A Heart Rate Variability-based Smart Approach to Analyze Frailty in Older Adults - Paulo do Vale Madeiro, João; César Cortez, Arnaldo Aires Peixoto Júnior, Paulo; Alexandre Lôbo Marques, João; Alisson Pessoa Guimarães, Antônio; Hebert da Silva Felix, John (The Smart Computing Review, 2015)
This paper presents an algorithm that applies metrics derived from automatic QRS detection and segmentation in electrocardiogram signals for analyzing Heart Rate Variability to study the evolution of metrics in the frequency domain of a clinical procedure. The analysis was performed on three sets of elderly people, who are categorized according to frailty phenotype. The first set was comprised of frail elderly, the second pre-frail elderly, and the third robust elderly. Investigators from many disciplines have been encouraged to contribute to the understanding of molecular and physiological changes in multiple systems that may increase the vulnerability of frail elderly. In this work, the frailty phenotype can be characterized by unintentional weight loss, as self-reported, fatigue assessed by self-report, grip strength (measured directly), physical activity level assessed by self-report and gait speed (measured). The results obtained demonstrate the existence of significant differences between Heart Rate Variability metrics for the three groups, especially considering a higher preponderance for sympathetic nervous system for the group of robust patients in response to postural maneuver.

A prospective analysis about eEmployee retention strategies for Macao hotel industry - Chong, Ka Yee (University of Saint Joseph, 2019)
Keywords: Theses and Dissertations Master of Business Administration (MBA); University of Saint Joseph

A Quantum Field Formulation for a Pandemic Propagation - Arraut, Ivan; Marques, João Alexandre Lobo; Fong, Simon James; Li, Gloria; Gois, Francisco Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
In this chapter, a mathematical model explaining generically the propagation of a pandemic is proposed, helping in this way to identify the fundamental parameters related to the outbreak in general. Three free parameters for the pandemic are identified, which can be finally reduced to only two independent parameters. The model is inspired in the concept of spontaneous symmetry breaking, used normally in quantum field theory, and it provides the possibility of analyzing the complex data of the pandemic in a compact way. Data from 12 different countries are considered and the results presented. The application of nonlinear quantum physics equations to model epidemiologic time series is an innovative and promising approach.
Keywords: COVID-19; Mathematical modeling; Nonlinear analysis; Quantum field theory

A review on multimodal machine learning in medical diagnostics - Yan, Keyue; Li, Tengyue; Marques, João Alexandre Lobo; Gao, Juntao; Fong, Simon James; Yan, Keyue; Li, Tengyue; Marques, João Alexandre Lobo; Gao, Juntao; Fong, Simon James (Mathematical Biosciences and Engineering, 2023)
Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.
Cc_license_type: cc_by Number: mbe-20-05-382 Primary_atype: Mathematical Biosciences and Engineering Subject_term: Review Subject_term_id: Review

A Review on Open Innovation and Absorptive Capacity in Small and Medium Enterprises during the last decade - Analyzing Bibliometrics to Understand the Development of the Field - Ip, Chi Hang; Lobo Marques, Joao Alexandre; Dos Santos Silva, Bruno Riccelli; Cortez, Paulo Cesar; Barbosa, Alvaro (Proceedings of the 2023 14th International Conference on E-business, Management and Economics, 2023)
Small and medium-sized enterprises (SMEs) can benefit significantly from open innovation by gaining access to a broader range of resources and expertise using absorptive capacitive, and increasing their visibility and reputation. Nevertheless, multiple barriers impact their capacity to absorb new technologies or adapt to develop them. This paper aims to perform an analysis of relevant topics and trends in Open Innovation (OI) and Absorptive Capacity (AC) in SMEs based on a bibliometric review identifying relevant authors and countries, and highlighting significant research themes and trends. The defined string query is submitted to the Web of Science database, and the bibliometric analysis using VOSviewer software. The results indicate that the number of scientific publications has consistently increased during the past decade, indicating a growing interest of the scientific community, reflecting the industry interest and possibly adoption of OI, considering Absorptive. This bibliometric analysis can provide insights on the most relevant regions the research areas are under intensive development.
Keywords: Absorptive Capacity; Bibliometric analysis; Open innovation; SMEs; VOSviewer

AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions - Lôbo Marques, João Alexandre; Bernardo Gois, Francisco Nauber; Nunes da Silveira, Jarbas Aryel; Li, Tengyue; Fong, Simon James (Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data, 2022)
The area of clinical decision support systems (CDSS) is facing a boost in research and development with the increasing amount of data in clinical analysis together with new tools to support patient care. This creates a vibrant and challenging environment for the medical and technical staff. This chapter presents a discussion about the challenges and trends of CDSS considering big data and patient-centered constraints. Two case studies are presented in detail. The first presents the development of a big data and AI classification system for maternal and fetal ambulatory monitoring, composed by different solutions such as the implementation of an Internet of Things sensors and devices network, a fuzzy inference system for emergency alarms, a feature extraction model based on signal processing of the fetal and maternal data, and finally a deep learning classifier with six convolutional layers achieving an F1-score of 0.89 for the case of both maternal and fetal as harmful. The system was designed to support maternal–fetal ambulatory premises in developing countries, where the demand is extremely high and the number of medical specialists is very low. The second case study considered two artificial intelligence approaches to providing efficient prediction of infections for clinical decision support during the COVID-19 pandemic in Brazil. First, LSTM recurrent neural networks were considered with the model achieving R2=0.93 and MAE=40,604.4 in average, while the best, R2=0.9939, was achieved for the time series 3. Second, an open-source framework called H2O AutoML was considered with the “stacked ensemble” approach and presented the best performance followed by XGBoost. Brazil has been one of the most challenging environments during the pandemic and where efficient predictions may be the difference in saving lives. The presentation of such different approaches (ambulatory monitoring and epidemiology data) is important to illustrate the large spectrum of AI tools to support clinical decision-making.
Keywords: Artificial intelligence; Clinical decisions; Computer-aided diagnostic systems; Deep learning; Patient-centric data

An Analysis of Celebrities and KOLs Influence Consumer Decisions on the Luxury Fashion Market in Macau - Ip, San U. (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

