This page has only limited features, please log in for full access.
The primary purpose of the accounting profession is to provide quality information to the market that facilitates the allocation of resources. The context in which it operates must attend to some stressors that can affect the professional’s meaning of the work. Meaningful work (MW) is based on the concept of valuable work and work well done, so it is directly related to the concept of quality at work, which is a constant concern in the accounting profession. The method used to determine meaningful work identifies the set of job quality indexes, as defined by the European Working Conditions Survey (EWCS), related to the MW. This paper has used an integer programming genetic algorithm (GA) to determine the JQIs and the statistically significant combinations. The findings showed that JQIs, skills development and discretion (SD), and physical environment (PE) positively and intensely relate to MW. Likewise, reduction of the work intensity (WI) and improvement of the social environment (SE) are related in the same direction as the MW. On the other hand, the results showed different indicator weightings depending on the age of the accountants. This paper shows the importance that accountants attribute to professional competence and how, throughout their careers, the JQI that most relate to MW is changing, from a social vision to preferences where the care of personal time also prevails.
José-Joaquín Del-Pozo-Antúnez; Horacio Molina-Sánchez; Francisco Fernández-Navarro; Antonio Ariza-Montes. Accountancy as a Meaningful Work. Main Determinants from a Job Quality and Optimization Algorithm Approach. Sustainability 2021, 13, 9308 .
AMA StyleJosé-Joaquín Del-Pozo-Antúnez, Horacio Molina-Sánchez, Francisco Fernández-Navarro, Antonio Ariza-Montes. Accountancy as a Meaningful Work. Main Determinants from a Job Quality and Optimization Algorithm Approach. Sustainability. 2021; 13 (16):9308.
Chicago/Turabian StyleJosé-Joaquín Del-Pozo-Antúnez; Horacio Molina-Sánchez; Francisco Fernández-Navarro; Antonio Ariza-Montes. 2021. "Accountancy as a Meaningful Work. Main Determinants from a Job Quality and Optimization Algorithm Approach." Sustainability 13, no. 16: 9308.
The traditional machine-part cell formation problem simultaneously clusters machines and parts in different production cells from a zero–one incidence matrix that describes the existing interactions between the elements. This manuscript explores a novel alternative for the well-known machine-part cell formation problem in which the incidence matrix is composed of non-binary values. The model is presented as multiple-ratio fractional programming with binary variables in quadratic terms. A simple reformulation is also implemented in the manuscript to express the model as a mixed-integer linear programming optimization problem. The performance of the proposed model is shown through two types of empirical experiments. In the first group of experiments, the model is tested with a set of randomized matrices, and its performance is compared to the one obtained with a standard greedy algorithm. These experiments showed that the proposed model achieves higher fitness values in all matrices considered than the greedy algorithm. In the second type of experiment, the optimization model is evaluated with a real-world problem belonging to Human Resource Management. The results obtained were in line with previous findings described in the literature about the case study.
Jose del Pozo-Antúnez; Francisco Fernández-Navarro; Horacio Molina-Sánchez; Antonio Ariza-Montes; Mariano Carbonero-Ruz. The Machine-Part Cell Formation Problem with Non-Binary Values: A MILP Model and a Case of Study in the Accounting Profession. Mathematics 2021, 9, 1768 .
AMA StyleJose del Pozo-Antúnez, Francisco Fernández-Navarro, Horacio Molina-Sánchez, Antonio Ariza-Montes, Mariano Carbonero-Ruz. The Machine-Part Cell Formation Problem with Non-Binary Values: A MILP Model and a Case of Study in the Accounting Profession. Mathematics. 2021; 9 (15):1768.
Chicago/Turabian StyleJose del Pozo-Antúnez; Francisco Fernández-Navarro; Horacio Molina-Sánchez; Antonio Ariza-Montes; Mariano Carbonero-Ruz. 2021. "The Machine-Part Cell Formation Problem with Non-Binary Values: A MILP Model and a Case of Study in the Accounting Profession." Mathematics 9, no. 15: 1768.
The aim of this work is to characterize the process of constructing mathematical knowledge by higher education students in a distance learning course. This was done as part of an algebra course within engineering degrees in a Colombian university. The study used a Transformative Sequential Design in mixed methods research. The analysis also determined the kinds of mathematical knowledge attained by the students and its relationship to the Colombian social and cultural context. The students acquired declarative, procedural, and conditional knowledge, while the learning strategies were often superficial. In a context where power is distant, students take on a passive approach to learning despite being highly respectful towards the educator. Thus, the educational system has the educator at the center.
