Modelling the barriers that are preventing e-commerce to thrive – a case study from Portugal, international journal of business excellence - Silva, Susana Costa e; Machado, Joana Costa; Martins, Carla; Duarte, Paulo; Marques, João Alexandre Lobo (International Journal of Business Excellence, 2021)
The identification of barriers for e-commerce to thrive in specific countries is a topic of great interest. This work proposes two models to study the barriers to B2C e-commerce adoption in Portugal, highlighting obstacles less exploited by previous research: the impact of offline shopping pleasure and the influence of the distance to shopping malls on online shopping intent. An online survey was conducted based on different constructs. A multivariate OLS hierarchical regression was used to analyse the proposed models regarding the intention to buy online and the number of online purchases. The results revealed that customer satisfaction is a strong predictor of intent to buy online and that perceived product risk remains a barrier to e-commerce. Consumers living in high urbanised areas have more propensity to buy online. Helpful information is provided regarding the impact of context, culture, product, and individual barriers, showing that multichannel strategies are best suited for success.
Keywords: E-commerce barriers; Online shopping; Portugal; Retail

Towards an efficient prognostic model for fetal state assessment - Silva Neto, Manuel Gonçalves da; Madeiro, João Paulo do Vale; Marques, João Alexandre Lobo; Gomes, Danielo G. (Measurement, 2021)
Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.
1 citations (Crossref) [2022-09-21]
Keywords: Cardiotocography; Classification; Fetal state assessment; Prognostic model; System design

Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning Techniques - Nguyen Van, Sau; Lobo Marques, J. A.; Biala, T. A.; Li, Ye (Computer Methods and Programs in Biomedicine, 2021)
Background and objective Intrauterine Growth Restriction (IUGR) is a condition in which a fetus does not grow to the expected weight during pregnancy. There are several well documented causes in the literature for this issue, such as maternal disorder, and genetic influences. Nevertheless, besides the risk during pregnancy and labour periods, in a long term perspective, the impact of IUGR condition during the child development is an area of research itself. The main objective of this work is to propose a machine learning solution to identify the most significant features of importance based on physiological, clinical or socioeconomic factors correlated with previous IUGR condition after 10 years of birth. Methods In this work, 41 IUGR (18 male) and 34 Non-IUGR (22 male) children were followed up 9 years after the birth, in average (9.1786 ± 0.6784 years old). A group of machine learning algorithms is proposed to classify children previously identified as born under IUGR condition based on 24-hours monitoring of ECG (Holter) and blood pressure (ABPM), and other clinical and socioeconomic attributes. In additional, an algorithm of relevance determination based on the classifier is also proposed, to determine the level of importance of the considered features. Results The proposed classification solution achieved accuracy up to 94.73%, and better performance than seven state-of-the-art machine learning algorithms. Also, relevant latent factors related to HRV and BP monitoring are proposed, such as: day-time heart rate (day-time HR), day-night systolic blood pressure (day-night SBP), 24-hour standard deviation (SD) of SBP, dropped, morning cortisol creatinine, 24-hour mean of SDs of all NN intervals for each 5 minutes segment (24-hour SDNNi), among others. Conclusion With outstanding accuracy of our proposed solutions, the classification system and the indication of relevant attributes may support medical teams on the clinical monitoring of IUGR children during their childhood development.
2 citations (Crossref) [2022-09-21]
Keywords: ABPM (Ambulatory Blood Pressure Monitoring); HRV (Heart Rate Variability); IUGR (Intrauterine Growth Restriction); Machine Learning

IoT-Based Smart Health System for Ambulatory Maternal and Fetal Monitoring - Marques, João Alexandre Lobo; Han, Tao; Wu, Wanqing; Madeiro, João Paulo do Vale; Neto, Aloísio Vieira Lira; Gravina, Raffaele; Fortino, Giancarlo; de Albuquerque, Victor Hugo C. (IEEE Internet of Things Journal, 2021)
The adoption of IoT for smart health applications is a relevant tool for distributed and intelligent automatic diagnostic systems. This work proposes the development of an integrated solution to monitor maternal and fetal signals for high-risk pregnancies based on IoT sensors, feature extraction based on data analytics, and an intelligent diagnostic aid system based on a 1-D convolutional neural network (CNN) classifier. The fetal heart rate and a group of maternal clinical indicators, such as the uterine tonus activity, blood pressure, heart rate, temperature, and oxygen saturation are monitored. Multiple data sources generate a significant amount of data in different formats and rates. An emergency diagnostic subsystem is proposed based on a fog computing layer and the best accuracy was 92.59% for both maternal and fetal emergency. A smart health analytics system is proposed for multiple feature extraction and the calculation of linear and nonlinear measures. Finally, a classification technique is proposed as a prediction system for maternal, fetal, and simultaneous health status classification, considering six possible outputs. Different classifiers are evaluated and a proposed CNN presented the best results, with the F1-score ranging from 0.74 to 0.91. The results are validated based on the diagnosis provided by two specialists. The results show that the proposed system is a viable solution for maternal and fetal ambulatory monitoring based on IoT.
9 citations (Crossref) [2022-09-21] Conference Name: IEEE Internet of Things Journal
Keywords: Artificial intelligence (AI); Biomedical monitoring; Cloud computing; Feature extraction; Fetal heart rate; Internet of Things; Medical diagnostic imaging; Monitoring; convolutional neural networks (CNNs); feature extraction; fetal monitoring; health analytics; maternal monitoring

Predictive Models for Decision Support in the COVID-19 Crisis - Marques, João Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, Jose; Fong, Simon James (Springer International Publishing, 2021)
COVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations. Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.

Sustainable Buildings’ Projects – A Perspective from Consultants and Contractors based in Macau SAR, China - J.P.P. Santos, Eduardo; Lobo Marques, Joao; Phyllips, Jenny O. L. (The 2021 12th International Conference on E-business, Management and Economics, 2021)
China growing awareness of sustainability has brought out relevant aspects to move towards a green environment. Since its subscription in 2016, China has been committed to implementing the Paris Agreement, and the Greater Bay Area (GBA) development plan prioritizes ecology and pursuing green development. The primary purpose of this research is to perceive the companies' insights concerning the implementation of sustainable buildings’ projects in Macau. For this multi-case study analysis, primary data was gathered from interviews with two groups involved in the construction projects’ lifecycle: Consultants and Contractors, to analyze different perceptions and concerns. The interviews considered two different themes about the main topic: (1) Perception on Companies’ Experience in Sustainable Projects; (2) Key Drivers towards Sustainable Buildings’ Projects’ Implementation. In conclusion, according to the analyzed data, it is essential to notice that companies’ background and the market particularities affect their corporate performance specially connected to the green construction frameworks. The data also indicate that it is necessary to move towards regulations and policies to change corporate and people's mindset.
0 citations (Crossref) [2022-09-21]
Keywords: Construction industry; Green building; Sustainable Policies; Sustainable building

A Prospective analysis about employee retention strategies for Macao Hotel Industry - Chong, Ka Yee; Lobo Marques, Joao A. (The 2021 12th International Conference on E-business, Management and Economics, 2021)
Human resources are essential to the survival, success, and long-term growth of a company. Hotel is an industry requiring a high level of human resources for delivering high-quality personal service to the hotel guests to maintain its competitiveness in the business environment. With the rapid economic growth in Macao started in 2002, all the industries have been growing fast and competing fiercely for the limited manpower in Macao. However, the Macao hotel industry has been losing its attractiveness in the Macao labor market and needs to rely on non-local workers with a limited stay in Macao. The management team of the Macao hotel industry is looking for a solution to maintain a stable workforce. Therefore, a study has been conducted on the effectiveness of its employee retention strategies. A questionnaire was designed to collect the preferences of the employees and interviews were conducted to understand the perspective of the management team toward the employee retention strategies. The study shows the employee strategies are focused on key employees’ interests such as career development and prospect. However, the communication between the management team and employees failed and led to employee turnover.
0 citations (Crossref) [2022-09-21]
Keywords: Employee retention; Employee turnover reasons; Macao hotel industry; Macao shortage of manpower

Predictive models to the COVID-19 - Bernardo Gois, Francisco Nauber; Lima, Alex; Santos, Khennedy; Oliveira, Ramses; Santiago, Valdir; Melo, Saulo; Costa, Rafael; Oliveira, Marcelo; Henrique, Francisco das Chagas Douglas Marques; Neto, José Xavier; Martins Rodrigues Sobrinho, Carlos Roberto; Lôbo Marques, João Alexandre (Data Science for COVID-19, 2021)
Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease's behavior and the progression of the virus's natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with R2 score of 0.99 to short-term predictions and 0.93 to long-term predictions.
Keywords: COVID-19; Forecast; Holt Winters; Kalman filter; Machine learning; Prophet; SEIR

The Probability Flow in the Stock Market and Spontaneous Symmetry Breaking in Quantum Finance - Arraut, Ivan; Lobo Marques, João Alexandre; Gomes, Sergio (Mathematics, 2021)
The spontaneous symmetry breaking phenomena applied to Quantum Finance considers that the martingale state in the stock market corresponds to a ground (vacuum) state if we express the financial equations in the Hamiltonian form. The original analysis for this phenomena completely ignores the kinetic terms in the neighborhood of the minimal of the potential terms. This is correct in most of the cases. However, when we deal with the martingale condition, it comes out that the kinetic terms can also behave as potential terms and then reproduce a shift on the effective location of the vacuum (martingale). In this paper, we analyze the effective symmetry breaking patterns and the connected vacuum degeneracy for these special circumstances. Within the same scenario, we analyze the connection between the flow of information and the multiplicity of martingale states, providing in this way powerful tools for analyzing the dynamic of the stock markets.
Number: 21 Publisher: Multidisciplinary Digital Publishing Institute
Keywords: Hermiticity; conservation of the information; degenerate vacuum; flow of information; martingale condition; random fluctuations; spontaneous symmetry breaking; vacuum condition