A review of design intelligence: progress, problems, and challenges

Tang, Yongchuan; Huang, Jiangjie; Yao, Meng-ting; Jia, Wei; Li, Wei; He, Yongxing; Li, Zejian

doi:10.1631/fitee.1900398

Cited by 30 publications

(17 citation statements)

References 116 publications

(137 reference statements)

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“…In other aspects of tourism teaching, the expert-assisted teaching of artificial intelligence can also establish the human reasoning model learning tools and many other applications, showing better practicability [ 12 ]. The system adopts the form of hyperlink, and the required materials can be called out with a click of the mouse so that we can get the information related to the scene in the scene and have a deeper and more comprehensive understanding of the scenic spots [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Tourism Teaching System Based on Artificial Intelligence Technology

Xing

2022

Computational Intelligence and Neuroscience

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The tourism teaching system provides all kinds of teaching resources for students and shares good teachers, which greatly improves the quality of teaching and learning, and it enables students to teach and learn randomly in the system. The mode of educational information dissemination has also changed, resulting in great changes in educational models, ideas, and methods. The tourism teaching system overcomes the shortcomings of traditional education. In the 21st century, the means of education and teaching will be ITS-based, and technology will be used to optimize teaching. With the rapid development of network technology, it has been widely used in the field of education, and the way of educational information dissemination has also changed, which has led to great changes in educational models, concepts, and methods. Therefore, the combination of artificial intelligence technology and tourism teaching system is the focus of current educational research. In this paper, the tourism teaching system is designed based on artificial intelligence technology. Combined with the application status of the tourism teaching system in education, a personalized cognitive student model is designed by combining artificial intelligence technology with student behavior analysis, and an intelligent and personalized educational environment is developed. Design and implement a simple web-based tourism teaching system, use a personalized cognitive student model to model students, and use an expert question bank and self-built question bank to test students’ learning to provide a basis for students’ next learning.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of Tourism Teaching System Based on Artificial Intelligence Technology

Xing

2022

Computational Intelligence and Neuroscience

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“…Currently, it is still challenging to predict the visual successfulness of a product scheme before a new design concept is put on the market. Designers and marketing professionals are still carrying heavy works in evaluation and analysis in pre-design and development [84], and it might be influenced by empirical and subjective factors. An increasing numbers of intelligent design methods have been applied in design industry [85], and deep learning approaches were proved to be effective in understanding product perception pattern and social manufacturing paradigm [86] [87].…”

Section: Discussionmentioning

confidence: 99%

Product Design Award Prediction Modeling: Design Visual Aesthetic Quality Assessment via DCNNs

Xing

et al. 2020

IEEE Access

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A visual aesthetic is a crucial determinant of product design evaluation. Through the analysis of image features, not only can we evaluate the aesthetic level, but also we can reveal the whole quality of the design proposal. We assume that it could be a potential pattern to predict the ultimate success of the proposal in product design that a visual aesthetic can be a cue for award classification modeling. Consequently, we conduct investigation on a dataset of over 10,003 design submissions in a design competition held once a year from 2008 to 2018 in order to manifest the assumption. Due to the remarkable performance of deep convolutional neural networks (DCNNs), we compare seven deep learning methods to explore an optimal model for design award prediction based on product image analysis. The result of the experiments indicates that the proposed method achieves comparative accuracy in design award classification result predication, with the optimal classification accuracy of 70.79% using the SEFL-ResNet (Squeeze and Excitation-Focal Loss-ResNet) method.

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“…The measurement of the beauty of images with those methods is called computational aesthetics (CA) [60]. Brachmann and Redies [61] and Tang et al [62] give detailed reviews of concepts and methods of experimental CA. Some of the studies are explained in the following subsections, including computational methods with and without 'learning', and hint at some of the VDP that are also within the scope of this research.…”

Section: Computational Analysis Of Visual Aestheticsmentioning

confidence: 99%

“…CA has attracted interest from AI researchers in recent years [62]. A comprehensive survey on image aesthetic quality assessment, including the review of datasets, various aesthetic tasks and applications using recent AI methods, was carried out by Yang et al [63].…”

Section: Ai-based Studies In Camentioning

confidence: 99%

“…Most of the studies in the art domain commonly use largescale and available databases, such as the AVA dataset [77], for visual aesthetic assessment [62,78]. However, for research like ours, which focuses on a design aspect that has not been considered before, a specially curated dataset was necessary -as in the work by Llamas et al [79].…”

Section: Curating the Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Detecting visual design principles in art and architecture through deep convolutional neural networks

Demir

Cekmis

Yesilkaynak

et al. 2021

Automation in Construction

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Visual design is associated with the use of some basic design elements and principles. Those are applied by the designers in the various disciplines for aesthetic purposes, relying on an intuitive and subjective process. Thus, numerical analysis of design visuals and disclosure of the aesthetic value embedded in them are considered as hard. However, it has become possible with emerging artificial intelligence technologies. This research aims at a neural network model, which recognizes and classifies the design principles over different domains. The domains include artwork produced since the late 20th century; professional photos; and facade pictures of contemporary buildings. The data collection and curation processes, including the production of computationally-based synthetic dataset, is genuine. The proposed model learns from the knowledge of myriads of original designs, by capturing the underlying shared patterns. It is expected to consolidate design processes by providing an aesthetic evaluation of the visual compositions with objectivity.

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A review of design intelligence: progress, problems, and challenges

Cited by 30 publications

References 116 publications

Design and Implementation of Tourism Teaching System Based on Artificial Intelligence Technology

Design and Implementation of Tourism Teaching System Based on Artificial Intelligence Technology

Product Design Award Prediction Modeling: Design Visual Aesthetic Quality Assessment via DCNNs

Detecting visual design principles in art and architecture through deep convolutional neural networks

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