International Journal of Neutrosophic Science

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https://doi.org/10.54216/IJNS

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Volume 20 , Issue 1 , PP: 174-183, 2023 | Cite this article as | XML | Html | PDF | Review Article

Metaheuristics and Neutrosophic Sets for COVID-19 Detection: A review study

M. A. El-Shorbagy 1 * , Hossam A. Nabwey 2 , Mustafa Inc 3 , Mostafa M. A. Khater 4

  • 1 Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia - (ma.hassan@psau.edu.sa)
  • 2 Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom 32511, Egypt - (eng_hossam21@yahoo.com)
  • 3 Science Faculty, Firat University, 23119, Elazig, Turkey - (minc@firat.edu.tr)
  • 4 School of Medical Informatics and Engineering, Xuzhou Medical University, 209 Tongshan Road, 221004, Xuzhou, Jiangsu Province, PR China - (mostafa.khater2024@yahoo.com)
  • Doi: https://doi.org/10.54216/IJNS.200114

    Received: December 21, 2022 Accepted: January 11, 2023
    Abstract

    The fast spread of COVID-19 has been a problem for several nations since February 2020. Computer-aided diagnostic technologies that are both effective and affordable are urgently needed to help ease the burden on healthcare systems. Researchers are delving further into the feasibility of using image analysis to detect COVID-19 in X-ray and CT-scan pictures of patients. In the past ten years, deep learning has surpassed every other method for classifying images. However, deep learning-based approaches' effectiveness is very sensitive to the design of the underlying deep neural network. In recent years, metaheuristics and neutrosophic sets have become more popular as a means of fine-tuning the structure of deep networks. Because of their adaptability, simplicity, and task dependence, metaheuristics have been extensively employed to tackle many difficult non-linear optimization problems. To correctly identify COVID-19 patients from their chest X-rays, the authors of this research made a review of a neurotrophic model and metaheuristics methods.

    Keywords :

    Metaheuristics , Neutrosophic Sets , COVID-19 , X-ray ,

    References

    [1]  Nivetha  Martin  ,  Florentin  Smarandache  ,  broumi  said,  COVID-19  Decision-Making  Model  using 

    Extended  Plithogenic  Hypersoft  Sets  with  Dual  Dominant  Attributes,  International  Journal  of 

    Neutrosophic Science, Vol. 13 , No.2 , (2021) : 75-86

    [2]  M. Kaur, V. Kumar, V. Yadav, D. Singh, N. Kumar, and N. N. Das, “Metaheuristic-based deep COVID-19 screening model from chest X-ray images,” Journal of Healthcare Engineering, vol. 2021, 2021.

    [3]  N. A. Samee et al., “Metaheuristic optimization through deep learning classification of COVID -19 in chest 

    X-ray images,” Computers, Materials and Continua, pp. 4193–4210, 2022.

    [4]  A. J. da S. Rodrigues and G. L. Lima, “A metaheuristic to support the distribution of COVID-19 vaccines,” 

    Production, vol. 31, 2021.

    [5]  S.  Chakraborty  and  K.  Mali,  “SuFMoFPA:  A  superpixel  and  meta-heuristic  based  fuzzy  image 

    segmentation approach to explicate COVID-19 radiological images,”  Expert Systems with Applications, 

    vol. 167, p. 114142, 2021.

    [6]  M. Canayaz, “MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristicbased feature selection on X-ray images,” Biomedical Signal Processing and Control, vol. 64, p. 102257, 

    2021.

    [7]  Marcelo Viteri Villa , Lester Wong  Vázquez , Roberto Zúñiga Viteri, Neutrsophic Health Analysis in 

    Times of COVID-19, International Journal of Neutrosophic Science, Vol. 18 , No. 3 , (2022) : 218 -226

    [8]  J.  De  Anda-Suárez,  V.  Calzada-Ledesma,  D.  A.  Gutiérrez-Hernández,  R.  Santiago-Montero,  L.  F. 

    Villanueva-Jiménez,  and  S.  Rodríguez-Miranda,  “A  novel  metaheuristic  framework  based  on  the 

    generalized  boltzmann  distribution  for  COVID-19  spread characterization,”  IEEE  Access,  vol.  10, pp. 

    7326–7340, 2022.

    [9]  L. Fenga and C. Del Castello, “COVID-19: Metaheuristic Optimization-Based Forecast Method on TimeDependent Bootstrapped Data,” Journal of Probability and Statistics, vol. 2021, 2020.

    [10]  J. Dey, A. Sarkar, B. Chowdhury, and S. Karforma, “Episode of COVID -19 telepsychiatry session key 

    origination upon swarm-based metaheuristic and neural perceptron blend,” SN Computer Science, vol. 2, 

    no. 6, pp. 1–16, 2021.

