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Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

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arXiv 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Roshanzamir, Mohamad Joloudari, Javad Hassannataj Shoeibi, Afshin Jafari, Mahboobeh Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Panahiazar, Maryam Zare, Assef Shariful Islam, Sheikh Mohammed Rajendra, U. Acharya Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid hospital Iran University of Medical Sciences Tehran Iran Institute for Computational Health Sciences University of California San Francisco United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel) achieved the highest classification accuracy of 99.02% in accurate detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： data mining

Implementation strategies in phonopy and phono3py

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arXiv 2023年

作者： Togo, Atsushi Chaput, Laurent Tadano, Terumasa Tanaka, Isao Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki Tsukuba305-0047 Japan Center for Elements Strategy Initiative for Structural Materials Kyoto University Sakyo Kyoto606-8501 Japan Université de Lorraine LEMTA Centre National de la Recherche Scientifique Unité Mixte de Recherche 7563 BP 70239 Cedex Vandœuvre54506 France Research Center for Magnetic and Spintronic Materials National Institute for Materials Science Ibaraki Tsukuba305-0047 Japan Department of Materials Science and Engineering Kyoto University Sakyo Kyoto606-8501 Japan Nanostructures Research Laboratory Japan Fine Ceramics Center Atsuta Nagoya456-8587 Japan

Scientific simulation codes are public property sustained by the community. Modern technology allows anyone to join scientific software projects, from anywhere, remotely via the internet. The phonopy and phono3py codes are widely used open source phonon calculation codes. This review describes a collection of computational methods and techniques as implemented in these codes and shows their implementation strategies as a whole, aiming to be useful for the community. Some of the techniques presented here are not limited to phonon calculations and may therefore be useful in other area of condensed matter physics. Copyright © 2023, The Authors. All rights reserved.

关键词： Phonons

食品系统与气候变化、空气污染之间的相互作用综述（英文）

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engineering 2025年第1期 215-233页

作者： Chaopeng Hong Rui Zhong Mengyao Xu Peidong He Huibin Mo Yue Qin Danna Shi Xinlei Chen Kebin He Qiang Zhang Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and Ecology Shenzhen International Graduate School Tsinghua University State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex College of Environmental Sciences and Engineering Peking University Institute of Data and Information Shenzhen International Graduate School Tsinghua University State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University

Food systems are deeply affected by climate change and air pollution, while being key contributors to these environmental challenges. Understanding the complex interactions among food systems, climate change, and air pollution is crucial for mitigating climate change, improving air quality, and promoting the sustainable development of food systems. However, the literature lacks a comprehensive review of these interactions, particularly in the current phase of rapid development in the field. To address this gap, this study systematically reviews recent research on the impacts of climate change and air pollution on food systems, as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution. In addition, this study summarizes various strategies for mitigation and adaptation, including adjustments in agricultural practices and food supply chains. Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions. This review offers a critical overview of current research on the interactions among food systems, climate change, and air pollution and highlights future research directions to support the transition to sustainable food systems.

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Collaborative Perception datasets for Autonomous Driving: A Review

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arXiv 2025年

作者： Wang, Naibang Shang, Deyong Gong, Yan Hu, Xiaoxi Song, Ziying Yang, Lei Huang, Yuhan Wang, Xiaoyu Lu, Jianli Beijing100083 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing100084 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing Jiaotong University China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore School of Mechatronics Engineering Harbin Institute of Technology 92 West Dazhi St. Harbin China Department of Electronic & Electrical Engineering University of Bath United Kingdom

Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion. With the advancement of Vehicle-to-Everything (V2X) communication, numerous collaborative perception datasets have emerged, varying in cooperation paradigms, sensor configurations, data sources, and application scenarios. However, the absence of systematic summarization and comparative analysis hinders effective resource utilization and standardization of model evaluation. As the first comprehensive review focused on collaborative perception datasets, this work reviews and compares existing resources from a multi-dimensional perspective. We categorize datasets based on cooperation paradigms, examine their data sources and scenarios, and analyze sensor modalities and supported tasks. A detailed comparative analysis is conducted across multiple dimensions. We also outline key challenges and future directions, including dataset scalability, diversity, domain adaptation, standardization, privacy, and the integration of large language models. To support ongoing research, we provide a continuously updated online repository of collaborative perception datasets and related literature: https://***/frankwnb/Collaborative-Perceptio n-datasets-for-Autonomous-Driving. © 2025, CC BY-SA.

