111 results match your criteria Applied Soft Computing[Journal]


Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification.

Appl Soft Comput 2022 May 13:108966. Epub 2022 May 13.

Graduate Program on Teleinformatics Engineering, Federal University of Ceará, Fortaleza/CE, Brazil.

The COVID-19 pandemic continues to wreak havoc on the world's population's health and well-being. Successful screening of infected patients is a critical step in the fight against it, with radiology examination using chest radiography being one of the most important screening methods. For the definitive diagnosis of COVID-19 disease, reverse-transcriptase polymerase chain reaction remains the gold standard. Read More

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Dealing with distribution mismatch in semi-supervised deep learning for COVID-19 detection using chest X-ray images: A novel approach using feature densities.

Appl Soft Comput 2022 Jul 10;123:108983. Epub 2022 May 10.

School of Computer Science, University of Nottingham, United Kingdom.

In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learning models usually need large labelled datasets to be effective. Semi-supervised deep learning is an attractive alternative, where unlabelled data is leveraged to improve the overall model's accuracy. Read More

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Generalized Susceptible-Exposed-Infectious-Recovered model and its contributing factors for analysing the death and recovery rates of the COVID-19 pandemic.

Appl Soft Comput 2022 Jul 11;123:108973. Epub 2022 May 11.

School of Information Science and Technology, Nantong University, Nantong 226019, PR China.

COVID-19 is a highly contagious disease that has infected over 136 million people worldwide with over 2.9 million deaths as of 11 April 2021. In March 2020, the WHO declared COVID-19 as a pandemic and countries began to implement measures to control the spread of the virus. Read More

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COVID-19 prognosis using limited chest X-ray images.

Appl Soft Comput 2022 Jun 25;122:108867. Epub 2022 Apr 25.

Indian Institute of Technology, Hauz Khas, New Delhi, 110016, Delhi, India.

The COrona VIrus Disease 2019 (COVID-19) pandemic is an ongoing global pandemic that has claimed millions of lives till date. Detecting COVID-19 and isolating affected patients at an early stage is crucial to contain its rapid spread. Although accurate, the primary viral test 'Reverse Transcription Polymerase Chain Reaction' (RT-PCR) for COVID-19 diagnosis has an elaborate test kit, and the turnaround time is high. Read More

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A deep fuzzy model for diagnosis of COVID-19 from CT images.

Appl Soft Comput 2022 Jun 22;122:108883. Epub 2022 Apr 22.

Rajnagar Mahavidyalaya, Rajnagar, Birbhum, India.

From early 2020, a novel coronavirus disease pneumonia has shown a global "pandemic" trend at an extremely fast speed. Due to the magnitude of its harm, it has become a major global public health event. In the face of dramatic increase in the number of patients with COVID-19, the need for quick diagnosis of suspected cases has become particularly critical. Read More

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Pre-trained ensemble model for identification of emotion during COVID-19 based on emergency response support system dataset.

Appl Soft Comput 2022 Jun 18;122:108842. Epub 2022 Apr 18.

Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli, India.

The COVID-19 precautions, lockdown, and quarantine implemented throughout the epidemic resulted in a worldwide economic disaster. People are facing unprecedented levels of intense threat, necessitating professional, systematic psychiatric intervention and assistance. New psychological services must be established as quickly as possible to support the mental healthcare needs of people in this pandemic condition. Read More

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An based emergency response plan evaluation with MULTIMOORA method in group decision making.

Appl Soft Comput 2022 Jun 9;122:108812. Epub 2022 Apr 9.

School of Management, Wuhan University of Technology, Wuhan Hubei, 430070, China.

The eruption of COVID-19 at the beginning of 2020 has sounded the alarm, making experts pay more attention to public health emergency events. A suitable emergency response plan plays a vital role in handling emergency events. Therefore, this paper focuses on the evaluation of emergency response plans among a set of group in the comprehensive prospect, and an emergency decision making method integrated with the interval type-2 fuzzy information based on the third generation prospect theory ( ) and the extended MULTIMOORA method is proposed. Read More

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A comparative study for predictive monitoring of COVID-19 pandemic.