    [11]  V. Ganesan, P. Rajarajeswari, V. Govindaraj, K. B. Prakash, and J. Naren, “Post-COVID-19 Emerging 

    Challenges and Predictions on People, Process, and Product by Metaheuristic Deep Learning Algorithm,” 

    in Machine Intelligence and Soft Computing, Springer, 2021, pp. 275–287.

    [12]  A. Dey et al., “MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using 

    deep features,” Scientific reports, vol. 11, no. 1, pp. 1–15, 2021.

    [13]  M.  Zivkovic,  A.  Petrovic,  N.  Bacanin,  S.  Milosevic,  V.  Veljic,  and  A.  Vesic,  “The  covid-19  images 

    classification  by  mobilenetv3  and  enhanced  sine  cosine  metaheuristics,”  in  Mobile  Computing  and 

    Sustainable Informatics, Springer, 2022, pp. 937–950.

    [14]  Z. Fei, Y. Ryeznik, A. Sverdlov, C. W. Tan, and W. K. Wong, “An overview of healthcare data analytics 

    with applications to the COVID-19 pandemic,” IEEE Transactions on Big Data, 2021.

    [15]  C. Li, P. Han, M. Zhou, and M. Gu, “Design of multimodal hub -and-spoke transportation network for 

    emergency relief under COVID-19 pandemic: A meta-heuristic approach,”  Applied Soft Computing, p. 

    109925, 2022.

    [16]  D. Wolfinger, M. Gansterer, K. F. Doerner, and N. Popper, “A large neighbourhood search metaheuristic 

    for the contagious disease testing problem,” European journal of operational research, vol. 304, no. 1, pp. 

    169–182, 2023.

    [17]  J.  Dey,  A.  Sarkar,  S.  Karforma,  and  B.  Chowdhury,  “Metaheuristic  secured  transmission  in  Telecare 

    Medical Information System (TMIS) in the face of post-COVID-19,” Journal of Ambient Intelligence and 

    Humanized Computing, pp. 1–22, 2021.

    [18]  R. A. K. Sherwani, H. Shakeel, M. Saleem, W. B. Awan, M. Aslam, and M. Farooq, “A new neutrosophic 

    sign test: An application to COVID-19 data,” PloS one, vol. 16, no. 8, p. e0255671, 2021.

    [19]  N.  E.  M.  Khalifa,  F.  Smarandache,  G.  Manogaran,  and  M.  Loey,  “A  study  of  the  neutrosophic  set 

    significance on deep transfer learning models: An experimental case on a limited covid -19 chest x-ray 

    dataset,” Cognitive Computation, pp. 1–10, 2021.

    [20]  Priyanka  Majumder  ,  Florentin  Smarandache,  Analyzing  the  Sustainability  of  Industry  Affected  in 

    COVID-19  Pandemic  Scenario  Using  Cosine  Similarity  Measure  under  SVPNS  and  PNN  Model, 

    International Journal of Neutrosophic Science, Vol. 19 , No. 3 , (2022) : 16 -28

    [21]  K.  Karuppiah,  B.  Sankaranarayanan,  S.  M.  Ali,  and  S.  K.  Paul,  “Key  challenges  to  sustainable 

    humanitarian supply chains: lessons from the covid-19 pandemic,” Sustainability, vol. 13, no. 11, p. 5850, 

    2021.

    [22]  R. Das, A. Mukherjee, and B. C. Tripathy, “Application of neutrosophic similarity measures in Covid -19,” 

    Annals of Data Science, vol. 9, no. 1, pp. 55–70, 2022.

    [23]  E. Çakır, M. A. Taş, and Z.  Ulukan, “Neutrosophic Fuzzy Weighted Saving Heuristic for COVID-19 

    Vaccination,” in 2021 Systems and Information Engineering Design Symposium (SIEDS), 2021, pp. 1–4.

    [24]  I. M. Hezam, M. K. Nayeem, A. Foul, and A. F. Alrasheedi, “COVID-19 Vaccine: A neutrosophic MCDM 

    approach for determining the priority groups,” Results in physics, vol. 20, p. 103654, 2021.

    [25]  W. M. Shaban, A. H. Rabie, A. I. Saleh, and M. A. Abo-Elsoud, “A new COVID-19 Patients Detection 

    Strategy  (CPDS)  based  on  hybrid  feature  selection  and  enhanced  KNN  classifier,”  Knowledge-Based 

    Systems, vol. 205, p. 106270, 2020.

    [26]  H. M. Afify, A. Darwish, K. K. Mohammed, and A. E. Hassanien, “An Automated CAD System of CT

    Chest Images for COVID-19 Based on Genetic Algorithm and K-Nearest Neighbor Classifier.,” Ingénierie 

    des Systèmes d Inf., vol. 25, no. 5, pp. 589–594, 2020.

    [27]  T. Akram et al., “A novel framework for rapid diagnosis of COVID -19 on computed tomography scans,” 

    Pattern analysis and applications, vol. 24, no. 3, pp. 951–964, 2021.