Elizabeth Martinez-Villarraga; Isabel Lopez-Cobo; David Becerra-Alonso; Francisco Fernández-Navarro. Characterizing Mathematics Learning in Colombian Higher Distance Education. Mathematics 2021, 9, 1740 .
AMA StyleElizabeth Martinez-Villarraga, Isabel Lopez-Cobo, David Becerra-Alonso, Francisco Fernández-Navarro. Characterizing Mathematics Learning in Colombian Higher Distance Education. Mathematics. 2021; 9 (15):1740.
Chicago/Turabian StyleElizabeth Martinez-Villarraga; Isabel Lopez-Cobo; David Becerra-Alonso; Francisco Fernández-Navarro. 2021. "Characterizing Mathematics Learning in Colombian Higher Distance Education." Mathematics 9, no. 15: 1740.
This paper reports perceptions of higher education lecturers who switched from classical face-to-face teaching to online teaching due to the unexpected circumstances caused by the COVID-19 pandemic. Based on a validated theoretical model about the roles of instructors in online settings, the authors document the perceptions of experienced face-to-face lecturers regarding their performance in online roles and the perceived importance of the formal and informal support they received during the process of adapting to a sudden online context. The study was based on the Q-sort methodology. Among other conclusions, our research reveals that the best performance we elicited pertained to the technical role, followed by the managerial role and the support received through informal channels. Worryingly, the worst performance pertained to promoting life skills. This finding is especially alarming considering both the UNESCO humanistic vision of universities as promoters of university community development and wellbeing and SDG 4.7 of Agenda 2030, which states that education should ensure that all learners acquire the knowledge and skills needed to promote sustainable development through education on sustainable development and lifestyles. This article is meant to provide guidelines to traditional universities to help them overcome weaknesses and enhance strengths when switching to online learning.
Pilar Gómez-Rey; Francisco Fernández-Navarro; María Vázquez-De Francisco. Identifying Key Variables on the Way to Wellbeing in the Transition from Face-to-Face to Online Higher Education due to COVID-19: Evidence from the Q-Sort Technique. Sustainability 2021, 13, 6112 .
AMA StylePilar Gómez-Rey, Francisco Fernández-Navarro, María Vázquez-De Francisco. Identifying Key Variables on the Way to Wellbeing in the Transition from Face-to-Face to Online Higher Education due to COVID-19: Evidence from the Q-Sort Technique. Sustainability. 2021; 13 (11):6112.
Chicago/Turabian StylePilar Gómez-Rey; Francisco Fernández-Navarro; María Vázquez-De Francisco. 2021. "Identifying Key Variables on the Way to Wellbeing in the Transition from Face-to-Face to Online Higher Education due to COVID-19: Evidence from the Q-Sort Technique." Sustainability 13, no. 11: 6112.
Extreme Learning Machine (ELM) algorithms have achieved unprecedented performance in supervised machine learning tasks. However, the preconfiguration of the nodes in the hidden layer in ELM models through randomness does not always lead to a suitable transformation of the original features. Consequently, the performance of these models relies on broad exploration of these feature mappings, generally using a large number of nodes in the hidden layer. In this paper, a novel ELM architecture is presented, called Negative Correlation Hidden Layer ELM (NCHL-ELM), based on the Negative Correlation Learning (NCL) framework. This model incorporates a parameter into each node in the original ELM hidden layer, and these parameters are optimized by reducing the error in the training set and promoting the diversity among them in order to improve the generalization results. Mathematically, the ELM minimization problem is perturbed by a penalty term, which represents a measure of diversity among the parameters. A variety of regression and classification benchmark datasets have been selected in order to compare NCHL-ELM with other state-of-the-art ELM models. Statistical tests indicate the superiority of our method in both regression and classification problems.
Carlos Perales-González; Francisco Fernández-Navarro; Javier Pérez-Rodríguez; Mariano Carbonero-Ruz. Negative Correlation Hidden Layer for the Extreme Learning Machine. Applied Soft Computing 2021, 109, 107482 .
