• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2469-2490
• /
• 2019

A low-energy adaptive clustering hierarchy (LEACH) protocol is a low-power adaptive cluster routing protocol which was proposed by MIT's Chandrakasan for sensor networks. In the LEACH protocol, the selection mode of cluster-head nodes is a random selection of cycles, which may result in uneven distribution of nodal energy and reduce the lifetime of the entire network. Hence, we propose a new selection method to enhance the lifetime of network, in this selection function, the energy consumed between nodes in the clusters and the power consumed by the transfer between the cluster head and the base station are considered at the same time. Meanwhile, the improved FTBA algorithm integrating the curve strategy is proposed to enhance local and global search capabilities. Then we combine the improved BA with LEACH, and use the intelligent algorithm to select the cluster head. Experiment results show that the improved BA has stronger optimization ability than other optimization algorithms, which the method we proposed (FTBA-TC-LEACH) is superior than the LEACH and LEACH with standard BA (SBA-LEACH). The FTBA-TC-LEACH can obviously reduce network energy consumption and enhance the lifetime of wireless sensor networks (WSNs).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.1
• /
• pp.208-223
• /
• 2015

To solve the problem of face recognition with complex changes and further improve the efficiency, a new near-infrared face recognition algorithm which fuses E-GV-LBP and FKNN algorithm is proposed. Firstly, it transforms near infrared face image by Gabor wavelet. Then, it extracts LBP coding feature that contains space, scale and direction information. Finally, this paper introduces an improved FKNN algorithm which is based on spatial domain. The proposed approach has brought face recognition more quickly and accurately. The experiment results show that the new algorithm has improved the recognition accuracy and computing time under the near-infrared light and other complex changes. In addition, this method can be used for face recognition under visible light as well.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.627-630
• /
• 2022

Over the past few years, fake news has become one of the most significant problems. Since it is impossible to prevent people from spreading misinformation, people should analyze the news themselves. However, this process takes some time and effort, so the routine part of this analysis should be automated. There are many different approaches to this problem, but they only analyze the text and messages, ignoring the images. The fake news problem should be solved using a complex analysis tool to reach better performance. In this paper, we propose the approach of training an Artificial Intelligence using an unsupervised learning algorithm, combined with online data parsing tools, providing independence from subjective data set. Therefore it will be more difficult to spread fake news since people could quickly check if the news or article is trustworthy.

• Journal of Korean Library and Information Science Society
• /
• v.45 no.1
• /
• pp.297-317
• /
• 2014

This study is to produce baseline data, in its purpose of improving the quality of life of industrial workers as well as the business competitiveness, though improvements in reading culture. The data produced in this study will be based on questionnaire, regarding sub-topics of the reading environment, the status of reading culture, and remaining demands. The results argue that the responding workers in Siheung Smart Hub Complex had inadequate environment of reading, while had weak status of reading culture. We propose the ways of improvements in the reading culture of the worker in Siheung Smart Hub Complex, as well as their reading infrastructure.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.4
• /
• pp.1437-1459
• /
• 2020

Wireless Sensor Networks (WSN) is a distributed Sensor network whose terminals are sensors that can sense and check the environment. Sensors are typically battery-powered and deployed in where the batteries are difficult to replace. Therefore, maximize the consumption of node energy and extend the network's life cycle are the problems that must to face. Low-energy adaptive clustering hierarchy (LEACH) protocol is an adaptive clustering topology algorithm, which can make the nodes in the network consume energy in a relatively balanced way and prolong the network lifetime. In this paper, the novel multi-objective LEACH protocol is proposed, in order to solve the proposed protocol, we design a multi-objective coupling algorithm based on bat algorithm (BA), glowworm swarm optimization algorithm (GSO) and bacterial foraging optimization algorithm (BFO). The advantages of BA, GSO and BFO are inherited in the multi-objective coupling algorithm (MBGF), which is tested on ZDT and SCH benchmarks, the results are shown the MBGF is superior. Then the multi-objective coupling algorithm is applied in the multi-objective LEACH protocol, experimental results show that the multi-objective LEACH protocol can greatly reduce the energy consumption of the node and prolong the network life cycle.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.2
• /
• pp.771-789
• /
• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

• Korean Educational Research Journal
• /
• v.40 no.2
• /
• pp.1-16
• /
• 2019

The society of intelligence-information complex is a fresh world that connects things, knowledge and calculation with human. What is the condition of educational reform in this world? Robinson and Aronica(2015) suggest educational reform at the center of organic agriculture, in which they focus on the dignity of human as an organic being. Human consists in an intelligence and a life. We have to ask to ourselves what is the human in this Age. The development of AI represented by deep-learning will be an actual condition in the educational reform. In the other hand, the combination with an information technology and art rises a question about a life itself. So, we have to ask the question seriously that overlap what is the human and what is a life. Two questions about human and a life cast a philosophical paradox in the age of AI.

• The Journal of Bigdata
• /
• v.8 no.1
• /
• pp.35-47
• /
• 2023

It is crucial to develop effective and efficient big data analytics methods for problem-solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.

• Asia pacific journal of information systems
• /
• v.30 no.1
• /
• pp.31-52
• /
• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.1
• /
• pp.217-224
• /
• 2019

The intelligent robot industry is a complex which encompasses all fields of science and technology, and its marketability and industrial impact are remarkable. Major countries in the world have been strengthening their policies to foster the intelligent robot industry, but discussions on liability issues and legal actions that are accompanied by the related big or small accidents are still insufficient. In this study, therefore, the patent law by artificial intelligence robots and the legislation for relevant legal actions at the criminal law level are presented. Patent law legislation by artificial intelligence robots should comply with the followings. First, the electronic human being other than humans ought to be given legal personality, which is the subject of patent infringement. Even if artificial intelligence has legal personality, legal responsibility will be varied depending on the judgment of whether the accident has occurred due to the malfunction of the artificial intelligence itself or due to the human intervention with malicious intention. Second, artificial intelligence as a subject of actors and responsibility should be distinguished strictly; in other words, the injunction is the responsibility of the intelligent robot itself, but the financial repayment is the responsibility of the owner. In the criminal law legislation, regulations for legal punishment of intelligent robot manufacturing companies and manufacturers should be prepared promptly in case of legal violation, by amending the scope of application of Article 47 (Penal Provisions) of the Intelligent Robots Development and Distribution Promotion Act. In this way, joint penal provisions, which can clearly distinguish the responsibilities of the related parties, should be established to contribute to the development of the fourth industrial revolution.