{
    "article_id": 1052,
    "public_id": "160ac6b4-329e-40d4-8c32-35fd116b8e83",
    "internal_identifier": null,
    "source_identifier": "42:57",
    "status": "published",
    "type": "research_article",
    "title": "Standart ve Geliştirilen Klonal Seçim Algoritması Kullanılarak Aritmilerin Sınıflandırılması",
    "title_secondary": null,
    "abstract": "Bu çalışmada Yapay Bağışıklık Sistemine(YBS) dayalı Klonal Seçim Algoritması'nın (KSA) Matlab ortamında yazılımı gerçekleştirilmiştir. Ayrıca KSA'na koşul tabanlı bir yapı eklenerek geliştirilmiştir. Geliştirilen Koşul-Tabanlı Klonal Seçim Algoritması (KT-KSA) ve standart KSA aritmi teşhisinde kullanılmıştır. Aritmiler APC, LBBB, RBBB, SVTA ve normal vuru olarak Physionet veri tabanından elde edilmiştir. Öznitelik çıkarmak için Pan-Topmkins algoritması kullanılmış ve vurulara ait şekilsel öznitelikleri olan QRS alanı, QRS genişliği, QRS genliği ve RR aralıkları hesaplanmıştır. Öznitelikler sisteme giriş verisi olarak verilip KSA ve KT-KSA ile eğitim işlemi gerçekleştirilmiş ve sınıflandırma kNN kullanılarak yapılmıştır. Sonuç olarak KT-KSA’nın %90,98 hassasiyet ile KSA’nın ise %80,84 hassasiyet ile aritmileri sınıflandırdığı tespit edilmiştir. Koşul tabanlı sistemin KSA’yı güçlendirdiği ortaya konulmuştur.",
    "language": "tr",
    "english_title": "Classification of Arrhythmias Using Standard and Improved Clonal Selection Algorithms",
    "english_abstract": "In this study, the software of The Clone selection algorithm(KSA) based on artificial immune system (YBS) was performed in MATLAB environment. It was also developed by adding a conditional structure to KSA. Developed Condition-Based Clonal Selection Algorithm (KT-KSA) and standard KSA were used for arrhythmia diagnosis. Arrhythmias were obtained from APC, LBBB, RBBB, SVTA and the Physionet database as a normal stroke. The Pan-Topmkins algorithm was used to extract the attribute and the QRS domain, QRS width, QRS amplitude and RR intervals with the formal attributes of the scales were calculated. Attributes were given as input data to the system and training was carried out with KSA and KT-KSA and the classification was made using kNN. As a result, when the sensitivity results were examined, it was found out that KT-KSA was the highest with 90,98% and KSA with 80,84%.",
    "doi": null,
    "doi_url": null,
    "resource_url": "https://www.set-science.com/a/160ac6b4-329e-40d4-8c32-35fd116b8e83",
    "pdf_url": "https://www.set-science.com/a/160ac6b4-329e-40d4-8c32-35fd116b8e83/pdf",
    "og_image": "https://www.set-science.com/assets/img/og-card-v2.png",
    "authors": [
        {
            "public_id": "6ea52018-05d5-4af4-ac71-203e61865115",
            "given": "Ziynet ",
            "family": "Pamuk  ",
            "full": "Ziynet  Pamuk  ",
            "literal": null,
            "orcid": null,
            "orcid_url": null,
            "email": "ziynet.pamuk@beun.edu.tr",
            "corresponding": true,
            "sequence": "first",
            "affiliations": [
                {
                    "name": "Zonguldak Bülent Ecevit Üniversitesi ",
                    "department": null,
                    "country": null,
                    "country_code": null,
                    "ror": null,
                    "ror_url": null
                }
            ]
        }
    ],
    "keywords": [],
    "translations": [
        {
            "language": "en",
            "title": "Classification of Arrhythmias Using Standard and Improved Clonal Selection Algorithms",
            "abstract": "In this study, the software of The Clone selection algorithm(KSA) based on artificial immune system (YBS) was performed in MATLAB environment. It was also developed by adding a conditional structure to KSA. Developed Condition-Based Clonal Selection Algorithm (KT-KSA) and standard KSA were used for arrhythmia diagnosis. Arrhythmias were obtained from APC, LBBB, RBBB, SVTA and the Physionet database as a normal stroke. The Pan-Topmkins algorithm was used to extract the attribute and the QRS domain, QRS width, QRS amplitude and RR intervals with the formal attributes of the scales were calculated. Attributes were given as input data to the system and training was carried out with KSA and KT-KSA and the classification was made using kNN. As a result, when the sensitivity results were examined, it was found out that KT-KSA was the highest with 90,98% and KSA with 80,84%.",
            "keywords": [
                "Aritmi, Yapay Bağışıklık Sistemi, Klonal Seçim Algoritması"
            ]
        }
    ],
    "subjects": [],
    "subject_names": [],
    "published_date": "2019-06-01",
    "published_year": "2019",
    "received_date": null,
    "accepted_date": null,
    "volume": "4",
    "issue": null,
    "page_first": "301",
    "page_last": "305",
    "article_number": null,
    "conference": {
        "short_name": "ISAS2019-ENS",
        "full_title": "3rd International Symposium on Innovative Approaches in Scientific Studies (Engineering and Natural Sciences)",
        "name": "3rd International Symposium on Innovative Approaches in Scientific Studies (Engineering and Natural Sciences)",
        "acronym": "ISAS2019-ENS",
        "location": "Ankara, Türkiye",
        "start_date": "2019-04-19",
        "end_date": null,
        "public_id": "ed3b36e9-38cf-4aeb-a135-833c969cc89f"
    },
    "proceedings": {
        "title": "3rd International Symposium on Innovative Approaches in Scientific Studies (Engineering and Natural Sciences)",
        "series_title": "Setsci conference indexing system",
        "publisher": "SET Technology",
        "isbn": null,
        "eisbn": null,
        "issn": "2618-6446",
        "eissn": null,
        "doi": null,
        "doi_prefix": null,
        "slug": "setsci-volume-4-2019-legacy-42",
        "public_id": "24d215ef-a545-4351-9725-6e9bdb167b3c",
        "url": "https://www.set-science.com/p/setsci-volume-4-2019-legacy-42"
    },
    "license": {
        "name": "CC BY 4.0",
        "url": "https://creativecommons.org/licenses/by/4.0/",
        "code": "CC-BY-4.0",
        "open_access": true
    },
    "copyright": {
        "holder": "SET Technology",
        "year": "2019"
    },
    "funding_statement": null,
    "funders": [],
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    "history": {
        "published": "2019-06-01"
    }
}