AMA StyleCarlos Perales-González, Francisco Fernández-Navarro, Javier Pérez-Rodríguez, Mariano Carbonero-Ruz. Negative Correlation Hidden Layer for the Extreme Learning Machine. Applied Soft Computing. 2021; 109 ():107482.
Chicago/Turabian StyleCarlos Perales-González; Francisco Fernández-Navarro; Javier Pérez-Rodríguez; Mariano Carbonero-Ruz. 2021. "Negative Correlation Hidden Layer for the Extreme Learning Machine." Applied Soft Computing 109, no. : 107482.
New diversification strategies, along with other naive strategies as 1/N portfolios, have been proposed in the literature as a method for overcoming concentration limitations of the mean–variance model. However, it is not clear whether these strategies outperform the classical mean–variance model in all scenarios. Motivated by these points, this manuscript contributes an experimental study in which 11 diversification and mean–variance strategies are compiled and compared with a complete repository of 10 portfolio time series problems with three different estimation windows (composing a total of 30 datasets) and then evaluated using four performance metrics. Additionally, a novel purely data-driven method for determining the optimal value of the hyper-parameter associated with each approach is also proposed. Unlike results previously found in the literature, the empirical results obtained in this study show that equally weighed models obtain the worst ranking in all evaluation metrics except for the stability index, which is hypothetically due to the hyper-parameter optimization raising the transaction cost debate.
Luisa Martínez-Nieto; Francisco Fernández-Navarro; Mariano Carbonero-Ruz; Teresa Montero-Romero. An experimental study on diversification in portfolio optimization. Expert Systems with Applications 2021, 181, 115203 .
AMA StyleLuisa Martínez-Nieto, Francisco Fernández-Navarro, Mariano Carbonero-Ruz, Teresa Montero-Romero. An experimental study on diversification in portfolio optimization. Expert Systems with Applications. 2021; 181 ():115203.
Chicago/Turabian StyleLuisa Martínez-Nieto; Francisco Fernández-Navarro; Mariano Carbonero-Ruz; Teresa Montero-Romero. 2021. "An experimental study on diversification in portfolio optimization." Expert Systems with Applications 181, no. : 115203.
In this paper, a non-linear multi-dimensional (machine learning-based) index for accountants that relates work engagement scores (according to accountants’ perceptions) with the seven Job Quality Indices (JQI) (proposed by Eurofound) has been proposed. The goal of the research is two-fold, namely, (i) to quantify the extent to which the JQI variables explain the work engagement scores, and (ii) to determine which JQI variables most affect the work engagement scores. The best performing regression model achieved a competitive root mean square percentage, highlighting that the selected variables primarily determine the work engagement values. Other important findings include (i) that the work engagement index is mainly influenced by the social environment index and (ii) that the skills and discretion and prospects indices are also crucial in the promotion of the work engagement of accountants. The instrument implemented could be employed by human resources practitioners to propose efficient human resources strategies that improve both individual well-being and company performance in the accounting sector.
Jose Joaquin del Pozo-Antúnez; Horacio Molina-Sánchez; Antonio Ariza-Montes; Francisco Fernández-Navarro. Promoting work Engagement in the Accounting Profession: a Machine Learning Approach. Social Indicators Research 2021, 157, 653 -670.
AMA StyleJose Joaquin del Pozo-Antúnez, Horacio Molina-Sánchez, Antonio Ariza-Montes, Francisco Fernández-Navarro. Promoting work Engagement in the Accounting Profession: a Machine Learning Approach. Social Indicators Research. 2021; 157 (2):653-670.
Chicago/Turabian StyleJose Joaquin del Pozo-Antúnez; Horacio Molina-Sánchez; Antonio Ariza-Montes; Francisco Fernández-Navarro. 2021. "Promoting work Engagement in the Accounting Profession: a Machine Learning Approach." Social Indicators Research 157, no. 2: 653-670.
