BIBLIOMETRIC ANALYSIS OF PUBLICATIONS RELATED TO ARTIFICIAL INTELLIGENCE AND ITS COMPONENTS IN THE COVID-19 PERIOD

Yıl 2022, Cilt: 9 Sayı: 3, 220 - 233, 01.09.2022

Tuğçe Karayel , Mehmet Nurullah Kurutkan

https://doi.org/10.52880/sagakaderg.1070774

Öz

Purpose: The main purpose of this study is to conduct a bibliometric analysis of publications in the field of Covid-19 and artificial intelligence. The performance of the field, its conceptual and social structure, the thematic development map and the identification of its main clusters serve this purpose.
Method: In this article, R-based Bibliometrix, VOSviwer, SciMAT and Citespace software were used. Web of Science articles dec dec 2020-2021 have been downloaded as raw data from the Core collection with the search staretit. In total, 1367 articles were studied. Conceptual and social structure analyses were carried out from information structures with performance analyses. The process has been completed with the analysis of engine themes and main clusters.
Finding: From the point of view of conceptual structure analyses, it was determined that the studies were analyzed under the headings classification, diagnosis and treatment. According to the results of the social structure, the USA, China, India, Italy and the UK are both the most broadcasting countries and the countries that are most open to dec-country cooperation. According to thematic diagram analysis, themes based on artificial intelligence tools and algorithms used in “Transfer- Learning and Support Vector Machines”, covid 19 disease diagnosis, social media, mental health and covid process have come to the fore.
Result: The results of the bibliometric analysis provided information about the quality of published studies on COVID-19 and artificial intelligence, as well as research areas. In particular, artificial intelligence applications based on “Transfer- Learning” and “Support Vector Machines”, forecasting and social media data have the potential to become popular research topics.

Anahtar Kelimeler

Covid-19, Artificial İntelligence, Machine Learning, Artificial Neural Networks Deep Learning

COVİD 19 SÜRECİNDE YAPAY ZEKÂ VE BİLEŞENLERİ İLE İLGİLİ YAYINLARIN BİBLİYOMETRİK ANALİZİ

Öz

Amaç: Bu çalışmanın temel amacı, Covid-19 ve yapay zekâ alanındaki yayınların bibliyometrik analizini yapmaktır. Alanın performansı, kavramsal ve sosyal yapısı, tematik gelişim haritası ve ana kümelerinin tespiti bu amaç altında ortaya çıkarılmıştır.
Yöntem: Bu çalışmada, R tabanlı Bibliometrix, VOSviwer, SciMAT ve Citespace yazılımları kullanılmıştır. Arama stratejisi ile 2020-2021 yılları arasındaki Web of Science makaleleri Core koleksiyonundan ham veri olarak indirilmiştir. Toplamda 1367 makale incelenmiştir. Performans analizleri ile bilgi yapılarından kavramsal ve sosyal yapı analizleri gerçekleştirilmiştir. Motor temalar ile ana kümeler analizi ile süreç tamamlanmıştır.
Bulgu: Kavramsal yapı analizleri açısından bakıldığında çalışmaların sınıflandırma, teşhis ve tedavi başlıkları altında analiz edildiği tespit edildi. Sosyal yapı sonuçlarına göre ise ABD, Çin, Hindistan, İtalya ve İngiltere hem en çok yayın yapan ülkelerdir hem de en çok ülkeler arası iş birliğine açık olan ülkelerdir. Tematik diyagram analizlerine göre “Transfer- Learning ve Support Vector Machines”, covid-19 hastalık teşhisi, sosyal medya, zihin sağlığı ve covid sürecinde kullanılan yapay zekâ araç ve algoritmalarına dayalı temalar ön plana çıkmıştır.
Sonuç: Bibliyometrik analiz sonuçları, COVID-19 ve yapay zekâ ile ilgili yayınlanmış çalışmaların kalitesi ve araştırma alanları hakkında bilgi verdi. Özellikle “Transfer- Learning” ile “Support Vector Machines”, forecasting ve sosyal medya verilerine dayalı yapay zekâ uygulamaları popüler araştırma konuları olma potansiyeli taşımaktadır.

Anahtar Kelimeler

Covid-19, Yapay Zekâ, Makine Öğrenmesi, Yapay Sinir Ağları, Derin Öğrenme

Kaynakça

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