Shahadat Uddin

TL;DR: It is found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies), however, the Random Forest algorithm showed superior accuracy comparatively.

TL;DR: This paper explores a co-authorship network of a relatively young and emerging research discipline to understand its trend of evolution pattern and proximity of efficiency and applies approaches to analyze longitudinal network data.

TL;DR: A model was developed that employed supervised machine learning algorithms to identify the presentation features predicting COVID-19 disease diagnoses with high accuracy and found that the XGBoost algorithm performed with the highest accuracy (>85%) to predict and select features that correctly indicate CO VID-19 status for all age groups.

TL;DR: This study examines how citation count of a scientific publication is influenced by different centrality measures of its co-author(s) in a co-authorship network and reveals that degree centrality and betweenness centrality values of authors in aCo-Authorship network are positively correlated with the strength of their scientific collaborations.

TL;DR: This study presents the evolutionary dynamics of multi level (i.e., individual, institutional and national) collaboration networks for exploring the emergence of collaborations in the research field of “steel structures” and finds that the average distance between countries about two and institutes five and for authors eight meaning that only about eight steps are necessary to get from one randomly chosen author to another.