• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.4
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• pp.541-549
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• 2019

Recently, Korea has been attracting the attention of smart city as a solution to urban problems along with the era of Forth Industrial Revolution. However, tourism, industry, and specific high-class residential complexes are being formed, and the disabled with information such as the elderly and disabled who can actually get help are not experienced. This study establishes the concept of smart city suitable for domestic and derives the priority of physical and non-physical elements of smart city considering information weakness. Smart City considering disabled with information has concluded that not only physical elements but also non-physical components are important, and derive the differences between the experts and the priorities of actual information weak people. We will propose a smart city development direction that takes into account information weak people that can be developed and advanced in response to the needs of information weak people.

• Management & Information Systems Review
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• v.36 no.4
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• pp.33-51
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• 2017

With the advent of 'The Forth Industrial Revolution' and the growing demand for quality of life due to economic growth, needs for the quality of medical services are increasing. Artificial intelligence has been introduced in the medical field, but it is rarely used in chronic skin diseases that directly affect the quality of life. Also, atopic dermatitis, a representative disease among chronic skin diseases, has a disadvantage in that it is difficult to make an objective diagnosis of the severity of lesions. The aim of this study is to establish an intelligent severity recognition model of atopic dermatitis for improving the quality of patient's life. For this, the following steps were performed. First, image data of patients with atopic dermatitis were collected from the Catholic University of Korea Seoul Saint Mary's Hospital. Refinement and labeling were performed on the collected image data to obtain training and verification data that suitable for the objective intelligent atopic dermatitis severity recognition model. Second, learning and verification of various CNN algorithms are performed to select an image recognition algorithm that suitable for the objective intelligent atopic dermatitis severity recognition model. Experimental results showed that 'ResNet V1 101' and 'ResNet V2 50' were measured the highest performance with Erythema and Excoriation over 90% accuracy, and 'VGG-NET' was measured 89% accuracy lower than the two lesions due to lack of training data. The proposed methodology demonstrates that the image recognition algorithm has high performance not only in the field of object recognition but also in the medical field requiring expert knowledge. In addition, this study is expected to be highly applicable in the field of atopic dermatitis due to it uses image data of actual atopic dermatitis patients.