Appl Soft Comput 2022 Jun 7;122:108806. Epub 2022 Apr 7.

SBILab, Department of ECE, IIIT-Delhi, Delhi, India.

COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. Read More

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CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.

Appl Soft Comput 2022 May 29;121:108765. Epub 2022 Mar 29.

System Sciences and Industrial Engineering, Binghamton University, NY, USA.

Evaluating patient criticality is the foremost step in administering appropriate COVID-19 treatment protocols. Learning an Artificial Intelligence (AI) model from clinical data for automatic risk-stratification enables accelerated response to patients displaying critical indicators. Chest CT manifestations including ground-glass opacities and consolidations are a reliable indicator for prognostic studies and show variability with patient condition. Read More

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Analysis of supply chain resilience drivers in oil and gas industries during the COVID-19 pandemic using an integrated approach.

Appl Soft Comput 2022 May 28;121:108756. Epub 2022 Mar 28.

Department of Industrial Engineering and Engineering Management, University of Sharjah, Sharjah, United Arab Emirates.

The COVID-19 pandemic has significantly affected the supply chains (SCs) of many industries, including the oil and gas (O&G) industry. This study aims to identify and analyze the drivers that affect the resilience level of the O&G SC under the COVID-19 pandemic. The analysis helps to understand the driving intensity of one driver over those of others as well as drivers with the highest driving power to achieve resilience. Read More

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COVID-WideNet-A capsule network for COVID-19 detection.

Appl Soft Comput 2022 Jun 29;122:108780. Epub 2022 Mar 29.

School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, N.L, Mexico.

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. Read More

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An FGM decomposition-based fuzzy MCDM method for selecting smart technology applications to support mobile health care during and after the COVID-19 pandemic.

Appl Soft Comput 2022 May 23;121:108758. Epub 2022 Mar 23.

Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung City, Taiwan.

In a fuzzy multicriteria decision-making (MCDM) problem, a decision maker may have differing viewpoints on the relative priorities of criteria. However, traditional methods merge these viewpoints into a single one, which leads to an unrepresentative decision-making result. Several recent methods identify the multiple viewpoints of a decision maker by decomposing the decision maker's fuzzy judgment matrix into several symmetric fuzzy subjudgment matrices, which is an inflexible strategy. Read More

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Interpretable Temporal Attention Network for COVID-19 forecasting.

Appl Soft Comput 2022 May 9;120:108691. Epub 2022 Mar 9.

State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering, University of Macau, 999078, Macao Special Administrative Region of China.

The worldwide outbreak of coronavirus disease 2019 (COVID-19) has triggered an unprecedented global health and economic crisis. Early and accurate forecasts of COVID-19 and evaluation of government interventions are crucial for governments to take appropriate interventions to contain the spread of COVID-19. In this work, we propose the Interpretable Temporal Attention Network (ITANet) for COVID-19 forecasting and inferring the importance of government interventions. Read More

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Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network.

Appl Soft Comput 2022 Apr 14;119:108610. Epub 2022 Feb 14.

Department of EEE, Birla Institute of Technology and Science (BITS) Pilani, Hyderabad, India.

The Coronavirus disease 2019 (COVID19) pandemic has led to a dramatic loss of human life worldwide and caused a tremendous challenge to public health. Immediate detection and diagnosis of COVID19 have lifesaving importance for both patients and doctors. The availability of COVID19 tests increased significantly in many countries, thereby provisioning a limited availability of laboratory test kits Additionally, the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID 19 is costly and time-consuming. Read More

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A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach.

Appl Soft Comput 2022 Apr 3;119:108528. Epub 2022 Feb 3.

Department of Computer Science and Engineering, University of Kalyani, India.

Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm the presence of this virus, some radiological investigations find some important features from the CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful to automatically process the CT scan images without any manual annotation and helpful in the easy interpretation. The proposed approach is based on artificial cell swarm optimization and will be known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented in the Matlab environment. Read More

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COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland.