An Exploratory Comparison of Stock Prices Prediction using Multiple Machine Learning Approaches based on Hong Kong Share Market - Lin, Chinyang; Lobo Marques, João Alexandre (Proceedings of the 2023 14th International Conference on E-business, Management and Economics, 2023)
Stock price prediction has always been challenging due to its volatility and unpredictability. This paper performs a preliminary exploratory comparison that utilizes Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms to forecast the stock market in Hong Kong. It considers a public dataset publicly available and uses feature engineering to extract relevant features. Then, LSTM and SVM algorithms are applied to predict stock prices. Our results show that the proposed machine learning techniques can predict stock prices in Hong Kong's share market with the error metrics presented, and, for this purpose, LSTM achieved better results than SVM, with MSE = 0.0026, RMSE = 0.0508, MAE = 0.0406, and MAPE = 1.325.
Keywords: Long Short-Term Memory (LSTM); Machine Learning; Stock price prediction; Support Vector Machine (SVM); Time-series analysis

An Exploratory Study into How the Application of Neurosciences can Assist in Understanding the Decision-Making Process Under Stress - Furtado, Filipa Magalhães de Moura e Mota (University of Saint Joseph, 2021)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

ANALYSIS AND MODEL PROPOSITION OF USER WILLINGNESS TO ADOPT MOBILE BANKING FINANCE+ SCENARIO SERVICES UNDER THE BACKGROUND OF DIGITAL TRANSFORMATION - Lin, Jingying (University of Saint Joseph, 2024)
In the wave of digital transformation, Chinese banks have taken digital and scenario-based finance as primary strategic goals. The goal is to revolutionize the mobile banking experience and encourage frequent use of mobile banking services. However, assessing customer satisfaction with the various financial and contextual services mobile banking provides is crucial. The main objective of this study is to propose a model based on users' perception of financial usage in mobile banking scenarios and how the development of mobile banking finance and scenarios affects users' choice motivations. The study examined the interview records of 12 mobile banking users through qualitative in-depth interviews and utilized Nvivo qualitative analysis software to analyze the interview content. Through repeated thinking, sorting, and differentiating the data, nine core coding categories were formed. The coding was further refined and deepened to include Financial professionalism, Security, Marketing Stimulation, Innovative Products, Use Experience, Strong Relationship, Trust, Perceived usefulness, and Willingness to use. Based on these categories, a theoretical model of user willingness in the financial scenario of mobile banking has been proposed by referring to the optimized TAM model. The results may provide support to the banking industry in Macau in understanding customers' needs and fostering the positive development of mobile finance and the scene field in Macau
Keywords: Banking in Macau; Finance+ scenarios; Grounded Theory; TAM model; Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph; Willingness to use

Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models - Fong, Simon James; Marques, João Alexandre Lobo; Li, G.; Dey, N.; Crespo, Rubén G.; Herrera-Viedma, E.; Gois, F. Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, was not originally designed for COVID19. We used the simple, commonly used SEIR model to retrospectively analyse the initial pandemic data from Singapore. Here, the SEIR model was combined with the actual published Singapore pandemic data, and the key parameters were determined by maximizing the nonlinear goodness of fit R2 and minimizing the root mean square error. These parameters served for the fast and directional convergence of the parameters of an improved model. To cover the quarantine and asymptomatic variables, the existing SEIR model was extended to an infectious disease model with a greater number of population compartments, and with parameter values that were tuned adaptively by solving the nonlinear dynamics equations over the available pandemic data, as well as referring to previous experience with SARS. The contribution presented in this paper is a new model called the adaptive SEAIRD model; it considers the new characteristics of COVID19 and is therefore applicable to a population including asymptomatic carriers. The predictive value is enhanced by tuning of the optimal parameters, whose values better reflect the current pandemic.
Keywords: Adaptive SEAIRD model; Adaptive SVEAIRD model; Asymptomatic cases; Prediction models

Analysis of the Impact of Environmentally Sustainable Practices on Macau Hotels Guest Engagement - Santos, Ana Sofia Kong (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

Artificial neural network-based approaches for computer-aided disease diagnosis and treatment - Marques, João Alexandre Lôbo; Gois, Francisco Nauber Bernardo; Madeiro, João Paulo do Vale; Li, Tengyue; Fong, Simon James (Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data, 2022)
The adoption of computer-aided diagnosis and treatment systems based on different types of artificial neural networks (ANNs) is already a reality in several hospital and ambulatory premises. This chapter aims to present a discussion focused on the challenges and trends of adopting these computerized systems, highlighting solutions based on different types and approaches of ANN, more specifically, feed-forward, recurrent, and deep convolutional architectures. One section is focused on the application of AI/ANN solutions to support cardiology in different applications, such as the classification of the heart structure and functional behavior based on echocardiography images; the automatic analysis of the heart electric activity based on ECG signals; and the diagnosis support of angiogram images during surgical interventions. Finally, a case study is presented based on the application of a deep learning convolutional network together with a recent technique called transfer learning to detect brain tumors using an MRI images data set. According to the findings, the model has a high degree of specificity (precision of 0.93 and recall of 0.94 for images with no brain tumor) and can be used as a screening tool for images that do not contain a brain tumor. The f1-score for images with brain tumor was 0.93. The results achieved are very promising and the proposed solution may be considered to be used as a computer-aided diagnosis tool based on deep learning convolutional neural networks. Future works will consider other techniques and compare them with the one presented here. With the comprehensive approach and overview of multiple applications, it is valid to conclude that computer-aided diagnosis and treatment systems are important tools to be considered today and will be an essential part of the trend of personalized medicine over the coming years.
Keywords: Artificial intelligence; Computer-aided diagnosis and treatment; Deep learning; Medical imaging; Neural networks

Assessment of Methodologies Employed by Change Agents in Managing Organizational Change: A Case Study in the Hospitality-Gaming Industry - Tagulao, Thea Clarice Saavedra (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Categorization of Foreign Aid Donors: A Critical Review of the Criteria in Light of China's Reemergence as a Donor - Diakite, Ansoumane Douty; Marques, João Alexandre Lobo (Journal of Global South Studies, 2023)
Over the past several decades, the dichotomy between traditional and emerging donors has been based upon the notion that emerging donors (such as China) support authoritarian regimes and use foreign aid to pursue their economic interests at the expense of the poor in the recipient countries. Accordingly, Western donors, media, and scholars portray Chinese aid as non-poverty-focused. This study aims to review and analyze whether the dichotomy between traditional and emerging donors is still relevant in the current aid system and to propose a new and rigorous criterion for recategorizing donors. In terms of methodology, this study relies on secondary data, including scholarly works on traditional and emerging donors and foreign aid policy documents. Conclusions based on the research indicate that the divide between traditional donors and (re)emerging donors is becoming more ambiguous. The literature review indicates that the two donors’ aids had a mixed impact and that their approaches were similar. This paper highlights the importance of developing different recategorization criteria depending on the impact of aid.
Number: 2
Keywords: China and development assistance committee donors; aid policies; categorization of donors; sustainable development goal; traditional and emerging donors

Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers - Bernardo Gois, Francisco Nauber; Lobo Marques, Joao Alexandre; Fong, Simon James (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94.

Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability - Pordeus, Daniel; Ribeiro, Pedro; Zacarias, Laíla; Paulo Madeiro, João; Lobo Marques, Joao Alexandre; Miguel Rodrigues, Pedro; Leite, Camila; Alves Neto, Manoel; Aires Peixoto Jr, Arnaldo; de Oliveira, Adriel (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.
Keywords: COVID-19; Electrocardiogram (ECG) signal; Heart Rate Variability (HRV) indices; Severity; Signal processing

Computerized Systems for Diagnosis and Treatment of COVID-19 - Lobo Marques, Joao Alexandre; Fong, Simon James (Springer International Publishing, 2023)
Keywords: Artificial Intelligence; Biofeedback; Computerized Diagnostic Support; Covid-19; Signal and Image Processing

COVID-19 Classification Using CT Scans with Convolutional Neural Networks - Motta, Pedro Crosara; Cesar Cortez, Paulo; Lobo Marques, Jao Alexandre (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
Even with more than 12 billion vaccine doses administered globally, the Covid-19 pandemic has caused several global economic, social, environmental, and healthcare impacts. Computer Aided Diagnostic (CAD) systems can serve as a complementary method to aid doctors in identifying regions of interest in images and help detect diseases. In addition, these systems can help doctors analyze the status of the disease and check for their progress or regression. To analyze the viability of using CNNs for differentiating Covid-19 CT positive images from Covid-19 CT negative images, we used a dataset collected by Union Hospital (HUST-UH) and Liyuan Hospital (HUST-LH) and made available at the Kaggle platform. The main objective of this chapter is to present results from applying two state-of-the-art CNNs on a Covid-19 CT Scan images database to evaluate the possibility of differentiating images with imaging features associated with Covid-19 pneumonia from images with imaging features irrelevant to Covid-19 pneumonia. Two pre-trained neural networks, ResNet50 and MobileNet, were fine-tuned for the datasets under analysis. Both CNNs obtained promising results, with the ResNet50 network achieving a Precision of 0.97, a Recall of 0.96, an F1-score of 0.96, and 39 false negatives. The MobileNet classifier obtained a Precision of 0.94, a Recall of 0.94, an F1-score of 0.94, and a total of 20 false negatives.

Covid-19 Detection Based on Chest X-Ray Images Using Multiple Transfer Learning CNN Models - dos Santos Silva, Bruno Riccelli; Cesar Cortez, Paulo; Crosara Motta, Pedro; Lobo Marques, Joao Alexandre (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
The gold standard to detect SARS-CoV-2 infection considers testing methods based on Polymerase Chain Reaction (PCR). Still, the time necessary to confirm patient infection can be lengthy, and the process is expensive. In parallel, X-Ray and CT scans play an important role in the diagnosis and treatment processes. Hence, a trusted automated technique for identifying and quantifying the infected lung regions would be advantageous. Chest X-rays are two-dimensional images of the patient’s chest and provide lung morphological information and other characteristics, like ground-glass opacities (GGO), horizontal linear opacities, or consolidations, which are typical characteristics of pneumonia caused by COVID-19. This chapter presents an AI-based system using multiple Transfer Learning models for COVID-19 classification using Chest X-Rays. In our experimental design, all the classifiers demonstrated satisfactory accuracy, precision, recall, and specificity performance. On the one hand, the Mobilenet architecture outperformed the other CNNs, achieving excellent results for the evaluated metrics. On the other hand, Squeezenet presented a regular result in terms of recall. In medical diagnosis, false negatives can be particularly harmful because a false negative can lead to patients being incorrectly diagnosed as healthy. These results suggest that our Deep Learning classifiers can accurately classify X-ray exams as normal or indicative of COVID-19 with high confidence.

COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review - Ribeiro, Pedro; Marques, João Alexandre Lobo; Rodrigues, Pedro Miguel (Bioengineering, 2023)
Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease.
Number: 2 Publisher: Multidisciplinary Digital Publishing Institute
Keywords: COVID-19; artificial intelligence; computerized diagnostic systems; image processing; signal processing

Crisis Management – Dystopic Scenario VS Real-World Pandemic: The Case of Saramago’s Blindness that Anticipated the Initial Managerial Approach to Covid-19 Pandemic Taken by Unprepared Governments Worldwide - Rouxinol, Hernâni Tiago Caveirinhas (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living - Luo, Renfei; Marques, João Alexandre Lôbo; Ong, Kok-Leong; Fong, Simon (International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH), 2020)
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...
0 citations (Crossref) [2022-09-21] Publisher: IGI Global

EFFECTIVENESS ANALYSIS OF WATERFALL AND AGILE PROJECT MANAGEMENT METHODOLOGIES – A CASE STUDY FROM MACAU'S CONSTRUCTION INDUSTRY - Marques, Joao Alexandre Lobo; Morais, João José Bragança dos Reis; Alves, José; Gonçalves, Marcus (Revista Gestão em Análise, 2023)
The adoption of project management techniques is a crucial decision for corporate governance in construction companies since the management of areas such as risk, cost, and communications is essential for the success or failure of an endeavor. Nevertheless, different frameworks based on traditional or agile methodologies are available with several approaches, which may create several ways to manage projects. The primary purpose of this work is to investigate the adequate project management methodology for the construction industry from a general perspective and consider a case study from Macau. The methodology considered semi-structured interviews and a survey comparing international and local project managers from the construction industry. The interviews indicate that most construction project managers still follow empirical methods with no specific methodology but consider the adoption of traditional waterfall approaches. In contrast, according to the survey, most project managers and construction managers agree that the project's efficacy needs to increase, namely in planning, waste minimization, communication increase, and focus on the Client's feedback. In addition, there seems to be a clear indication that agile methodology could be implemented in several types of projects, including hospitality development projects. A hybrid development approach based on the Waterfall and Agile methodologies as a tool for the project management area may provide a more suitable methodology for project managers to follow.
Number: 1
Keywords: PMBOK; agile; construction industry; project management methodologies

Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices - Rodrigues, Pedro Miguel; Madeiro, João Paulo; Marques, João Alexandre Lobo (Bioengineering, 2023)
In recent years, the integration of Machine Learning (ML) techniques in the field of healthcare and public health has emerged as a powerful tool for improving decision-making processes [...]
Number: 7 Publisher: Multidisciplinary Digital Publishing Institute
Keywords: n/a

Epidemic analytics for decision supports in COVID19 crisis - Lobo Marques, Joao Alexandre; Fong, Simon (Springer, 2022)
Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.

Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

Evaluation of ECG Non-linear Features in Time-Frequency Domain for the Discrimination of COVID-19 Severity Stages - Ribeiro, Pedro; Pordeus, Daniel; Zacarias, Laíla; Leite, Camila; Alves Neto, Manoel; Aires Peixoto Jr, Arnaldo; de Oliveira, Adriel; Paulo Madeiro, João; Lobo Marques, Joao Alexandre; Miguel Rodrigues, Pedro (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
In 2020, the World Health Organization declared the Coronavirus Disease 19 a global pandemic. While detecting COVID-19 is essential in controlling the disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a CT-Scan room. In other cases, the devices are not available. The adoption of ambulatory monitoring examinations, such as Electrocardiography (ECG), can be considered a viable tool to address the patient’s cardiovascular condition and to act as a predictor for future disease outcomes. In this investigation, ten non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) extracted from 2 ECG signals (collected from 2 different patient’s positions). Windows of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested: (Low vs. Moderate, Low vs. Severe, Moderate vs. Severe) and 1 Multi-class comparison (All vs. All)). The results indicate that some statistically significant parameter distributions were found for all comparisons. (Low vs. Moderate—Approximate Entropy p-value = 0.0067 < 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 < 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 < 0.05, All vs. All—Correlation Dimension p-value = 0.0185 < 0.05. The non-linear analysis of the time-frequency representation of the ECG signal can be considered a promising tool for describing and distinguishing the COVID-19 severity activity along its different stages.
Keywords: COVID-19; ECG signals; Non-linear analysis; Statistical analysis

Evaluation of mathematical models for QRS feature extraction and QRS morphology classification in ECG signals - do Vale Madeiro, João Paulo; Lobo Marques, João Alexandre; Han, Tao; Coury Pedrosa, Roberto (Measurement, 2020)
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.
13 citations (Crossref) [2022-09-21]
Keywords: ECG feature extraction; Mathematical modeling; Morphology classification; QRS complex delineation

Exploratory Analysis of Project Management Adoption and Maturity Level of IT Companies–A Comparison between Macao and Hengqin - Wong, Ka Seng; Lobo Marques, João Alexandre (Journal of Advanced Management Science, 2023)

Exploratory Data Analysis on Clinical and Emotional Parameters of Pregnant Women with COVID-19 Symptoms - Lobo Marques, Joao Alexandre; Macedo, Danielle S.; Motta, Pedro; dos Santos Silva, Bruno Riccelli; Carvalho, Francisco Herlanio Costa; Kehdi, Renata Castro; Cavalcante, Letícia Régia Lima; da Silva Viana, Marylane; Lós, Deniele; Fiorenza, Natália Gindri (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
The scientific literature indicates that pregnant women with COVID-19 are at an increased risk for developing more severe illness conditions when compared with non-pregnant women. The risk of admission to an ICU (Intensive Care Unit) and the need for mechanical ventilator support is three times higher. More significantly, statistics indicate that these patients are also at 70% increased risk of evolving to severe states or even death. In addition, other previous illnesses and age greater than 35 years old increase the risk for the mother and the fetus, including a higher number of cesarean sections, higher systolic and diastolic maternal blood pressure, increasing the risk of eclampsia, and, in some cases, preterm birth. Additionally, pregnant women have more Emotional lability/fluctuations (between positive and negative feelings) during the entire pregnancy. The emotional instability and brain fog that takes place during gestation may open vulnerability for neuropsychiatric symptoms of long COVID, which this population was not studied in depth. The present Chapter characterizes the database presented in this work with clinical and survey data collected about emotions and feelings using the Coronavirus Perinatal Experiences—Impact Survey (COPE-IS). Pregnant women with or without COVID-19 symptoms who gave birth at the Assis Chateaubriand Maternity Hospital (MEAC), a public maternity of the Federal University of Ceara, Brazil, were recruited. In total, 72 mother-infant dyads were included in the study and are considered in this exploratory analysis. The participants have undergone serological tests for SARS-CoV-2 antibody detection and a nasopharyngeal swab test for COVID-19 diagnoses by RT-PCR. A comprehensive Exploratory Data Analysis (EDA) is performed using frequency distribution analysis of multiple types of variables generated from numerical data, multiple-choice, categorized, and Likert-scale questions.

Exploring EEG Signal Features for Predicting Post Cardiac Arrest Prognosis - Guilherme Cunha Santos, Antonio; Alexandre Lobo Marques, Joao; Rigo Jr., Luis; Paulo Madeiro, João (2023)

Facial expression for marketing and consumer behavior: A Bibliometric analysis from 2013-2023 using Data Visualization Tools - Qian, Cheng; Lobo Marques, Joao Alexandre (Proceedings of the 2023 14th International Conference on E-business, Management and Economics, 2023)
The classification of emotions based on facial expressions have been a new topic of research in recent years, especially in marketing and consumer behavior areas. However, there is lack of studies to understand how the research topic is developed in terms of bibliometric data. Therefore, the purpose of this work is to provide a bibliometric analysis of the research on the analysis of facial expressions for marketing and consumer behavior, identifying the state of the art, the latest research direction, and other indicators. We extracted data from Web of Science (WOS) platform, considering its core database, resulting in a total of 117 articles. The software Vosviewer was used to analyze the data and graphically visualize the results. This study indicates some of the most influential authors citations and coupling analysis in this specific field, identifies journals with the most published articles, and provide trends of the research area based on the analysis of keywords and corresponding number of articles per year. The results shows that 11 articles (9.4%) were cited more than 100 times, and the two most prolific authors published 5 articles, and the two most influential authors are Bouaziz Sofien and Pauly mark(270 citations) in this field. Of the 117 articles retrieved by WOS, more than 70% were published in high impact journals. The bibliometric analysis of the existing work in this study provides a valuable and reliable reference for researchers in this field and makes a reasonable prediction of the research direction trends.
Keywords: Bibliometric analysis; Consumer; Facial expression; Marketing; Research; VOSviewer

