• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.39-46
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• 2021

AI is deeply applied to various algorithms that assists us, not only daily technologies like translator and Face ID, but also contributing to innumerable fields in industry, due to its dominance. In this research, we provide convenience through AI categorization, extracting the only data that users need, with objective classification, rather than verifying all data to find from the internet, where exists an immense number of contents. In this research, we propose a model using LSTM(Long-Short Term Memory Network), which stands out from text classification, and compare its performance with models of RNN(Recurrent Neural Network) and BiLSTM(Bidirectional LSTM), which is suitable structure for natural language processing. The performance of the three models is compared using measurements of accuracy, precision, and recall. As a result, the LSTM model appears to have the best performance. Therefore, in this research, text classification using LSTM is recommended.

• Annual Conference of KIPS
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• 2021.05a
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• pp.411-414
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• 2021

One of the elements of technology that has become extremely critical within the field of education today is Deep learning. It has been especially used in the area of natural language processing, with some word-representation vectors playing a critical role. However, some of the low-resource languages, such as Swahili, which is spoken in East and Central Africa, do not fall into this category. Natural Language Processing is a field of artificial intelligence where systems and computational algorithms are built that can automatically understand, analyze, manipulate, and potentially generate human language. After coming to discover that some African languages fail to have a proper representation within language processing, even going so far as to describe them as lower resource languages because of inadequate data for NLP, we decided to study the Swahili language. As it stands currently, language modeling using neural networks requires adequate data to guarantee quality word representation, which is important for natural language processing (NLP) tasks. Most African languages have no data for such processing. The main aim of this project is to recognize and focus on the classification of words in English, Swahili, and Korean with a particular emphasis on the low-resource Swahili language. Finally, we are going to create our own dataset and reprocess the data using Python Script, formulate the syllabic alphabet, and finally develop an English, Swahili, and Korean word analogy dataset.

• Journal of KIISE
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• v.44 no.2
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• pp.179-185
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• 2017

Document summarization aims to generate a summary that is consistent and contains the highly related sentences in a document. In this study, we implemented for document summarization that extracts highly related sentences from a whole document by considering both similarities and entailment relations between sentences. Accordingly, we proposed a new algorithm, TextRank-NLI, which combines a Recurrent Neural Network based Natural Language Inference model and a Graph-based ranking algorithm used in single document extraction-based summarization task. In order to evaluate the performance of the new algorithm, we conducted experiments using the same datasets as used in TextRank algorithm. The results indicated that TextRank-NLI showed 2.3% improvement in performance, as compared to TextRank.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.3
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• pp.307-322
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• 1992

For designing the radar integrated data network, we construct the network structure with a spatial hieratchy decomposition scheme. The RIDN can be decomposed into several subent classes, those of which are composed of the several group classes of radar sites, In a group class. The communication nodes of a radar site are modeled by the software modules formulated with the statistical attributes of discrete events. And we get the analysis over the network through the separately constructed infra group level models which were coded with the C language.From the result of the simulation. We could findthe fact that the data integration system;s performance approaches to the theordtically calculated value after being stable. And also we could get the packet processing status of a communication module’s inner processor which is difficult to oberve through the mathematical calculation tin the subnet model of the integrated data network.

• ETRI Journal
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• v.26 no.4
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• pp.360-366
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• 2004

Synchronization Markup Language (SyncML) is a specification of a common data synchronization framework for synchronizing data on networked devices. SyncML is designed for use between mobile devices that are intermittently connected to a network and network services that are continuously available on the network. We have designed and developed a data synchronization system based on the SyncML protocol and evaluated the throughput of the system using the stochastic Petri nets package (SPNP) and analyzed the relationship between the arrival rate and the system resources. Using this model, we evaluate various performance measures in different situations, and we estimate the relationship between the arrival rate and the system resources. From the results, we can estimate the optimal amount of resources due to the arrival rate before deploying the developed system.

• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.203-210
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• 2017

In this paper, we propose an effective neural network model for image caption generation and model transfer. This model is a kind of multi-modal recurrent neural network models. It consists of five distinct layers: a convolution neural network layer for extracting visual information from images, an embedding layer for converting each word into a low dimensional feature, a recurrent neural network layer for learning caption sentence structure, and a multi-modal layer for combining visual and language information. In this model, the recurrent neural network layer is constructed by LSTM units, which are well known to be effective for learning and transferring sequence patterns. Moreover, this model has a unique structure in which the output of the convolution neural network layer is linked not only to the input of the initial state of the recurrent neural network layer but also to the input of the multimodal layer, in order to make use of visual information extracted from the image at each recurrent step for generating the corresponding textual caption. Through various comparative experiments using open data sets such as Flickr8k, Flickr30k, and MSCOCO, we demonstrated the proposed multimodal recurrent neural network model has high performance in terms of caption accuracy and model transfer effect.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.247-249
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• 2014

Ubiquitous Sensor Network (USN) environments, sensors and sensor nodes, and coming from heterogeneous sensor networks consist of one another, the characteristics of each component are also very diverse. Thus the sensor and the sensor nodes to interoperability between metadata for a single definition, management is very important. For this, the standard language for modeling sensor SensorML (Sensor Model Language) has. In this paper, sensor devices, sensor nodes and sensor networks for information technology in the application stage XMDR-DAI -based metadata to define the USN. The proposed XMDR-DAI USN based store and retrieve metadata for a method for effectively agent technology. Metadata of the proposed sensor is based SensorML USN environment by maintaining interoperability 50-200 USN middleware or a metadata management system for managing metadata in applications can be utilized directly.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• Journal of the Korea Computer Graphics Society
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• v.2 no.2
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• pp.69-74
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• 1996

With the emergence of integrated global market and increased competition, many companies are interested in sharing the product model data. One of the solutions is to share the product model data over the computer network or the internet using a standard format. CAD/CAM, STEP, and internet technologies make it possible to share the product model data. This paper presents methods to visualize 3D STEP geometry data on the internet. To create an internet-based STEP model visualizer, the programming language Java and 3D scene description language VRML have been experimented. The STEP geometry data can be displayed either by Java applets of by a VRML browser. These visualization technologies are applied to a PDM development. Engineers who have a low cost web browser can share the expensive design information even at a remote site.