• Journal of Korea Society of Industrial Information Systems
• /
• v.28 no.5
• /
• pp.109-126
• /
• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.

• Information Systems Review
• /
• v.22 no.3
• /
• pp.15-30
• /
• 2020

In the real estate industry, the latest changes in the Fourth Industrial Revolution, such as big data analytics, machine learning, and VR (virtual reality), combine to bring about industry change. Proptech is a new term combining properties and technology. This study aims to derive and analyze from a comprehensive perspective the quality factors (systems, services, interfaces, information) for mobile real estate brokerage services that are well known and used in the domestic market. The surveys in this study were conducted online and offline and a total of 161 samples were used for statistical analysis. As a result, all hypotheses were approved to except system quality and service quality. The results show that the domestic proptech companies who are mostly focused on real estate brokerage services, peer-to-peer lending, advertising platforms and apartments need to grow in various fields of proptech business of other countries including Europe, USA and China.

• Information Systems Review
• /
• v.20 no.4
• /
• pp.25-41
• /
• 2018

Applying predictive analytics to enterprise processes is an effective way to reduce operation costs and enhance productivity. Accordingly, the ability to predict business processes and performance indicators are regarded as a core capability. Recently, several works have predicted processes using deep learning in the form of recurrent neural networks (RNN). In particular, the approach of predicting the next step of activity using static or dynamic RNN has excellent results. However, few studies have given attention to applying deep learning in the form of dynamic RNN to predictions of process performance indicators. To fill this knowledge gap, the study developed an approach to using process mining and dynamic RNN. By utilizing actual data from a large domestic company, it has applied the suggested approach in estimating timely stocking in purchasing process, which is an important indicator of the process. The analytic methods and results of this study were presented and some implications and limitations are also discussed.

• Advances in nano research
• /
• v.16 no.6
• /
• pp.623-638
• /
• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.30 no.1
• /
• pp.82-88
• /
• 2024

As the modern maritime industry rapidly progresses through technological advancements, data processing technology is emphasized as a key driver of this development. Natural language processing is a technology that enables machines to understand and process human language. Through this methodology, we aim to develop a model that predicts the proportions of outcomes when entering new written judgments by analyzing the rulings of the Marine Safety Tribunal and learning the cause-providing ratios of previously adjudicated ship collisions. The model calculated the cause-providing ratios of the accident using the navigation applied at the time of the accident and the weight of key keywords that affect the cause-providing ratios. Through this, the accuracy of the developed model could be analyzed, the practical applicability of the model could be reviewed, and it could be used to prevent the recurrence of collisions and resolve disputes between parties involved in marine accidents.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.141-166
• /
• 2018

Information technology improves the efficiency of humanities research. In humanities research, information technology can be used to analyze a given topic or document automatically, facilitate connections to other ideas, and increase our understanding of intellectual history. We suggest a method to identify and automatically analyze the relationships between arguments contained in unstructured data collected from humanities writings such as books, papers, and articles. Our method, which is called history mining, reveals influential relationships between arguments and the philosophers who present them. We utilize several classification algorithms, including a deep learning method. To verify the performance of the methodology proposed in this paper, empiricists and rationalism - related philosophers were collected from among the philosophical specimens and collected related writings or articles accessible on the internet. The performance of the classification algorithm was measured by Recall, Precision, F-Score and Elapsed Time. DNN, Random Forest, and Ensemble showed better performance than other algorithms. Using the selected classification algorithm, we classified rationalism or empiricism into the writings of specific philosophers, and generated the history map considering the philosopher's year of activity.

• Journal of Digital Convergence
• /
• v.17 no.12
• /
• pp.345-352
• /
• 2019

This study aims to analyze the factors affecting the length of stay in elderly pediatric inpatients in traffic accidents. We used Korean National Hospital Discharge In-depth Injury data on the discharged from 2012 to 2016. Statistically significant factors affecting the length of stay are admission route, Charlson Comorbidity Index(CCI), injury parts, operation, results, hospital area, and beds for hospitals. The length of stay was shorter in the case of the admission route of the outpatient department than the emergency room, the results were not improved or death rather than improved, and the bed size was 500-999 beds or over 1000 beds rather than 100-299 beds. However, the length of stay was longer in the case of CCI score was 1-2 or over 3 rather than 0, injury parts were other parts rather than head/neck, when the operation was yes, and when the hospital area was a province, metropolitan rather than Seoul. This study intends to understand the medical characteristics of inpatient to prevent pedestrian traffic accidents in accordance with the population aging. Based on this finding, we wish to be used as the basic data for the establishment of policies to effectively manage traffic safety and medical resources in consideration of the characteristics of the elderly people.

• The Journal of Society for e-Business Studies
• /
• v.19 no.4
• /
• pp.1-19
• /
• 2014

Efforts to identify user's recognition which exists in the big data are being conducted actively. They try to measure scores of people's view about products, movies and social issues by analyzing statements raised on Internet bulletin boards or SNS. So this study deals with the problem of determining how to find the emotional vocabulary and the degree of these values. The survey methods are using the results of previous studies for the basic emotional vocabulary and degree, and inferring from the dictionary's glosses for the extended emotional vocabulary. The results were found to have the 4 emotional words lists (vocabularies) as basic emotional list, extended 1 stratum 1 level list from basic vocabulary's glosses, extended 2 stratum 1 level list from glosses of non-emotional words, and extended 2 stratum 2 level list from glosses' glosses. And we obtained the emotional degrees by applying the weight of the sentences and the emphasis multiplier values on the basis of basic emotional list. Experimental results have been identified as AND and OR sentence having a weight of average degree of included words. And MULTIPLY sentence having 1.2 to 1.5 weight depending on the type of adverb. It is also assumed that NOT sentence having a certain degree by reducing and reversing the original word's emotional degree. It is also considered that emphasis multiplier values have 2 for 1 stratum and 3 for 2 stratum.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.4
• /
• pp.44-57
• /
• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• Korean Journal of Construction Engineering and Management
• /
• v.23 no.3
• /
• pp.13-24
• /
• 2022

Construction projects are exposed to various types of risks, which tend to increase. The increasing risks call for contractors' more attentions to forecasting and dealing with these risks. One of the measures to deal with contractors' risks is to forecast or estimate risk cost and include it in the construction budget. Although various researches in relation to risk cost have been observed, little attention has been paid to general contractors' perceptions and current practices in estimating risk cost of construction budget. The objective of the study is to identify and discuss key characteristics and implications based on the survey and analysis of general contractors' perceptions and current practices in estimating risk cost of construction budget. The study shows that there is a gap between the perception and the practice of estimating risk cost, that is, high perception of the importance of risk cost and a relatively low level of practice. It suggests that historical cost data, guidelines and corporate-level standard procedures are required to improve the current practice in addition to sufficient time allocations for risk cost estimating. It discusses that there is a need for using sophisticated estimating techniques including bid data analytics despite a low level of the current adoption, and also proposes that research and development in the field of the sophisticated estimating techniques should be further implemented in order to increase their practicality.