Profiles of millennial reviewers and gamification can contribute to digital sustainability as a driver of innovation and growth. The study aims to detect if there are profiles of reviewers that can be grouped together, in order to apply a specific gamification to them and to make it sustainable over time. In this way, more information will be generated through the reviews that will help responsible consumers to choose better in their purchase decisions. The objective of this study is twofold. First, it aims to characterize online product reviewers based on their intrinsic motivations and self-perception when they comment, identifying their main motivations. Second, it aims to classify these individuals based on the acceptance of gamification elements while commenting on and relating them to the intrinsic attributes that determine their behaviors. A survey method design was used to capture responses from 187 millennial reviewers of Amazon in Spain. The relationships between motivations and the types of reviewer were extracted from the accommodation of the dataset using decision trees (DTs), specifically, the J48 algorithm. To contribute to the second objective, this paper elaborates a typology of reviewer analysis based on cluster analysis and DTs. It is confirmed that online product reviewers can be characterized based on their intrinsic motivations, which are mainly egoistic motives, competence and social relatedness. The obtained results show that the J48 DT provides excellent classification accuracy of approximately 95% in identifying reviewers based on intrinsic motivations. Similarly, egoistic intrinsic motives are decisive in focusing gamification strategies.
Alejandro García-Jurado; José Pérez-Barea; Francisco Fernández-Navarro. Towards Digital Sustainability: Profiles of Millennial Reviewers, Reputation Scores and Intrinsic Motivation Matter. Sustainability 2021, 13, 3297 .
AMA StyleAlejandro García-Jurado, José Pérez-Barea, Francisco Fernández-Navarro. Towards Digital Sustainability: Profiles of Millennial Reviewers, Reputation Scores and Intrinsic Motivation Matter. Sustainability. 2021; 13 (6):3297.
Chicago/Turabian StyleAlejandro García-Jurado; José Pérez-Barea; Francisco Fernández-Navarro. 2021. "Towards Digital Sustainability: Profiles of Millennial Reviewers, Reputation Scores and Intrinsic Motivation Matter." Sustainability 13, no. 6: 3297.
Ensembles are a widely implemented approach in the machine learning community and their success is traditionally attributed to the diversity within the ensemble. Most of these approaches foster diversity in the ensemble by data sampling or by modifying the structure of the constituent models. Despite this, there is a family of ensemble models in which diversity is explicitly promoted in the error function of the individuals. The negative correlation learning (NCL) ensemble framework is probably the most well-known algorithm within this group of methods. This article analyzes NCL and reveals that the framework actually minimizes the combination of errors of the individuals of the ensemble instead of minimizing the residuals of the final ensemble. We propose a novel ensemble framework, named global negative correlation learning (GNCL), which focuses on the optimization of the global ensemble instead of the individual fitness of its components. An analytical solution for the parameters of base regressors based on the NCL framework and the global error function proposed is also provided under the assumption of fixed basis functions (although the general framework could also be instantiated for neural networks with nonfixed basis functions). The proposed ensemble framework is evaluated by extensive experiments with regression and classification data sets. Comparisons with other state-of-the-art ensemble methods confirm that GNCL yields the best overall performance.
Carlos Perales-Gonzalez; Francisco Fernandez-Navarro; Mariano Carbonero-Ruz; Javier Perez-Rodriguez. Global Negative Correlation Learning: A Unified Framework for Global Optimization of Ensemble Models. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -12.
AMA StyleCarlos Perales-Gonzalez, Francisco Fernandez-Navarro, Mariano Carbonero-Ruz, Javier Perez-Rodriguez. Global Negative Correlation Learning: A Unified Framework for Global Optimization of Ensemble Models. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-12.
Chicago/Turabian StyleCarlos Perales-Gonzalez; Francisco Fernandez-Navarro; Mariano Carbonero-Ruz; Javier Perez-Rodriguez. 2021. "Global Negative Correlation Learning: A Unified Framework for Global Optimization of Ensemble Models." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-12.
The mean-variance (MV) portfolio is typically formulated as a quadratic programming (QP) problem that linearly combines the conflicting objectives of minimizing the risk and maximizing the expected return through a risk aversion profile parameter. In this formulation, the two objectives are expressed in different units, an issue that could definitely hamper obtaining a more competitive set of portfolio weights. For example, a modification in the scale in which returns are expressed (by one or percent) in the MV portfolio, implies a modification in the solution of the problem. Motivated by this issue, a novel mean squared variance (MSV) portfolio is proposed in this paper. The associated optimization problem of the proposed strategy is very similar to the Markowitz optimization, with the exception of the portfolio mean, which is presented in squared form in our formulation. The resulting portfolio model is a non-convex QP problem, which has been reformulated as a mixed-integer linear programming (MILP) problem. The reformulation of the initial non-convex QP problem into an MILP allows for future researchers and practitioners to obtain the global solution of the problem via the use of current state-of-the-art MILP solvers. Additionally, a novel purely data-driven method for determining the optimal value of the hyper-parameter that is associated with the MV and MSV approaches is also proposed in this paper. The MSV portfolio has been empirically tested on eight portfolio time series problems with three different estimation windows (composing a total of 24 datasets), showing very competitive performance in most of the problems.
