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  1. Article ; Online: An agent-based model to evaluate the COVID-19 transmission risks in facilities.

    Cuevas, Erik

    Computers in biology and medicine

    2020  Volume 121, Page(s) 103827

    Abstract: The rapid spread of the coronavirus disease (COVID-19) has become a global threat affecting almost all countries in the world. As countries reach the infection peak, it is planned to return to a new normal under different coexistence conditions in order ... ...

    Abstract The rapid spread of the coronavirus disease (COVID-19) has become a global threat affecting almost all countries in the world. As countries reach the infection peak, it is planned to return to a new normal under different coexistence conditions in order to reduce the economic effects produced by the total or partial closure of companies, universities, shops, etc. Under such circumstances, the use of mathematical models to evaluate the transmission risk of COVID-19 in various facilities represents an important tool in assisting authorities to make informed decisions. On the other hand, agent-based modeling is a relatively new approach to model complex systems composed of agents whose behavior is described using simple rules. Different from classical mathematical models (which consider a homogenous population), agent-based approaches model individuals with distinct characteristics and provide more realistic results. In this paper, an agent-based model to evaluate the COVID-19 transmission risks in facilities is presented. The proposed scheme has been designed to simulate the spatiotemporal transmission process. In the model, simulated agents make decisions depending on the programmed rules. Such rules correspond to spatial patterns and infection conditions under which agents interact to characterize the transmission process. The model also includes an individual profile for each agent, which defines its main social characteristics and health conditions used during its interactions. In general, this profile partially determines the behavior of the agent during its interactions with other individuals. Several hypothetical scenarios have been considered to show the performance of the proposed model. Experimental results have demonstrated that the simulations provide useful information to produce strategies for reducing the transmission risks of COVID-19 within the facilities.
    MeSH term(s) Betacoronavirus ; COVID-19 ; Computational Biology ; Computer Simulation ; Coronavirus Infections/epidemiology ; Coronavirus Infections/transmission ; Disease Susceptibility/epidemiology ; Health Behavior ; Health Facilities ; Humans ; Models, Biological ; Pandemics/statistics & numerical data ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/transmission ; Population Dynamics/statistics & numerical data ; SARS-CoV-2 ; Systems Analysis
    Keywords covid19
    Language English
    Publishing date 2020-05-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2020.103827
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  2. Article ; Online: An agent-based model to evaluate the COVID-19 transmission risks in facilities

    Cuevas, Erik

    Computers in Biology and Medicine

    2020  Volume 121, Page(s) 103827

    Keywords Health Informatics ; Computer Science Applications ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2020.103827
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: An agent-based model to evaluate the COVID-19 transmission risks in facilities

    Cuevas, Erik

    Comput Biol Med

    Abstract: The rapid spread of the coronavirus disease (COVID-19) has become a global threat affecting almost all countries in the world. As countries reach the infection peak, it is planned to return to a new normal under different coexistence conditions in order ... ...

    Abstract The rapid spread of the coronavirus disease (COVID-19) has become a global threat affecting almost all countries in the world. As countries reach the infection peak, it is planned to return to a new normal under different coexistence conditions in order to reduce the economic effects produced by the total or partial closure of companies, universities, shops, etc. Under such circumstances, the use of mathematical models to evaluate the transmission risk of COVID-19 in various facilities represents an important tool in assisting authorities to make informed decisions. On the other hand, agent-based modeling is a relatively new approach to model complex systems composed of agents whose behavior is described using simple rules. Different from classical mathematical models (which consider a homogenous population), agent-based approaches model individuals with distinct characteristics and provide more realistic results. In this paper, an agent-based model to evaluate the COVID-19 transmission risks in facilities is presented. The proposed scheme has been designed to simulate the spatiotemporal transmission process. In the model, simulated agents make decisions depending on the programmed rules. Such rules correspond to spatial patterns and infection conditions under which agents interact to characterize the transmission process. The model also includes an individual profile for each agent, which defines its main social characteristics and health conditions used during its interactions. In general, this profile partially determines the behavior of the agent during its interactions with other individuals. Several hypothetical scenarios have been considered to show the performance of the proposed model. Experimental results have demonstrated that the simulations provide useful information to produce strategies for reducing the transmission risks of COVID-19 within the facilities.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #380456
    Database COVID19

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  4. Article: Mathematical Optimization Strategy for Effectiveness Profile Estimation in Two-Dose Vaccines and Its Use in Designing Improved Vaccination Strategies Focused on Pandemic Containment.

    González-Sánchez, Óscar A / Zaldívar, Daniel / Cuevas, Erik / González-Ortiz, L Javier

    Vaccines

    2024  Volume 12, Issue 1

    Abstract: Since late 2019, most efforts to control the COVID-19 pandemic have focused on developing vaccines. By mid-2020, some vaccines fulfilled international regulations for their application. However, these vaccines have shown a decline in effectiveness ... ...

