Published in Journal of Big Data
Text clustering is the task of grouping a set of texts so that text in the same group will be more similar than those from a different group. The process of grouping text manually requires a significant amount of time and labor. Therefore, automation utilizing machine learning is necessary. One of the most frequently used method to represent textua...
Published in Journal of Big Data
With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) and large-scale distributed resources on computer clusters. Current DPNN approaches implement the network parameter update...
Published in Journal of Big Data
The study aimed to present an integrated model for evaluation of big data (BD) challenges and analytical methods in recommender systems (RSs). The proposed model used fuzzy multi-criteria decision making (MCDM) which is a human judgment-based method for weighting of RSs’ properties. Human judgment is associated with uncertainty and gray information...
Published in Journal of Big Data
Performance is a critical concern when reading and writing data from billions of records stored in a Big Data warehouse. We introduce two scopes for query performance improvement. One is to improve the performance of lookup queries after data deletion in Big Data systems that use Eventual Consistency. We propose a scheme to improve lookup performan...
Published in Journal of Big Data
Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool...
Published in Journal of Big Data
BackgroundThis paper explores machine learning algorithms and approaches for predicting alum income to obtain insights on the strongest predictors and a ‘high’ earners’ class.MethodsIt examines the alum sample data obtained from a survey from a multicampus Mexican private university. Survey results include 17,898 and 12,275 observations before and ...
Published in Journal of Big Data
Scientometrics is the field of study and evaluation of scientific measures such as the impact of research papers and academic journals. It is an important field because nowadays different rankings use key indicators for university rankings and universities themselves use them as Key Performance Indicators (KPI). The purpose of this work is to propo...
Published in Journal of Big Data
The creation of new knowledge from manipulating and analysing existing knowledge is one of the primary objectives of any cognitive system. Most of the effort on Big Data research has been focussed upon Volume and Velocity, while Variety, “the ugly duckling” of Big Data, is often neglected and difficult to solve. A principal challenge with Variety i...
Published in Journal of Big Data
Online Social Networks (OSNs) are a popular platform for communication and collaboration. Spammers are highly active in OSNs. Uncovering spammers has become one of the most challenging problems in OSNs. Classification-based supervised approaches are the most commonly used method for detecting spammers. Classification-based systems suffer from limit...
Published in Journal of Big Data
The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological da...
