• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.158-159
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• 2017

Sentiment analysis is the review of written or verbal communications to determine some measure of emotion or feeling in the communication. Search engines are one of the most popular sites visited on the Internet generating hundreds of billions of hits per month worldwide. Obviously privacy policies related to these search sites are extremely important. Our study reviews the privacy policies of the two largest search engines, Google and Baidu to determine the overall sentiment of their privacy policies. Significant individual findings and significant differences were found using several sentiment and opinion analysis methods.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.55-61
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• 2019

In order to realize foreign language education, various existing educational media have been provided, but there are disadvantages in that the cost of the parish and the media program is high and the real-time responsiveness is poor. In this paper, we propose an artificial intelligence English conversation system based on VR and speech recognition. We used Google CardBoard VR and Google Speech API to build the system and developed artificial intelligence algorithms for providing virtual reality environment and talking. In the proposed speech recognition server system, the sentences spoken by the user can be divided into word units and compared with the data words stored in the database to provide the highest probability. Users can communicate with and respond to people in virtual reality. The function provided by the conversation is independent of the contextual conversations and themes, and the conversations with the AI assistant are implemented in real time so that the user system can be checked in real time. It is expected to contribute to the expansion of virtual education contents service related to the Fourth Industrial Revolution through the system combining the virtual reality and the voice recognition function proposed in this paper.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.379-384
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• 2021

With people's continuous research and exploration in the field of artificial intelligence, more relatively mature artificial intelligence technology is applied in people's daily life. Mirrors are the most commonly used daily necessities in life, and it should be applied to artificial intelligence. The research results of this paper show that the smart mirror designed based on the raspberry pi displays weather, temperature, greetings, and has a human-mirror interaction function. The research method of this paper uses the Raspberry pi 3B + as the core controller and Google Assistant as the intelligent control. When connected to the network via Raspberry Pi's own WiFi, the mirror can automatically display and update time, weather and news information features. You can wake up the Google Assistant using keywords, then control the mirror to play music, remind the time, It implements the function of smart mirror voice interaction. Also, all the hardware used in this study is modular assembly. Later, it is convenient for user to assemble by himself later. It is suitable for market promotion at an affordable price.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.379-384
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• 2019

This study is an online survey user experience study. The purpose of this research is user experience research to make use and development of online surveys. In-depth interviews were conducted with 8 native Koreans who were not experienced with Naver and Google, and were surveyed using Peter Morville's Honeycomb model. In addition, we performed evaluation through tasks and think-aloud. Naver is highly useful, usable, desirable and findable, and Google can confirm its superior accessible and flexible. Research has shown that improvements in usability and ease of functioning are needed by reclassifying and moving menu categories. Online survey user experience that has not been studied previously can predict the direction of usability improvement and can help the user side. We hope that this research will improve the usability of online surveys, and will lead to various related research.

• Journal of The Korean Association of Information Education
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• v.23 no.4
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• pp.293-302
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• 2019

In this study, we developed the google spreadsheet education program which focuses on the education data visualization. And we applied it to forth and fifth grade students, and then verified its effect. The developed program was applied to 29 forth and fifth grade students for 36 classes in six days. Application of the program In order to verify the effect on creativity, Torrance Tests of Creative Thinking Figures A and B were introduced and pretests and post tests were carried out. As a result of the verification, it was found that the google spreadsheet education program which focuses on the education data visualization has positive effects on the creativity factors of the elementary school forth and fifth grade students.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1403-1410
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• 2020

Recently, as interest in non-face-to-face experiences and services increases, the demand for web video contents that can be easily consumed using mobile devices such as smartphones or tablets is rapidly increasing. To cope with these requirements, in this paper we propose a technique to efficiently produce video contents that can provide experience of visiting famous places (i.e., stage tour) in animation or movies. To this end, an image dataset was established by collecting images of stage areas using Google Maps and Google Street View APIs. Afterwards, a deep learning-based style transfer method to apply the unique style of animation videos to the collected street view images and generate the video contents from the style-transferred images was presented. Finally, we showed that the proposed method could produce more interesting stage-tour video contents through various experiments.

