A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field.
One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a...
| Publicado en: | BioMed Research International pp. 1 - 13 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/20/2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158084105&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158084105 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/20/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158084105 158084105 158084105 10.1155/2022/2239152 158084105 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field. aug: au: Alohali, Yousef A. Fayed, Mahmoud S. Mesallam, Tamer Abdelsamad, Yassin Almuhawas, Fida Hagr, Abdulrahman affil: College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia sug: subj: Machine Learning Saudi Arabia Prediction Models Citation Analysis Serial Publications Research, Medical Specialties, Medical Human Saudi Arabia Natural Language Processing Bibliometrics Algorithms Descriptive Statistics Linear Regression Decision Trees Random Forest Neural Networks (Computer) ab: One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|