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IEEJ Transactions on Electronics, information and Systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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A Systematic Review: Remote Sensed Hyperspectral Image Segmentation and Caption Generation Using Deep Learning Methods 1

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2nd EAI International Conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

作者： Badhe, Namdeo Baban Bharadi, Vinayak Ashok Giri, Nupur Alegavi, Sujata Yele, Vijaykumar Department of Information Technology Finolex Academy of Management and Technology P-60 P-60/1 Midc Mirjole Block Maharashtra Ratnagiri415639 India Department of Computer Engineering Vivekanand Education Society’s Institute of Technology Hashu Adwani Memorial Complex Collector’s Colony Maharashtra Mumbai400074 India Mumbai400101 India Mumbai400101 India

ISBN: (数字)9783031660443

ISBN: (纸本)9783031660436

Hyperspectral images (HSIs) exhibit a high-dimensional nature, capturing data across numerous wavelengths in the electromagnetic spectrum, often spanning thousands of bands. It has found widespread applications in various real-life scenarios due to its ability to leverage the rich spectral information contained within each pixel. Deep Learning (DL) schemes offer a huge variety of chances to resolve traditional imaging tasks and also for approaching various simulating issues in the spatial-spectral region. This review work provides a systematic review of the relevant existing techniques based on HSI segmentation and image captioning. Initially, other DL methods like, Deep Belief Network (DBN), Convolutional Neural Network (CNN), Autoencoders, Fully Convolutional Neural Network (FCNN), UNet, and Graph Convolutional Network (GCN) are discussed. Secondly, a significant computer vision problem that has recently evolved is image captioning, which tries to automatically produce English explanations of an input image. Therefore, image captioning has garnered growing interest within the realm of remote sensing. This survey summarizes the relevant methods and concentrates on the feature extraction-based methods and attention mechanism-based techniques, which plays a significant role in image caption generation tasks. Finally, it provides the research gaps and its appropriate solution at the end of each survey. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Convolutional neural networks

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis 27

Exploring the Impact of Random Distribution Choices on Parti...

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27th International Conference on Knowledge Based and Intelligent information and engineering Sytems, KES 2023

作者： Kolodziejczyk, Joanna Tarasenko, Yuliia Sulikowski, Piotr Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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A New Belief-Based Incomplete Pattern Unsupervised Classification Method : Extended Abstract 39

A New Belief-Based Incomplete Pattern Unsupervised Classific...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Zuowei Liu, Zhe Ma, Zongfang Zhang, Yiru Wang, Hao University of Rennes 1 Institut de Recherche en Informatique et Systèmes Aléatoires France Xi'an University of Architecture and Technology School of Information and Control Engineering China Norwegian University of Science and Technology Department of Computer Science Norway

ISBN: (纸本)9798350322279

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets. © 2023 IEEE.

关键词： Uncertainty analysis

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DeepJDOT: Deep joint distribution optimal transport in facial recognition 2

DeepJDOT: Deep joint distribution optimal transport in facia...

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2nd International Conference on Mechatronics and Smart Systems, CONF-MSS 2024

作者： Cai, Yuchen Wen, Chenglong Chen, Haoyuan Liu, Yijia Peng, Haofei Computer Science and Software Engineer Department Rose-Hulman Institute of Technology Terre Haute47803 United States Department of Physics University of Toronto TorontoM5S 1A1 Canada Department of Physics University of Washington Seattle98195 United States Department of Water Conservancy and Civil Engineering China Agricultural University Beijing100083 China Department of Information Engineering Xi'An Jiaotong University Xi'an710000 China

This paper explores the application of deep learning in domain adaptation, with a particular focus on using the Deep Joint Distribution Optimal Transportation (DeepJDOT) method for face recognition. We test DeepJDOT on a series of visual domain adaptation tasks and find that it exhibits high accuracy and competitive advantage, especially under smaller image sizes, compared to recent state-of-the-art competitors. This research not only highlights the potential of deep learning in domain adaptation but also underscores DeepJDOT as an effective approach for improving the performance of applications like face recognition. Furthermore, we introduce a domain adaptation-tailored loss function based on optimal transport to further enhance model performance. This study provides valuable insights and methods for research in visual domain adaptation and face recognition. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Deep Learning-based Source Code Classification and Visualization of Decision Rationales

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Procedia computer Science 2024年 246卷 2753-2761页

作者： Shinichi Oeda Seiya Mitsumori Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan

In programming education, it is crucial to provide instruction that is tailored to students’ proficiency levels. For this purpose, an objective evaluation of each student’s coding ability is essential. Additionally, it is necessary to understand the characteristics of source code written by both beginners and advanced students. Previous research has successfully converted the structural information of source code into graphs and assessed coding skills using deep learning, achieving high accuracy. However, it remains unclear which specific structural elements significantly influence these assessments. This study addresses this gap by transforming source code into abstract syntax trees and developing a model that uses Graph Convolutional Networks to categorize code as either beginner or advanced users based on learned structural information. Furthermore, we apply Integrated Gradients to visualize the decision-making basis of our model and elucidate the structural characteristics distinguishing source code written by beginner and advanced users.