    Abstract Since late 2019, most efforts to control the COVID-19 pandemic have focused on developing vaccines. By mid-2020, some vaccines fulfilled international regulations for their application. However, these vaccines have shown a decline in effectiveness several weeks after the last dose, highlighting the need to optimize vaccine administration due to supply chain limitations. While methods exist to prioritize population groups for vaccination, there is a lack of research on how to optimally define the time between doses when two-dose vaccines are administrated to such groups. Under such conditions, modeling the real effect of each vaccine on the population is critical. Even though several efforts have been made to characterize vaccine effectiveness profiles, none of these initiatives enable characterization of the individual effect of each dose. Thus, this paper presents a novel methodology for estimating the vaccine effectiveness profile. It addresses the vaccine characterization problem by considering a deconvolution of relevant data profiles, treating them as an optimization process. The results of this approach enabled the independent estimation of the effectiveness profiles for the first and second vaccine doses and their use to find sweet spots for designing efficient vaccination strategies. Our methodology can enable a more effective and efficient contemporary response against the COVID-19 pandemic, as well as for any other disease in the future.
    Language English
    Publishing date 2024-01-12
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2703319-3
    ISSN 2076-393X
    ISSN 2076-393X
    DOI 10.3390/vaccines12010081
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  5. Article ; Online: AltWOA: Altruistic Whale Optimization Algorithm for feature selection on microarray datasets.

    Kundu, Rohit / Chattopadhyay, Soham / Cuevas, Erik / Sarkar, Ram

    Computers in biology and medicine

    2022  Volume 144, Page(s) 105349

    Abstract: The data-driven modern era has enabled the collection of large amounts of biomedical and clinical data. DNA microarray gene expression datasets have mainly gained significant attention to the research community owing to their ability to identify diseases ...

    Abstract The data-driven modern era has enabled the collection of large amounts of biomedical and clinical data. DNA microarray gene expression datasets have mainly gained significant attention to the research community owing to their ability to identify diseases through the "bio-markers" or specific alterations in the gene sequence that represent that particular disease (for example, different types of cancer). However, gene expression datasets are very high-dimensional, while only a few of those are "bio-markers". Meta-heuristic-based feature selection effectively filters out only the relevant genes from a large set of attributes efficiently to reduce data storage and computation requirements. To this end, in this paper, we propose an Altruistic Whale Optimization Algorithm (AltWOA) for the feature selection problem in high-dimensional microarray data. AltWOA is an improvement on the basic Whale Optimization Algorithm. We embed the concept of altruism in the whale population to help efficient propagation of candidate solutions that can reach the global optima over the iterations. Evaluation of the proposed method on eight high dimensional microarray datasets reveals the superiority of AltWOA compared to popular and classical techniques in the literature on the same datasets both in terms of accuracy and the final number of features selected. The relevant codes for the proposed approach are available publicly at https://github.com/Rohit-Kundu/AltWOA.
    MeSH term(s) Algorithms ; Altruism ; Animals ; Gene Expression Profiling ; Neoplasms/genetics ; Oligonucleotide Array Sequence Analysis ; Whales/genetics
    Language English
    Publishing date 2022-03-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2022.105349
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  6. Article: COVID-19 detection from CT scans using a two-stage framework.

    Basu, Arpan / Sheikh, Khalid Hassan / Cuevas, Erik / Sarkar, Ram

    Expert systems with applications

    2022  Volume 193, Page(s) 116377

    Abstract: Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause serious ailments in infected individuals and complications may lead to death. X-rays and Computed Tomography ( ...

    Abstract Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause serious ailments in infected individuals and complications may lead to death. X-rays and Computed Tomography (CT) scans can be used for the diagnosis of the disease. In this context, various methods have been proposed for the detection of COVID-19 from radiological images. In this work, we propose an end-to-end framework consisting of deep feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images. For feature extraction, we utilize three deep learning based Convolutional Neural Networks (CNNs). For FS, we use a meta-heuristic optimization algorithm, Harmony Search (HS), combined with a local search method, Adaptive
    Language English
    Publishing date 2022-01-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2017237-0
    ISSN 0957-4174
    ISSN 0957-4174
    DOI 10.1016/j.eswa.2021.116377
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  7. Article ; Online: Artificial Bee Colony (ABC) algorithm and its use in digital image processing

    Erik Cuevas

    Inteligencia Artificial, Vol 18, Iss 55, Pp 50-

    2015  Volume 68

    Abstract: Classical methods often face great difficulties in solving image processing problems in images containing noise and distortions. Under such conditions, the use of bio-inspired optimization approaches has been extended. This paper explores the use of the ... ...

