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The immense growth in online research publications has attracted the research community to extract valuable information from scientific resources by exploring online digital libraries and publishers' websites. The metadata stored in a machine comprehendible form can facilitate a precise search to enlist most related articles by applying semantic queries to the document's metadata and the structural elements. The online search engines and digital libraries offer only keyword-based search on full-body text, which creates excessive results. The research community in recent years has adopted different approaches to extract structural information from research documents. We have distributed the content of an article into two logical layouts and metadata levels. This strategy has given our technique an advantage over the state-of-the-art (SOTA) extracting metadata with diversified publication styles. The experimental results have revealed that the proposed approach has shown a significant gain in performance of 20.26% to 27.14%. [ABSTRACT FROM AUTHOR]

The renowned international scientific societies nominate researchers for awards based on qualitative judgments every year. Qualitative judgment uses subjective assessments based on information that is not quantifiable. The way of assessing the quality of the work has not been established or disclosed, nor do we have any qualitative evaluation criteria. We can assess the quality of the researcher's work by mapping the quantitative parameters to qualitative judgments. To date, the scientific community has presented more than 50 research assessment quantitative parameters, including publication count, citation count, h-index, and its variants. The contemporary state-of-the-art in authors ranking does not determine the best parameter that effectively maps on experts' qualitative evaluation. Moreover, these parameters have been evaluated by using same scenarios. In such scenarios, the value and effect of each parameter over the others are complicated to ascertain. Therefore, they must be assessed in inequitable scenarios. The purpose of this research is to identify the significant parameters that map on qualitative judgments of international scientific societies in Civil Engineering (CE) for award nominations. We will identify the rank of author assessment parameters, which includes published papers, citations, No of years since 1st publication, citations in h-core, authors/paper, citations/paper, citations/year, h-index, g-index, hg-index, A-index, R-index, e-index, and f-index. We have evaluated these parameters on the dataset from the discipline of Civil Engineering (CE). The data set contains 250 non-award winners and 250 award winners from prestigious scientific societies of CE. The h-index and its variants have been ranked based on their effectiveness for awardees using Logistic Regression. The award-winning researchers have less number of average authors/paper than the non-awardees. The authors/paper has achieved the highest effectiveness of 67% for awardees. Furthermore, we have also analyzed the ratio of awardees in the ranked list of 50, 100, and 150 researchers by author assessment parameters. The authors/papers have outperformed all other indices by elevating 62% and 66% of the award recipients in its ranked list of 100 and 150 researchers. In the ranked list of 50 researchers, publications elevate 54% awardees, and Authors/papers achieved the second-highest elevation score of awardees of 50%. [ABSTRACT FROM AUTHOR]

Citations play a pivotal role in indicating various aspects of scientific literature. Quantitative citation analysis approaches have been used over the decades to measure the impact factor of journals, to rank researchers or institutions, to discover evolving research topics etc. Researchers doubted the pure quantitative citation analysis approaches and argued that all citations are not equally important; citation reasons must be considered while counting. In the recent past, researchers have focused on identifying important citation reasons by classifying them into important and non-important classes rather than individually classifying each reason. Most of contemporary citation classification techniques either rely on full content of articles, or they are dominated by content based features. However, most of the time content is not freely available as various journal publishers do not provide open access to articles. This paper presents a binary citation classification scheme, which is dominated by metadata based parameters. The study demonstrates the significance of metadata and content based parameters in varying scenarios. The experiments are performed on two annotated data sets, which are evaluated by employing SVM, KLR, Random Forest machine learning classifiers. The results are compared with the contemporary study that has performed similar classification employing rich list of content-based features. The results of comparisons revealed that the proposed model has attained improved value of precision (i.e., 0.68) just by relying on freely available metadata. We claim that the proposed approach can serve as the best alternative in the scenarios wherein content in unavailable. [ABSTRACT FROM AUTHOR]

The scientific community has proposed diversified set of parameters to rank researchers, including publications, citations, h-index, different variants and extensions of h-index. However, there is a debate in the scientific Community which index ranks authors in a better way. Current state-of-the-art depicts that these indices are evaluated on imaginary case scenarios and small datasets. Furthermore, these indices are evaluated on different datasets, making it difficult to grasp the contribution and importance of each index over the others. To analyze the individual behavior of each index, these indices should comprehensively be evaluated on some extensive data set. This study emphasizes on the scrutiny of h-index, some of its variants and extensions to rank authors. These indices are evaluated using a comprehensive data set of Civil Engineering field. For the evaluation of results obtained from these indices, first correlation was calculated among indices. There exists weak correlation between various indices, which demonstrates that the author’s rankings acquired from these indices are not identical. Secondly, occurrences of awardees are checked in all ranked lists. The prestigious award winners of four Civil Engineering societies are considered as a benchmark. In top 10% of ranked list, maximum 47% of the awardees were brought by Wu-index. Overall, none of the index succeeded in bringing 100% awardees to the top rankings. Highest number of awardees on top of all ranked lists are found to be from ACI (American Concrete Institute), which shows ACI might be dependent on these indices for its criterion to honor awards. [ABSTRACT FROM AUTHOR]