• Journal of dental hygiene science
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• v.20 no.4
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• pp.206-212
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• 2020

Background: A Teachable Machine is a kind of machine learning web-based tool for general persons. In this paper, the feasibility of Google's Teachable Machine (ver. 2.0) was studied in the diagnosis of the tooth-marked tongue. Methods: For machine learning of tooth-marked tongue diagnosis, a total of 1,250 tongue images were used on Kaggle's web site. Ninety percent of the images were used for the training data set, and the remaining 10% were used for the test data set. Using Google's Teachable Machine (ver. 2.0), machine learning was performed using separated images. To optimize the machine learning parameters, I measured the diagnosis accuracies according to the value of epoch, batch size, and learning rate. After hyper-parameter tuning, the ROC (receiver operating characteristic) analysis method determined the sensitivity (true positive rate, TPR) and specificity (false positive rate, FPR) of the machine learning model to diagnose the tooth-marked tongue. Results: To evaluate the usefulness of the Teachable Machine in clinical application, I used 634 tooth-marked tongue images and 491 no-marked tongue images for machine learning. When the epoch, batch size, and learning rate as hyper-parameters were 75, 0.0001, and 128, respectively, the accuracy of the tooth-marked tongue's diagnosis was best. The accuracies for the tooth-marked tongue and the no-marked tongue were 92.1% and 72.6%, respectively. And, the sensitivity (TPR) and specificity (FPR) were 0.92 and 0.28, respectively. Conclusion: These results are more accurate than Li's experimental results calculated with convolution neural network. Google's Teachable Machines show good performance by hyper-parameters tuning in the diagnosis of the tooth-marked tongue. We confirmed that the tool is useful for several clinical applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.349-354
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• 2020

The preliminary investigation to expand the green space requires a lot of cost and time. In this paper, we solve the problem by measuring the ratio of green space in a specific region through a convolutional neural network based the green space classification using Google Earth images. First, the proposed method collects various region images in Google Earth and learns them by using the convolutional neural network. The proposed method divides the image recursively to measure the green space ratio of the specific region, and it determines whether the divided image is green space using a trained convolutional neural network model, and then the green space ratio is calculated using the regions determined as the green space. Experimental results show that the proposed method shows high performance in measuring green space ratios in various regions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.907-913
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• 2022

Approximately 70% of smartphone users around the world use Android operating system-based smartphones, and malicious apps targeting these Android platforms are constantly increasing. Google has provided "Google Play Protect" to respond to the increasing number of Android targeted malware, preventing malicious apps from being installed on smartphones, but many malicious apps are still normal. It threatens the smartphones of ordinary users registered in the Google Play store by disguising themselves as apps. However, most people rely on antivirus programs to detect malicious apps because the average user needs a great deal of expertise to check for malicious apps. Therefore, in this paper, we propose a method to classify unnecessary malicious permissions of apps by using only the categories and permissions that can be easily confirmed by the app, and to easily detect malicious apps through the classified permissions. The proposed method is compared and analyzed from the viewpoint of undiscovered rate and false positives with the "commercial malicious application detection program", and the performance level is presented.

• Trans-
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• v.13
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• pp.117-140
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• 2022

Recently, artificial intelligence technology is being used throughout the industry. Currently, Currently, AI art generators are used in the NFT industry, and works using them have been exhibited and sold. AI art generators in the art field include Gated Photos, Google Deep Dream, Sketch-RNN, and Auto Draw. AI art generators in the music field are Beat Blender, Google Doodle Bach, AIVA, Duet, and Neural Synth. The characteristics of AI art generators are as follows. First, AI art generator in the art field are being used to create new works based on existing work data. Second, it is possible to quickly and quickly derive creative results to provide ideas to creators, or to implement various creative materials. In the future, AI art generators are expected to have a great influence on content planning and production such as visual art, music composition, literature, and movie.