关键词： Sensor data fusion

Uncertainty-aware semi-supervised method using large unlabelled and limited labeled COVID-19 data

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arXiv 2021年

作者： Alizadehsani, Roohallah Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Gorriz, Juan M. Hussain, Sadiq Arco, Juan E. Sani, Zahra Alizadeh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Dept. of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

Continuous black-box optimization with quantum annealing and random subspace coding

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arXiv 2021年

作者： Izawa, Syun Kitai, Koki Tanaka, Shu Tamura, Ryo Tsuda, Koji Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences University of Tokyo Kashiwa277-8561 Japan Department of Mechanical Engineering University of Tokyo Tokyo113-8654 Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama223-8522 Japan Green Computing System Research Organization Waseda University Tokyo162-0042 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan

A black-box optimization algorithm such as Bayesian optimization finds extremum of an unknown function by alternating inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply quantum annealing (QA) to overcome the difficulty in the continuous black-box optimization. As QA specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution of QA has to be translated back. Our method has the following three parts: 1) Random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector. 2) A quadratic unconstrained binary optimization (QUBO) defined by acquisition function based on nonnegative-weighted linear regression model which is solved by QA. 3) A penalization scheme to ensure that the QA solution can be translated back. It is shown in benchmark tests that its performance using D-Wave AdvantageTM quantum annealer is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up a new possibility of quantum annealing and other QUBO solvers including quantum approximate optimization algorithm (QAOA) using a gated-quantum computers, and expand its range of application to continuous-valued problems. © 2021, CC BY.

关键词： Annealing

Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta

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Nexus (New York, N.Y.) 2024年第2期1卷 100016页

作者： Mitchell Daneker Shengze Cai Ying Qian Eric Myzelev Arsh Kumbhat He Li Lu Lu Department of Statistics and Data Science Yale University New Haven CT 06511 USA. Department of Chemical and Biomolecular Engineering University of Pennsylvania Philadelphia PA 19104 USA. These authors contributed equally. Institute of Cyber-System & Control College of Control Science & Engineering Zhejiang University Hangzhou 310058 China. School of Chemical Materials and Biomedical Engineering University of Georgia Athens GA 30602 USA. Department of Mathematics University of Pennsylvania Philadelphia PA 19104 USA. Department of Mathematics ETH Zurich 8092 Zurich Switzerland. Wu Tsai Institute Yale University New Haven CT 06510 USA. Lead contact.

Aortic dissection is a life-threatening event that is responsible for significant morbidity and mortality in individuals ranging in age from children to older adults. A better understanding of the complex hemodynamic environment inside the aorta enables clinicians to assess patient-specific risk of complications and administer timely interventions. In this study, we propose to develop and validate a new computational framework, warm-start physics-informed neural networks (WS-PINNs), to address the limitations of the current approaches in analyzing the hemodynamics inside the false lumen (FL) of type B aortic dissection vessels reconstructed from apolipoprotein null mice infused with AngII, thereby significantly reducing the amount of required measurement data and eliminating the dependency of predictions on the accuracy and availability of the inflow/outflow boundary conditions. Specifically, we demonstrate that the WS-PINN models allow us to focus on assessing the 3D flow field inside FL without modeling the true lumen and various branched vessels. Furthermore, we investigate the impact of the spatial and temporal resolutions of MRI data on the prediction accuracy of the PINN model, which can guide the data acquisition to reduce time and financial costs. Finally, we consider the use of transfer learning to provide faster results when looking at similar but new geometries. Our results indicate that the proposed framework can enhance the capacity of hemodynamic analysis in vessels with aortic dissections, with the promise of eventually leading to improved prognostic ability and understanding of the development of aneurysms.