Francisco Fernández-Navarro; Luisa Martínez-Nieto; Mariano Carbonero-Ruz; Teresa Montero-Romero. Mean Squared Variance Portfolio: A Mixed-Integer Linear Programming Formulation. Mathematics 2021, 9, 223 .
AMA StyleFrancisco Fernández-Navarro, Luisa Martínez-Nieto, Mariano Carbonero-Ruz, Teresa Montero-Romero. Mean Squared Variance Portfolio: A Mixed-Integer Linear Programming Formulation. Mathematics. 2021; 9 (3):223.
Chicago/Turabian StyleFrancisco Fernández-Navarro; Luisa Martínez-Nieto; Mariano Carbonero-Ruz; Teresa Montero-Romero. 2021. "Mean Squared Variance Portfolio: A Mixed-Integer Linear Programming Formulation." Mathematics 9, no. 3: 223.
On $19^{\text{th}}$ March, the World Health Organisation declared a pandemic. Through this global spread, many nations have witnessed exponential growth of confirmed cases brought under control by severe mass quarantine or lockdown measures. However, some have, through a different timeline of actions, prevented this exponential growth. Currently as some continue to tackle growth, others attempt to safely lift restrictions whilst avoiding a resurgence. This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by a novel soft computing approach that makes concurrent use of a neural network model, to predict the daily slope increase of cumulative infected, and an optimiser, with a parametrisation of the government restriction time series, to understand the best set of mitigating actions. Data for two territories, Italy and Taiwan, have been gathered to model government restrictions in travelling, testing and enforcement of social distance measures as well as people connectivity and adherence to government actions. It is found that a larger and earlier testing campaign with tighter entry restrictions benefit both regions, resulting in significantly less confirmed cases. Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan's lack of nationwide lockdown, i.e. earlier government actions could have contained the growth to a degree that a widespread lockdown would have been avoided, or at least delayed. The results, found with a purely data-driven approach, are in line with the main findings of mathematical epidemiological models, proving that the proposed approach has value and that the data alone contains valuable knowledge to inform decision makers.
Annalisa Riccardi; Jessica Gemignani; Francisco Fernandez-Navarro; Anna Heffernan. Optimisation of Non-Pharmaceutical Measures in COVID-19 Growth via Neural Networks. IEEE Transactions on Emerging Topics in Computational Intelligence 2021, 5, 79 -91.
AMA StyleAnnalisa Riccardi, Jessica Gemignani, Francisco Fernandez-Navarro, Anna Heffernan. Optimisation of Non-Pharmaceutical Measures in COVID-19 Growth via Neural Networks. IEEE Transactions on Emerging Topics in Computational Intelligence. 2021; 5 (1):79-91.
Chicago/Turabian StyleAnnalisa Riccardi; Jessica Gemignani; Francisco Fernandez-Navarro; Anna Heffernan. 2021. "Optimisation of Non-Pharmaceutical Measures in COVID-19 Growth via Neural Networks." IEEE Transactions on Emerging Topics in Computational Intelligence 5, no. 1: 79-91.
Extreme learning machine (ELM) has shown to be a suitable algorithm for classification problems. Several ensemble meta-algorithms have been developed in order to generalize the results of ELM models. Ensemble approaches introduced in the ELM literature mainly come from boosting and bagging frameworks. The generalization of these methods relies on data sampling procedures, under the assumption that training data are heterogeneously enough to set up diverse base learners. The proposed ELM ensemble model overcomes this strong assumption by using the negative correlation learning (NCL) framework. An alternative diversity metric based on the orthogonality of the outputs is proposed. The error function formulation allows us to develop an analytical solution to the parameters of the ELM base learners, which significantly reduce the computational burden of the standard NCL ensemble method. The proposed ensemble method has been validated by an experimental study with a variety of benchmark datasets, comparing it with the existing ensemble methods in ELM. Finally, the proposed method statistically outperforms the comparison ensemble methods in accuracy, also reporting a competitive computational burden (specially if compared to the baseline NCL-inspired method).
