Çankaya GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.
 

Computational algorithms for the analysis of cancer virotherapy model

dc.contributor.authorRaza, Ali
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorRafiq, Muhammad
dc.contributor.authorAbbas, Syed Zaheer
dc.contributor.authorSiddique, Abubakar
dc.contributor.authorJaved, Umer
dc.contributor.authorNaz, Mehvish
dc.contributor.authorFatima, Arooj
dc.contributor.authorMunawar, Tayyba
dc.contributor.authorBatool, Hira
dc.contributor.authorNazir, Zaighum
dc.contributor.authorID56389tr_TR
dc.date.accessioned2024-02-29T12:04:41Z
dc.date.available2024-02-29T12:04:41Z
dc.date.issued2022
dc.departmentÇankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractCancer is a common term for many diseases that can affect any part of the body. In 2020, ten million people will die due to cancer. A worldwide leading cause of death is cancer by theWorld Health Organization (WHO) report. Interaction of cancer cells, viral therapy, and immune response are identified in this model. Mathematical and computational modeling is an effective tool to predict the dynamics of cancer virotherapy. The cell population is categorized into three parts like uninfected cells (x), infected cells (y), virus-free cells (v), and immune cells (z). The modeling of cancerlike diseases is based on the law of mass action (the rate of change of reacting substances is directly proportional to the product of interacting substances). Positivity, boundedness, equilibria, threshold analysis, are part of deterministic modeling. Later on, a numerical analysis is designed by using the standard and non-standard finite difference methods. The non-standard finite difference method is developed to study the long-term behavior of the cancer model. For its efficiency, a comparison of the methods is investigated.en_US
dc.identifier.citationRaza, Ali;...et.al. (2022). "Computational algorithms for the analysis of cancer virotherapy model", Computers, Materials and Continua, Vol.71, No.2, pp.3621-3634.en_US
dc.identifier.doi10.32604/cmc.2022.023286
dc.identifier.endpage3634en_US
dc.identifier.issn15462218
dc.identifier.issue2en_US
dc.identifier.startpage3621en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/7396
dc.identifier.volume71en_US
dc.language.isoenen_US
dc.relation.ispartofComputers, Materials and Continuaen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlgorithmsen_US
dc.subjectCancer Diseaseen_US
dc.subjectEpidemic Modelen_US
dc.subjectStability Analysisen_US
dc.titleComputational algorithms for the analysis of cancer virotherapy modeltr_TR
dc.titleComputational Algorithms for the Analysis of Cancer Virotherapy Modelen_US
dc.typeArticleen_US
dspace.entity.typePublication

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Article.pdf
Size:
1.53 MB
Format:
Adobe Portable Document Format
Description:
Yayıncı Sürümü

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: