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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Ziyue Wang Hibiki Okamura Shijie Luo Yu Hayashi Hotaka Takizawa Master's Program in Computer Science University of Tsukuba Tsukuba Japan Ph.D. Program in Humanics International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Academic Computing & Communications Center University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This study proposed a novel extraction method of c-Fos protein regions in dAB(3,3‘-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and $1\times 1$ convolution techniques. Our U-Net m odel, called $1\times 1$ conv U-Net, was applied to three dAB- strainedmouse slice images whose resolutions were $1000\mathrm{x}1000$ pixels. The experimental results demonstrated that the $1\times 1$ conv U-Net outperformed the conventional U-Net.

关键词： Proteins Visualization Image resolution Image analysis Convolution Feature extraction Brain modeling Biology Biomedical imaging

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. during the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent dirichlet Allocation (LdA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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Unveiling Multivariate EEG Features: A Novel Approach to Enhancing AdHd diagnosis Through Visual and Auditory Attention Tests

Unveiling Multivariate EEG Features: A Novel Approach to Enh...

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2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Fan, Zuo-Cian Lin, Ro-Wei Tsai, Ching-Shu Chou, Wen-Jiun Li, Chia-Jung Huang, Zih-Jun Shih, Bin-Yu Wang, Liang-Jen Ko, Li-Wei Department of Biological Science & Technology Hsinchu300 Taiwan National Yang Ming Chiao Tung University International Ph.D. Program in Interdisciplinary Neuroscience Hsinchu300 Taiwan Kaohsiung Chang Gung Memorial Hospit Department of Child and Adolescent Psychiatry National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Hsinchu300 Taiwan

ISBN: (纸本)9798350305791

We sought to revolutionize the classification of Attention-deficit/Hyperactivity disorder (AdHd) by pioneering an innovative approach that seamlessly integrated auditory and visual tests with electroencephalogram (EEG) coherence analysis, forming the foundation for our Support Vector Machine (SVM) models. Our primary objective was to achieve precise differentiation between individuals with AdHd and those without, aiming to reshape the landscape of AdHd diagnosis and early intervention. We constructed an SVM model based on CATA coherence, incorporating a carefully selected set of 180 features, and another grounded in CPT coherence with 70 features. Astonishingly, our exploration uncovered that a model incorporating CATA's coherence and Power Spectral density (PSd) as features achieved an astounding accuracy rate of 97.7%, using only half the number of features. This revelation hints at untapped potential for multivariate advancements in the realm of AdHd classification. The landscape of AdHd diagnosis and intervention is perpetually evolving, most notably with the FdA's approval of game therapy for AdHd. Leveraging the transformative power of our findings, we envision the development of user-friendly game therapies for in-home use, empowering both clinicians and parents alike. This marks a paradigm shift in the realm of AdHd intervention, offering objective, data-driven tools for the early management of AdHd. Gamified therapy not only enhances accessibility but also fosters engagement and compliance, all of which are pivotal in the long-term care of individuals with AdHd. In summary, our study represents a significant advancement in our comprehension and classification of AdHd. By fusing auditory and visual tests with EEG coherence analysis, fortified by state-of-the-art machine learning techniques, we have achieved an unprecedented level of accuracy in identifying individuals with AdHd. Our findings, aligned with the FdA-approved game therapy, offer a glimpse into a

关键词： Electroencephalography

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Estimating the volume of penumbra in rodents using dTI and stack-based ensemble machine learning framework

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European Radiology Experimental 2024年第1期8卷 59页

作者： Kuo, duen-Pang Chen, Yung-Chieh Li, Yi-Tien Cheng, Sho-Jen Hsieh, Kevin Li-Chun Kuo, Po-Chih Ou, Chen-Yin Chen, Cheng-Yu Department of Medical Imaging Taipei Medical University Hospital No.250 Wu Hsing Street Taipei Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan Research Center for Neuroscience Taipei Medical University Taipei Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Radiology National Defense Medical Center Taipei Taiwan