    [28]  M. A. Albadr, S. Tiun, M. Ayob, and F. Al-Dhief, “Genetic algorithm based on natural selection theory 

    for optimization problems,” Symmetry, vol. 12, no. 11, p. 1758, 2020.

    [29]  M. Abdel-Basset, L. Abdle-Fatah, and A. K. Sangaiah, “An improved Lévy based whale optimization 

    algorithm for bandwidth-efficient virtual machine placement in cloud computing environment,”  Cluster 

    Computing, vol. 22, no. 4, pp. 8319–8334, 2019.

    [30]  Z. Yan, S. Wang, B. Liu, and X. Li, “Application of whale optimization algorithm in optimal allocation of 

    water resources,” in E3S Web of Conferences, 2018, vol. 53, p. 4019.

    [31]  E.-S. M. El-Kenawy, A. Ibrahim, S. Mirjalili, M. M. Eid, and S. E. Hussein, “Novel feature selection and 

    voting classifier algorithms for COVID-19 classification in CT images,” IEEE access, vol. 8, pp. 179317–

    179335, 2020.

    [32]  T. Goel, R. Murugan, S.  Mirjalili, and D. K. Chakrabartty, “Automatic screening of covid-19 using an 

    optimized generative adversarial network,” Cognitive computation, pp. 1–16, 2021.

    [33]  M. Abdel-Basset, V. Chang, and R. Mohamed, “HSMA_WOA: A hybrid novel Slime mould algorithm 

    with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images,” 

    Applied soft computing, vol. 95, p. 106642, 2020.

    [34]  X. Li, Z. Zhang, and C. Huang, “An EPC forecasting method for stock index based on integrating empirical 

    mode decomposition, SVM and cuckoo search algorithm,” Journal of Systems Science and Information, 

    vol. 2, no. 6, pp. 481–504, 2014.

    [35]  X.-S. Yang and M. Karamanoglu, “Nature-inspired computation and swarm intelligence: a state-of-the-art 

    overview,” Nature-Inspired Computation and Swarm Intelligence, pp. 3–18, 2020.

    [36]  D. Yousri, M. Abd Elaziz, L. Abualigah, D. Oliva, M. A. A. Al-Qaness, and A. A. Ewees, “COVID-19 Xray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed 

    distributions,” Applied Soft Computing, vol. 101, p. 107052, 2021.

    [37]  S.  C.  Satapathy,  D.  J.  Hemanth,  S.  Kadry,  G.  Manogaran,  N.  M.  S.  Hannon,  and  V.  Rajinikanth, 

    “Segmentation and evaluation of COVID-19 lesion from CT scan slices-a study with Kapur/Otsu function 

    and Cuckoo Search Algorithm,” 2020.

    [38]  M. Riaz, M. Bashir, and I. Younas, “Metaheuristics based COVID-19 detection using medical images: A 

    review,” Computers in Biology and Medicine, p. 105344, 2022.

    [39]  S. H. Basha, A. M. Anter, A. E. Hassanien, and A. Abdalla, “Hybrid intelligent model for classifying chest 

    X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic,” Soft Computing, pp. 

    1–16, 2021

    Cite This Article As :
    A., M.. , Hossam, . , Inc, Mustafa. , M., Mostafa. Metaheuristics and Neutrosophic Sets for COVID-19 Detection: A review study. International Journal of Neutrosophic Science, vol. , no. , 2023, pp. 174-183. DOI: https://doi.org/10.54216/IJNS.200114
    A., M. Hossam, . Inc, M. M., M. (2023). Metaheuristics and Neutrosophic Sets for COVID-19 Detection: A review study. International Journal of Neutrosophic Science, (), 174-183. DOI: https://doi.org/10.54216/IJNS.200114
    A., M.. Hossam, . Inc, Mustafa. M., Mostafa. Metaheuristics and Neutrosophic Sets for COVID-19 Detection: A review study. International Journal of Neutrosophic Science , no. (2023): 174-183. DOI: https://doi.org/10.54216/IJNS.200114
    A., M. , Hossam, . , Inc, M. , M., M. (2023) . Metaheuristics and Neutrosophic Sets for COVID-19 Detection: A review study. International Journal of Neutrosophic Science , () , 174-183 . DOI: https://doi.org/10.54216/IJNS.200114
    A. M. , Hossam . , Inc M. , M. M. [2023]. Metaheuristics and Neutrosophic Sets for COVID-19 Detection: A review study. International Journal of Neutrosophic Science. (): 174-183. DOI: https://doi.org/10.54216/IJNS.200114
    A., M. Hossam, . Inc, M. M., M. "Metaheuristics and Neutrosophic Sets for COVID-19 Detection: A review study," International Journal of Neutrosophic Science, vol. , no. , pp. 174-183, 2023. DOI: https://doi.org/10.54216/IJNS.200114