• Journal of Internet Computing and Services
• /
• v.11 no.2
• /
• pp.129-142
• /
• 2010

The ubiquitous healthcare environment is one of the systems that benefit from wireless sensor network. But one of the challenges with wireless sensor network is its high loss rates when transmitting data. Data from the biosensors may not reach the base stations which can result in missing values. This paper proposes the Health Monitor Agent (HMA) to gather data from the base stations, predict missing values, classify symptom patterns into medical conditions, and take appropriate action in case of emergency. This agent is applied in the Ubiquitous Healthcare Environment and uses data from the biosensors and from the patient’s medical history as symptom patterns to recognize medical conditions. In the event of missing data, the HMA uses a predictive algorithm to fill missing values in the symptom patterns before classification. Simulation results show that the predictive algorithm using the HMA makes classification of the symptom patterns more accurate than other methods.

• Journal of Internet Computing and Services
• /
• v.18 no.5
• /
• pp.61-67
• /
• 2017

In this paper we introduce two experimental results from classification of Instagram images and some valuable lessons from them. We have tried some experiments for evaluating the competitive power of Convolutional Neural Network(CNN) in classification of real social network images such as Instagram images. We used AlexNet and ResNet, which showed the most outstanding capabilities in ImageNet Large Scale Visual Recognition Challenge(ILSVRC) 2012 and 2015, respectively. And we used 240 Instagram images and 12 pre-defined categories for classifying social network images. Also, we performed fine-tuning using Inception V3 model, and compared those results. In the results of four cases of AlexNet, ResNet, Inception V3 and fine-tuned Inception V3, the Top-1 error rates were 49.58%, 40.42%, 30.42%, and 5.00%. And the Top-5 error rates were 35.42%, 25.00%, 20.83%, and 0.00% respectively.

• Journal of Internet Computing and Services
• /
• v.20 no.1
• /
• pp.57-65
• /
• 2019

In the case of Domeggook B2B online shopping malls, it has a market share of over 70% with more than 2 million members and 800,000 items are sold per one day. However, since the same or similar items are stored and registered in different catalog entries, it is difficult for the buyer to search for items, and problems are also encountered in managing B2B large shopping malls. Therefore, in this study, we developed a catalog entry auto classification and recommendation system for products by using semi-supervised machine learning method based on previous huge shopping mall purchase information. Specifically, when the seller enters the item registration information in the form of natural language, KoNLPy morphological analysis process is performed, and the Naïve Bayes classification method is applied to implement a system that automatically recommends the most suitable catalog information for the article. As a result, it was possible to improve both the search speed and total sales of shopping mall by building accuracy in catalog entry efficiently.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.2
• /
• pp.55-62
• /
• 2022

A smart tourism chatbot is needed as a user interface to efficiently provide smart tourism services such as recommended travel products, tourist information, my travel itinerary, and tour guide service to tourists. We have been developed a smart tourism app and a smart tourism information system that provide smart tourism services to tourists. We also developed a smart tourism chatbot service consisting of khaiii morpheme analyzer, rule-based intention classification, and tourism information knowledge base using Neo4j graph database. In this paper, we develop the Korean and English smart tourism Name Entity (NE) datasets required for the development of the NER model using the pre-trained language models (PLMs) for the smart tourism chatbot system. We create the tourism information NER datasets by collecting source data through smart tourism app, visitJeju web of Jeju Tourism Organization (JTO), and web search, and preprocessing it using Korean and English tourism information Name Entity dictionaries. We perform training on the KoBERT-CRF NER model using the developed Korean and English tourism information NER datasets. The weight-averaged precision, recall, and f1 scores are 0.94, 0.92 and 0.94 on Korean and English tourism information NER datasets.

• Journal of the Korean Society for information Management
• /
• v.19 no.3
• /
• pp.111-122
• /
• 2002

In order to investigate information seeking behavior of web search users, this study analyzes transaction logs posed by users of NAVER, a major Korean Internet search service. We present a session definition method for Web transaction log analysis, a way of cleaning original logs and a query classification method. We also propose a query term definition method that is necessary for Korean Web transaction log analysis. It is expected that this study could contribute to the development and implementation of more effective Web search systems and services.

