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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ming-Che Cheng Yi-Hsien Hsieh Te-Cheng Hsu Tung-Hung Su Che Lin Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Dept. of Internal Medicine Division of Gastroenterolgy and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly hampers the development and performance of these models. Addressing this challenge is crucial for enhancing clinical decision-making and patient care. Existing methods for handling missing data, ranging from simple imputation to more sophisticated approaches, often fall short of capturing the temporal dynamics inherent in EHRs. To bridge this gap, we introduce the Deep Stochastic Time-series Imputation (Deep STI) algorithm, an innovative end-to-end deep learning model that seamlessly integrates a sequence-to-sequence generative network with a prediction network. Deep STI is designed to leverage the observed time-series data in EHRs, learning to infer missing values from the temporal context with high accuracy. We evaluated Deep STI on the liver cancer data from the National Taiwan University Hospital (NTUH), Taiwan. Our results showed that Deep STI achieved better 5-year hepatocellular carcinoma predictions (19.21% in the area under the precision-recall curve) than extreme gradient boosting (18.15%) and Transformer (18.09%). The ablation study also illustrates the efficacy of our generative architecture design compared to regular imputations. This approach not only promises to improve the reliability of disease analysis in the presence of incomplete data but also sets a new standard for utilizing EHRs in predictive healthcare. Our work aims to advance the field of healthcare analytics and open new avenues for research in deep learning applications to EHRs.

关键词： Deep learning Time series analysis Stochastic processes Predictive models Transformers Imputation Reliability Electronic medical records Standards Diseases

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Imbalance fault detection in BLDC motors through audio signals using the density of maxima and CNN

Imbalance fault detection in BLDC motors through audio signa...

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Brazilian Symposium on Computing System engineering (SBESC)

作者： Yuri C. Gouveia Alisson V. Brito Tiago P. Nascimento Jorge Gabriel Ramos Abel Lima Filho Graduate Program in Informatics Universidade Federal da Paraiba Joao Pessoa Brazil Laboratory of System Engineering and Robotics Universidade Federal da Paraiba Joao Pessoa Brazil Department of Physics Universidade Federal da Paraiba Joao Pessoa Brazil Department of Mechanical Engineering Universidade Federal da Paraiba Joao Pessoa Brazil

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are increasingly used for various tasks. However, increased usage leads to more failures, posing risks to humans and the environment. This study identifies propeller imbalance faults in Brushless DC (BLDC) motors of UAVs. It compares two fault detection methodologies based on chaotic behavior: the alreadyknown CNN approach, and the SAC-DM+CNN approach. Both methods involve acquiring audio signals, generating spectrograms, and training a Convolutional Neural Network (CNN) model. Results show that the CNN approach achieves 98.18% accuracy, while the SAC-DM+CNN approach achieves 71.43% accuracy in classifying propeller balance faults.

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Retraction Note: A proposal for Internet of Smart Home Things based on BCI system to aid patients with amyotrophic lateral sclerosis

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Neural Computing and Applications 2024年第28期36卷 17761-17761页

作者： de Oliveira Júnior, Wilson G. de Oliveira, Juliana M. Munoz, Roberto de Albuquerque, Victor Hugo C. Graduate Program in Applied Informatics Laboratory of Bioinformatics University of Fortaleza Fortaleza Brazil School of Informatics Engineering Universidad de Valparaíso Valparaíso Chile

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Cyber Security for Hoax News Detection with Similarity Algorithm

Cyber Security for Hoax News Detection with Similarity Algor...