Appl Soft Comput 2022 Feb 20;116:108324. Epub 2021 Dec 20.

Centre for Public Health, Queen's University of Belfast, Belfast, Northern Ireland, United Kingdom.

Mobile health (mHealth) technologies, such as symptom tracking apps, are crucial for coping with the global pandemic crisis by providing near real-time, in situ information for the medical and governmental response. However, in such a dynamic and diverse environment, methods are still needed to support public health decision-making. This paper uses the lens of strong structuration theory to investigate networks of COVID-19 symptoms in the Belfast metropolitan area. Read More

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February 2022

Robust weakly supervised learning for COVID-19 recognition using multi-center CT images.

Appl Soft Comput 2022 Feb 13;116:108291. Epub 2021 Dec 13.

Royal Brompton Hospital, London, UK.

The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Read More

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February 2022

A novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine.

Appl Soft Comput 2022 Feb 15;116:108280. Epub 2021 Dec 15.

Department of Computer Engineering, Tekirdag Namik Kemal University, 59860, Corlu, Tekirdag, Turkey.

B-cell epitope prediction research has received growing interest since the development of the first method. B-cell epitope identification with the aid of an accurate prediction method is one of the most important steps in epitope-based vaccine development, immunodiagnostic testing, antibody production, disease diagnosis, and treatment. Nevertheless, using experimental methods in epitope mapping is very time-consuming, costly, and labor-intensive. Read More

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February 2022

One Shot Model For The Prediction of COVID-19 And Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features.

Appl Soft Comput 2022 Feb 14;116:108261. Epub 2021 Dec 14.

CitAI Research Center, Department of Computer Science, City University of London, United Kingdom.

We present a novel framework that integrates segmentation of lesion masks and prediction of COVID-19 in chest CT scans in one shot. In order to classify the whole input image, we introduce a type of associations among lesion mask features extracted from the scan slice that we refer to as affinities. First, we map mask features to the affinity space by training an affinity matrix. Read More

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February 2022

Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms.

Appl Soft Comput 2022 Feb 9;116:108264. Epub 2021 Dec 9.

LARODEC Laboratory, Institut Supérieur de Gestion de Tunis, Université de Tunis, 41 Rue de la liberté, Le Bardo 2000, Tunisia.

The speed by which the COVID-19 pandemic spread throughout the world makes the emergency services unprepared to answer all the patients' requests. The Tunisian ministry of health established a protocol planning the sample collection from the patients at their location. A triage score is first assigned to each patient according to the symptoms he is showing, and his health conditions. Read More

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February 2022

Multi-COVID-Net: Multi-objective optimized network for COVID-19 diagnosis from chest X-ray images.

Appl Soft Comput 2022 Jan 9;115:108250. Epub 2021 Dec 9.

Department of Radiology, Silchar Medical College and Hospital, Assam, 788014, India.

Coronavirus Disease 2019 (COVID-19) had already spread worldwide, and healthcare services have become limited in many countries. Efficient screening of hospitalized individuals is vital in the struggle toward COVID-19 through chest radiography, which is one of the important assessment strategies. This allows researchers to understand medical information in terms of chest X-ray (CXR) images and evaluate relevant irregularities, which may result in a fully automated identification of the disease. Read More

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January 2022

Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images.

Appl Soft Comput 2022 Jan 5;115:108190. Epub 2021 Dec 5.

Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, 15071, A Coruña, Spain.

Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. Read More

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January 2022

Machine learning-based forecasting of firemen ambulances' turnaround time in hospitals, considering the COVID-19 impact.

Appl Soft Comput 2021 Sep 4;109:107561. Epub 2021 Jun 4.

Service Départemental d'Incendie et de Secours du Doubs, Besançon, France.

When ambulances' turnaround time (TT) in emergency departments is prolonged, it not only affects the victim severely but also causes unavailability of resources in emergency medical services (EMSs) and, consequently, leaves a locality unprotected. This problem may worsen with abnormal situations, e.g. Read More

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September 2021

A calibrated piecewise-linear FGM approach for travel destination recommendation during the COVID-19 pandemic.

