• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.166-178
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• 2023

This study aims to introduce and assess CNN Deep Learning methods to analyze visual landscape images on social media with embedded user perceptions and experiences. This study analyzed visual landscape images by focusing on a healing place. For the study, seven adjectives related to healing were selected through text mining and consideration of previous studies. Subsequently, 50 evaluators were recruited to build a Deep Learning image. Evaluators were asked to collect three images most suitable for 'healing', 'healing landscape', and 'healing place' on portal sites. The collected images were refined and a data augmentation process was applied to build a CNN model. After that, 15,097 images of 'healing' and 'healing landscape' on portal sites were collected and classified to analyze the visual landscape of a healing place. As a result of the study, 'quiet' was the highest in the category except 'other' and 'indoor' with 2,093 (22%), followed by 'open', 'joyful', 'comfortable', 'clean', 'natural', and 'beautiful'. It was found through research that CNN Deep Learning is an analysis method that can derive results from visual landscape image analysis. It also suggested that it is one way to supplement the existing visual landscape analysis method, and suggests in-depth and diverse visual landscape analysis in the future by establishing a landscape image learning dataset.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.228-231
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• 2022

Most CCTVs operated by public institutions for crime prevention and parking enforcement are located on roads. The angle of these CCTV's view is often changed for various reasons, such as bolt loosening by vibration or shocking by vehicles and workers, etc. In order to effectively provide AI services based on the collected images, the service target area(ROI, Region Of Interest) must be provided without interruption within the image. This is also related to the viewpoint of effective operation of computing power for image analysis. This study explains how to maximize the application of artificial intelligence technology by setting the ROI based on the marker on the road, setting the image analysis to be possible only within the area, and studying the process of finding the ROI.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.1-10
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• 2019

The present research aims to identify the issues that occurred when uploading stock photos to the internet-based stock image agencies and to develop technical solutions based on web service technologies. We identify the issues by examination of previous studies and stock photo uploading systems of major three agencies currently in service. As such, we develop web service technology by focusing on the following matters. First, we apply an automatic tag system to ensure convenience. Second, to ensure safety, we apply a technology that easily enables prevention of portrait rights violations and trademark infringements. We also prepare for measures against possible harmfulness. Third, to ensure completeness, we apply a method which resolves upload failure issues that frequently occurred in the past. In particular, the present research is significant as it applies an automatic image analysis system based on Google Cloud Vision API as the artificial intelligence-based image processing technology. In addition, we develop a web service program which improves user access by using SNS-type screen composition.

• The Research Journal of the Costume Culture
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• v.30 no.5
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• pp.766-778
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• 2022

Image-oriented information is becoming increasingly important on social networking services (SNS); the background of this trend is the popularity of selfies. Currently, camera applications using augmented reality (AR) and artificial intelligence (AI) technologies are gaining traction. An AR camera app is a smartphone application that converts selfies into various interesting forms using filters. In this study, we investigated the change of keywords according to the time flow of selfies in Goolgle News articles through semantic network analysis. Additionally, we examined the effects of using an AR camera app on appearance satisfaction and self-esteem when taking a selfie. Semantic network analysis revealed that in 2013, postings of specific people were the most prominent selfie-related keywords. In 2019, keywords appeared regarding the launch of a new smartphone with a rear-facing camera for selfies; in 2020, keywords related to communication through selfies appeared. As a result of examining the effect of the degree of use of the AR camera app on appearance satisfaction, it was found that the higher the degree of use, the higher the user's interest in appearance. As a result of examining the effect of the degree of use of the AR camera app on self-esteem, it was found that the higher the degree of use, the higher the user's negative self-esteem.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.861-866
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• 2023

Unlike conventional smart farms that require chemical fertilizers and large spaces, aquaponics farming, which utilizes the symbiotic relationship between aquatic organisms and crops to grow crops even in abnormal environments such as environmental pollution and climate change, is being actively researched. Different crops require different environments and nutrients for growth, so it is necessary to configure the ratio of aquatic organisms optimized for crop growth. This study proposes a method to measure the degree of growth based on area and volume using image processing techniques in an aquaponics environment. Tilapia, carp, catfish, and lettuce crops, which are aquatic organisms that produce organic matter through excrement, were tested in an aquaponics environment. Through 2D and 3D image analysis of lettuce and real-time data analysis, the growth degree was evaluated using the area and volume information of lettuce. The results of the experiment proved that it is possible to manage cultivation by utilizing the area and volume information of lettuce. It is expected that it will be possible to provide production prediction services to farmers by utilizing aquatic life and growth information. It will also be a starting point for solving problems in the changing agricultural environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.115-122
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• 2017

Deep learning is artificial intelligence technology that can teach people like themselves who need machine learning. Deep learning has become of the most promising in the development of artificial intelligence to understand the world and detection technology, and Google, Baidu and Facebook is the most developed in advance. In this paper, we discuss the kind of deep learning frameworks, compare and analyze the efficiency of the image and speech recognition field of it.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.333-336
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• 2020

AI-powered robot news is drawing attention as artificial intelligence technology is fully spread in the news distribution field. Robot news still has many technical and ethical problems, but academic research on this is insufficient. This study analyzes the issue of robot writing in artificial intelligent based robot journalism industry using SWOT analysis. As a result, the advantages of big data processes, accurate information gathering, high efficiency and disadvantages such as lack of independent arguments and lack of evidence and opportunities for technical development, government support, academic development, and industrial applications, and threats such as uncritical acceptance and lack of talent have been found. This study suggests three future-oriented directions, such as human-machine collaboration, intelligent news, and chat-bot, through previous studies on the development direction of robot journalism-based article writing.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.499-500
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• 2022

차량용 블랙박스의 대중화와 '스마트 국민 제보' 애플리케이션 도입에 따른 영향으로 교통법규 위반 공익신고 건수가 급증하면서 대응해야 할 담당 경찰 인력이 부족한 상황이다. 이러한 인력 부족 문제를 해결하기 위해서 인공지능(AI) 알고리즘을 활용하여 신고된 영상의 위법 여부를 자동으로 분석할 필요가 있다. 본 논문에서는 공익신고의 대부분을 차지하고 있는 진로변경 위반 영상 분석을 위한 객체 인식 방법에 대한 연구 내용을 기술한다. 이 연구에서는 딥러닝 알고리즘과 컴퓨터 비전 알고리즘을 통해 진로변경 위반 분석에 필요한 차량과 실선 객체를 인식하여 진로변경 위반 영상 분석에 활용할 수 있도록 한다.