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

作者： Yunlong Xie Banghua Yang Xi Lu Minmin Zheng Cunxiu Fan Xiaoying Bi Shu zhou Yingjie Li School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Power Station Automation Technology Research Center of Brain Computer Engineering Shanghai University China Department of Neurology Shanghai Changhai Hospital PR. China Shanghai institute for advanced communication and data science School of Communication and Information Engineering Shanghai University China

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

At present, only professional doctors can use the professional scales to diagnose depression and anxiety in clinical practice. In recent years, the problems of detecting the presence of anxiety or depression using Electroencephalography (EEG) has received attention as a way to implement assistant diagnosis, and some researchers explored that there are differences in the degree of prefrontal lateralization and functional connectivity of brain networks between patients with anxiety and depression and normal people. In this paper, we proposed a new approach that combines functional connectivity of brain networks and convolutional neural networks (CNN) for EEG-based anxiety and depression recognition. EEG data are collected from subjects consisting ten healthy controls and ten patients with anxiety or depression. In this way, we achieved 67.67% classification accuracy. It points out the way to further explore the application of functional connectivity of brain networks and deep learning technology in EEG about patients with anxiety and depression.

关键词： Electroencephalography Electrodes Brain modeling Feature extraction Task analysis Convolution Scalp

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Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network

Predicting Origin-Destination Flow via Multi-Perspective Gra...

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International Conference on data engineering

作者： Hongzhi Shi Quanming Yao Qi Guo Yaguang Li Lingyu Zhang Jieping Ye Yong Li Yan Liu Beijing National Research Center for Information Science and Technology (BNRist) Department of Electronic Engineering Tsinghua University Beijing China 4 Paradigm Inc. Hong Kong China College of Computer Science Sichuan University Chengdu China Google Inc. Mountain View United States AI Labs Didi Chuxing Beijing China Department of Computer Science University of Southern California Los Angeles United States

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation. However, it is extremely challenging because of three reasons: first, correlations exist between both origins and destinations; second, the correlations are dynamic across the time; at last, there are multiple correlations from different aspects. To the best of our knowledge, existing models for OD flow prediction cannot tackle all of these three issues simultaneously. We propose Multi-Perspective Graph Convolutional Networks (MPGCN) to capture the complex dependencies. Our proposed model first utilizes long short-term memory (LSTM) network to extract temporal features for each OD pair and then learns the spatial dependency of origins and destinations by a two-dimensional graph convolutional network. Furthermore, we design a dynamic graph together with two static graphs to capture the complicated spatial dependencies and use an average strategy to obtain the final predicted OD flow. We conduct extensive experiments on two large-scale and real-world datasets, which not only demonstrate our design philosophy but also validate the effectiveness of the proposed model.

关键词： Correlation Feature extraction Predictive models Tensile stress Two dimensional displays Convolution Transportation

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Prediction of solar energetic events impacting space weather conditions

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Advances in Space research 2024年

作者： Georgoulis, Manolis K. Yardley, Stephanie L. Guerra, Jordan A. Murray, Sophie A. Ahmadzadeh, Azim Anastasiadis, Anastasios Angryk, Rafal Aydin, Berkay Banerjee, Dipankar Barnes, Graham Bemporad, Alessandro Benvenuto, Federico Bloomfield, D. Shaun Bobra, Monica Campi, Cristina Camporeale, Enrico DeForest, Craig E. Emslie, A. Gordon Falconer, David Feng, Li Gan, Weiqun Green, Lucie M. Guastavino, Sabrina Hapgood, Mike Kempton, Dustin Kitiashvili, Irina Kontogiannis, Ioannis Korsos, Marianna B. Leka, K.D. Massa, Paolo Massone, Anna Maria Nandy, Dibyendu Nindos, Alexander Papaioannou, Athanasios Park, Sung-Hong Patsourakos, Spiros Piana, Michele Rawafi, Nour E. Sadykov, Viacheslav M. Toriumi, Shin Vourlidas, Angelos Wang, Haimin L. Wang, Jason T. Whitman, Kathryn Yan, Yihua Zhukov, Andrei N. Research Center for Astronomy and Applied Mathematics of the Academy of Athens Athens11527 Greece Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Department of Meteorology University of Reading ReadingRG6 6ET United Kingdom Paseo Manuel de Lardizabal 4 San Sebastián20018 Spain CIRES University of Colorado BoulderCO80309 United States NOAA Space Weather Prediction Center BoulderCO80305 United States School of Cosmic Physics Dublin Institute for Advanced Studies Dunsink Observatory DublinD15 XR2R Ireland Computer Science Department Georgia State University AtlantaGA30303 United States National Observatory of Athens Penteli15236 Greece Uttarakhand Nainital263001 India NorthWest Research Associates BoulderCO80301 United States INAF Turin Astrophysical Observatory via Osservatorio 20 10025 Pino Torinese Torino Italy MIDA Dipartimento di Matematica Università di Genova via Dodecaneso 35 16146 Genova Italy State of California Office of Data and Innovation 401 I Street Ste 200 SacramentoCA95814 United States Southwest Research Institute BoulderCO80302 United States Department of Physics & Astronomy Western Kentucky University Bowling GreenKY42101 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States NASA/Marshall Space Flight Center HuntsvilleAL35812 United States Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Mullard Space Science Laboratory University College London Holmbury St. MaryRH5 6NT United Kingdom University of Chinese Academy of Sciences Nanjing211135 China Visiting Scientist RAL Space STFC Rutherford Appleton Laboratory Oxfordshire DidcotOX11 0QX United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States An der Sternwarte 16 Potsdam14482 Germany Dipartime

aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015), we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (ai) methodologies, in variants of machine and deep learning, developed particularly for tackling Big data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and ai methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the

关键词： Solar energy

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Opti-SW: An improved gene sequence alignment algorithm

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Ingenierie des Systemes d'Information 2018年第6期23卷 73-85页

作者： Li, Leixiao Gao, Jing Liu, Yanfeng College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China

This paper aims to improve the speed and complexity of Smith-Waterman (SW) algorithm. For this purpose, the SW algorithm was improved by reducing the complexity and task load of the computation of the scoring matrix without sacrificing the alignment accuracy. Then, the optimized algorithm, denoted as the Opti-SW, was verified through experiment. The results show that the Opti-SW boasts low time complexity, fast computing speed and light computing load. The research findings shed new light on the database search for gene sequences. © 2018 Lavoisier.

关键词： Genes

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Lymgradingnet: Follicular Lymphoma Grading Guided by Fusion Strategy with Pet and Ct

SSRN

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SSRN 2024年

作者： Qian, Chunjun He, Lulu Jiang, Qiuhui Zhou, Hang Jiang, Chong Jiang, Zekun Meng, Xianglian Teng, Yue Ding, Chongyang Xu, Bing Li, Xin Ni, Xinye Hertfordshire College Changzhou Institute of Technology NO. 666 Liaohe Road Xinbe District Jiangsu Changzhou213032 China Department of Hematology The First Affiliated Hospital of Xiamen University Institute of Hematology School of Medicine Xiamen University No. 55 Zhenhai Road Siming District Fujian Xiamen361003 China Department of Nuclear Medicine Qilu Hospital of Shandong University 107 Wenhua West Road Lixia District Shandong Jinan250012 China Department of Nuclear Medicine West China Hospital Sichuan University No. 37 Guoxue Alley Wuhou District Sichuan Chengdu610041 China West China Biomedical Big Data Center West China Hospital Sichuan University No. 37 Guoxue Alley Wuhou District Sichuan Chengdu610041 China School of Computer Science and Information Engineering Changzhou Institute of Technology NO. 666 Liaohe Road Xinbe District Jiangsu Changzhou213032 China Department of Nuclear Medicine Nanjing Drum Tower Hospital Affiliated Hospital of Nanjing University Medical School NO. 321 Zhongshan Road Gulou District Jiangsu Nanjing210008 China Department of Nuclear Medicine First Affiliated Hospital of Nanjing Medical University Jiangsu Province Hospital NO. 300 Guangzhou Road Gulou District Jiangsu Nanjing210028 China Changzhou NO.2 People’s Hospital Nanjing Medical University Jiangsu Province Engineering Research Center of Medical Physics Center of Medical Physics Nanjing Medical University No. 68 Gehu Middle Road Wujin District Jiangsu Nanjing213003 China Nanjing University China

Follicular lymphoma (FL) is a non-Hodgkin lymphoma and an indolent B-cell lymphoproliferative disorder of transformed follicular center B cells. In the diagnosis, FL should be graded by counting the number of centroblasts in the pathological image, which is time-consuming. This study proposes a FL grading method based on the PET and CT images. We propose a 3D-DDcGAN to fuse the simultaneously collected PET and CT images. In the 3D-DDcGAN, the surface-level measurement and chrominance information constraint are considered to construct the loss function. Then, due to the uncertainty between the FL grades I and II, the BayesianResNet (ResNet is improved by introducing Bayes' theorem) is adopted for the FL grading. Our method is trained and tested on mixed data consisting of FL grades I-III and DLBCL. Finally, the evaluation metrics for our method are accuracy 0.871, precision 0.875, recall 0.777, macro-averaged F1-score 0.816, and micro-averaged F1-score 0.866. Through comparative experiments, we find that appropriate image fusion methods are helpful for FL grading in PET-CT images, which may be extended to the other diseases' grading. The method based on deep learning and medical imaging will help assist in disease grading, identifying the FL subtypes, and guiding personalized treatment plans. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Emotion correlation mining through deep learning models on natural language text

