• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• Journal of KIISE:Software and Applications
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• v.27 no.9
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• pp.931-941
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• 2000

본 논문은 가상환경 속에서 사용되는 다감각(시각, 청각, 후각, 촉각)요소가 현실감에 끼치는 영향력을 평가한다. 이번 실험에 80명의 피실험자가 참여하였으며, 여러 감각 요소들을 조합한 가상환경을 경험하게 하여 얻은 실험 데이터를 분석함으로써 각 요소들의 영향도를 평가한다. 주된 평가 요소는 가상 현실 환경 속에서 피실험자가 주관적으로 느끼는 현심감과, 감각 요소들이 피실험자들의 기억력에 미치는 영향도를 객관적으로 측정한 결과를 가지고 분산분석법을 사용하여 검증하여 본다. 이번 실험을 통하여 우리는 이러한 다중 감감 요소들이 단일한 감각요소가 있을 때에 비하여 가상 환경 속에서의 사물에 대한 기억력과 현실감을 증가시킬 수 있다는 결과를 얻는다. 또한 피실험자가 느꼈던 가상환경 속의 현실감과 사물에 대한 기억력에 대하여, 시각적인 요소를 증가시키는 것보다 그 외의 다른 요소(청각, 후각, 촉각)들을 첨가시키는 것이 현실감과 기억력을 배가시키는 요소로써 더 효과적이고 민감하게 반응한다.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.2
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• pp.97-101
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• 2007

Correct localization of epileptogenic zone is important for the successful epilepsy surgery. Both ictal perfusion single photon emission computed tomography (SPECT) and interictal F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information in the presurgical localization of intractable partial epilepsy. These imaging modalities have excellent diagnostic sensitivity in medial temporal lobe epilepsy and provide good presurgical information in neocortical epilepsy. Also provide functional information about cellular functions to better understand the neurobiology of epilepsy and to better define the ictal onset zone, symptomatogenic zone, propagation pathways, functional deficit zone and surround inhibition zones. Multimodality imaging and developments in analysis methods of ictal perfusion SPECT and new PET ligand other than FDG help to better define the localization.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1002-1003
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• 2019

Face recognition is an extensively researched area of computer vision. Visible, infrared, thermal, and 3D modalities have been used against various challenges of face recognition such as illumination, pose, expression, partial information, and disguise. In this paper we present a multi-modal approach to face recognition using convolutional neural networks. We use visible and thermal face images as two separate inputs to a multi-input deep learning network for face recognition. The experiments are performed on IRIS visible and thermal face database and high face verification rates are achieved.

• Journal of Information Science Theory and Practice
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• v.7 no.3
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• pp.21-30
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• 2019

Information about the formation and filling (in 2014 to 2016) of a terminological dictionary on electronics and radioengineering and collective work (in 2017 to 2018) with a data bank "TERMIN" is presented in this article. In purpose of creating an instrument of navigating the modern scientific-technical space a net of terms with set semantic links is described. This set is based on the analysis of terms' definitions (each term is checked for inclusion in the definitions of all other terms; the definitions were borrowed from reputable reference editions: encyclopedias, dictionaries, reference books). The created model of a system that consists of different information sources, in which it (information) is indexed by the terminology of Russian State Rubricator of Scientific and Technical Information rubrics and/or keywords, is described. There is an access for the search in all these sources in the system. Searching inquiries are referred to in the language of these rubrics or formulated by arbitrary terms. The system is to refer to information sources and give out relevant information. In accordance with this model, semantic links of various types, which allow expanding a search at different modalities of query, should be set among data bank terms. Obtained links will have to increase semantic matching, i.e., they can provide actual understanding of the meaning of the information that is being sought.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.259-270
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• 2022

Artificial intelligence (AI) has been studied in various fields of medical imaging. Currently, top-notch deep learning (DL) techniques have led to high diagnostic accuracy and fast computation. However, they are rarely used in real clinical practices because of a lack of reliability concerning their results. Most DL models can achieve high performance by extracting features from large volumes of data. However, increasing model complexity and nonlinearity turn such models into black boxes that are seldom accessible, interpretable, and transparent. As a result, scientific interest in the field of explainable artificial intelligence (XAI) is gradually emerging. This study aims to review diverse XAI approaches currently exploited in medical imaging. We identify the concepts of the methods, introduce studies applying them to imaging modalities such as computational tomography (CT), magnetic resonance imaging (MRI), and endoscopy, and lastly discuss limitations and challenges faced by XAI for future studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.51-73
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• 2023

Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNetB0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

• International Commerce and Information Review
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• v.11 no.2
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• pp.49-66
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• 2009

The DDA negotiations, in 9th multilateral trade round, has focused on nine sectors including agriculture, non-agricultural market access, and service. After August 2004, member countries have intensified negotiations in order to reduce gaps between countries perspective. So most attention of members countries has been focused on agricultural trade and non-agricultural market access. Agricultural negotiation confront tough challenges because of different positions among members countries, and are not expected to reach perfect forms of modalities. Nevertheless based on the fact that many countries nearly reached agreement on some core. Under this circumstance, Korea has to prepare more practical strategics and more effective individual commitments to minimized the agricultural market opening. The other way, some Korean agricultural products will be exported by the DDA negotiation. Recently the understanding of Third-Party Logistics and Logistics Outsourcing are receiving increased attention as means of becoming competitive in agricultural products export improvement. So this paper presents a in-depth analysis for third-party logistics and its implications for Korea agricultural product export system improvement in E-trade Era.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.157-163
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• 2005

In this paper, we present the multimodal system based on the fusion of two user-friendly biometric modalities: Iris and Face. In order to reach robust identification and verification we are going to combine two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. In addition, the Synergetic Neural Network(SNN) is used to obtain matching score of the preprocessed data. This system can operate in two modes: to identify a particular person or to verify a person's claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.147-156
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• 2016

A modalized Łukasiewicz three-valued propositional logic will be proposed in this paper which there are three modalities [t]; [m]; [f] to represent the three values t; m; f; respectively. And a Gentzen-typed deduction system will be given so that the the system is sound and complete with respect to the Łukasiewicz three-valued semantics Ł$_3$, which are given in soundness theorem and completeness theorem.