    Abstract Classical methods often face great difficulties in solving image processing problems in images containing noise and distortions. Under such conditions, the use of bio-inspired optimization approaches has been extended. This paper explores the use of the Artificial Bee Colony (ABC) algorithm for digital image processing seen as an optimization problem. ABC is a heuristic algorithm motivated by the biological behaviour of honey-bees which has been successfully employed to solve complex optimization problems. In this paper, image segmentation and circle detection tasks are considered as examples, both issues approached as optimization problems. In segmentation, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. On the other hand, the circle detector uses a combination of three edge points as parameters to construct candidate circles. A matching function determines is such candidate circles are actually present in the image. Experimental results show that the generated solutions are able to solve properly the considered problems.
    Keywords Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2015-06-01T00:00:00Z
    Publisher Asociación Española para la Inteligencia Artificial
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Special issue: Bio-inspired algorithms and Bio-systems.

    Cuevas, Guest Editors Erik / Oliva, Diego / Osuna, Valentín

    Mathematical biosciences and engineering : MBE

    2020  Volume 17, Issue 3, Page(s) 2400–2401

    MeSH term(s) Algorithms ; Biology
    Language English
    Publishing date 2020-03-29
    Publishing country United States
    Document type Editorial ; Introductory Journal Article
    ZDB-ID 2265126-3
    ISSN 1551-0018 ; 1551-0018
    ISSN (online) 1551-0018
    ISSN 1551-0018
    DOI 10.3934/mbe.2020129
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  9. Article: Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans.

    Bandyopadhyay, Rajarshi / Basu, Arpan / Cuevas, Erik / Sarkar, Ram

    Applied soft computing

    2021  Volume 111, Page(s) 107698

    Abstract: Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in infected individuals. The more severe cases may lead to death. Automated methods which can ...

    Abstract Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in infected individuals. The more severe cases may lead to death. Automated methods which can detect COVID-19 in radiological images can help in the screening of patients. In this work, a two-stage pipeline composed of feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images is proposed. For feature extraction, a state-of-the-art Convolutional Neural Network (CNN) model based on the DenseNet architecture is utilised. To eliminate the non-informative and redundant features, the meta-heuristic called Harris Hawks optimisation (HHO) algorithm combined with Simulated Annealing (SA) and Chaotic initialisation is employed. The proposed approach is evaluated on the SARS-COV-2 CT-Scan dataset which consists of 2482 CT-scans. Without the Chaotic initialisation and the SA, the method gives an accuracy of around 98.42% which further increases to 98.85% on the inclusion of the two and thus delivers better performance than many state-of-the-art methods and various meta-heuristic based FS algorithms. Also, comparison has been drawn with many hybrid variants of meta-heuristic algorithms. Although HHO falls behind a few of the hybrid variants, when Chaotic initialisation and SA are incorporated into it, the proposed algorithm performs better than any other algorithm with which comparison has been drawn. The proposed algorithm decreases the number of features selected by around 75% , which is better than most of the other algorithms.
    Language English
    Publishing date 2021-07-14
    Publishing country United States
    Document type Journal Article
    ISSN 1568-4946
    ISSN 1568-4946
    DOI 10.1016/j.asoc.2021.107698
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  10. Article ; Online: MTRRE-Net: A deep learning model for detection of breast cancer from histopathological images.

    Chattopadhyay, Soham / Dey, Arijit / Singh, Pawan Kumar / Oliva, Diego / Cuevas, Erik / Sarkar, Ram

    Computers in biology and medicine

    2022  Volume 150, Page(s) 106155

    Abstract: Histopathological image classification has become one of the most challenging tasks among researchers due to the fine-grained variability of the disease. However, the rapid development of deep learning-based models such as the Convolutional Neural ... ...

    Abstract Histopathological image classification has become one of the most challenging tasks among researchers due to the fine-grained variability of the disease. However, the rapid development of deep learning-based models such as the Convolutional Neural Network (CNN) has propelled much attentiveness to the classification of complex biomedical images. In this work, we propose a novel end-to-end deep learning model, named Multi-scale Dual Residual Recurrent Network (MTRRE-Net), for breast cancer classification from histopathological images. This model introduces a contrasting approach of dual residual block combined with the recurrent network to overcome the vanishing gradient problem even if the network is significantly deep. The proposed model has been evaluated on a publicly available standard dataset, namely BreaKHis, and achieved impressive accuracy in overcoming state-of-the-art models on all the images considered at various magnification levels.
    MeSH term(s) Humans ; Female ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/pathology ; Deep Learning ; Neural Networks, Computer ; Breast/pathology
    Language English
    Publishing date 2022-09-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2022.106155
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