arXiv

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arXiv 2020年

作者： Wang, Xinzhi Kou, Luyao Sugumaran, Vijayan Luo, Xiangfeng Zhang, Hui School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China Institute of Public Safety Research Department of Engineering Physics Tsinghua University Beijing Beijing100084 China Key Laboratory of City Integrated EmergencyResponse Science Tsinghua University Beijing100084 China Department of Decision and Information Sciences Oakland University RochesterMI48309 United States Center for Data Science and Big Data Analytics Oakland University RochesterMI48309 United States

Emotion analysis has been attracting researchers’ attention. Most previous works in the artificial-intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. The correlation among emotions contributes to the failure of emotion recognition. In this article, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from Web news. The correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural-network models are presented. The emotion confusion law is extracted through an orthogonal basis. The emotion evolution law is evaluated from three perspectives: one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets: 1) the titles;2) the bodies;and 3) the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love–anger and sadness–anger. In objective news, it is easy to recognize text emotion as love and cause fear–joy circulation. These findings could provide insights for applications regarding affective interaction, such as network public sentiment, social media communication, and human–computer interaction. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Accurate machine-learning based modeling of apparent viscosity of waxy oils mixed with polymeric wax inhibitors

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Flow Measurement and Instrumentation 2025年

作者： Hongyan Lei Muntadher Abed Hussein Tarak Vora Anupam Yadav Asha Rajiv Aman Shankhyan Sachin Jaidka Mehul Manu Issa Mohammed Kadhim Zainab Jamal Hamoodah Fadhil Faez Mehrdad Mottaghi College of Computer and Electrical Engineering Hunan University of Arts and Science Changde hunan 415000 China Department of sciecnes Al-Manara College for Medical Sciences (Maysan) Iraq Marwadi University Research Center Department of Civil Engineering Faculty of Engineering & Technology Marwadi University Rajkot-360003 Gujarat India Department of Computer engineering and Application GLA University Mathura-281406 India Department of Physics & Electronics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Physics Department of Applied Sciences Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali 140307 Punjab India Department of Allied Science (Physics) Graphic Era Hill University Bhimtal India Graphic Era Deemed to be University Dehradun Uttarakhand-248002 India Department of Medical Laboratories Technology Al-Nisour University College Nisour Seq. Karkh Baghdad Iraq Mazaya University college An Nasiriyah Dhi Qar Iraq Department of Dentistry College of Dentistry The Islamic University Najaf Iraq Department of medical analysis Medical laboratory technique college the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of medical analysis Medical laboratory technique college the Islamic University of Babylon Babylon Iraq Faculty of chemistry Kabul University Kabul Afghanistan

While producing and then transporting waxy oils through pipeline network systems, wax precipitation commonly leads to issues such as wax build-up, gel creation, and decreased flow, negatively affecting pipeline efficiency. To address these problems, the petroleum industry frequently uses polymeric flow improvers to enhance the rheological properties, thereby increasing the mobility and flowability of oils. Experimental methods to measure oil viscosity are both expensive and time-consuming; hence, having reliable data driven frameworks are necessary in this regard. This work presents the use of accurate artificial intelligence methods including K-nearest neighbors, convolutional neural network, multi-layer perceptron artificial neural network, decision tree, support vector machine, adaptive boosting, random forest and ensemble learning to create smart methods for calculating the apparent viscosity of waxy crude oils that are doped with polymeric additives by actual databank sourced from prior studies. A databank containing 526 datapoints was used to both prepare and validate these machine learning algorithms. The predicted results were then evaluated against actual lab data using both graphical and statistical methods. The outcomes showed a strong agreement between the determined and real values for the constructed models. These developed models offer a way of accurate estimation capability bypassing the need for costly, labor-intensive, and time-consuming experimental procedures.

