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A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis

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A five-layer deep convolutional neural network with stochast...

作者： Zhang, Yu-Dong Satapathy, Suresh Chandra Liu, Shuaiqi Li, Guang-Run School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia School of Computer Engg KIIT Deemed To University Bhubaneswar India College of Electronic and Information Engineering Hebei University BaodingHebei071002 China Department of Imaging Jinhu People’s Hospital No. 160 Jinhu Shenhua Street Huai’an China

Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements: (i) We introduced stochastic pooling to replace average pooling and max pooling;(ii) We combined conv layer with batch normalization layer and obtained the conv block (CB);(iii) We combined dropout layer with fully connected layer and obtained the fully connected block (FCB). Our algorithm achieved a sensitivity of 93.28% ± 1.50%, a specificity of 94.00% ± 1.56%, and an accuracy of 93.64% ± 1.42%, in identifying COVID-19 from normal subjects. We proved using stochastic pooling yields better performance than average pooling and max pooling. We compared different structure configurations and proved our 3CB + 2FCB yields the best performance. The proposed model is effective in detecting COVID-19 based on chest CT images. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

Beyond tracking: Using deep learning to discover novel interactions in biological swarms

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arXiv 2021年

作者： Choi, Taeyeong Pyenson, Benjamin Liebig, Juergen Pavlic, Theodore P. Lincoln Institute for Agri-food Technology University of Lincoln Riseholme Park Lincoln United Kingdom School of Life Sciences Social Insect Research Group Arizona State University TempeAZ85281 United States School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States School of Sustainability Arizona State University TempeAZ85281 United States

Most deep-learning frameworks for understanding biological swarms are designed to fit perceptive models of group behavior to individual-level data (e.g., spatial coordinates of identified features of individuals) that have been separately gathered from video observations. Despite considerable advances in automated tracking, these methods are still very expensive or unreliable when tracking large numbers of animals simultaneously. Moreover, this approach assumes that the human-chosen features include sufficient features to explain important patterns in collective behavior. To address these issues, we propose training deep network models to predict system-level states directly from generic graphical features from the entire view, which can be relatively inexpensive to gather in a completely automated fashion. Because the resulting predictive models are not based on human-understood predictors, we use explanatory modules (e.g., Grad-CAM) that combine information hidden in the latent variables of the deep-network model with the video data itself to communicate to a human observer which aspects of observed individual behaviors are most informative in predicting group behavior. This represents an example of augmented intelligence in behavioral ecology - knowledge co-creation in a human-AI team. As proof of concept, we utilize a 20-day video recording of a colony of over 50 Harpegnathos saltator ants to showcase that, without any individual annotations provided, a trained model can generate an \importance map" across the video frames to highlight regions of important behaviors, such as dueling (which the AI has no a priori knowledge of), that play a role in the resolution of reproductive-hierarchy re-formation. Based on the empirical results, we also discuss the potential use and current challenges to further develop the proposed framework as a tool to discover behaviors that have not yet been considered crucial to understand complex social dynamics within biological colle

关键词： Video recording

Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy

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arXiv 2021年

作者： Wu, Jianfeng Zhu, Wenhui Su, Yi Gui, Jie Lepore, Natasha Reiman, Eric M. Caselli, Richard J. Thompson, Paul M. Chen, Kewei Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States Banner Alzheimer’s Institute Phoenix United States School of Cyber Science and Engineering Southeast University Nanjing China CIBORG Lab Department of Radiology Children’s Hospital Los Angeles Los Angeles United States Department of Neurology Mayo Clinic Arizona Scottsdale United States Imaging Genetics Center Stevens Neuroimaging and Informatics Institute University of Southern California Marina del Rey United States

Biomarker-assisted diagnosis and intervention in Alzheimer’s disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM). © 2021, CC BY-NC-SA.

关键词： Deposition

Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review

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arXiv 2024年

作者： Mao, Lingchao Wang, Hairong Hu, Leland S. Tran, Nhan L. Canoll, Peter D. Swanson, Kristin R. Li, Jing H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States Mathematical NeuroOncology Lab Precision Neurotherapeutics Innovation Program Mayo Clinic Arizona 5777 East Mayo Blvd Support Services Building Suite 2-700 PhoenixAZ85054 United States Department of Radiology Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S Mill Ave TempeAZ85281 United States Department of Neurosurgery Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States Department of Cancer Biology Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States Department of Pathology and Cell Biology Columbia University Medical Center 630 West 168th Street New YorkNY10032 United States

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine *** Codes 92B99 Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge representation

Towards a more realistic citation model: The key role of research team sizes

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arXiv 2020年

作者： Milojević, Staša Center for Complex Networks and Systems Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

