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Faculty of Business and Law

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Between 2000 and 2024
- Between 2020 and 2024
  - 2020

Results 15 resources

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 69–87). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_5
Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 15–39). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_2
Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Forecasting COVID-19 Time Series Based on an Autoregressive Model. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 41–54). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_3
Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 55–68). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_4
Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 89–98). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_6
Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Prediction for Decision Support During the COVID-19 Pandemic. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 1–13). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_1
Li, T., Marques, J. A. L., & Fong, S. (2020). Health and Well-Being Education: Extending the SCARF Learning Analytics Model for Identifying the Learner Happiness Indicators. International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH), 2(2), 42–53. https://doi.org/10.4018/IJEACH.2020070105

The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and th...
Luo, R., Marques, J. A. L., Ong, K.-L., & Fong, S. (2020). Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living. International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH), 2(2), 54–64. https://doi.org/10.4018/IJEACH.2020070106

Crowdsensing exploits the sensing abilities offered by smart phones and users' mobility. Users can mutually help each other as a community with the aid of crowdsensing. The potential of crowdsensing has yet to be fully realized for improving public health. A protocol based on gamification to encoura...
do Vale Madeiro, J. P., Lobo Marques, J. A., Han, T., & Coury Pedrosa, R. (2020). Evaluation of mathematical models for QRS feature extraction and QRS morphology classification in ECG signals. Measurement, 156, 107580. https://doi.org/10.1016/j.measurement.2020.107580

It is plausible to assume that the component waves in ECG signals constitute a unique human characteristic because morphology and amplitudes of recorded beats are governed by multiple individual factors. According to the best of our knowledge, the issue of automatically classifying different ’identities’ of QRS morphology has not been explored within the literature. This work proposes five alternative mathematical models for representing different QRS morphologies providing the extraction of a set of features related to QRS shape. The technique incorporates mechanisms of combining the mathematical functions Gaussian, Mexican-Hat and Rayleigh probability density function and also a mechanism for clipping the waveform of those functions. The searching for the optimal parameters which minimize the normalized RMS error between each mathematical model and a given QRS search window enables to find an optimal model. Such modeling behaves as a robust alternative for delineating heartbeats, classifying beat morphologies, detecting subtle and anomalous changes, compression of QRS complex windows among others. The validation process evaluates the ability of each model to represent different QRS morphology classes within 159 full ECG signal records from QT database and 584 QRS search windows from MIT-BIH Arrhythmia database. From the experimental results, we rank the winning rates for which each mathematical model best models and also discriminates the most predominant QRS morphologies Rs, rS, RS, qR, qRs, R, rR’s and QS. Furthermore, the average time errors computed for QRS onset and offset locations when using the corresponding winner mathematical models for delineation purposes were, respectively, 12.87±8.5 ms and 1.47±10.06 ms.
Marques, J., Reis, J., Phillips, J. O. L., & Diakite, A. (2020). The Importance of Readiness for Change, a Leadership Perspective Based on a Case Study in Macau, SAR China. Journal of Advanced Management Science, 116–120. https://doi.org/10.18178/joams.8.4.116-120
Arraut, I., Au, A., Tse, A. C., & Marques, J. A. L. (2020). On the probability flow in the Stock market I: The Black-Scholes case. ArXiv.Org, 1, 1–10. https://search.proquest.com/docview/2332255379?pq-origsite=primo

It is known that the probability is not a conserved quantity in the stock market, given the fact that it corresponds to an open system. In this paper we analyze the flow of probability in this system by expressing the ideal Black-Scholes equation in the Hamiltonian form. We then analyze how the non-conservation of probability affects the stability of the prices of the Stocks. Finally, we find the conditions under which the probability might be conserved in the market, challenging in this way the non-Hermitian nature of the Black-Scholes Hamiltonian.
Lampo, A. (2020). Adapting the Unified Theory of Acceptance and Use of Technology 2 (Utaut 2) to Explain Acceptance of Battery Electric Vehicles: Evidence From Macau [PhD in Business Administration, University of Saint Joseph]. http://library-opac.usj.edu.mo/cgi-bin/koha/opac-detail.pl?biblionumber=174665

Vehicles solely powered by electricity are a major technological innovation that combines individual transportation needs and environmental sustainability, yet their market penetration is low. Research has traditionally indicated factors such as the vehicle’s purchasing price, driving range, and charging time as the main barriers to adoption. However, the decision to adopt a technology also depends on what the technology represents to the user; therefore, other factors may be important to explain individuals’ behavior. This study is a quantitative and cross-sectional look at the behavioral intention to adopt battery electric vehicles (BEVs) technology in the context of Macau. The research builds on the unified theory of acceptance and use of technology 2 (UTAUT 2) (Venkatesh et. al., 2012) to explain the characteristics of the local consumers. Besides the addition of image and environmental concern to the theoretical model, the study also put forward and evaluate the construct of technology show-off, an original measure of the visible and experiential characteristics of a technology. A sample of 236 Macau residents was analyzed by structural equation modeling (SEM). The analysis of the data supported the explanatory and predictive power of our model and helped to describe the idiosyncrasies of local residents. The results provide insights related to individual technology acceptance that could be useful in designing more accurate strategies and fostering the uptake of BEVs in Macau or markets that share similarities
Lan, K., Liu, L., Li, T., Chen, Y., Fong, S., Marques, J. A. L., Wong, R. K., & Tang, R. (2020). Multi-view convolutional neural network with leader and long-tail particle swarm optimizer for enhancing heart disease and breast cancer detection. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-04769-y
Lei, W. C. (2020). The Road to Service Export Diversification: Gambling and Convention in Macao. In E. Lau, B. Simonetti, I. Trinugroho, & L. M. Tan (Eds.), Economics and Finance Readings: Selected Papers from Asia-Pacific Conference on Economics & Finance, 2019. Springer Singapore. https://doi.org/10.1007/978-981-15-2906-1

This book is a compilation of the best papers presented at the APEF 2019 conference which was held on 25th and 26th July 2019 at the Grand Copthorne Waterfront in Singapore. With a great number of submissions, it presents the latest research findings in economics and finance and discusses relevant issues in today's world. The book is a useful resource for readers who want access to economics, finance and business research focusing on the Asia-Pacific region.
Marques, J. A. L., Cortez, P. C., Madeiro, J. P. V., Albuquerque, V. H. C., Fong, S. J., & Schlindwein, F. S. (2020). Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures. Journal of Supercomputing, 76(2), 1305–1320. https://doi.org/10.1007/s11227-018-2570-8

Last update from database: 5/25/24, 12:28 AM (UTC)

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