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Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets using Graph Machine Learning

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arXiv 2023年

作者： Sivarajkumar, Sonish Tandale, Pratyush Bhardwaj, Ankit Johnson, Kipp W. Titus, Anoop Glicksberg, Benjamin S. Khader, Shameer Yadav, Kamlesh K. Subramanian, Lakshminarayanan Molecular Robotics Kerala India School of Computing and Information University of Pittsburgh Pennsylvania PA United States Health Informatics & Data Science Georgetown University WashingtonDC United States Department of Computer Science Courant Institute of Mathematical Sciences New York University New YorkNY United States Institute for Next Generation Healthcare Mount Sinai Health System New YorkNY United States Department of Preventive Cardiology DeBakey Heart & Vascular Center Houston Methodist HoustonTX United States Hasso Plattner Institute for Digital Health Icahn School of Medicine at Mount Sinai New YorkNY United States Faculty of Medicine Imperial College London London United Kingdom School of Engineering Medicine Texas A&M University HoustonTX United States Department of Translational Medical Sciences Center for Genomic and Precision Medicine Texas A&M University HoustonTX United States

Transcription factors (TFs) play a vital role in the regulation of gene expression thereby making them critical to many cellular processes. In this study, we used graph machine learning methods to create a compendium of TF cascades using data extracted from the STRING database. A TF cascade is a sequence of TFs that regulate each other, forming a directed path in the TF network. We constructed a knowledge graph of 81,488 unique TF cascades, with the longest cascade consisting of 62 TFs. Our results highlight the complex and intricate nature of TF interactions, where multiple TFs work together to regulate gene expression. We also identified 10 TFs with the highest regulatory influence based on centrality measurements, providing valuable information for researchers interested in studying specific TFs. Furthermore, our pathway enrichment analysis revealed significant enrichment of various pathways and functional categories, including those involved in cancer and other diseases, as well as those involved in development, differentiation, and cell signaling. The enriched pathways identified in this study may have potential as targets for therapeutic intervention in diseases associated with dysregulation of transcription factors. We have released the dataset, knowledge graph, and graphML methods for the TF cascades, and created a website to display the results, which can be accessed by researchers interested in using this dataset. Our study provides a valuable resource for understanding the complex network of interactions between TFs and their regulatory roles in cellular processes. Copyright © 2023, The Authors. All rights reserved.

关键词： Transcription factors

Face image super-resolution reconstruction via mapping matrix and multilayer model

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IAENG International Journal of Computer Science

IAENG International Journal of Computer Science 2020年第1期47卷 68-74页

作者： Wang, Yan Wang, Jianchun Li, Fengju Zhou, Yancong Xiong, Min Li, Ming College of Information Engineering Tianjin University of Commerce Tianjin300134 China Agriculture Information Department Tianjin Academy of Agricultural Sciences Tianjin300192 China School of Information Hebei University of Technology Tianjin300401 China Tianjin Zhongwei Aerospace Data System Technology Company Tianjin300301 China

Making full use of the information provided by the training set has always been the target of face image super-resolution reconstruction in the case of small samples. To solve the problem of full usage, we propose a novel algorithmvia mapping matrix and multilayer model. Firstly, we double the training set by the flip method. Then, we extract one-step and two-step gradient features in the training set, and divide these face images and their feature images into many multi-scale patches. The reconstructed training set is formed by the patches which are in the same positions. We adopt high-resolution and low-resolution feature images simultaneously to build weights for the mapping matrix. To reduce local ghosting and blurring of the reconstructed image, the matrix constraint is constructed through processing the distance of different low-resolution and high-resolution feature image blocks. Last, the multilayer model is built by using image patches of different scales so that the reconstruction can reflect the degradation process of the image. The experimental results on the small sample database FERET, FEI and CAS-PEAL-R1 show that the proposed method can achieve better face image reconstruction quality compared with state-of-the-art methods. © 2020 International Association of Engineers.

关键词： Optical resolving power