• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1975-1988
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• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.99-102
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• 2021

The article considers one of the areas of development of artificial intelligence where there is the development of computer intelligent systems capable of performing functions traditionally considered intelligent - language comprehension, inference, use of accumulated knowledge, learning, pattern recognition, as well as learn and explain their decisions. It is found that informational intellectual systems are promising in their development. The article is devoted to intelligent information systems and technologies in educational activities, ie issues of organization, design, development and application of systems designed for information processing, which are based on the use of artificial intelligence methods.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.131-142
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• 2021

Recently, there has been an active service that provides customized news to news subscribers. In this study, we intend to design a customized news service system through Deep Learning-based Social Network Service (SNS) activity analysis, applying real news and avoiding fake news. In other words, the core of this study is the study of delivery methods and delivery devices to provide customized news services based on analysis of users, SNS activities. First of all, this research method consists of a total of five steps. In the first stage, social network service site access records are received from user terminals, and in the second stage, SNS sites are searched based on SNS site access records received to obtain user profile information and user SNS activity information. In step 3, the user's propensity is analyzed based on user profile information and SNS activity information, and in step 4, user-tailored news is selected through news search based on user propensity analysis results. Finally, in step 5, custom news is sent to the user terminal. This study will be of great help to news service providers to increase the number of news subscribers.

• Korean Journal of Artificial Intelligence
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• v.8 no.2
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• pp.23-28
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• 2020

Purpose: Today, we often hear complaints from customers: poor quality, poor service, expensive prices, etc. Customer complaints are an indication that the company's products and services do not meet customer requirements, which in turn causes customer complaints. An important content of corporate marketing practice is how to use the opportunity of handling customer complaints to win the trust of customers and gain a competitive advantage. According to the concept of marketing, the way for an enterprise to obtain profits is to continuously meet the needs of customers. However, with increasingly fierce market competition and the overall formation of a buyer's market, providing high-quality products and high-efficiency and high-level services have become the eternal theme of enterprises. Therefore, meeting the actual needs of customers and effectively handling customer complaints are issues that we must take seriously. Research design, data, and methodology: This article mainly analyzes the causes of customer complaints, proposes relevant solutions for different types of complaints, builds a customer complaint management system, improves the efficiency and ability of handling complaints, and provides more references and basis for enterprises to solve customer complaints. Conclusions: To further improve the quality of enterprise products and service standards, to help enterprises increase customer loyalty and satisfaction, and to enable enterprises to gain advantages in the increasingly competitive global market.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.510-518
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• 2022

This paper confirmed the technical reliability of mobile-based sarcopenia prediction and monitoring system. In implementing the developed system, we designed using only sensors built into a smartphone without a separate external device. The prediction system predicts the possibility of sarcopenia without visiting a hospital by performing the SARC-F survey, the 5-time chair stand test, and the rapid tapping test. The Monitoring system tracks and analyzes the average walking speed in daily life to quickly detect the risk of sarcopenia. Through this, it is possible to rapid detection of undiagnosed risk of undiagnosed sarcopenia and initiate appropriate medical treatment. Through prediction and monitoring system, the user may predict and manage sarcopenia, and the developed system can have a positive effect on reducing medical demand and reducing medical costs. In addition, collected data is useful for the patient-doctor communication. Furthermore, the collected data can be used for learning data of artificial intelligence, contributing to medical artificial intelligence and e-health industry.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.451-460
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• 2022

Recently, several deep learning studies have been reported to automatically identify the location of diagnostic devices using endoscopic data. In previous studies, there was no design to determine whether the configuration of the dataset resulted in differences in the accuracy in which artificial intelligence models perform image classification. Studies that are based on large amounts of data are likely to have different results depending on the composition of the dataset or its proportion. In this study, we intended to determine the existence and extent of accuracy according to the composition of the dataset by compiling it into three main types using larynx, esophagus, gastroscopy, and laryngeal endoscopy images.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.15-19
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• 2022

This study is to understand the relationship between turnover and various conditions. Turnover refers to workers moving from one company to another, which exists in various ways and forms. Currently, a large number of workers are considering many turnover rates to satisfy their income levels, distance between work and residence, and age. In addition, they consider changing jobs a lot depending on the type of work, the decision-making ability of workers, and the level of education. The company needs to accept the conditions required by workers so that competent workers can work for a long time and predict what measures should be taken to convert them into long-term workers. The study was conducted because it was necessary to predict what conditions workers must meet in order to become long-term workers by comparing various conditions and turnover using regression and decision trees. It used Microsoft Azure machines to produce results, and it found that among the various conditions, it looked for different items for long-term work. Various methods were attempted in conducting the research, and among them, suitable algorithms adopted algorithms that classify various kinds of algorithms and derive results, and among them, two decision tree algorithms were used to derive results.

• The Journal of Industrial Distribution & Business
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• v.14 no.2
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• pp.21-33
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• 2023

Purpose: After using the Internet, the world is changing through several paradigms, and the retail industry, which is essential to living in the world, is also changing rapidly. In this review paper, the requirements that the retail industry should consider and prepare in accordance with the rapidly changing paradigm were reviewed according to the current situation of the times. Research design, data, and methodology: It is a review of technological development using PRISMA flow diagram, retail change, and necessity in April 2022, and a review of the digital environment to be applied to the retail industry in the future. Results As the current situation and changes of retail, and the development of IT technology, reviews on the retail business applying the 4th Industrial Revolution, the Internet of Things and artificial intelligence were collected, and the direction of the retail industry was suggested. Conclusions: The direction for the retail industry in preparation for developing technologies was presented. In addition, this study is a review paper that suggests the need for research on active introduction of new technologies to the beauty market that is very close to human life and economically helpful as IT technology for the 4th industrial revolution develops rapidly.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.7-15
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• 2023

Purpose: The purpose of this study is to investigate how consumers perceive electric vehicles and factors that affect attitude, satisfaction, and intention to use electric vehicles and to explore policy issues regarding climate change and global environment. By classifying actual and potential users, this study developed the following research questions: i) factors including economic feasibility, sociality, environmental sustainability, inefficiency, inconvenience, convenience, and uncertainty affect attitude to electric vehicles; ii) attitude to electric vehicles affects actual consumers' satisfaction; and iii) attitude to electric vehicles affects potential users' intention to use. Research design, data and methodology: This study conducted an online survey and applied factor and regression analyses and ANOVA to test hypotheses. Results: The results of this study found that economic feasibility and convenience factors significantly affect attitude in both cases of actual and potential users. How actual users perceive efficiency of electric vehicles negatively and uncertain issues such as battery technology affect attitude to electric vehicles. Conclusions: This study provides policy implications that foster promotional policies for the adoption of electric vehicles for environment and regulate negative aspects. This study also provides managerial implications for manufacturers to develop better technology competences to enhance reliability on electric vehicles.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.