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International Conference on informatics and Computing (ICIC)

作者： Sy.Yuliani Yakub David Agustriawan Irmawati Dinar Ajeng Kristiyanti Shahrin Sahib Mohd Faizal Abdollah Mohammed Nasser Al-Mhiqani Yusuf Durachman The Informatic Study Program Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Faculty of Information Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Cyber Security University of Huddersfield United Kingdom Department of Information System State Islamic University of Syarif Hidayatullah Jakarta Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Hoax news is false information disseminated to deceive or mislead audiences, often with the aim of swaying opinions or creating confusion. The rise of social media has amplified the spread of hoax news, particularly in sensitive areas such as politics and health. In response to this growing issue, this paper proposes a Natural Language Processing (NLP) approach of detecting hoax news using the Smith-Waterman similarity algorithm. By comparing news content with a curated dataset of verified hoaxes, the system calculates a similarity score to assess the likelihood of the news being false. The results of this study show that news articles analyzed using the Smith-Waterman algorithm achieve a high accuracy, with a similarity score exceeding $93 \%$ for news inputs over 100 words. Furthermore, the proposed system demonstrates an efficient processing time, completing the analysis in approximately 6.57 seconds. These findings underscore the algorithm’s potential for real-time application in detecting fake news on social media and other digital platforms. This research aims not only to enhance the technical capabilities of hoax detection systems but also to foster greater media literacy and a more informed public.

关键词： Accuracy Social networking (online) Scalability Approximation algorithms Real-time systems Natural language processing Fake news informatics Optimization Testing

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Meta-EHR: A meta-learning approach for electronic health records with a high imbalanced ratio and missing rate

Meta-EHR: A meta-learning approach for electronic health rec...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Hsu-Hsiang Chang Te-Cheng Hsu Yi-Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Institute of Communications Engineering National Tsing Hua University (NTHU) Hsinchu Taiwan Department of Electrical Engineering National Taiwan University (NTU) Taipei Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

Data imbalance is a practical and crucial issue in deep learning. Moreover, real-world datasets, such as electronic health records (EHR), often suffer from high missing rates. Both issues can be understood as noises in data that may lead to bad generalization results for standard deep-learning algorithms. This paper introduces a novel meta-learning approach to deal with these noise issues in an EHR dataset for a binary classification task. This meta-learning approach leverages the information from a selected subset of balanced, low-missing rate data to automatically assign proper weight to each sample. Such weights would enhance the informative samples and suppress the opposites during training. Furthermore, the meta-learning approach is model-agnostic for deep learning-based architectures that simultaneously handle the high imbalanced ratio and high missing rate problems. Through experiments, we demonstrate that this meta-learning approach is better in extreme cases. In the most extreme one, with an imbalance ratio of 172 and a 74.6% missing rate, our method outperforms the original model without meta-learning by as much as 10.3% of the area under the receiver-operating characteristic curve (AUROC) and 3.2% of the area under the precision-recall curve (AUPRC). Our results mark the first step towards training a robust model for extremely noisy EHR datasets.

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Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

Computational and Systems Oncology

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Computational and Systems Oncology 2023年第3期3卷

作者： Ai, Xusheng Smith, Melissa C Feltus, Frank Alex Electrical and Computer Engineering Department Clemson University Clemson SC United States Department of Genetics and Biochemistry Biomedical Data Science & Informatics Program Clemson Center for Human Genetics Clemson University Clemson SC United States

The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA-seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA-seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio-informatics. Finally, we propose potential directions for future research. © 2023 The Authors. Computational and Systems Oncology published by Wiley Periodicals LLC.

关键词： biomarkers data augmentation generative adversarial network RNA sequencing

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Sensing Architecture in Smart City: A Review Paper

Sensing Architecture in Smart City: A Review Paper

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International Conference on ICT For Smart Society (ICISS)

作者： Mindit Eriyadi Suhono Harso Supangkat Fadhil Hidayat Doctoral Program of Instiut Teknologi Bandung Bandung Indonesia School of Applied Science Universitas Telkom Bandung Indonesia School of Electrical Engineering and Informatics Instiut Teknologi Bandung Bandung Indonesia Smart City and Community Innovation Center Institut Teknologi Bandung Bandung Indonesia

Relevant smart city research involves many highly futuristic applications, such as smart transportation, in which smart roads warn drivers of impending traffic jams, smart parking, which communicates the location of available parking spaces to drivers, and smart environments, which allow homes and workplaces to fully automatically adjust their temperature to save energy. Complexity in the city sensing section is caused by the complexity of urban systems. The enormous number of sensor types and technologies utilized for municipal data collecting provide a problem. Some of the issues in city sensing are node deployment, sensing management, sensor network types, and their derivative variables, necessitating the development of a city sensing architecture capable of contributing to advances in efficiency, accuracy, and real-timeness.