Fast Cluster-learning with Prior Probability from Big Dataset - Li, Tengyue; Fong, Simon; Lobo Marques, Joao Alexandre; Wong, Raymond K. (2018 5th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2018)
Association Rule Mining by Aprior method has been one of the popular data mining techniques for decades, where knowledge in the form of item-association rules is harvested from a dataset. The quality of item-association rules nevertheless depends on the concentration of frequent items from the input dataset. When the dataset becomes large, the items are scattered far apart. It is known from previous literature that clustering helps produce some data groups which are concentrated with frequent items. Among all the data clusters generated by a clustering algorithm, there must be one or more clusters which contain suitable and frequent items. In turn, the association rules that are mined from such clusters would be assured of better qualities in terms of high confidence than those mined from the whole dataset. However, it is not known in advance which cluster is the suitable one until all the clusters are tried by association rule mining. It is time consuming if they were to be tested by brute-force. In this paper, a statistical property called prior probability is investigated with respect to selecting the best out of many clusters by a clustering algorithm as a pre-processing step before association rule mining. Experiment results indicate that there is correlation between prior probability of the best cluster and the relatively high quality of association rules generated from that cluster. The results are significant as it is possible to know which cluster should be best used for association rule mining instead of testing them all out exhaustively.
0 citations (Crossref) [2022-09-21] ISSN: 2640-0146
Keywords: Association Rule Mining; Big Data; Clustering; Clustering algorithms; Data mining; Itemsets; Preprocessing; Prior Probability; Probabilistic logic; Probability distribution

Forecasting COVID-19 Time Series Based on an Autoregressive Model - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

Framework proposition for identification, impact analysis and intervention on causes of delay in construction projects in the Greater Bay based on descriptive analytics and artificial intelligence - Felix, Fung Ka Chun (University of Saint Joseph, 2023)

Health and Well-Being Education: Extending the SCARF Learning Analytics Model for Identifying the Learner Happiness Indicators - Li, Tengyue; Marques, Joao Alexandre Lobo; Fong, Simon (International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH), 2020)
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...
0 citations (Crossref) [2022-09-21] Publisher: IGI Global

IDENTIFICATION OF CRITICAL FACTORS TO DETERMINE THE INTENTION OF GOVERNMENT SERVICE MINI-PROGRAM ADOPTION THROUGH MOBILE PAYMENT PLATFORMS IN MACAU AND THE GREATER BAY AREA - Lai Chi Meng; Marques, João Alexandre Lobo (University of Saint Joseph, 2023)
Government service mini-programs have become an integral component of eGovernment in the Greater Bay Area, and successful eGovernment is necessary for building a smart city. Service quality and citizens' trust play a vital role in urban integration and in-depth cooperation in the Bay Area. The ubiquitous nature of mini-programs based on WeChat and Alipay provides excellent flexibility in accessing government services. Technology advantages, mutual recognition of cross-border data, and online transactions bring value and benefits to citizens. However, the mechanism of mini-program adoption has not been elaborated. Homogenization, conflict of regulations, and policy effectiveness are issues of great concern. This study employed Self-Determination Theory and Motivation Theory, proposed an empirical model based on the extended SOR paradigm, and aimed to identify the critical factors determining the intention of government service mini-program adoption from the user’s perspective. Six hundred and nine valid samples were collected from Macau, Hong Kong, Guangzhou, and Shenzhen through online survey platforms. The findings suggested that service quality, trust in eGovernment, ubiquity, and social influence constituted the determinants of intention to adopt. Service quality and ubiquity were salient determinants, and a great extent of service quality and ubiquity could promote perceived value and intention. Citizens' trust in government service mini-programs was reasonable, where benevolence, integrity, and competence were crucial indicators of trust. Social influence amplified and transmitted risk perception while perceived risk significantly reduced intention. Perceived value positively associated with the four determinants and enhanced user intention; it acted as a mediator with high explanatory power in the model. Government support received positive ratings from citizens; it negatively regulated the relationship between intention and the determinants respectively, implying that excessive intervention from the government could lead to inhibition. Finally, we proposed relevant implications and suggestions for the GBA government agents and policymakers
Keywords: Thesis and Dissertations PhD in Government Studies (D-GS); University of Saint Joseph

Influence of management in change readiness maturity of teams in organizations - Reis, Joana Mateus (University of Saint Joseph, 2019)
Keywords: Theses and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Innovation Analysis and Research on the Mode of KOL Live Commerce - Fong, Hou Man (University of Saint Joseph, 2024)
With the rapid development of digital media, internet celebrity live streaming has become a key factor in influencing the consumer decision-making of adolescents, presenting unique regional characteristics in different socio-cultural and economic contexts. This study investigates the differences in consumption habits among adolescents in Macau and Mainland China and their impact on the innovation and reform of the commercial model of internet celebrity live streaming. The methodology employs a questionnaire survey and data analysis to systematically compare the consumption behavior of adolescents in Macao and mainland China, collecting live streaming consumption habits of adolescents in both regions. Statistical methods are used to compare and analyze the consumption patterns within the regions. The analysis indicates that influencers, as internet celebrities with a large number of fans on social media, have a significant impact on adolescents' consumption decisions through their recommendations and evaluations. Firstly, the convenience and diversity of e-commerce platforms provide adolescents with a wealth of consumption choices, such as characteristics and usage effects of products. Secondly, the recommendations and evaluations of influencers have become an important reference for adolescents' consumption. Results show that adolescents in Macau tend to seek entertainment and interaction in their consumption of internet celebrity live streaming, whereas those in Mainland China place greater emphasis on the practicality of the live streaming content and the cost-effectiveness of the products. Moreover, the study reveals the roles of socio-cultural and economic levels in the differences in consumption between the two regions. Based on these insights, it is recommended that live streaming platforms should advance the innovation and reform of their business models to cater to different market characteristics—such as optimizing content recommendation algorithms, enhancing interactive elements, and improving the integration of e-commerce features, thereby promoting business sustainability and economic benefits
Keywords: Adolescent Consumption Habits; Internet Celebrity; Live Streaming; Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Intersectoral Linkages in Booming Sector Economies - Gomes, Sérgio Ricardo Santos; Marques, Joao Alexandre Lobo; Reis, Ricardo Ferreira (2022 13th International Conference on E-business, Management and Economics, 2022)