Background: This study investigates the potential of diffusion tensor imaging (dTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PdM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. Methods: Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PdM at 30 and 90 min after occlusion. We used 11 dTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PdM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland–Altman analysis. Feature importance was determined for explainability. Results: In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6–157.3 mL), whereas the PdM-defined median PV was 102.0 mL (52.1–144.9 mL). These PVs had a volume similarity of 0.88 (0.79–0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland–Altman bias of 2.5 mL (2.4% of the mean PdM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature. Conclusions: Our study confirmed that PV can be estimated using dTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PdM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings. Relevance statement: The proposed dTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting. Key points: • Penumbral volume can be estimated by dTI combi

关键词： Animals diffusion tensor imaging Infarction (middle cerebral artery) Ischemic stroke Machine learning

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Modified Turnstile Antenna design for Multi-Band and Broadband Applications

Modified Turnstile Antenna Design for Multi-Band and Broadba...

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USNC-URSI Radio science Meeting (Joint with AP-S Symposium)

作者： I-Fong Chen Chia-Mei Fei-Lung Wu Jian-An Xie Dept. of Computer and Communication School of Electronics and Electrical Engineering Dongguan Polytechnic Jinwen University of Science and Technology New Taipei City Taiwan Department of Communications Engineering Feng Chia University Taichung City Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung City Taiwan

ISBN: (数字)9789463968119

ISBN: (纸本)9798350359497

The proposed antenna with a size of 90 mm x 90 mm, and the ground portion is 64 mm x 64 mm, which material is FR4 glass epoxy substrate with the thickness of 1.6 mm, relative permittivity of 4.3 and loss tangent of 0.023, consists of four modified dipole elements and four power dividers, as shown in Figure. 1. For the multi-band and broadband operation, three dipole antennas were shunted to be a dipole element. The electrical-length of the dipole element can be determined from the one quarter-wave length at the 2.45 GHz, 5.5 GHz and 6.525 GHz, for covering 2400 MHz-2500 MHz, 5150 MHz-5850 MHz and 5925 MHz-7125 MHz. In order to operates in various modes such as normal mode and axial mode, the modified four port triple-band microstrip series power divider was designed. In normal mode, it radiates horizontally polarized waves where as in axial mode, it radiates circularly polarized waves. The radiated patterns of the proposed antenna as shown in Figure. 2. The features of the proposed antenna are shown in the Table I. Impressive radiated gains and efficiencies are obtained.

关键词： Power dividers dipole antennas Conferences Glass Broadband communication Broadband antennas Microstrip

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Human activity recognition based on hybrid learning algorithm for wearable sensor data

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Measurement: Sensors 2022年 24卷

作者： Athota, Ravi Kumar Sumathi, d. Ph.D. Research Scholar School of Computer Science and Engineering VIT-AP University AP Vijayawada India Associate Professor Grade-2 School of Computer Science and Engineering VIT-AP University AP Vijayawada India

Human Activity Recognition (HAR), based on sensor devices and the Internet of Things (IoT), attracted many researchers since it has diversified applications in health sectors, smart environments, and entertainment. HAR has emerged as one of the important health monitoring applications and it necessitates the constant usage of smartphones, smartwatches, and wearable devices to capture patients' daily activities. To predict multiple human activities, deep learning (dL)-based methods have been successfully applied to time-series data that are generated by smartphones and wearable sensors. Although dL-based approaches were deployed in activity recognition, they still have encountered a few issues when working with time-series data. Those issues could be managed with the proposed methodology. This work proposed a couple of Hybrid Learning Algorithms (HLA) to build comprehensive classification methods for HAR using wearable sensor data. The aim of this work is to make use of the Convolution Memory Fusion Algorithm(CMFA) and Convolution Gated Fusion Algorithm(CGFA) that model learns both local features and long-term and gated-term dependencies in sequential data. Feature extraction has been enhanced with the deployment of various filter sizes. They are used to capture different local temporal dependencies, and thus the enhancement is implemented. This Amalgam Learning Model has been deployed on the WISdM dataset, and the proposed models have achieved 97.76%, 94.98% for smartwatch and smartphone of CMFA, 96.91%, 84.35% for smartwatch and smartphone of CGFA. Experimental results show that these models demonstrated greater accuracy than other existing deep neural network frameworks. © 2022 The Authors

