• International Journal of Computer Science & Network Security
• /
• v.24 no.7
• /
• pp.143-147
• /
• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.10
• /
• pp.1435-1440
• /
• 2021

With the advent of the low interest rate era, many investors are flocking to the stock market. In the past stock market, people invested in stocks labor-intensively through company analysis and their own investment techniques. However, in recent years, stock investment using artificial intelligence and data has been widely used. The success rate of stock prediction through artificial intelligence is currently not high, so various artificial intelligence models are trying to increase the stock prediction rate. In this study, we will look at various artificial intelligence models and examine the pros and cons and prediction rates between each model. This study investigated as stock prediction programs using artificial intelligence artificial neural network (ANN), deep learning or hierarchical learning (DNN), k-nearest neighbor algorithm(k-NN), convolutional neural network (CNN), recurrent neural network (RNN), and LSTMs.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.2
• /
• pp.76-82
• /
• 2017

Malware, including ransomware to quickly detect, in this study, to provide an analysis method of malicious code through the image analysis that has been learned in the deep learning of artificial intelligence. First, to analyze the 2,400 malware data, and learning in artificial neural network Convolutional neural network and to image data. Extracts subgraphs to convert the graph of abstracted image, summarizes the set represent malware. The experimentally analyzed the malware is not how similar. Using deep learning of artificial intelligence by classifying malware and It shows the possibility of accurate malware detection.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.528-529
• /
• 2023

Due to vast points and irregular structure, labeling full points in large-scale point clouds is highly tedious and time-consuming. To resolve this issue, we propose a novel point-based transformer network in weakly-supervised semantic segmentation, which only needs 0.1% point annotations. Our network introduces general local features, representing global factors from different neighborhoods based on their order positions. Then, we share query point weights to local features through point attention to reinforce impacts, which are essential in determining sparse point labels. Geometric encoding is introduced to balance query point impact and remind point position during training. As a result, one point in specific local areas can obtain global features from corresponding ones in other neighborhoods and reinforce from its query points. Experimental results on benchmark large-scale point clouds demonstrate our proposed network's state-of-the-art performance.

• Spatial Information Research
• /
• v.22 no.1
• /
• pp.19-26
• /
• 2014

Recently, ICT environments have been increasingly developed and the pattern of the war also has been changed to NCW. The development of communication and network technology, for example, C4I and TDL(Tactical Data Link), has been prosperous and rapid. But the geospatial intelligence field which is the basis of the network frames relatively has not been developed. The purpose of this paper is to foster the geospatial governance in terms of the defense perspective. In order to do that, this paper deals with the U.S. Intelligence Community(IC) and the U.S. Army Global Information Grid(GIG), LandWarNet and those could be good examples of roles and statuses of geospatial intelligence. IC has been produced essential intelligence which is required for policymakers and military leaders. IC has several stove-piped intelligence process systems which have been separately developed and competed. And so as to complete GIG, the U.S. Army adopted LandWarNet. The U.S. Corps of Engineers organized the Army Geospatial Center(AGC) on 1 October 2009 to support LandWarNet. In order to develop NCW, we should recognize geospatial intelligence as the basis of network framework and make a central leading organization of defense geospatial intelligence. The mission of Korea Defense Geospatial-Intelligence Agency should be changed from producing GEOINT to a strategic GEOINT agency. The Army should organize a laboratory of geospatial intelligence field. The mission of producing GEOINT should be transferred to a geospatial intelligence battalion which is newly organized.

• The Journal of the Korea Contents Association
• /
• v.20 no.4
• /
• pp.86-98
• /
• 2020

The artificial intelligence (AI) is a technology that will lead the future connective and intelligent era by combining with almost all industries in manufacturing and service industry. Although Korea is one of the world's leading artificial intelligence group with the United States, Japan, and Germany, but its competitiveness in terms of artificial intelligence patent is relatively low compared to others. Therefore, it is necessary to carry out quantitative analysis of artificial intelligence patents in various aspects in order to examine national competitiveness, major industries and future development directions in artificial intelligence technology. In this study, we use the IPC technology classification code to estimate the overall life cycle and the speed of development of the artificial intelligence technology. We collected patents related to artificial intelligence from 2008 to 2018, and analyze patent trends through one-dimensional statistical analysis, two-dimensional statistical analysis and network analysis. We expect that the technological trends of the artificial intelligence industry discovered from this study will be exploited to the strategies of the artificial intelligence technology and the policy making of the government.

• The Journal of Information Technology
• /
• v.10 no.3
• /
• pp.1-14
• /
• 2007

We have studied the methods to connect homes with a digital network and apply home automation to education. We presented the possibility of integrated education where public education at school can be extended to home. The hardwares related with home automation are continuously in development. We have demonstrated the materials appropriate for the levels of individual students with home network information, and studied methods of education.

• Journal of Intelligence and Information Systems
• /
• v.1 no.1
• /
• pp.23-41
• /
• 1995

Business researchers have traditionally used statistical techniques for classification. In late 1980's, inductive learning started to be used for business classification. Recently, neural network began to be a, pp.ied for business classification. This study reviews the business classification studies, identifies a neural network a, pp.oach as the most powerful classification tool, and discusses the problems and issues in neural network a, pp.ications.

• Journal of Information Science Theory and Practice
• /
• v.10 no.spc
• /
• pp.12-24
• /
• 2022

This study attempted to investigate changes in collaboration structure for each stage of national Research and Development (R&D) in the artificial intelligence (AI) field through analysis of a co-author network for papers written under national R&D projects. For this, author information was extracted from national R&D outcomes in AI from 2014 to 2019. For such R&D outcomes, NTIS (National Science & Technology Information Service) information from the KISTI (Korea Institute of Science and Technology Information) was utilized. In research collaboration in AI, power function structure, in which research efforts are led by some influential researchers, is found. In other words, less than 30 percent is linked to the largest cluster, and a segmented network pattern in which small groups are primarily developed is observed. This means a large research group with high connectivity and a small group are connected with each other, and a sporadic link is found. However, the largest cluster grew larger and denser over time, which means that as research became more intensified, new researchers joined a mainstream network, expanding a scope of collaboration. Such research intensification has expanded the scale of a collaborative researcher group and increased the number of large studies. Instead of maintaining conventional collaborative relationships, in addition, the number of new researchers has risen, forming new relationships over time.

• Journal of Digital Convergence
• /
• v.15 no.5
• /
• pp.225-233
• /
• 2017

In this paper, we collected the articles related to artificial intelligence among SCI(E) journals published by Korean authors in 'Web of Science' and conducted frequency analysis and keyword network analysis. As a result of the analysis, the artificial intelligence thesis showed an average growth of about 10% per year, but the relative ratio decreased. As time went on, we could confirm that there is a lot of practical and applied research in artificial intelligence research. Unlike the US 'National Strategy for Artificial Intelligence Research and Development,' the field of research in Korea was focused on local and technical aspects. Therefore, Korea should go beyond the theoretical and technical iterations of artificial intelligence, and research should be carried out to present a comprehensive future direction.