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Anomaly Detection in Climate data Using Stacked and Densely Connected Long Short-Term Memory Model

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Journal of computers (Taiwan) 2020年第4期31卷 42-53页

作者： Saleh Abbas, Bahtiar Wahyono, Teguh Heryadi, Yaya Soeparno, Haryono Doctor of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Industrial Engineering Bina Nusantara University Jakarta Indonesia

Climate anomalies are considered as an important factor closely related to many disasters causing many human losses, such as airline crash, wildfires, drought and flooding in many areas. Many researchers have projected that the rising global temperature will increase the draught, especially in the mid-latitude areas. Taking those problems into account, studies on anomaly detection in climate are crucial. While climate prediction aims to analyze and model regular pattern of climate, climate anomaly studies aim to model climate deviation from its previous general patterns. Long Short Term Memory (LSTM) is a method employed in this research because it has been proven to work effectively in several anomaly detection studies, especially for data with similar characteristics. This paper presents empirical results of using Basic LSTM, Densely Connected (DC) LSTM, and Stacked-DC LSTM models to detect temperature anomaly as a manifestation of climate change in Semarang City. The results show that Stacked DC LSTM produced higher accuracy in detecting anomaly than the other two methods. © 2020 computer Society of the Republic of China. All rights reserved.

关键词： Long short-term memory

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Cocreating the Visualization of Digital Mobility Outcomes: Delphi-Type Process With Patients

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JMIR Formative research 2025年 9卷 e68782页

作者： Lumsdon, Jack Wilson, Cameron Alcock, Lisa Becker, Clemens Benvenuti, Francesco Bonci, Tecla van den Brande, Koen Brittain, Gavin Brown, Philip Buckley, Ellen Caruso, Marco Caulfield, Brian Cereatti, Andrea Delgado-Ortiz, Laura Del Din, Silvia Evers, Jordi Garcia-Aymerich, Judith Gaßner, Heiko Gur Arieh, Tova Hansen, Clint Hausdorff, Jeffrey M. Hiden, Hugo Hume, Emily Kirk, Cameron Maetzler, Walter Megaritis, Dimitrios Rochester, Lynn Scott, Kirsty Sharrack, Basil Sutton, Norman Vereijken, Beatrix Vogiatzis, Ioannis Yarnall, Alison Keogh, Alison Cantu, Alma Population Health Sciences Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom School of Clinical Medicine Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom NIHR Newcastle Biomedical Research Centre Newcastle University The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle upon Tyne United Kingdom Robert Bosch Society for Medical Research Stuttgart Germany Digital Geriatrics Unit Medical Centre University of Heidelberg Heidelberg Germany Mobilise-D Patient and Public Advisory Group Newcastle Upon Tyne United Kingdom School of Mechanical Aerospace and Civil Engineering University of Sheffield Sheffield United Kingdom Insigneo Institute for Silico Medicine University of Sheffield Sheffield United Kingdom Department of Neuroscience Sheffield NIHR Translational Neuroscience BRC Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom Sheffield Institute for Translational Neuroscience University of Sheffield Sheffield United Kingdom The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne United Kingdom Division of Clinical Medicine University of Sheffield Sheffield United Kingdom Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Insights Centre Data Analytics University College Dublin Dublin Ireland School of Public Health Physiotherapy and Sports Science University of College Dublin Dublin Ireland Barcelona Institute for Global Health Barcelona Spain Department of Medicine and Life Sciences Pompeu Fabra University Barcelona Spain Center for Biomedical Research Madrid Spain McRoberts BV The Hague Netherlands Department of Molecular Neurology University Hospital Erlangen Erlangen Germany Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany Departmen

Background: Recent technological advances in wearable devices offer new potential for measuring mobility in real-world contexts. Mobilise-D has validated digital mobility outcomes to provide novel outcomes and end points in clinical research of 4 different long-term health conditions (Parkinson disease, multiple sclerosis, chronic obstructive pulmonary disease, and proximal femoral fracture). These outcomes also provide unique information that is important to patients;however, there is limited literature that explores the optimal methods to achieve this, such as the best way to visualize patients’ data. Objective: This study aimed to identify meaningful outcomes for each condition and how to best visualize them from the perspective of end users. Methods: Using a Delphi-type protocol with patients as subject matter experts, we gathered iterative feedback on the cocreation of visualizations through 3 rounds of questionnaires. An open-ended questionnaire was used in round 1 to understand what aspects of mobility were most influenced by their health condition. These responses were mapped onto relevant digital mobility outcomes and walking experiences and then prioritized for visualization. Using patient responses, we worked alongside researchers, clinicians, and a patient advisory group to develop visualizations that depicted a week of mobility data. During rounds 2 and 3, participants rated usefulness and ease of understanding on a 5-point Likert scale and provided unstructured feedback in comment boxes for each visualization. Visualizations were refined using the feedback from round 2 before receiving further feedback in round 3. Results: Participation varied across rounds 1 to 3 (n=48, n=79, and n=78, respectively). Round 1 identified important outcomes and contexts for each health condition, such as walking speed and stride length for people with Parkinson disease or multiple sclerosis and number of steps for people with chronic obstructive pulmonary disease or prox

关键词： cocreation data visualization digital mobility outcomes mobility wearable devices

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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