Carlos Perales-González; Mariano Carbonero-Ruz; Javier Pérez-Rodríguez; David Becerra-Alonso; Francisco Fernández-Navarro. Negative correlation learning in the extreme learning machine framework. Neural Computing and Applications 2020, 32, 13805 -13823.
AMA StyleCarlos Perales-González, Mariano Carbonero-Ruz, Javier Pérez-Rodríguez, David Becerra-Alonso, Francisco Fernández-Navarro. Negative correlation learning in the extreme learning machine framework. Neural Computing and Applications. 2020; 32 (17):13805-13823.
Chicago/Turabian StyleCarlos Perales-González; Mariano Carbonero-Ruz; Javier Pérez-Rodríguez; David Becerra-Alonso; Francisco Fernández-Navarro. 2020. "Negative correlation learning in the extreme learning machine framework." Neural Computing and Applications 32, no. 17: 13805-13823.
This article describes the development of an application for the grading and provision of feedback on educational processes. The too, named EduZinc, enables instructors to go through the complete process of creating and evaluating the activities and materials of a course. The application enables for the simultaneous management of two teaching-related aspects: (a) creation of individualized learning products (activities, tests and exams) and (b) automatic grading (for every learning product; automated creation of student, class, and competency-based reports; and delivery of personalized reports to students, instructors and tutors). The system also has a series of warnings in place to notify instructors and tutors when a student is falling behind. As a means to reward the efforts made during the course, the program keeps relevant statistics, notifying when a student is excelling in the course.
David Becerra-Alonso; Isabel Lopez-Cobo; Pilar Gómez-Rey; Francisco Fernández-Navarro; Elena Barbera. EduZinc: a tool for the creation and assessment of student learning activities in complex open, online, and flexible learning environments. Distance Education 2020, 41, 86 -105.
AMA StyleDavid Becerra-Alonso, Isabel Lopez-Cobo, Pilar Gómez-Rey, Francisco Fernández-Navarro, Elena Barbera. EduZinc: a tool for the creation and assessment of student learning activities in complex open, online, and flexible learning environments. Distance Education. 2020; 41 (1):86-105.
Chicago/Turabian StyleDavid Becerra-Alonso; Isabel Lopez-Cobo; Pilar Gómez-Rey; Francisco Fernández-Navarro; Elena Barbera. 2020. "EduZinc: a tool for the creation and assessment of student learning activities in complex open, online, and flexible learning environments." Distance Education 41, no. 1: 86-105.
Carlos Perales-González; Mariano Carbonero-Ruz; David Becerra-Alonso; Javier Pérez-Rodríguez; Francisco Fernández-Navarro. Regularized ensemble neural networks models in the Extreme Learning Machine framework. Neurocomputing 2019, 361, 196 -211.
AMA StyleCarlos Perales-González, Mariano Carbonero-Ruz, David Becerra-Alonso, Javier Pérez-Rodríguez, Francisco Fernández-Navarro. Regularized ensemble neural networks models in the Extreme Learning Machine framework. Neurocomputing. 2019; 361 ():196-211.
Chicago/Turabian StyleCarlos Perales-González; Mariano Carbonero-Ruz; David Becerra-Alonso; Javier Pérez-Rodríguez; Francisco Fernández-Navarro. 2019. "Regularized ensemble neural networks models in the Extreme Learning Machine framework." Neurocomputing 361, no. : 196-211.
M. Pérez-Ortiz; A.M. Durán-Rosal; Pedro Antonio Gutiérrez; Javier Sanchez-Monedero; Athanasia Nikolaou; Francisco Fernández-Navarro; C. Hervás-Martínez. On the use of evolutionary time series analysis for segmenting paleoclimate data. Neurocomputing 2019, 326-327, 3 -14.
AMA StyleM. Pérez-Ortiz, A.M. Durán-Rosal, Pedro Antonio Gutiérrez, Javier Sanchez-Monedero, Athanasia Nikolaou, Francisco Fernández-Navarro, C. Hervás-Martínez. On the use of evolutionary time series analysis for segmenting paleoclimate data. Neurocomputing. 2019; 326-327 ():3-14.