• International Journal of Internet, Broadcasting and Communication
• /
• v.8 no.2
• /
• pp.55-65
• /
• 2016

Distributed Denial of service attack is one of the major threats nowadays especially to the government infrastructure that give huge impact to the reputation and interrupt the services and resource. Our survey start with brief introduction about DDoS attacks, we illustrate the trends and incident happened at government from various countries. We then provide an extensive literature review on the existing research about implication, types of attacks and initiative to defence against the DDoS attacks. Our discussion aims to identify the trends in DDoS attacks, in depth impact of DDoS attacks to government infrastructure, classification of attacks and techniques against the attacks. And we will use for a fire fight safety and management.

• Journal of Internet Computing and Services
• /
• v.19 no.4
• /
• pp.35-44
• /
• 2018

Monitoring through Synthesis Aperture Radar (SAR) is responsible for marine safety from floating icebergs. However, there are limits to distinguishing between icebergs and ships in SAR images. Convolutional Neural Network (CNN) is used to distinguish the iceberg from the ship. The goal of this paper is to increase the accuracy of identifying icebergs from SAR images. The metrics for performance evaluation uses the log loss. The two-layer CNN model proposed in research of C.Bentes et al.[1] is used as a benchmark model and compared with the four-layer CNN model using data augmentation. Finally, the performance of the final CNN model using the VGG-16 pre-trained model is compared with the previous model. This paper shows how to improve the benchmark model and propose the final CNN model.

• Journal of the Korean Home Economics Association
• /
• v.38 no.12
• /
• pp.85-99
• /
• 2000

The purpose of this study was to classify consumers barred on satisfaction with electronic commerce and to understand group differences in personal and behavioral characteristics. An on-line survey among hitel users was conducted from August 5th through 14th of 1999 and 678 replies were analyzed. This study identified four groups of consumers based on satisfaction with electronic commerce. They were 'the generally satisfied'. 'the generally dissatisfied', 'the satisfied with e-commerce while dissatisfied with the Internet', and 'the dissatisfied with e-commerce while satisfied with the Internet'. Demographic and behavioral characteristics of the four groups were significantly different. Implications on consumer education, consumer policy, and customer services of e-business were discussed.

• Korean Journal of Clinical Pharmacy
• /
• v.26 no.4
• /
• pp.324-329
• /
• 2016

Background: With the development of information and communication technology, there is a growing number of people looking for health information on the internet. This pilot study was performed to analyze the pattern of pharmacist consultation on the internet portal site. Methods: Questions and answers posted on the portal site "Naver Jisik-iN" from March 1st to 31th in 2016 were collected. Medications asked were categorized into prescription drugs, nonprescription drugs, sanitary aids, emergency drugs, functional health foods, and others. Medications were subcategorized into 14 according to the anatomical therapeutic chemical classification system. Questions were divided into 10 categories based on Hepler's drug-related problems. Results: Of the 955 cases, females accounted for 59.5% and inquirers from 11 to 40 years old, 89.4%. The number of prescription drugs, nonprescription drugs, sanitary aids, emergency drugs, functional health foods, and others were 428 (44.8%), 328 (34.3%), 31 (3.3%), 2 (0.2%), 122 (12.8%), and 44 (4.6%), respectively. Questions for drugs acting on alimentary tract and metabolism took up 27.4% followed by those on nervous system, 13.6% and anti-infectives for systemic use, 12.2%. Pharmacist consultation regarding drug information, drug interaction, and adverse reaction occupied 47.9%, 15.2%, and 12.3%, respectively. Conclusion: Health counseling through online is predicted to increase steadily, so pharmacists should broaden their boundaries beyond off-line pharmacies to meet social needs.

• International journal of advanced smart convergence
• /
• v.9 no.3
• /
• pp.183-191
• /
• 2020

Today, taking photos using smartphones has become an essential element of modern people. According to these social changes, modern people need a larger storage capacity, and the number of unnecessary photos has increased. To support the storage, cloud-based photo storage services from various platforms have appeared, and many people are using the services. As the number of photos increases, it is difficult for users to find the photos they want, and it takes a lot of time to organize. In this paper, we propose a cloud-based photo management service that facilitates photo management by classifying photos and recommending unnecessary photos using deep learning. The service provides the function of tagging photos by identifying what the subject is, the function of checking for wrongly taken photos, and the function of recommending similar photos. By using the proposed service, users can easily manage photos and use storage capacity efficiently.