• Electronics and Telecommunications Trends
• /
• v.38 no.6
• /
• pp.22-30
• /
• 2023

Recently, artificial intelligence has been in the spotlight across various fields. Artificial intelligence uses massive amounts of data to train machine learning models and performs various tasks using the trained models. For model training, large, high-quality data sets are essential, and database systems have provided such data. Driven by advances in artificial intelligence, attempts are being made to improve various components of database systems using artificial intelligence. Replacing traditional complex algorithm-based database components with their artificial-intelligence-based counterparts can lead to substantial savings of resources and computation time, thereby improving the system performance and efficiency. We analyze trends in the application of artificial intelligence to database systems.

• Rapid Communication in Photoscience
• /
• v.1 no.2
• /
• pp.41-41
• /
• 2012

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.2
• /
• pp.297-306
• /
• 2004

It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

• Korean Journal of Radiology
• /
• v.25 no.2
• /
• pp.123-125
• /
• 2024

• Clinical Endoscopy
• /
• v.56 no.1
• /
• pp.14-22
• /
• 2023

Barrett's esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett's esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett's esophagus and elaborate on potential artificial intelligence in the future.

• Bulletin of the Korean Chemical Society
• /
• v.32 no.spc8
• /
• pp.3113-3116
• /
• 2011

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.3
• /
• pp.67-75
• /
• 2016

This paper investigates how artificial individuals with behavioral memory circuits adaptively evolve with respect to given environments on a cell-level simulation framework simulating artificial individuals. This makes it possible for us to analyse the advantages of artificial individuals with behavioral memory circuits against the simple artificial individuals that can do only simple reactions with respect to the environments and to know which advanced reactions are possible. In order to do this analysis, we experimented various tests on a specific prey pattern and examined the results. As a first experiment, we tested that artificial individuals with four memory steps competed against from those without memory step to those with three memory steps. Experimental results showed that the artificial individuals with four memory steps were superior to most others. However, artificial individuals with two memory steps were better than those with four memory steps. This was caused that the artificial individuals with two memory steps could evolve faster than those of four memory steps. In a second experiment that all types of artificial individuals are simultaneously evolved, the artificial individuals with two memory steps also showed the best result in the experiment. We could conclude that the artificial individuals with memory was better than those without memory and the best memory steps of artificial individuals were depended on the complexity of prey patterns.

• Journal of The Korean Association of Information Education
• /
• v.25 no.5
• /
• pp.791-801
• /
• 2021

In this paper, an educational case that can be applied as artificial intelligence literacy education for preservice teachers and incumbent teachers was studied. To this end, a case of educating the operating principle of an artificial neural network that recognizes images is proposed. This training case focuses on the basic principles of artificial neural network operation and implementation, and applies the method of finding parameter optimization solutions required for artificial neural network implementation in a spreadsheet. In this paper, we focused on the artificial neural network of supervised learning method. First, as an artificial neural network principle education case, an artificial neural network education case for recognizing two types of images was proposed. Second, as an artificial neural network extension education case, an artificial neural network education case for recognizing three types of images was proposed. Finally, the results of analyzing artificial neural network training cases and training satisfaction analysis results are presented. Through the proposed training case, it is possible to learn about the operation principle of artificial neural networks, the method of writing training data, the number of parameter calculations executed according to the amount of training data, and parameter optimization. The results of the education satisfaction survey for preservice teachers and incumbent teachers showed a positive response result of over 70% for each survey item, indicating high class application suitability.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_3
• /
• pp.1399-1414
• /
• 2018

Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.56 no.3
• /
• pp.202-212
• /
• 2020

In order to replace sardine baits for octopus pot, an efficacy experiment to lure with alternative bait (fermented skate or chicken skin in artificial crab or northern clam) pots and sardine pot were conducted in a circular water tank. The soaking time of the sardine bait was divided into two categories: six days or less and seven days or more. The behavioral response of octopus to the artificial bait pots and sardine pot were investigated. In the comparison of the luring effects between pots with fermented skate inside artificial crab or northern clam and sardine pot, the pot with artificial crab + fermented skate had better results than the other pots in the section distribution (31.6%) and the number of times the pot was entered into (20.0%) (p > 0.05). In the comparison of the luring effects between pots with chicken skin inside artificial crab or northern clam and sardine pot, the pot with northern clam + chicken skin had better results than the other pots in the section distribution (22.6%) and number of times the pot was entered into (55.6%) (p < 0.05). The results were also better compared to those of pot with artificial crab + fermented skate. From these results, it seems that in the luring effect aspect, sardine bait can be replaced with artificial bait consisting of chicken skin inside northern clam.