ABSTRACT Accurately predicting scientific impact and ranking researchers remains a central yet complex challenge in research evaluation. Traditional metrics such as citation counts, publication totals, hybrid measures, and h‐type indices each capture limited aspects of scholarly influence, making it difficult to establish a universally accepted standard. This study proposes a novel composite index designed to enhance the robustness and fairness of researcher ranking. A dataset of 1060 neuroscience researchers comprising both awardees and non‐awardees was analysed to evaluate the ability of existing indices to identify top‐performing scientists. The five indices most strongly associated with awardees were selected and further refined using deep learning models to determine their distinctiveness and combined effectiveness. Eleven statistical models were then tested to integrate the most independent pair of indices. The H2 upper and K indices exhibited the highest disjointness value (0.97), and their harmonic mean produced the most balanced and consistent performance with an average impact score of 0.76. The resulting composite index outperformed traditional metrics, offering a more comprehensive and unbiased measure of researcher impact. This approach demonstrates a scalable and data‐driven framework for improving the accuracy of scientific evaluation and ranking systems.

Summary Social networking websites such as Twitter provide a platform where users share their opinions about different news, events, and products. A recent research has identified that 81% of users search online first before purchasing products. Reviews are written in natural language and needs sentiment analysis for opinion extraction. Various approaches have been proposed to perform sentiment classification based on polarity bearing words in reviews such as noun, verb, adverb, and an adjective. Prior researchers have also identified the role of an adverb as a feature. However, impact analysis of adverb forms, are not yet studied and remains an open research area. This study focused on the following tasks: (1) impact of different forms of adverbs that are not studied for sentiment classification; (2) analysis of possible combinations of eight forms that are 255. The different forms are Adverb (RA), Degree Adverbs (RG), Degree Comparative Adverbs (RGR), General Adverbs (RR), General Comparative Adverbs (RRR), Locative Adverbs (RL), Prep. Adverb (RP), and Adverbs of time (RT); (3) comparison with benchmark dataset. Dataset of 5513 tweets is used to evaluate the idea. The findings of this work show that RRR and RR are important polarities bearing words for neutral opinions, RL for positive, and RP for negative opinions.

Sequence similarity is a commonly used measure to compare proteins. With the increasing use of ontologies, semantic (function) similarity is getting importance. The correlation between these measures has been applied in the evaluation of new semantic similarity methods, and in protein function prediction. In this research, we investigate the relationship between the two similarity methods. The results suggest absence of a strong correlation between sequence and semantic similarities. There is a large number of proteins with low sequence similarity and high semantic similarity. We observe that Pearson's correlation coefficient is not sufficient to explain the nature of this relationship. Interestingly, the term semantic similarity values above 0 and below 1 do not seem to play a role in improving the correlation. That is, the correlation coefficient depends only on the number of common GO terms in proteins under comparison, and the semantic similarity measurement method does not influence it. Semantic similarity and sequence similarity have a distinct behavior. These findings are of significant effect for future works on protein comparison, and will help understand the semantic similarity between proteins in a better way.

Recommending relevant content to the learner is a challenging task for any e-Learning management system. This paper has critically reviewed the literature and identified the strategies being used to recommend relevant content to learners.

Classification is an important task in data mining. Classification is about organizing data into relevant nodes in taxonomy. In scientific domain, classification of documents to predefined category (ies) is an important research problem and supports number of tasks such as: information retrieval, finding experts, recommender systems etc. In Computer Science, the ACM categorization system is commonly used for organizing research papers in the topical hierarchy defined by the ACM. Accurately assigning a research paper to a predefined category (ACM topic) is a difficult task especially when the paper belongs to multiple topics. In the past, different approaches have been applied to find the actual topics of a paper such as content based analysis, metadata analysis, and semantic analysis etc. However, in this paper, we exploit the reference section of a research paper to discover topics of the paper. It is assumed that in most of the cases, papers belonging to the same or similar category are cited by an author. We have evaluated our technique for a dataset of Journal of Universal Computer Science (J.UCS). Our system collected 1460 documents from J. UCS along with their predefined topics assigned by authors and verified by journal's administration. The system used 1010 documents for training dataset. The system extracted references from training dataset and grouped them in a Topic Reference pair such as TR {Topic, Reference}. Subsequently, the system was tested on the remaining 450 documents. The references of the focused paper are parsed and compared in the pair TR {Topic, Reference}. The system collects corresponding list of topics matched with the references in the said pair. Subsequently multiple weights are assigned during the process of this matching. The system was able to predict the first node in the ACM topic (topic A to K) with 70% accuracy.