• Journal of Physiology & Pathology in Korean Medicine
• /
• v.35 no.5
• /
• pp.132-138
• /
• 2021

Artificial intelligence technology sheds light on new ways of innovating acupuncture research. As acupoint selection is specific to target diseases, each acupoint is generally believed to have a specific indication. However, the specificity of acupoint selection may be not always same with the specificity of acupoint indication. In this review, we propose that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Using forward inference, the prescribed acupoints for each disease can be quantified for the specificity of acupoint selection. Using reverse inference, targeted diseases for each acupoint can be quantified for the specificity of acupoint indication. It is noteworthy that the selection of an acupoint for a particular disease does not imply the acupoint has specific indications for that disease. Electronic medical record includes various symptoms and chosen acupoint combinations. Data mining approach can be useful to reveal the complex relationships between diseases and acupoints from clinical data. Combining the clinical information and the bodily sensation map, the spatial patterns of acupoint indication can be further estimated. Interoperable medical data should be collected for medical knowledge discovery and clinical decision support system. In the era of artificial intelligence, machine learning can reveal the associations between diseases and prescribed acupoints from large scale clinical data warehouse.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.4
• /
• pp.911-923
• /
• 2016

Machine learning becomes increasingly important in the big data era. Logistic regression is a type of classification in machine leaning, and has been widely used in various fields, including medicine, economics, marketing, and social sciences. Rhipe that integrates R and Hadoop environment, has not been discussed by many researchers owing to the difficulty of its installation and MapReduce implementation. In this paper, we present the MapReduce implementation of Gradient Descent algorithm and Newton-Raphson algorithm for logistic regression using Rhipe. The Newton-Raphson algorithm does not require a learning rate, while Gradient Descent algorithm needs to manually pick a learning rate. We choose the learning rate by performing the mixed procedure of grid search and binary search for processing big data efficiently. In the performance study, our Newton-Raphson algorithm outpeforms Gradient Descent algorithm in all the tested data.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.2
• /
• pp.66-76
• /
• 2020

We The 4th industrial revolution, the core characteristics of super-intelligence, hyper-connective, and ultrareality, has been actualized. New technologies such as artificial intelligence, big data, the Internet of Things, and high-tech video have begun to be applied to media and advertising. With the introduction of new technologies in the advertising field, innovative changes in advertising types, advertising effects, advertising methods, and advertising contents are expected. Accordingly, We intends to design a future advertising roadmap development by predicting how future advertising will change and develop through future technologies in the 4th industrial revolution era. To design the roadmap, this study analyzes changes in advertising technology, consumer, and media as changes in the advertising environment in the 4th industrial revolution era, and identifies the core changing trends, advertising factors in future advertising through the Delphi Survey on experts in advertising and future fields. We identifies how the future advertising technology, types, media, effects, and fields are developed by the changes of future advertising environments, including technology, consumers, and media in the 4th industrial revolution era. Hence it is expected to help the advertising industry and experts to prepare for future changes.

• Journal of Cadastre & Land InformatiX
• /
• v.45 no.1
• /
• pp.169-179
• /
• 2015

Recently, as the amount of data increases rapidly, the development of IT technology entered the 'Big Data' era, dealing with large-volume of data at once. In the spatial field, a spatial data service technology is required to use that various and big amount of data. In this study, firstly, we explained the technology of typical spatial information data services abroad, and then we have developed large KML data processing techniques those can be applied as KML format to VWorld desktop. The test was conducted using a large KML data in order to verify the development KML partitioned methods and tools. As a result, the index file and the divided files are produced and it was visible in VWorld desktop.

• The Journal of the Korea Contents Association
• /
• v.21 no.5
• /
• pp.149-156
• /
• 2021

This study started from the question of what we think about big data as the term "big data" was used and discussed in our daily lives in the era of the 4th industrial revolution. For the analysis, the final 30 Q samples were selected based on prior research related to big data, and 23 respondents were secured for Q analysis, and the following results were derived. First, the explanatory power of each type was 34.30% for , 8.03% for , 7.21% for , and 6.24% for , showing a total of 55.69%. Second, the Q sample emphasized by respondents by each type shows various occupational distributions in , and for 'big data', it is 'digital' and future'. So they were named 「Digital Type」. In , the distribution of 'social workers' was high, and for 'big data', 'future', 'collaboration', 'welfare', 'local residents', and 'defense' were emphasized. It was named 「welfare type」. In , the job distribution of respondents appeared evenly, and it was named as 「Convergence Type」. Because it emphasized statements such as 'convergence', 'digital', 'future', and 'sports'. is composed of association officials, sports instructors, and graduate students, and was named 「Artificial Intelligence Type」, because it emphasizes 'artificial intelligence', 'new paradigm', 'network', and 'sports'. In the age of knowledge industrialization and knowledge informatization that followed industrialization and informatization, how to process and utilize the numerous data accumulated over the years is an important task. Right now, in sports, more than anything else, it is necessary to continuously seek ways to utilize and activate accumulated big data.