Appl Soft Comput 2021 Sep 6;109:107535. Epub 2021 Jun 6.

Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung City, Taiwan.

After months of lockdown due to the COVID-19 pandemic, more people are planning regional trips because overseas travel is still not feasible. However, choosing a suitable travel destination during the COVID-19 pandemic is challenging because the factors critical to the selection process are very different from those usually considered. Furthermore, without sufficient literature or data for reference, existing methods based on psychological analyses or mining past experiences may not be applicable. Read More

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September 2021

An efficiency-based interval type-2 fuzzy multi-criteria group decision making for makeshift hospital selection.

Appl Soft Comput 2022 Jan 4;115:108243. Epub 2021 Dec 4.

School of Statistics, Jiangxi University of Finance and Economics, Nanchang 330013, China.

Since makeshift hospitals have strong ability in blocking the spread of the virus, how to design some methods to select the reasonable sites of makeshift hospitals is vitally important for containing COVID-19. This paper investigates an efficiency-based multi-criteria group decision making (MCGDM) method by combining the best-worst method (BWM) and data envelopment analysis (DEA) in trapezoidal interval type-2 fuzzy (TrIT2F) environment. This MCGDM method is called , where the is used to determine the weights of criteria and decision-makers, and the is employed to rank alternatives by measuring their overall efficiencies. Read More

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January 2022

Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis.

Appl Soft Comput 2022 Jan 24;115:108088. Epub 2021 Nov 24.

National- Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Read More

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January 2022

Novel multi-site graph convolutional network with supervision mechanism for COVID-19 diagnosis from X-ray radiographs.

Appl Soft Comput 2022 Jan 16;114:108041. Epub 2021 Nov 16.

School of Automation, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.

The novel Coronavirus disease 2019 (COVID-2019) has become a global pandemic and affected almost all aspects of our daily life. The total number of positive COVID-2019 cases has exponentially increased in the last few months due to the easy transmissibility of the virus. It can be detected using the nucleic acid test or the antibodies blood test which are not always available and take several hours to get the results. Read More

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January 2022

MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services.

Appl Soft Comput 2021 Jun 18;104:107199. Epub 2021 Feb 18.

Department of Logistics, Military Academy, University of Defence in Belgrade, 11000 Belgrade, Serbia.

Assessing and ranking private health insurance companies provides insurance agencies, insurance customers, and authorities with a reliable instrument for the insurance decision-making process. Moreover, because the world's insurance sector suffers from a gap of evaluation of private health insurance companies during the COVID-19 outbreak, the need for a reliable, useful, and comprehensive decision tool is obvious. Accordingly, this article aims to identify insurance companies' priority ranking in terms of healthcare services in Turkey during the COVID-19 outbreak through a multi-criteria performance evaluation methodology. Read More

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An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation.

Appl Soft Comput 2021 Dec 12;113:107947. Epub 2021 Oct 12.

Department of Health Administration and Policy George Mason University, Fairfax, VA, 22030, USA.

COVID-19 infection segmentation has essential applications in determining the severity of a COVID-19 patient and can provide a necessary basis for doctors to adopt a treatment scheme. However, in clinical applications, infection segmentation is performed by human beings, which is time-consuming and generally introduces bias. In this paper, we developed a novel evolvable adversarial framework for COVID-19 infection segmentation. Read More

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December 2021

A two-layer nested heterogeneous ensemble learning predictive method for COVID-19 mortality.

Appl Soft Comput 2021 Dec 7;113:107946. Epub 2021 Oct 7.

Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

The COVID-19 epidemic has had a great adverse impact on the world, having taken a heavy toll, killing hundreds of thousands of people. In order to help the world better combat COVID-19 and reduce its death toll, this study focuses on the COVID-19 mortality. First, using the multiple stepwise regression analysis method, the factors from eight aspects (economy, society, climate etc. Read More

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December 2021