• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.285-307
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• 2009

This paper introduces DICE, a Domain-Independent text Classification Engine. DICE is robust, efficient, and domain-independent in terms of software and architecture. Each module of the system is clearly modularized and encapsulated for extensibility. The clear modular architecture allows for simple and continuous verification and facilitates changes in multiple cycles, even after its major development period is complete. Those who want to make use of DICE can easily implement their ideas on this test bed and optimize it for a particular domain by simply adjusting the configuration file. Unlike other publically available tool kits or development environments targeted at general purpose classification models, DICE specializes in text classification with a number of useful functions specific to it. This paper focuses on the ways to locate the optimal states of a practical text classification framework by using various adaptation methods provided by the system such as feature selection, lemmatization, and classification models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Computers and Concrete
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• v.30 no.5
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• pp.301-310
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• 2022

Cement-based sensors have been widely used as structural health monitoring systems, however, their long-term sensing performance have not actively investigated. In this study, a deep learning-based methodology is adopted to predict the long-term piezoresistive properties of cement-based sensors. Samples with different multi-walled carbon nanotube contents (0.1, 0.3, and 0.5 wt.%) are fabricated, and piezoresistive tests are conducted over 10,000 loading cycles to obtain the training data. Time-dependent degradation is predicted using a modified long short-term memory (LSTM) model. The effects of different model variables including the amount of training data, number of epochs, and dropout ratio on the accuracy of predictions are analyzed. Finally, the effectiveness of the proposed approach is evaluated by comparing the predictions for long-term piezoresistive sensing performance with untrained experimental data. A sensitivity of 6% is experimentally examined in the sample containing 0.1 wt.% of MWCNTs, and predictions with accuracy up to 98% are found using the proposed LSTM model. Based on the experimental results, the proposed model is expected to be applied in the structural health monitoring systems to predict their long-term piezoresistice sensing performances during their service life.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1653-1670
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• 2022

The fear that location privacy may be compromised greatly hinders the development of location-based service. Accordingly, some schemes based on the distributed architecture in peer-to-peer network for location privacy protection are proposed. Most of them assume that mobile terminals are mutually trusted, but this does not conform to realistic scenes, and they cannot make requirements for the level of location privacy protection. Therefore, this paper proposes a scheme for location attribute-based security authentication and private sharing data group, so that they trust each other in peer-to-peer network and the trusted but curious mobile terminal cannot access the initiator's query request. A new identifier is designed to allow mobile terminals to customize the protection strength. In addition, the caching mechanism is introduced considering the cache capacity, and a cache replacement policy based on deep reinforcement learning is proposed to reduce communications with location-based service server for achieving location privacy protection. Experiments show the effectiveness and efficiency of the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.77-92
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• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1432-1438
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• 2015

We present a methodology to detect moving shadows in video sequences, which is considered as a challenging and critical problem in the most visual surveillance systems since 1980s. While most previous moving shadow detection methods used hand-crafted features such as chromaticity, physical properties, geometry, or combination thereof, our method can automatically learn features to classify whether image segments are shadow or foreground by using a deep learning architecture. Furthermore, applying Markov Random Field enables our system to refine our shadow detection results to improve its performance. Our algorithm is applied to five different challenging datasets of moving shadow detection, and its performance is comparable to that of state-of-the-art approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4456-4475
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• 2021

The system for re-identifying persons is used to find and verify the persons crossing through different spots using various cameras. Much research has been done to re-identify the person by utilising features with deep-learned or hand-crafted information. Deep learning techniques segregate and analyse the features of their layers in various forms, and the output is complex feature vectors. This paper proposes a distinctive framework called Integrated Level Feature Pattern (ILFP) framework, which integrates local and global features. A new deep learning architecture named modified XceptionNet (m-XceptionNet) is also proposed in this work, which extracts the global features effectively with lesser complexity. The proposed framework gives better performance in Rank1 metric for Market1501 (96.15%), CUHK03 (82.29%) and the newly created NEC01 (96.66%) datasets than the existing works. The mean Average Precision (mAP) calculated using the proposed framework gives 92%, 85% and 98%, respectively, for the same datasets.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.158-161
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• 2003

A cascade structured neural network called Sigma-Pi$_{t}$ Cascaded Hybrid Neural Network ($\sigma$$\pi$$_{t}$-CHNN) is Proposed. It is an extended version of the Sigma-Pi Cascaded extended Hybrid Neural Network ($\sigma$$\pi$-CHNN), where the classical multiplicative neuron ($\pi$-neuron) is replaced by the translated multiplicative ($\pi$$_{t}$-neuron) model. The learning algorithm of $\sigma$$\pi$$_{t}$-CHNN is composed of an evolutionary programming method, responsible for determining the network architecture, and of a Levenberg-Marquadt algorithm, responsible for tuning the weights of the network. The $\sigma$$\pi$$_{t}$-CHNN is evaluated in 2 pattern classification problems: the 2 spirals and the sonar problems. In the 2 spirals problem, $\sigma$$\pi$$_{t}$-CHNN can generate neural networks with 10％ less hidden neurons than that in previous neural models. In the sonar problem, $\sigma$$\pi$$_{t}$-CHNN can find the optimal solution for the problem i.e., a network with no hidden neurons. These results confirm the expanded information processing capabilities of $\sigma$$\pi$$_{t}$-CHNN, when compared to previous neural network models. network models.

• Journal of Advanced Navigation Technology
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• v.8 no.2
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• pp.155-162
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• 2004

Recently, significant interest is shown to creation rather inexpensive global systems communications on base of Low-Earth-Orbit Satellite Networks (LEOSN). One of problems of design and creation LEOSN is development of the stream control methods and estimation it's efficiency in such networks. The given problem is complicated, that the topology of the satellite networks varies in time. It essentially hinders the analytical decision of the given problem. An effective way of overcoming of these difficulties is simulation modeling. For realization of research experiments on learning the information streams routing algorithms in LEOSN a special program complex SANET was developed. In the given paper principles of development of LEOSN simulation models and architecture of the manager by the process of a simulation modeling of the unit are considered. Methods of promotion of modeling time and architecture of a simulator complex offered in the article allow to boost essentially efficiency of simulation analysis and to ensure simulation modeling of the satellite networks consisting of several hundreds space vehicles.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.95-102
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• 2008

The interactive virtual reality technology has used in scientific inquiry learning since it can overcome the restriction of real world and it draws user's interest and foster active participation. However, prior works are mostly designed for a specific inquiry learning lesson and it is quite difficult to use them for constructing other inquiry learning environments. Hence, we developed the integrated virtual reality system, SASILE (System for Augmenting Scientific Inquiry Learning Environments), that helps ease the development of the scientific inquiry learning environment. In this paper, we first describe the related works on supporting VR scientific inquiry learning systems, followed by the SASILE system architecture and implementation. Then, we illustrate the use of this system to develop a Virtual Moyangsung application for teaching a scientific structure of Korean traditional house by exploring and observing the convection currents as well as a Mars Rover application for estimating the asteroid impacts on Mars by measuring rock properties. Finally, we will discuss the future research directions for this system.