• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.35-40
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• 2019

It is important to constantly provide service in real-time multimedia applications using multicast. Transmission path reconstruction occurs in hybrid multicast using Internet Protocol (IP) multicast and ALM in order to adapt the network status to things like congestion. So, there is a problem in which real-time QoS is reduced, caused by an increase in end-to-end delay. In this paper, we want to solve this problem through multi-layer transmission path construction. In the proposed method, we deploy the control server and application layer overlay host (ALOH) in each multicast domain (MD) for hybrid multicast construction. After the control server receives the control information from an ALOH that joins the MD, it makes a group based on the hop count and sends it to the ALOH in each MD. The ALOH in the MD performs the role of sending the packet to another ALOH and constructs the multi-layered transmission path in order of priority by using control information that is received from the control server and based on the delay between neighboring ALOHs. When congestion occurs in, or is absent from, the ALOH in the upper MD, the ALOH selects the path with the highest priority in order to reduce end-to-end delay. Simulation results show that the proposed method could reduce the end-to-end delay to less than 289 ms, on average, under congestion status.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.87-94
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• 2009

Noise estimation and suppression are a crucial factor of many speech communication and recognition systems. In this paper, proposed algorithm is based on the ratio of variance normalized of noisy power spectrum in time-frequency domain. Our proposed algorithm tracks the threshold and controls the trade-off between residual noise and distortion. This algorithm is evaluated by the ITU-T P.835 signal distortion (SIG) and segment signal to noise ratio (SNR), and is superior to the conventional methods.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.91-104
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• 2007

Ontology matching technique is one of the most important techniques in the Semantic Web as well as in other areas. Ontology matching algorithm takes two ontologies as input, and finds out the matching relations between the two ontologies by using some parameters in the matching process. Ontology matching is very useful in various areas such as the integration of large-scale ontologies, the implementation of intelligent unified search, and the share of domain knowledge for various applications. In general cases, the performance of ontology matching is estimated by measuring the matching results such as precision and recall regardless of the requirements that came from the matching environment. Therefore, most research focuses on controlling parameters for the optimization of precision and recall separately. In this paper, we focused on the harmony of precision and recall rather than independent performance of each. The purpose of this paper is to propose a methodology that determines parameters for the desired ratio of precision and recall that is appropriate for the requirements of the matching environment.

• Journal of IKEEE
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• v.26 no.3
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• pp.341-348
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• 2022

Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.34-41
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• 2017

Vehicle to Infrastructure(V2I) communication means the technology between the vehicle and the roadside unit to provide the Intelligent Transportation Systems(ITS) and Telematic services. The vehicle collects information about the probe data through the evolved Node B(eNodeB) and after that eNodeB provides road conditions or traffic information to the vehicle. To provide these V2I communication services, we need a link adaptation technology that enables reliable and higher transmission rate. The receiver transmits the estimated Channel State Information(CSI) to transmitter, which uses this information to enable the link adaptation. However, due to the rapid channel variation caused by vehicle speed and the processing delay between the layers, the estimated CSI quickly becomes outdated. For this reason, channel interpolation and prediction scheme are needed to achieve link adaptation in V2I communication system. We propose the Advanced Discrete Prolate Spheroidal Sequence(ADPSS) channel interpolation and prediction scheme. The proposed scheme creates an orthonomal basis, and uses a correlation matrix to interpolate and predict channel. Also, smoothing is applied to frequency domain for noise removal. Simulation results show that the proposed scheme outperforms conventional schemes with the high speed and low speed vehicle in the freeway and urban environment.