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2024 IEEE-EMBS International Conference on Biomedical and Health informatics, BHI 2024

作者： Dionisio, Jefferson Rizwan, Muhammad Suzuki, Hidetaka Hassan, Kazi Mahmudul Chanpornpakdi, Ingon Lin, Lian-Yu Tanaka, Toshihisa Lin, Che Tokyo University of Agriculture and Technology Tokyo Japan Japanese Red Cross Musashino Hospital Department of Emergency and Critical Care Tokyo Japan National Taiwan University College of Medicine and Hospital Division of Cardiology Department of Internal Medicine Taipei Taiwan Artificial Intelligence of Things Taiwan International Graduate Program Academia Sinica Taipei Taiwan National Taiwan University Department of Electrical Engineering Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan National Taiwan University Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan Department of Smart Medicine and Health Informatics National Taiwan University Taipei Taiwan

ISBN: (纸本)9798350351552

Sudden cardiac arrest (SCA) poses a significant health challenge, necessitating accurate predictions of neurological outcomes in comatose patients, where good outcomes are defined as the recovery of most cognitive functions. Electroencephalogram (EEG) serves as a valuable biomarker for monitoring neurological states due to its rich, time-dependent information. This study aims to predict neurological outcomes using early EEG data by employing the Transformer model, which leverages multi-headed attention to identify patterns in lengthy sequences such as hour-long EEG recordings. Unlike traditional methods that use subsampled EEG epochs, we utilize the entire EEG sequences, subdivided into time steps, allowing our model to capture detailed temporal patterns via the attention mechanism. Moreover, we trained our proposed model using each EEG recording as an individual data sample but evaluated our model through aggregated patient-wise predictions. This approach allows us to boost the data sample size. Our results demonstrate promising predictive performance, achieving an AUROC of 0.82 and AUPRC of 0.90 on the holdout test set and an AUROC of 0.73 and AUPRC of 0.93 on an external test set with patient-wise predictions. This study highlights the potential of utilizing attention mechanisms to capture important time series progressions across EEG sequences for improving SCA prognosis. © 2024 IEEE.

关键词： Electroencephalography

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Spatially differentiated life cycle assessment of Thailand's transport: The implications from country-specific factors and alternative technologies

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Sustainable Production and Consumption 2025年 57卷 319-340页

作者： Vitoon Chotanapund Shabbir H. Gheewala Vladimir Strezov Nazmul Huda Jitti Mungkalasiri Trakarn Prapaspongsa Graduate Program in Environmental and Water Resources Engineering Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand School of Natural Sciences Faculty of Science and Engineering Macquarie University NSW 2109 Australia The Joint Graduate School of Energy and Environment (JGSEE) King Mongkut's University of Technology Thonburi Bangkok Thailand Center of Excellence on Energy Technology and Environment Ministry of Higher Education Science Research and Innovation Ministry of Education Bangkok Thailand School of Engineering Faculty of Science and Engineering Macquarie University NSW 2109 Australia Technology and Informatics Institute for Sustainability (TIIS) National Metal and Materials Technology Center (MTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand

This study proposed country-specific characterisation factors (CFs) for Thailand by modifying the Thai Spatially Differentiated Life Cycle Impact Assessment (ThaiSD) method and introducing monetary conversion factors to express environmental impacts in Thai Baht. Five impact categories were fully parameterised, including fine particulate matter formation (PMF), human toxicity (both cancer and non-cancer) (HT c and HT nc ), freshwater ecotoxicity (FET), and water scarcity (WS). Other spatialised CFs were selected from regionalisation models in ReCiPe2016 and IMPACT World+. When comparing the use of country-specific CFs with global average, the impact scores for PMF, HT c , HT nc , FET, photochemical ozone formation and terrestrial acidification demonstrated notably different levels, ranging from ±25 to ±50 %. The developed method was then applied to assess environmental impacts and costs of Thailand's transport sector, encompassing freight and passenger transport across roadway, railway, waterway, and aviation. Climate change (CC) was significantly attributed to human health and ecosystem quality impacts, while the major contributor to resource scarcity impact was fossil resource scarcity (FS). In some scenarios, non-exhaust emissions, particularly from freight trucks, accounted for 40–60 % of the total PMF impacts. Environmental costs of freight and passenger transport in Thailand were 0.08–3.64 Thai Baht per tonne-kilometre and 0.01–0.81 Thai Baht per passenger-kilometre, respectively. Among transport modes, trains were found to be the most environmentally favourable option for both passenger and freight transport, while aviation had the highest environmental impact for freight transport. In contrast, passenger aviation had a comparable environmental burden to passenger cars due to optimised occupancy rates. Despite the efficiency of modern internal combustion engine vehicles (ICEVs), particularly Euro 5 and 6, employing blended biodiesel was less effective in comp

关键词： Life cycle assessment Thai Spatially Differentiated Life Cycle Impact Assessment Freight transport Passenger transport Transport technologies

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU 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)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge Distillation

Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge D...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tin-Han Chi Kai-Chun Liu Chia-Yeh Hsieh Yu Tsao Chia-Tai Chan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems" using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms).

关键词： Computational modeling System performance Aging Transformers Reliability Fall detection Usability

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

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Deep Learning Methods for Plankton Identification: A Bibliometric Analysis and General Review 1

Deep Learning Methods for Plankton Identification: A Bibliom...

