• Korean Journal of Soil Science and Fertilizer
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• v.34 no.3
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• pp.153-157
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• 2001

To evaluate the effect of liquid pig manure application, the growth and yield of rice and the quality of infiltration water were investigated with application of different amounts of liquid manure. At this study, liquid pig manure was treated with 100, 200, 300 and 400% of recommending nitrogen fertilizer level, respectively. Liquid manure with application rate more than 200% of recommending N fertilizer level (11kg) caused to increase of plant height and number of tiller at panicle formation stage, but it caused the plant disease and pest and plant lodging. In those treatment, number of panicles per hill and number of spikelets per panicle were increased, but yield of rice was less than chemical fertilizer treatment due to low rate of ripeness and 1,000 grain weight. $NO_3-N$ concentration in infiltration water sample collected at 90 cm of soil depth was increased with increasing application amount of liquid manure. With liquid manure application more than 200% of recommending N fertilizer level, it affected negatively on yield and environment such as groundwater quality.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.169-176
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• 2009

Rule discovery is an operation that discovers patterns frequently occurring in a given database. Rule discovery makes it possible to find useful rules from a stock database, thereby recommending buying or selling times to stock investors. In this paper, we discuss storage structures for efficient processing of queries in a system that recommends stock investments. First, we propose five storage structures for efficient recommending of stock investments. Next, we discuss their characteristics, advantages, and disadvantages. Then, we verify their performances by extensive experiments with real-life stock data. The results show that the histogram-based structure improves the query performance of the previous one up to about 170 times.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.145-152
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• 2020

When conducting a health screening, it is important to select the most appropriate hospitals for the screening items. There are various packages in the screening hospitals, and the screening items and price are very different for each package. In this paper, we provide a method of recommending the screening packages in consideration of the customer's preferences such as screening items and minimum matching ratio. First, after collecting package information of hospitals, information such as basic items and optional items in the package are extracted. Then, we determine whether the client's screening items exist in the basic item or optional item of the package and calculate the matching rate of the package. Finally, we recommend screening packages with the lowest price while meeting the minimum matching rate suggested by the client. For performance analysis, we implement a prototype for recommending screening packages and provide the experimental results. The performance analysis shows that the proposed approach provides a real-time response time and recommends appropriate packages.

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

• The Journal of Information Technology and Database
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• v.8 no.1
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• pp.81-94
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• 2001

Providing explanations about recommending actions is one of the most important capabilities of expert systems. In fact, there exist many approaches incorporating this explanation facility into the system. Here we present briefly a new approach to generating these explanations and further attempt to investigate the impact of system explanations on user behaviors toward system-generated recommendations. For this experiment we designed a stock investment decision supporting system which, given a set of market situations, suggests an investment recommendation with explanations about the recommending action. Twenty-nine bank employees evaluated the output of the system in a laboratory setting. The results indicate that explanation facilities can make systems-generated advice more confident to users but cannot increase users'acceptance for the system conclusion.

• Journal of the Korean Institute of Plant Engineering
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• v.23 no.4
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• pp.57-64
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• 2018

User-based collaborative filtering is a method of recommending an item to a user based on the preference of the neighbor users who have similar purchasing history to the target user. User-based collaborative filtering is based on the fact that users are strongly influenced by the opinions of other users with similar interests. Item-based collaborative filtering is a method of recommending an item by comparing the similarity of the user's previously preferred items. In this study, we create a recommendation model using user-based collaborative filtering and item-based collaborative filtering with consumer's consumption data. Collaborative filtering is performed by using RFM (recency, frequency, and monetary) technique with purchasing data to recommend items with high purchase potential. We compared the performance of the recommendation system with the purchase amount and the performance when applying the RFM method. The performance of recommendation system using RFM technique is better.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1242-1250
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• 2022

When recommending cold items that do not have user-item interactions to users, even we adopt state-of-the-arts algorithms, the predicted information of cold items tends to have lower accuracy compared to warm items which have enough user-item interactions. The lack of information makes for recommender systems to recommend monotonic items which have a few top popular contents matched to user preferences. As a result, under-diversified items have a negative impact on not only recommendation diversity but also on recommendation accuracy when recommending cold items. To address the problem, we adopt a diversification algorithm which tries to make distributions of accumulated contents embedding of the two items groups, recommended items and the items in the target user's already interacted items, similar. Evaluation on a real world data set CiteULike shows that the proposed method improves not only the diversity but also the accuracy of cold item recommendation.

• Journal of the korean veterinary medical association
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• v.30 no.7
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• pp.406-423
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• 1994

Ethology is the scientific study of animal behavior, especially as it occurs in a natural environment. A background in ethology can provide practitioners a clearer understanding of the reasons why companion animals misbehave. Recommending behavioral treat

• Journal of The Korean Association of Information Education
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• v.19 no.2
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• pp.175-186
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• 2015

In this study, we developed the PATROL teaching and learning model by using digital textbooks in Smart Classrooms to correct the disadvantages of Flipped Classrooms. PATROL is an acronym for Planning, Action, Tracking, Recommending, Ordering, and Leading. In the Planning phase, teachers should make a lesson plan. Next, students take Action by watching online contents and completing assignments in their digital textbook. After that, Tracking is needed to analyze the students' activities and the results. Then, Recommending is used to provide suggested instructional activities to teachers based on that analysis. Next, Ordering requires that students request new materials for class activities. Finally, Leading allows teachers to provide materials at the appropriate level to their students based on the students' learning activities. Applying the PATROL model at two elementary schools resulted in an increase in student-directed speech as well as an increase in the number of group and individual activities. Teachers also had more time to walk around the classroom.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.103-109
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• 2009

We build the web document recommending agent system which offers a certain amount of web documents to each user by monitoring and learning the user's action of web browsing. We also propose a method of query expansion using the Korean thesaurus. The queries to search for new web documents generate a candidate set using the Korean thesaurus. We extract the words which are mostly correlated with the queries, among the words in the candidate set, by using TF-IDF and mutual information. Then, we expand the query. If we adopt the system of query expansion, we can recommend a lot of web documents which have potential interests to users. We thus conclude that the system of query expansion is more effective than a base system of recommending web-documents to users.