Development of a Decision Support System for Selection of Reviewers to Evaluate Research and Development Projects

Serdar Koçak, Yusuf Tansel Ä°ã§, Mustafa Sert, Kumru Didem Atalay and Berna Dengiz
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Serdar Koçak: The Scientific and Technological Research Council of Turkey, 06100 Kavaklıdere, Ankara, Turkey
Yusuf Tansel Ä°ã§: ��Department of Industrial Engineering, Baskent University, 06790, Etimesgut, Ankara, Turkey
Mustafa Sert: ��Department of Computer Engineering, Baskent University, 06790, Etimesgut, Ankara, Turkey
Kumru Didem Atalay: ��Department of Industrial Engineering, Baskent University, 06790, Etimesgut, Ankara, Turkey
Berna Dengiz: ��Department of Industrial Engineering, Baskent University, 06790, Etimesgut, Ankara, Turkey

International Journal of Information Technology & Decision Making (IJITDM), 2023, vol. 22, issue 06, 1991-2020

Abstract: The evaluation of Research and Development (R&D) projects consists of many steps depending on the government funding agencies and the support program. It is observed that the reviewer evaluation reports have a crucial impact on the support decisions of the projects. In this study, a decision support system (DSS), namely R&D Reviewer, is developed to help the decision-makers with the assignment of the appropriate reviewer to R&D project proposals. It is aimed to create an artificial intelligence-based decision support system that enables the classification of Turkish R&D projects with natural language processing (NLP) methods. Furthermore, we examine the reviewer ranking process by using fuzzy multi-criteria decision-making methods. The data in the database is processed primarily to classify the R&D projects and the word embedding model NLP, “Word2Vec†. Also, we designed the Convolutional Neural Network (CNN) model to select the features by using the automatic feature learning approach. Moreover, we incorporate a new integrated hesitant fuzzy VIKOR and TOPSIS methodology into the developed DSS for the reviewer ranking process.

Keywords: Reviewer selection; hesitant fuzzy sets; natural language processing (NLP); convolutional neural network (CNN) (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219622022500961

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