhandari, Mohit
    INJURY-INTERNATIONAL JOURNAL OF THE CARE OF THE INJURED, 2023, 54 : S69 - S73
  • [40] Machine learning, artificial intelligence and the prediction of dementia
    Merkin, Alexander
    Krishnamurthi, Rita
    Medvedev, Oleg N.
    CURRENT OPINION IN PSYCHIATRY, 2022, 35 (02) : 123 - 129