Investigation on citizen trust towards e-government services in GBA – A study on WeChat and Alipay government service mini-programs - Lai, Chimeng; Joao, Alexandre Lobo Marques (2022 13th International Conference on E-business, Management and Economics, 2022)
Citizens' trust in eGovernment is crucial for the successful implementation of new electronic services. This relationship in the Greater Bay Area (GBA) plays an essential role since the Government services rely on mobile mini-programs This study investigates the trust towards government service mini-programs in WeChat and Alipay. A user feedback questionnaire was designed, and a total of 609 valid samples were collected from Shenzhen, Guangzhou, Hong Kong, and Macau. The findings imply that competence, integrity, and benevolence are the key components of trust in e-government (TIEG). TIEG positively influences perceived value (PV), which positively affects citizens' Intention to adopt service mini-programs. PV significantly mediates the relationship between TIEG and Intention. Although TIEG does not effectively reduce perceived risk (PR), risk issues cannot be ignored in the adoption process. Finally, this article proposes relevant implications and suggestions for the GBA government agents and policy makers.
0 citations (Crossref) [2023-03-15]

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

Lead: An iOS Application to Help in the Construction of New Habits - de Aguiar, André Wescley Oliveira; Bezerra, Jagni Dasa Horta; Marques, João Alexandre Lobo; de Alexandria, Auzuir Ripardo (International Journal of Information and Electronics Engineering, 2019)

Leadership and Neurosciences - The analysis of emotional arousal during decision-making processes with decision-makers exposed to acute stress - Marques, Joao Alexandre Lobo (European Conference on Management Leadership and Governance, 2023)
Corporate leaders are constantly dealing with stress in parallel with continuous decision-making processes. The impact of acute stress on decision-making activities is a relevant area of study to evaluate the impact of the decisions made, and create tools and mechanisms to cope with the inevitable exposure to stress and better manage its impact. The intersection of leadership and neurosciences techniques is called Neuroleadership. In this work, an experiment is proposed to detect and measure the emotional arousal of two groups of business professionals, divided into two groups. The first one is the intervention/stress group, n=30, exposed to stressful conditions, and the control group, n=14, not exposed to stress. The participants are submitted to a sequence of computerized stimuli, such as watching videos, answering survey questions, and making decisions in a realistic office environment. The Galvanic Skin Response (GSR) biosensor monitors emotional arousal in real-time. The experiment design implemented stressors such as visual effects, defacement, unfairness, and time-constraint for the intervention group, followed by decision-making tasks. The results indicate that emotional arousal was statistically significantly higher for the intervention/stress group, considering Shapiro and Mann-Whitney tests. The work indicates that GSR is a reliable stress detector and may be useful to predict negative impacts on executive professionals during decision-making activities.
Number: 1

Lung Segmentation of Chest X-Rays Using Unet Convolutional Networks - dos Santos Silva, Bruno Riccelli; Cesar Cortez, Paulo; Gomes Aguiar, Rafael; Rodrigues Ribeiro, Tulio; Pereira Teixeira, Alexandre; Bernardo Gois, Francisco Nauber; Lobo Marques, Joao Alexandre (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
The gold standard to detect SARS-CoV-2 infection consider testing methods based on Polymerase Chain Reaction (PCR). Still, the time necessary to confirm patient infection can be lengthy, and the process is expensive. On the other hand, X-Ray and CT scans play a vital role in the auxiliary diagnosis process. Hence, a trusted automated technique for identifying and quantifying the infected lung regions would be advantageous. Chest X-rays are two-dimensional images of the patient’s chest and provide lung morphological information and other characteristics, like ground-glass opacities (GGO), horizontal linear opacities, or consolidations, which are characteristics of pneumonia caused by COVID-19. But before the computerized diagnostic support system can classify a medical image, a segmentation task should usually be performed to identify relevant areas to be analyzed and reduce the risk of noise and misinterpretation caused by other structures eventually present in the images. This chapter presents an AI-based system for lung segmentation in X-ray images using a U-net CNN model. The system’s performance was evaluated using metrics such as cross-entropy, dice coefficient, and Mean IoU on unseen data. Our study divided the data into training and evaluation sets using an 80/20 train-test split method. The training set was used to train the model, and the evaluation test set was used to evaluate the performance of the trained model. The results of the evaluation showed that the model achieved a Dice Similarity Coefficient (DSC) of 95%, Cross entropy of 97%, and Mean IoU of 86%.

Machine learning-based cardiac activity non-linear analysis for discriminating COVID-19 patients with different degrees of severity - Ribeiro, Pedro; Marques, João Alexandre Lobo; Pordeus, Daniel; Zacarias, Laíla; Leite, Camila Ferreira; Sobreira-Neto, Manoel Alves; Peixoto, Arnaldo Aires; de Oliveira, Adriel; Madeiro, João Paulo do Vale; Rodrigues, Pedro Miguel (Biomedical Signal Processing and Control, 2024)
Objective: This study highlights the potential of an Electrocardiogram (ECG) as a powerful tool for early diagnosis of COVID-19 in critically ill patients with limited access to CT–Scan rooms. Methods: In this investigation, 3 categories of patient status were considered: Low, Moderate, and Severe. For each patient, 2 different body positions have been used to collect 2 ECG signals. Then, from each collected signal, 10 non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) were extracted every 1s ECG time-series length to serve as entries for 19 Machine learning classifiers within a leave-one-out cross-validation procedure. Four different classification scenarios were tested: Low vs. Moderate, Low vs. Severe, Moderate vs. Severe and one Multi-class comparison (All vs. All). Results: The classification report results were: (1) Low vs. Moderate - 100% of Accuracy and 100% of F1–Score; (2) Low vs. Severe - Accuracy of 91.67% and an F1–Score of 94.92%; (3) Moderate vs. Severe - Accuracy of 94.12% and an F1–Score of 96.43%; and (4) All vs All - 78.57% of Accuracy and 84.75% of F1–Score. Conclusion: The results indicate that the applied methodology could be considered a good tool for distinguishing COVID-19’s different severity stages using ECG signals. Significance: The findings highlight the potential of ECG as a fast and effective tool for COVID-19 examination. In comparison to previous studies using the same database, this study shows a 7.57% improvement in diagnostic accuracy for the All vs All comparison.
Keywords: Accuracy; COVID-19; ECG signals; Machine learning classifiers; Non-linear analysis; –

Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures - Gois, Francisco Nauber Bernardo; Marques, João Alexandre Lobo; de Oliveira Dantas, Allberson Bruno; Santos, Márcio Costa; Neto, José Valdir Santiago; de Macêdo, José Antônio Fernandes; Du, Wencai; Li, Ye (Computer and Information Science, 2023)
Fast and efficient malaria diagnostics are essential in efforts to detect and treat the disease in a proper time. The standard approach to diagnose malaria is a microscope exam, which is submitted to a subjective interpretation. Thus, the automating of the diagnosis process with the use of an intelligent system capable of recognizing malaria parasites could aid in the early treatment of the disease. Usually, laboratories capture a minimum set of images in low quality using a system of microscopes based on mobile devices. Due to the poor quality of such data, conventional algorithms do not process those images properly. This paper presents the application of deep learning techniques to improve the accuracy of malaria plasmodium detection in the presented context. In order to increase the number of training sets, deep convolutional generative adversarial networks (DCGAN) were used to generate reliable training data that were introduced in our deep learning model to improve accuracy. A total of 6 experiments were performed and a synthesized dataset of 2.200 images was generated by the DCGAN for the training phase. For a real image database with 600 blood smears with malaria plasmodium, the proposed Deep Learning architecture obtained the accuracy of 100% for the plasmodium detection. The results are promising and the solution could be employed to support a mass medical diagnosis system.

Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures - Gois, Francisco Nauber Bernardo; Marques, João Alexandre Lobo; De Oliveira Dantas, Allberson Bruno; Santos, Márcio Costa; Neto, José Valdir Santiago; De Macêdo, José Antônio Fernandes; Du, Wencai; Li, Ye (Computer and Information Science, 2023)

Medical Information Extraction of Clinical Notes and Pictorial Visualisation of Electronic Medical Records Summary Interface - Singh, Praveen; Chaudhary, Gopal; Lobo Marques, Joao Alexandre (Smart Distributed Embedded Systems for Healthcare Applications, 2023)

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

NEUROFINANCE - ANALYZING AND UNDERSTANDING STOCK TRADER’S DECISION-MAKING PROCESS BASED ON REAL-TIME MONITORING OF EMOTIONAL REACTIONS - Hsu, Hsin-Tzu (University of Saint Joseph, 2023)
Human emotions can be meticulously associated with decision-making, and emotion can generate behaviours. Due to the fact that it could be bias and exhaustively complex to examine how human beings make choices, important groups of study in finance are stock traders and non-traders. The objective of this work is to analyze the connection between emotions and the decision-making process of investors and non-investors to understand how emotional arousal might dictate the process of deciding policy. As facial expressions are fleeting, neuroscience tools such as AFFDEX (Real-Time Facial Expression Analysis), Eye-Tracking, and GSR (galvanic skin response) were adopted to facilitate the experiment and its accompanying analysis process. Thirty-seven participants attended the study, ranging from 18 to 72 years old; the distribution of investors and non-investors was twenty-four and thirteen, respectively. The experiment initially disclosed a thought-provoking result between the two groups under the certainty and risk-seeking prospect theory; there were more risk-takers among non-investors at 75%, while investors were inclined toward certainty at 79.17%. The implication could be that the non-investing individuals were less complex in thought and therefore pursued higher returns besides a high probability of losing the game. In addition, the automatic emotion classification system indicates that when non-investors confronted a stock trending chart beyond their acquaintance or knowledge, they were psychologically exposed to fear, anger, sadness, and surprise. Investors, on the contrary, were detected with disgust, joy, contempt, engagement, sadness, and surprise, where sadness and surprise overlapped in both parties. Under time pressure conditions, 54.05% of investors or non-investors tend to make decisions after the peak(s) of emotional arousal. Variations were found in the deciding points of the slopes: 2.70% were decided right after the peak(s), 37.84% waited until the emotions turned stable, and 13.51% were determined as the emotional indicators started to slide downwards. Several combinations of emotional responses were associated with decisions. For example, negative emotions could induce passive decision-making, in this case, to sell the stock; nevertheless, it was also examined that as the slope slipped downwards to a particular horizontal point, the individuals became more optimistic and selected the "BUY" option. The support of physiological monitoring tools makes it possible to capture the individuals' responses and discover the science of decision-making. Future works may consider expanding the study to more significant demographic populations for further discoveries
Keywords: Decision-making; Emotions; Investors; Physiological Monitoring; Stock Trading; Theses and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Neurofinance: Exploratory Analysis Stock Trader's Decision-Making Process by Real-Time Monitoring of Emotional Reactions - Hsu, Hsin-Tzu; Marques, João Alexandre Lobo (European Conference on Management Leadership and Governance, 2023)
Human emotions can be associated with decision-making, and emotions can generate behaviors. Due to the fact that it could be biased and exhaustively complex to examine how human beings make choices, it is necessary to consider relevant groups of study, such as stock traders and non-traders in finance. This work aims to analyze the connection between emotions and the decision-making process of investors and non-investors submitted to the same set of stimuli to understand how emotional arousal might dictate the decision process. Neuroscience monitoring tools such as Real-Time Facial Expression Analysis (AFFDEX), Eye-Tracking, and Galvanic Skin Response (GSR) were adopted to monitor the related experiments of this paper and its accompanying analysis process. Thirty-seven participants attended the study, 24 were classified as stock traders, and 13 were non-traders; the mean age for the groups was 35 and 25, respectively. The designed experiment initially disclosed a thought-provoking result between the two groups under the certainty and risk-seeking prospect theory; there were more risk-takers among non-investors at 75%, while investors were inclined toward certainty at 79.17%. The implication could be that the non-investing individuals were less complex in thought and therefore pursued higher returns besides a high probability of losing the game. In addition, the automatic emotion classification system indicates that when non-investors confronted a stock trending chart beyond their acquaintance or knowledge, they were psychologically exposed to fear, anger, sadness, and surprise. On the contrary, investors were detected with disgust, joy, contempt, engagement, sadness, and surprise, where sadness and surprise overlapped in both parties. Under time pressure conditions, 54.05% of investors or non-investors tend to make decisions after the peak(s) of emotional arousal. Variations were found in the deciding points of the slopes: 2.70% were decided right after the peak(s), 37.84% waited until the emotions turned stable, and 13.51% were determined as the emotional indicators started to slide downwards. Several combinations of emotional responses were associated with decisions. For example, negative emotions could induce passive decision-making, in this case, to sell the stock; nevertheless, it was also examined that as the slope slipped downwards to a particular horizontal point, the individuals became more optimistic and selected the "BUY" option. Future works may consider expanding the study to larger sample size, different demographic groups, and other biometrics for further analysis and conclusions.
Number: 1