Chicago/Turabian StyleM. Pérez-Ortiz; A.M. Durán-Rosal; Pedro Antonio Gutiérrez; Javier Sanchez-Monedero; Athanasia Nikolaou; Francisco Fernández-Navarro; C. Hervás-Martínez. 2019. "On the use of evolutionary time series analysis for segmenting paleoclimate data." Neurocomputing 326-327, no. : 3-14.
The Job Demand-Control and Job Demand-Control-Support (JDCS) models constitute the theoretical approaches used to analyze the relationship between the characteristics of labor and occupational health. Few studies have investigated the main effects and multiplicative model in relation to the perceived occupational health of professional accountants. Accountants are subject to various types of pressure in performing their work; this pressure influences their health and, ultimately, their ability to perform a job well. The objective of this study is to investigate the effects of job demands on the occupational health of 739 accountants, as well as the role of the moderator that internal resources (locus of control) and external resources (social support) have in occupational health. The proposed hypotheses are tested by applying different models of neural networks using the algorithm of the Extreme Learning Machine. The results confirm the relationship between certain stress factors that affect the health of the accountants, as well as the direct effect that the recognition of superiors in occupational health has. Additionally, the results highlight the moderating effect of professional development and the support of superiors on the job’s demands.
José Joaquín Del Pozo-Antúnez; Antonio Ariza-Montes; Francisco Fernández-Navarro; Horacio Molina-Sánchez. Effect of a Job Demand-Control-Social Support Model on Accounting Professionals’ Health Perception. International Journal of Environmental Research and Public Health 2018, 15, 2437 .
AMA StyleJosé Joaquín Del Pozo-Antúnez, Antonio Ariza-Montes, Francisco Fernández-Navarro, Horacio Molina-Sánchez. Effect of a Job Demand-Control-Social Support Model on Accounting Professionals’ Health Perception. International Journal of Environmental Research and Public Health. 2018; 15 (11):2437.
Chicago/Turabian StyleJosé Joaquín Del Pozo-Antúnez; Antonio Ariza-Montes; Francisco Fernández-Navarro; Horacio Molina-Sánchez. 2018. "Effect of a Job Demand-Control-Social Support Model on Accounting Professionals’ Health Perception." International Journal of Environmental Research and Public Health 15, no. 11: 2437.
Higher-order factor analysis is a statistical method that consists of repeating steps of factor analysis. Studies of this type allow researchers and practitioners to visualize the hierarchical structure of the concept being studied. Unfortunately, the Socially Responsible Consumer (SRC) research community still remains unable to construct a second-order SRC index. Most researchers argue that the statistical requirements for the construction of the second-order index are not met. They typically try to construct the second-order index by applying linear factor analysis techniques. It is worth mentioning that this is a widespread practice in the social sciences. In this manuscript, we aim to show how better indices can be created by applying non-linear dimensionality reduction techniques. Specifically, we have modified the Unsupervised Extreme Learning Machine (UELM) method to promote orthogonality in the basis function space. These methods are able to model interactions among the input variables, but unfortunately, they are usually considered black boxes. To overcome this limitation, we propose the use of Global Sensitivity Analysis (GSA) techniques, which are able to estimate the importance of each variable by itself and in conjunction with the others. To test the methodology, we have used a sample of 703 Spanish consumers and a multidimensional SRC metric that considers both social and environmental issues. As expected, the non-linear techniques tend to enhance the results provided by the linear techniques.
Jose Javier Pérez-Barea; Francisco Fernández-Navarro; Maria Jose Montero-Simó; Rafael Araque-Padilla. A socially responsible consumption index based on non-linear dimensionality reduction and global sensitivity analysis. Applied Soft Computing 2018, 69, 599 -609.
AMA StyleJose Javier Pérez-Barea, Francisco Fernández-Navarro, Maria Jose Montero-Simó, Rafael Araque-Padilla. A socially responsible consumption index based on non-linear dimensionality reduction and global sensitivity analysis. Applied Soft Computing. 2018; 69 ():599-609.
Chicago/Turabian StyleJose Javier Pérez-Barea; Francisco Fernández-Navarro; Maria Jose Montero-Simó; Rafael Araque-Padilla. 2018. "A socially responsible consumption index based on non-linear dimensionality reduction and global sensitivity analysis." Applied Soft Computing 69, no. : 599-609.