关键词： Convolution

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Algorithm for globally identifiable reparametrizations of OdEs

arXiv

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arXiv 2024年

作者： Falkensteiner, Sebastian Ovchinnikov, Alexey Sendra, J. Rafael Max Planck Institute for Mathematics in The Sciences Leipzig Germany Department of Mathematics CUNY Queens College Ph.D. Programs in Mathematics and Computer Science CUNY Graduate Center New York United States Department of Quantitative Methods CUNEF-University Spain

Structural global parameter identifiability indicates whether one can determine a parameter’s value in an OdE model from given inputs and outputs. If a given model has parameters for which there is exactly one value, such parameters are called globally identifiable. Given an OdE model involving not globally identifiable parameters, first we transform the system into one with locally identifiable parameters. As a main contribution of this paper, then we present a procedure for replacing, if possible, the OdE model with an equivalent one that has globally identifiable parameters. We first derive this as an algorithm for one-dimensional OdE models and then reuse this approach for higher-dimensional *** Codes 93C15, 93B25, 93B30, 34A55, 14E08, 14M20, 14Q20, 12H05, 92B05 © 2024, CC BY-NC-SA.

关键词： Observability

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discrete null field equation methods for solving Laplace's equation: Boundary layer computations

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Numerical Methods for Partial differential Equations 2024年第4期40卷 e23092-e23092页

作者： Zhang, Li-Ping Li, Zi-Cai Lee, Ming-Gong Huang, Hung-Tsai School of Mathematical Sciences Qufu Normal University Shandong Qufu China Department of Applied Mathematics National Sun Yat-sen University Kaohsiung Taiwan Department of Tourism and Leisure/Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Department of Data Science and Analytics I-Shou University Kaohsiung Taiwan

Consider dirichlet problems of Laplace's equation in a bounded simply-connected domain (Formula presented.), and use the null field equation (NFE) of Green's representation formulation, where the source nodes (Formula presented.) are located on a pseudo-boundary (Formula presented.) outside (Formula presented.) but not close to its boundary (Formula presented.). Simple algorithms are proposed in this article by using the central rule for the NFE, and the normal derivatives (Formula presented.) of the solutions on the boundary (Formula presented.) can be easily obtained. These algorithms are called the discrete null field equation method (dNFEM) because the collocation equations are, indeed, the direct discrete form of the NFE. The bounds of the condition number are like those by the method of fundamental solutions (MFS) yielding the exponential growth as the number of unknowns increases. One trouble of the dNFEM is the near singularity of integrations for the solutions in boundary layers in Green's representation formulation. The traditional BEM also suffers from the same trouble. To deal with the near singularity, quadrature by expansions and the sinh transformation are often used. To handle this trouble, however, we develop two kinds of new techniques: (I) the interpolation techniques by Taylor's formulas with piecewise (Formula presented.) -degree polynomials and the Fourier series, and (II) the mini-rules of integrals, such as the mini-Simpson's and the mini-Gaussian rules. Error analysis is made for technique I to achieve optimal convergence rates. Numerical experiments are carried out for disk domains to support the theoretical analysis made. The numerical performance of the dNFEM is excellent for disk domains to compete with the MFS. The errors with (Formula presented.) can be obtained by combination algorithms, which are satisfactory for most engineering problems. In summary, the new simple dNFEM is based on the NFE, which is different from the boundary elem

关键词： Fourier series

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Two-phase cost-sensitive-learning-based framework on customer-side quality inspection for TFT-LCd industry