We propose a new citation model which builds on the existing models that explicitly or implicitly include "direct" and "indirect" (learning about a cited paper’s existence from references in another paper) citation mechanisms. Our model departs from the usual, unrealistic assumption of uniform probability of direct citation, in which initial differences in citation arise purely randomly. Instead, we demonstrate that a two-mechanism model in which the probability of direct citation is proportional to the number of authors on a paper (team size) is able to reproduce the empirical citation distributions of articles published in the field of astronomy remarkably well, and at different points in time. Interpretation of our model is that the intrinsic citation capacity, and hence the initial visibility of a paper, will be enhanced when more people are intimately familiar with some work, favoring papers from larger teams. While the intrinsic citation capacity cannot depend only on the team size, our model demonstrates that it must be to some degree correlated with it, and distributed in a similar way, i.e., having a power-law tail. Consequently, our team-size model qualitatively explains the existence of a correlation between the number of citations and the number of authors on a paper. Copyright © 2020, The Authors. All rights reserved.

关键词： Probability distributions

Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, ScienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

Long-short term spatiotemporal tensor prediction for passenger flow profile

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arXiv 2020年

作者： Li, Ziyue Yan, Hao Zhang, Chen Tsung, Fugee Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S Mill Ave TempeAZ85281 United States Industrial Engineering Tsinghua University Beijing China Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology

Spatiotemporal data is very common in many applications, such as manufacturing systems and transportation systems. It is typically difficult to be accurately predicted given intrinsic complex spatial and temporal correlations. Most of the existing methods based on var-ious statistical models and regularization terms, fail to preserve innate features in data alongside their complex correlations. In this paper, we focus on a tensor-based prediction and propose several practical techniques to improve prediction. For long-term prediction specifically, we propose the "Tensor Decomposition + 2-Dimensional Auto-Regressive Moving Average (2D-ARMA)" model, and an effective way to update prediction real-time;For short-term prediction, we propose to conduct tensor completion based on tensor clustering to avoid oversimplifying and ensure accuracy. A case study based on the metro passenger flow data is conducted to demonstrate the improved performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

Matrix Factorization with Interval-Valued Data

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Matrix Factorization with Interval-Valued Data

International Conference on Data engineering

作者： Mao-Lin Li Francesco Di Mauro K Selçuk Candan Maria Luisa Sapino School of Computing Informatics and Decision System Engineering Arizona State University Tempe United States Department of Computer Science University of Torino Turin Italy

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

With many applications relying on multi-dimensional datasets for decision making, matrix factorization (or decomposition) is becoming the basis for many knowledge discovery and machine learning tasks, from clustering, trend detection, anomaly detection, to correlation analysis. Unfortunately, a major shortcoming of matrix analysis operations is that, despite their effectiveness when the data is scalar, these operations become difficult to apply in the presence of non-scalar data, as they are not designed for data that include non-scalar observations, such as intervals. In this paper, we propose matrix decomposition techniques that consider the existence of interval-valued data. We show that naive ways to deal with such imperfect data may introduce errors in analysis and present factorization techniques that are especially effective when the amount of imprecise information is large.

关键词： Matrix decomposition Semantics Face Probabilistic logic Collaboration Conferences Data engineering

Small Cohort of Epilepsy Patients Showed Increased Activity on Facebook before Sudden Unexpected Death

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arXiv 2022年

作者： Wood, Ian B. Correia, Rion Brattig Miller, Wendy R. Rocha, Luis M. Center for Social and Biomedical Complexity Luddy School of Informatics Computing & Engineering Indiana University BloomingtonIN47408 United States Instituto Gulbenkian de Ciência Oeiras2780-156 Portugal CAPES Foundation Ministry of Education of Brazil DF Brasília Brazil School of Nursing Indiana University IndianapolisIN46202 United States Department of Systems Science and Industrial Engineering Binghamton University BinghamtonNY13902 United States

Sudden Unexpected Death in Epilepsy (SUDEP) remains a leading cause of death in people with epilepsy. Despite the constant risk for patients and bereavement to family members, to date the physiological mechanisms of SUDEP remain unknown. Here we explore the potential to identify putative predictive signals of SUDEP from online digital behavioral data using text and sentiment analysis. Specifically, we analyze Facebook timelines of six epilepsy patients deceased due to SUDEP, donated by surviving family members. We find preliminary evidence for behavioral changes detectable by text and sentiment analysis tools. Namely, in the months preceding their SUDEP event patient social media timelines show: i) increase in verbosity;ii) increased use of functional words;and iii) sentiment shifts as measured by different sentiment analysis tools. Combined, these results suggest that social media engagement, as well as its sentiment, may serve as possible early-warning signals for SUDEP in people with epilepsy. While the small sample of patient timelines analyzed in this study prevents generalization, our preliminary investigation demonstrates the potential of social media data as complementary data in larger studies of SUDEP and *** Codes 62P10 (Primary) 92D50, 68U15, 92D30 (Secondary) © 2022, CC BY-SA.

关键词： Sentiment analysis