Information about the paper titled "A PRELIMINARY INSTRUMENT TO FOSTER DEVELOPMENT OF LIFE SKILLS IN ONLINE LEARNING" at IATED Digital Library
Pilar Gómez-Rey; Elena Barbera; Francisco Fernández-Navarro; Jingjing Zhang. A PRELIMINARY INSTRUMENT TO FOSTER DEVELOPMENT OF LIFE SKILLS IN ONLINE LEARNING. EDULEARN18 Proceedings 2018, 9908 -9918.
AMA StylePilar Gómez-Rey, Elena Barbera, Francisco Fernández-Navarro, Jingjing Zhang. A PRELIMINARY INSTRUMENT TO FOSTER DEVELOPMENT OF LIFE SKILLS IN ONLINE LEARNING. EDULEARN18 Proceedings. 2018; ():9908-9918.
Chicago/Turabian StylePilar Gómez-Rey; Elena Barbera; Francisco Fernández-Navarro; Jingjing Zhang. 2018. "A PRELIMINARY INSTRUMENT TO FOSTER DEVELOPMENT OF LIFE SKILLS IN ONLINE LEARNING." EDULEARN18 Proceedings , no. : 9908-9918.
This study investigated learner support strategies that enable the success and completion of Massive Open Online Courses (MOOCs). It examined five MOOCs categorised into three groups according to their pedagogical approach and used in different learning settings: formal MOOCs, conventional MOOCs and professional MOOCs. A total of 4,202,974 units of variables (student behaviours and MOOC features) were analysed using Semi-Supervised Extreme Learning Machine (SSELM) and Global Sensitivity Analysis. In this study, the use of SSELM was compared to the state-of-art models (e.g. ELM, KELM, OP-ELM, PCA-ELM), and SSELM yielded 97.24% accuracy. Using unlabelled students helped improve the learning accuracy for the model, which confirms that SSELM is a good model to predict completion in MOOCs, considering the difficulty of labelling students in such an open and flexible learning environment. The findings show that designers and teachers should pay special attention to their students during the second quartile of the course (independently of the type of MOOC). The teachers’ presence during the course, his or her interactions with students and the quality of the videos presented are significant determinants of course completion.
Elena Barberà Gregori; Jingjing Zhang; Cristina Galván-Fernández; Francisco De Asís Fernández-Navarro. Learner support in MOOCs: Identifying variables linked to completion. Computers & Education 2018, 122, 153 -168.
AMA StyleElena Barberà Gregori, Jingjing Zhang, Cristina Galván-Fernández, Francisco De Asís Fernández-Navarro. Learner support in MOOCs: Identifying variables linked to completion. Computers & Education. 2018; 122 ():153-168.
Chicago/Turabian StyleElena Barberà Gregori; Jingjing Zhang; Cristina Galván-Fernández; Francisco De Asís Fernández-Navarro. 2018. "Learner support in MOOCs: Identifying variables linked to completion." Computers & Education 122, no. : 153-168.
In this paper, the neural network version of Extreme Learning Machine (ELM) is used as a base learner for an ensemble meta-algorithm which promotes diversity explicitly in the ELM loss function. The cost function proposed encourages orthogonality (scalar product) in the parameter space. Other ensemble-based meta-algorithms from AdaBoost family are used for comparison purposes. Both accuracy and diversity presented in our proposal are competitive, thus reinforcing the idea of introducing diversity explicitly.
Carlos Perales-González; Mariano Carbonero-Ruz; David Becerra-Alonso; Francisco Fernández-Navarro. A Preliminary Study of Diversity in Extreme Learning Machines Ensembles. Privacy Enhancing Technologies 2018, 302 -314.
AMA StyleCarlos Perales-González, Mariano Carbonero-Ruz, David Becerra-Alonso, Francisco Fernández-Navarro. A Preliminary Study of Diversity in Extreme Learning Machines Ensembles. Privacy Enhancing Technologies. 2018; ():302-314.
Chicago/Turabian StyleCarlos Perales-González; Mariano Carbonero-Ruz; David Becerra-Alonso; Francisco Fernández-Navarro. 2018. "A Preliminary Study of Diversity in Extreme Learning Machines Ensembles." Privacy Enhancing Technologies , no. : 302-314.