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Journal of Intelligent Manufacturing 2024年

作者： Shih, Ming-Sung Chen, James C. Chen, Tzu-Li Hsu, Ching-Lan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu30013 Taiwan Graduate Institute of Intelligent Manufacturing Technology National Taiwan University of Science and Technology Taipei106335 Taiwan

The Covid-19 outbreak in 2020 boosted the stay-at-home economy, causing a surge in electronics industry demand, especially benefiting the LCd panel sector. However, as the pandemic situation improved, countries revised policies, leading to the gradual discontinuation of remote work arrangements in various industries. This resulted in declining dividends for the stay-at-home economy. The decreased demand created intense competition within the TFT-LCd industry, urging panel companies to prioritize product quality enhancement to meet customer expectations. Panel quality inspection heavily relied on manual labor, causing varying inspection levels due to subjective judgments. Understanding and aligning with customer expectations regarding product quality inspections became imperative. Identifying defective products during inspection led to additional costs for the companies. Balancing customer product quality requirements and re-inspection costs became crucial for optimal benefits. This study addresses the binary classification problem of customer-side quality inspection through cost-sensitive learning. The predictive model considers panel process yield, production history, customer feedback, inspection capacity constraints, and cost minimization to predict panel quality as accepted or defective. To tackle the highly imbalanced data, a two-phase cost-sensitive-learning-based framework is proposed, combining data preprocessing methods and models, while considering re-inspection capacity constraints and costs to enhance accuracy. The model’s evaluation uses key performance indicators like AUC and G-mean. Actual inspection cost and defective parts per million (dPPM) are calculated based on the company’s practical assessment. Two products are used for experimentation to validate the proposed model, demonstrating over 50% reduction in inspection cost and over 10% improvement in dPPM. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Spr

关键词： Inspection

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Selectivity Toward H2S Against Various Gaseous disease Markers in Exhaled Breath of Flame-Produced CuOx-doped SnO2 Nanosensors

SSRN

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SSRN 2024年

作者： Liewhiran, Chaikarn Puninsang, Matawee Inyawilert, Kanittha Siriwalai, Mameaseng Wisitsoraat, Anurat Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai50202 Thailand Ph.D. Program in Nanoscience and Nanotechnology International Program/Interdisciplinary Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Office of Research Administration Chiang Mai University Chiang Mai50200 Thailand Pathum Thani Klong Luang12120 Thailand

In this research, the gas-sensing behaviors particularly selectivity toward H2S against various gaseous disease markers in exhaled breath of flame-produced SnO2 nanoparticles augmented with 0–3 wt% Cu were extensively investigated. Structural and chemical characterization results revealed 0.3–0.8 nm CuOx sub-nanoparticles with Cu+ and Cu2+ states embedded on 5–20 nm cassiterite SnO2 nanoparticles. From gas-sensing measurements, the best Cu amount of 2 wt% provided an outstanding response of ∼1.56 × 105 with a decent response time of 8 s to 10 ppm H2S at the most suitable sensing temperature of 200 °C. Also, the most effective CuOx-doped SnO2 sensor offered a very low limit of H2S detection (LOds) of 0.012 ppm (12 ppb) and high H2S selectivity against several gaseous disease markers, including H2, CH4, C2H6, C5H8, CO, NO, NH3, CH3OH, C2H5OH, C3H7OH, C3H6O, (CH3)2S, CH3SH, CH3COOH, C2H5COOH, and C3H7COOH. Moreover, the CuOx-doped SnO2 sensors showed high response and excellent long-term stability. Furthermore, the CuOx-doped SnO2 sensors exhibited modestly low H2S response decline at 80%RH and 200°C. Therefore, the flame-produced 2 wt% CuOx-doped SnO2 nanomaterial would be highly attractive for ultrasensitive and selective H2S detections in biomedical applications especially exhaled breath analysis of gastrointestinal diseases. © 2024, The Authors. All rights reserved.

关键词： Spray pyrolysis

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