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Computer Modelling of the Information Properties of Hyper Chaotic Lorenz System and Its Application in Secure Communication System 1

Computer Modelling of the Information Properties of Hyper Ch...

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1st Paris Van Java International Seminar on Computer, Science, engineering, and Technology, PVJ ISComSET 2020

作者： Rusyn, Volodymyr Mujiarto Mamat, Mustafa Azharul, Firmansyah Mada Sanjaya, W.S. Sambas, Aceng Dwipriyoko, Estiyan Sutoni, Akhmad Yuriy Fedkovych Chernivtsi National University Kotsybynsky str. 2 Chernivtsi58012 Ukraine Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala Terengganu Malaysia Department of Mechanical Engineering Sekolah Tinggi Teknologi Cileungsi Indonesia Department of Physics Universitas Islam Negeri Sunan Gunung Djati Bandung Indonesia Informatics Study Program Universitas Langlangbuana Bandung Indonesia Department of Industrial Engineering Universitas Suryakancana Cianjur Indonesia

This paper presents computer modeling, analysis and research of the hyper-chaotic Lorenz system based on programming interface that has been developed in LabView software environment. This study allows for generating and research of the main information properties of hyper-chaotic Lorenz system, focusing on time distribution of the four chaotic coordinates, phase portraits and Lyapunov exponents. The programming interface demonstrates the algorithm of masking and decrypt of the information carrier. © Published under licence by IOP Publishing Ltd.

关键词： Lyapunov methods

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Application for Airport Tenant Selection Using Weighted Product-based Decision Support System

Application for Airport Tenant Selection Using Weighted Prod...

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Electrical and Information Technology (IEIT), International Conference on

作者： Arwin Datumaya Wahyudi Sumari Muhamad Andika Saputra Astika Ayuningtyas Catur Sudarmadi Aerospace Intelligent System and Data Engineering Research Group Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Cognitive Artificial Intelligence Research Group Politeknik Negeri Malang Malang Indonesia Informatics Study Program Faculty of Industrial Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Aerospace Health Informatics Research Group Faculty of Industrial Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia

This research aims to develop an application for selecting the best new tenant at PT Angkasa Pura II Sultan Thaha Jambi Airport Branch by utilizing Weighted Product (WP) method as the decision support system. Our approach is descriptive research with data analysis technique using four prospective tenants coded A_1 to A_4. The data was collected through observation, interviews, documentation, and literature study. The steps for selecting the best new tenant using the WP method include determining the criteria, sorting the suitability of alternatives and criteria, determining the weight of each criterion and its normalization, calculating the score, S value as an alternative preference, and calculating the ranking, V value as the final alternative preference. Based on the final calculation results, the new tenant coded A_4 is the best alternative based on the data collected from the Financial Management (FHFM) Unit and the Commercial Unit, with a value of V_4 is 0.294.

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Improving Land Cover Segmentation Using Multispectral Dataset 8

Improving Land Cover Segmentation Using Multispectral Datase...

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8th International Conference on informatics and Computing, ICIC 2023

作者： Herlawati, Herlawati Handayanto, Rahmadya Trias Heryadi, Yaya Abdurachman, Edi Soeparno, Haryono Irwansyah, Edy Miranda, Eka Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Universitas Islam 45 Computer Engineering Bekasi Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Trisakti Institute of Transportation and Logistic Jakarta13410 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350342604

Computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia. © 2023 IEEE.

关键词： Landsat

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