• Knowledge Management Research
• /
• v.19 no.4
• /
• pp.59-76
• /
• 2018

With the arrival of the big data era, customer data and data mining analysis have gradually dominated the process of Customer Relationship Management (CRM). This phenomenon indicates that customer data along with the use of information techniques (IT) have become the basis for building a successful CRM strategy. However, some companies can not discover valuable information through a large amount of customer data, which leads to the failure of making appropriate business strategy. Without suitable strategies, the companies may lose the competitive advantage or probably go bankrupt. The purpose of this study is to propose CRM strategies by segmenting customers into VIPs and Non-VIPs and identifying purchase patterns using the the VIPs' transaction data and data mining techniques (K-means clustering and association rules) of online shopping mall in Korea. The results of this paper indicate that 227 customers were segmented into VIPs among 1866 customers. And according to 51,080 transactions data of VIPs, home product and women wear are frequently associated with food, which means that the purchase of home product or women wears mainly affect the purchase of food. Therefore, marketing managers of shopping mall should consider these shopping patterns when they build CRM strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3858-3874
• /
• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• Journal of Information Technology Services
• /
• v.14 no.2
• /
• pp.143-158
• /
• 2015

Companies and governments in an era of big data have been tried to create new values with their data resources. Among many data resources, many companies especially pay attention to data which is obtained from Social Network Service (SNS) because it reveals precise opinion of customers and can be used to estimate profiles of them from their social relationships. However, it is not only hard to collect, store, and analyze the data, but system applications are also insufficient. Therefore, this study proposes a S-POS (Social POS) system which consists of three parts; Twitter Side, POS Side and TPAS (Twitter&POS Analysis System). In this system, SNS data and POS data which are collected from Twitter Side and POS Side are stored in Mongo D/B. And it provides several services with POS terminal based on analysis and matching results which are generated from TPAS. Through S-POS system, we expect to efficient and effective store and sales managements of system users. Moreover, they can provide some differentiated services such as cross-selling and personalized recommendation services.

• KIISE Transactions on Computing Practices
• /
• v.22 no.1
• /
• pp.20-25
• /
• 2016

With the advent of the era of digital big data, the Hadoop platform has become widely used in various fields. However, the Hadoop MapReduce framework suffers from problems related to the increase of the name node's main memory and map tasks for the processing of large number of small files. In addition, a method for running C++-based tasks in the MapReduce framework is required in order to conjugate GPUs supporting hardware-based data parallelism in the MapReduce framework. Therefore, in this paper, we present a face detection system that generates a sequence file for images to process big data for images in the Hadoop platform. The system also deals with tasks for GPU-based face detection in the MapReduce framework using Hadoop Pipes. We demonstrate a performance increase of around 6.8-fold as compared to a single CPU process.

• Journal of Family Resource Management and Policy Review
• /
• v.27 no.1
• /
• pp.13-24
• /
• 2023

The 4th industrial revolution came deep into family life and changed the way of housework and care. The change in the family caused by the technological change of the 4th industrial revolution is remarkable in terms of socialization of housework. In this study, the socialization of housework, which is accelerating in the era of the 4th industrial revolution, was examined focusing on the change in the aspect of "household service" through the "platform". Since 2015, when technological changes in the 4th industrial revolution began to decline, related newspaper articles were extracted for daily and economic newspapers nationwide and analyzed big data. The results of big data analysis show that the platform economy using the 4th industrial revolution technology is rapidly spreading the socialization of housework not only at the business level but also at the public policy level. It has been confirmed that support for household services through the platform is growing into a new business area of companies, and at the public policy level, it is being treated as an important policy task in supporting work-family balance or responding to infectious diseases. This study is meaningful in that it provided an opportunity to reflect on the roles and tasks of the family, market, and state for housework and care in the future through changes in housework and care caused by the 4th industrial revolution technology.