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The evaluation of journals based on their influence is of interest fornumerous reasons. Various methods of computing a score have been proposed formeasuring the scientific influence of scholarly journals. Typically thecomputation of any of these scores involves compiling the citation informationpertaining to the journal under consideration. This involves significantoverhead since the article citation information of not only the journal underconsideration but also that of other journals for the recent few years need tobe stored. Our work is motivated by the idea of developing a computationallylightweight approach that does not require any data storage, yet yields a scorewhich is useful for measuring the importance of journals. In this paper, aregression analysis based method is proposed to calculate Journal InfluenceScore. Proposed model is validated using historical data from the SCImagoportal. The results show that the error is small between rankings obtainedusing the proposed method and the SCImago Journal Rank, thus proving that theproposed approach is a feasible and effective method of calculating scientificimpact of journals.

DSRS: Estimation and Forecasting of Journal Influence in the Science and Technology Domain via a Lightweight Quantitative Approach