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dc.contributor.authorMahmoud, Ahsanullah Y.
dc.contributor.authorNeagu, Daniel
dc.contributor.authorScrimieri, Daniele
dc.contributor.authorAbdullatif, Amr R.A.
dc.date.accessioned2022-12-13T16:50:13Z
dc.date.accessioned2023-01-30T11:13:45Z
dc.date.available2022-12-13T16:50:13Z
dc.date.available2023-01-30T11:13:45Z
dc.date.issued2022-11
dc.identifier.citationMahmoud AY, Neagu D, Scrimieri D et al (2022) Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective. 32nd Conference of Open Innovations Association (FRUCT). 9-11 Nov, Tampere, Finland.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19308
dc.descriptionYesen_US
dc.description.abstractImmunotherapy treatments can be essential sometimes and a waste of valuable resources in other cases, depending on the diagnosis results. Therefore, researchers in immunotherapy need to be updated on the current status of research by exploring: application domains e.g. warts, datasets e.g. immunotherapy, classifiers or algorithms e.g. kNN and software tools. The research objectives were: 1) to study the immunotherapy-related published literature from a supervised machine learning perspective. In addition, to reproduce immunotherapy classifiers reported in research papers. 2) To find gaps and challenges both in publications and practical work, which may be the basis for further research. Immunotherapy, diabetes, cryotherapy, exasens data and ”one unbalanced dataset” are explored. The results are compared with published literature. To address the found gaps in further research: novel experiments, unbalanced studies, focus on effectiveness and a new classifier algorithm are suggested.en_US
dc.language.isoenen_US
dc.publisherOpen Innovations Association (FRUCT)
dc.rights© 2022 Open Innovations Association (FRUCT). Reproduced in accordance with the publisher's self-archiving policy.
dc.subjectImmunotherapyen_US
dc.subjectClassificationen_US
dc.subjectMachine learningen_US
dc.subjectApplication domainsen_US
dc.subjectDatasetsen_US
dc.subjectAlgorithmsen_US
dc.subjectSoftware toolsen_US
dc.titleReview of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspectiveen_US
dc.status.refereedYesen_US
dc.date.Accepted2022-10-07
dc.typeConference paperen_US
dc.type.versionAccepted manuscripten_US
dc.identifier.doihttps://doi.org/10.23919/FRUCT56874.2022.9953853
dc.rights.licenseUnspecifieden_US
dc.date.updated2022-12-13T16:50:15Z
refterms.dateFOA2023-01-30T11:17:44Z
dc.openaccess.statusopenAccessen_US


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