• Korean Journal of Environment and Ecology
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• v.38 no.1
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• pp.55-66
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• 2024

This study conducted a survey to investigate the characteristics of fish communities and the inhabiting status of endangered species in the Byekgye Stream, Korea from April to September 2020. A total of 3,415 fish of 9 families and 31 species were collected from 7 survey stations during the survey period. The dominant species was Zacco koreanus (relative abundance of 31.2%), and the subdominant species was Z. platypus (15.0%), followed by Pungtungia herzi (11.7%), Acheilognathus yamatsutae (5.4%), A. lanceolata intermedia (4.8%), Rhinogobius brunneus (4.4%), and Pseudopungtungia tenuicorpa (4.3%). Among the fish species collected, 19 (61.3%) were identified as Korean endemic species, and two cold-water fish species sensitive to climate change (Rhynchocypris kumgangensis and Cottus koreanus) were collected. Four species were designated as class II endangered wildlife by the Ministry of Environment: A. signifer, P. tenuicorpa, Rhodeus pseudosericeus, and C. koreanus. A. signifer and P. tenuicorpa mainly inhabited the mid to lower streams, R. pseudosericeus in the midstream, and R. pseudosericeus in the upstream. P. tenuicorpa inhabited in large numbers, and estimating the age by total length-frequency distribution in July, the total length of the 26-35 mm group was estimated as 0 years old, the 54-75 mm group as 1 year old, 82-97 mm group as 2 years old, 104-109 mm group as 3 years or older. The cluster analysis showed that the dominance index decreased from upstream to downstream, but the diversity, evenness, and richness index increased. The water quality of Byekgye Stream was evaluated as good overall since the river health (fish assessment index, FAI) using fish was evaluated as excellent (5 stations) and good (2 stations). Byekgye Stream has relatively well-preserved habitats, but conservation measures are required as habitats are disturbed by river repair work in some parts of the midstream and downstream areas where many endangered species inhabit.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.