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A recommender system based on implicit feedback for selective dissemination of ebooks
(Information Sciences, 2018-10)
In this study, we describe a recommendation system for electronic books. The approach is based on implicit feedback derived from user's interaction with electronic content. User's behavior is tracked through several ...
Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction
(Applied Soft Computing Journal, 2020-08)
In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this ...
Analyzing the Scientific Evolution of Social Work Using Science Mapping
(Research on Social Work Practice, 2015-03)
Objectives: This article reports the first science mapping analysis of the social work field, which shows its conceptual structure
and scientific evolution. Methods: Science Mapping Analysis Software Tool, a bibliometric ...
Improving supervised learning classification methods using multi-granular linguistic modelling and fuzzy entropy
(IEEE Transactions on Fuzzy Systems, 2017-10)
Obtaining good classification results using supervised learning methods is critical if we want to obtain a high level of precision in the classification processes. The training data used for the learning process plays a ...
Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods
(Knowledge-Based Systems, 2017-12)
Classic multi-criteria group decision making models that have a high amount of alternatives are unmanageable for the experts. This is because they have to provide one value per each alternative and criteria. In this paper, ...
Characterizing highly cited papers in Social Work through H-Classics
(Scientometrics, 2015-02)
Highly cited papers are an important reference point in a research field. H-Classics is a new identification method of highly cited papers that is based on the H-index and is sensitive to the own characteristics of any ...
H-Classics: characterizing the concept of citation classics through H-index
(Scientometrics, 2014-03)
Citation classics identify those highly cited papers which are an important reference point in a research field. To identify a paper as a citation classic we have to fix a citation threshold value. Usually, this threshold ...
A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak
(Applied intelligence, 2021)
Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the medical system. The latest pandemic of COVID-19 ...
Enhancing big data feature selection using a hybrid correlation-based feature selection
(2021)
This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based feature selection (CFS), best first search ...
Modelling dynamics of coronavirus disease 2019 spread for pandemic forecasting based on Simulink
(Physical biology, 2021)
In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population ...