关键词： Abstract Syntax Tree Deep Learning Explainable AI Graph Convolutional Networks Integrated Gradients Source Code Classification

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Performance Evaluation of FC-RDVM and LDIWM Router Placement Methods for WMNs by WMN-PSODGA Hybrid Simulation System Considering Load Balancing and Chi-Square Distribution of Mesh Clients 1

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37th International Conference on Advanced information Networking and Applications, AINA 2023

作者： Barolli, Admir Sakamoto, Shinji Kulla, Elis Barolli, Leonard Takizawa, Makoto Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Information and Computer Science Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan Department of System Management Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Fukuoka Higashi-Ku811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Fukuoka Higashi-Ku811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University 3-7-2 Kajino-machi Koganei-shi Tokyo184-8584 Japan

ISBN: (数字)9783031284519

ISBN: (纸本)9783031284502

In this paper, we present WMN-PSODGA hybrid simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs). We consider Chi-square distribution of mesh clients and compare the results of a Fast Convergence Rational Decrement of Vmax Method (FC-RDVM) with Linearly Decreacing Inertia Weight Method (LDIWM). The simulation results show that FC-RDVM has better load balancing than LDIWM. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： MESH networking

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Latent State Estimation Method for Students Using Knowledge Tracing Combining with IRT 26

Latent State Estimation Method for Students Using Knowledge ...

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26th International Conference on Knowledge-Based and Intelligent information and engineering Systems, KES 2022

作者： Oeda, Shinichi Kakizaki, Toma Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan

The Intelligent Tutoring Systems (ITSs) are a system that provides an efficient learning environment by assigning questions that are suitable to the learner's skill state. To improve the performance of ITSs, conventional studies have proposed student models that can estimate skill states with high accuracy. However, these models were based on the assumption that the log data was correct. In other words, they do not take into account the possibility of students cheating or memorizing answers, assuming that the test was conducted fairly and impartially. In recent years, many examinations have been conducted online as a countermeasure against COVID-19 (coronavirus) infections, and students may be able to obtain hints for the examinations using the internet or books. In this way, it is important to know how students approach learning in order to estimate their skill state. In this study, we propose a method for estimating the latent state of learners using a model that combines Knowledge Tracing, the de-facto standard for student modeling method, and Item Response Theory. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent information & engineering Systems (KES 2022)

关键词： Students

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Data Preprocessing with IRM for skill extraction using NMF 26

Data Preprocessing with IRM for skill extraction using NMF

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26th International Conference on Knowledge-Based and Intelligent information and engineering Systems, KES 2022

作者： Oeda, Shinichi Iizumi, Shunsuke Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan

To provide examinees with appropriate question items, identifying and evaluating users' latent and acquired skills is important in e-learning systems. These latent skills are defined in terms of a Q-matrix. Constructing Q-matrices manually is relatively labor-intensive, because doing so requires expert insight as well as verbal examination to identify which skills students have mastered. Methods to extract a Q-matrix from examination results automatically using nonnegative matrix factorization (NMF) have been explored, and improved methods such as online NMF have been proposed, which extracts an immutable Q-matrix from time-series data. In this study, we propose a data preprocessing method to improve the accuracy of NMF using a co-clustering method called an infinite relational model (IRM). We also describe experiments conducted using synthetic data, in which learners'skills and the bias in the number of their responses were evaluated, and show that the results demonstrate the efficacy of the proposed approach. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent information & engineering Systems (KES 2022)

关键词： Non-negative matrix factorization

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SOSO: Symbiotic Organisms Search Optimization based Faster RCNN for Secure Data Storage in Cloud

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IETE Journal of Research 2024年第2期70卷 1196-1208页

作者： J. Thresa Jeniffer A. Chandrasekar S. Jothi 1 Research Scholar Department of Information Technology St. Joseph's College of Engineering Anna University Chennai India 2 Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai India

Because of the Internet of Things (IoT) and wearable devices, information obtained is subject to cyber attacks, making intrusion detection an essential component. The increased number of attacks on IoT devices exposes them to ongoing exploitation and data theft. Mirai, denial of Service (DoS), scan,etcare the major types of attacks conducted on IoT. Traditional attack detection algorithms have more disadvantages like lack of adaptability in all scenarios, low local minima computational complexity,etc. Thus developing a method that is suitable for all environments and has good convergence speed is taken as the motivation of this study. This paper presents a novel Symbiotic Organisms Search Algorithm (SOSA) optimized Faster region-based convolutional neural network (FRCNN) for attack detection and data type classification. The data to be stored in the cloud is analyzed whether they have restricted access, confidential, or unclassified. Depending upon the variety of data, the Optimal homomorphic encryption (OHE) approach is provided for security. The key selection process is done by an Elastic Collision Seeker Optimization Algorithm (ECSOA) which offers secure access to the data owner and prevents the data from unauthorized access. The model is optimized using the algorithm which improves the feature extraction performance. The effectiveness of the proposed model in attack detection is evaluated using two datasets namely the CSE-CIC-ID2018 dataset and the IoTID20 dataset. When compared to the existing techniques, the proposed model offers optimal performance in terms of multiclass attack detection.

关键词： Deep learning Intrusion detection IoT Machine learning Optimal homomorphic encryption Secure storage

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