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1st International Conference on Smart Technology, Applied informatics, and engineering, APICS 2022

作者： Ardhi, Ovide Decroly Wisnu Soeprobowati, Tri Retnaningsih Adi, Kusworo Prakasa, Esa Rachman, Arief School of Postgraduate Studies Universitas Diponegoro Doctoral of Information System Program Semarang Indonesia Vocational School Universitas Sebelas Maret Department of Informatics Engineering Surakarta Indonesia Universitas Diponegoro Faculty of Science of Mathematics Department of Biology Semarang Indonesia Universitas Diponegoro Faculty of Science of Mathematics Department of Physics Semarang Indonesia Bandung Indonesia Jakarta Indonesia

ISBN: (数字)9781665489850

ISBN: (纸本)9781665489850

This study investigates trends in deep learning research on microscopic images to identify and classify plankton. We performed a bibliometric analysis using VOSviewer to display the visualizations. The first step is to extract the relevant journals from the Scopus database. Extraction from relevant journals found 42 articles, including 21 journals and 21 conferences. We analyzed publication trends and analyzes of co-authorship, co-occurrence, and citations. In addition, it examines the collaborative relationship between authors by country, the emergence of co-author keywords, and the citation of journal sources. We found that the researchers performed plankton classification tasks that achieved high accuracy (>90%). The plankton identification method uses a deep learning architecture based on the Convolutional Neural Network. Among the architectures are AlexNet, GoogleNet, InceptionV3, VGGNet (16.19), ResNet (50.101), DenseNet, NasNet, MobileNetV2, ResidualNet, Shufflenet, EfficienNet, and CapsNet. Some researchers combine CNN with SparseConvNets, LMP, SVM, MorphoCluster, and Quantification Algorithm (CC, AC, PCC, PAC, Hdy). Some methods also implement transfer learning, fine-tuning, and advanced features. In addition to the CNN approach, End-to-End Object Detection with transformers is one of the new approaches in the future to identify plankton. © 2022 IEEE.

关键词： Deep learning

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PREFALLKD: PRE-IMPACT FALL DETECTION VIA CNN-VIT KNOWLEDGE DISTILLATION

arXiv

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arXiv 2023年

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai Department of Biomedical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for information Technology Innovation Academia Sinica Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan

关键词： Fall detection

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D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

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SN Computer Science 2021年第1期2卷 48页

作者： Demiray, Bekir Z. Sit, Muhammed Demir, Ibrahim Department of Computer Science University of Iowa Iowa City United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City United States Department of Civil and Environmental Engineering University of Iowa Iowa City United States Department of Electrical and Computer Engineering University of Iowa Iowa City United States

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network *** study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Deep learning DEM DEM reconstruction Generative adversarial networks (GANs) Super-resolution

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Utilizing Graph Neural Networks for Breast Cancer Prognosis Prediction with High-dimensional Genomic Data

Utilizing Graph Neural Networks for Breast Cancer Prognosis ...

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

作者： Tzu-Chen Huang 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 Hsinchu Taiwan Department of Electrical Engineering NTU Taipei Taiwan Master’s Program in Smart Medicine and Health Informatics NTU Taipei Taiwan Center for Computational and Systems Biology NTU Taipei Taiwan

An accurate prediction of breast cancer is essential to help physicians make appropriate treatment recommendations to reduce the chance of excessive treatment, avoiding unnecessary anxiety for patients. Cancer prognosis is highly related to patients’ genomic features, which are high-dimensional in nature. In this study, we utilize a systems biology feature selector for dimension reduction to select 20 prognostic biomarkers that are considered closely related to breast cancer prognosis from the high dimensional RNA Sequencing (RNA-Seq) data. Furthermore, we establish a graph neural network (GNN) and a multi-layer perception (MLP) graph-level readout method to better extract the underlying gene interactions from the corresponding gene interaction network (GIN). With the help of GINs, the model performs the best among all baseline models, especially in the area under the precision-recall curve (AUPRC) by as large as 23%. The results demonstrate that our approach using GNNs can successfully extract high-dimensional and complicated interactions within genomic data.

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LE-DTA: Local Extrema Convolution for Drug Target Affinity Prediction

LE-DTA: Local Extrema Convolution for Drug Target Affinity P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tanoj Langore 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 Hsinchu Taiwan Department of Electrical Engineering 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

One of the essential parts of drug discovery and design is the prediction of drug-target affinity (DTA). Researchers have proposed computational approaches for predicting DTA to circumvent the more expensive in vivo and in vitro tests. More recent approaches employed deep network architectures to obtain the features from the drug molecules and protein sequences. The drug compounds are represented as graphs and the target protein as a sequence to extract this information. In this work, we develop a new graph-based prediction model, termed LE-DTA, that utilizes local extrema convolutions for effective feature extraction. It focuses on the local and global extrema of graphs for node embedding. We investigated the performances of both the proposed models on three different benchmark datasets. Our proposed model showed improvement in CI by 1.12% and 0.35% and a reduction in MSE by 7.7% and 3.33% on the KIBA and BindingDB datasets, respectively. we also showed that despite using various pooling operations on our proposed model, we achieved an average reduction in MSE by 7% on the KIBA dataset and 3% improvement on the BindingDB dataset.

关键词： Drugs In vivo Neural networks Predictive models Network architecture Feature extraction Protein sequence

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