Neuromarketing and Global Branding Reaction Analysis Based on Real-Time Monitoring of Multiple Consumer's Biosignals and Emotions - Goncalves, Marcus V.; Marques, Joao Alexandre Lobo; Silva, Bruno Riccelli Santos; Luther, Valorie; Hayes, Sydney (2022)
Consumers' selections and decision-making processes are some of the most exciting and challenging topics in neuromarketing, sales, and branding. Multicultural influences and societal conditions are also crucial aspects to consider from a global perspective. Applying neuroscience tools and techniques in international marketing and consumer behavior is an emergent and multidisciplinary field that aims to understand consumers' thoughts, reactions, and selection processes in branding and sales. This study focuses on real-time monitoring of different physiological signals using eye-tracking, facial expressions recognition, and Galvanic Skin Response (GSR) acquisition methods to analyze consumers' responses, detect emotional arousal, measure attention or relaxation levels, analyze perception, consciousness, memory, learning, motivation, preference, and decision-making. The primary purpose of this research was to monitor human subjects' reactions to these signals during an experiment designed in three phases consisting of different types of branding advertisements. The non-advertisement exposition was also monitored during the gathering of survey responses at the end of each phase. A feature extraction module was implemented with a data analytics module to calculate statistical metrics and decision-making supporting tools based on Principal Component Analysis (PCA) and Feature Importance (FI) determination based on the Random Forest technique. The results indicate that when compared to image ads, video ads are more effective in attracting consumers' attention and creating more emotional arousal.
Keywords: Branding Reaction; Consumer Biosignal; Galvanic Skin Response; consumer decision-making; eye-tracking; neuromarketing; neurosciences

Neuromarketing and Global Branding Reaction Analysis Based on Real-Time Monitoring of Multiple Consumer's Biosignals and Emotions - Goncalves, Marcus; Lobo Marques, Joao Alexandre; Silva, Bruno Riccelli Santos; Luther, Valorie; Hayes, Sydney (Journal of International Business and Management, 2023)
Consumers' selections and decision-making processes are some of the most exciting and challenging topics in neuromarketing, sales, and branding. From a global perspective, multicultural influences and societal conditions are crucial to consider. Neuroscience applications in international marketing and consumer behavior is an emergent and multidisciplinary field aiming to understand consumers' thoughts, reactions, and selection processes in branding and sales. This study focuses on real-time monitoring of different physiological signals using eye-tracking, facial expressions recognition, and Galvanic Skin Response (GSR) acquisition methods to analyze consumers' responses, detect emotional arousal, measure attention or relaxation levels, analyze perception, consciousness, memory, learning, motivation, preference, and decision-making. This research aimed to monitor human subjects' reactions to these signals during an experiment designed in three phases consisting of different branding advertisements. The nonadvertisement exposition was also monitored while gathering survey responses at the end of each phase. A feature extraction module with a data analytics module was implemented to calculate statistical metrics and decision-making supporting tools based on Principal Component Analysis (PCA) and Feature Importance (FI) determination based on the Random Forest technique. The results indicate that when compared to image ads, video ads are more effective in attracting consumers' attention and creating more emotional arousal.

Neuromarketing As A Tool To Measure And Evaluate The Consumer Behaviour Of Guanding Teahouse's Social Media Advertisement - Zeng, Ian Mei; Lobo Marques, Joao Alexandre (Proceedings of the 2023 14th International Conference on E-business, Management and Economics, 2023)
This research aims to evaluate a Macau tea brand's social media advertising effectiveness with neuromarketing tools, including physiological monitoring that can measure emotional arousal. This research bridges the gap of social media marketing on Instagram for brands through the neuromarketing method. Data from 40 respondents were collected with iMotions software using neuroscientific tools. This research uses the stimuli of Guanding Teahouse, a newly established Macau tea brand, to evaluate social media advertising effectiveness. The neuroscientific tools – Galvanic Skin Response (GSR) sensors, Eye-tracking, Facial Expression Analysis (FEA) and emotion analysis are used to do the experiment. The data analysis was drawn from one representative respondent to measure the emotions and attention on the Instagram advertisements. Video 1 recorded 9 GSR peaks and Video 2 recorded 12 GSR peaks, both videos attention is ranging between 96-98 indexes. Results show that advertising videos should focus more on the products than the model. Moreover, the participant is more interested in Video 2, but the effectiveness of advertising is showing a lower focus on the brand and the tea. Future studies should consider comparing the video advertising effectiveness of Instagram stories and Instagram reels to prevent disruption of video on the stories ad.

Neuromarketing: Evaluating Consumer Emotions and Preferences to Improve Business Marketing Management - Zeng, Ian Mei; Marques, João Alexandre Lobo (European Conference on Management Leadership and Governance, 2023)
The invention of neuroscience has benefited medical practitioners and businesses in improving their management and leadership. Neuromarketing, a field that combines neuroscience and marketing, helps businesses understand consumer behaviour and how they respond to advertising stimuli. This study aims to investigate the consumer purchase intention and preferences to improve the marketing management of the brand, based on neuroscientific tools such as emotional arousal using Galvanic Skin Response (GSR) sensors, eye-tracking, and emotion analysis through facial expressions classification. The stimuli for the experiment are two advertisement videos from the Macau tea brand “Guanding Teahouse” followed by a survey. The experiment was conducted on 40 participants. 76.2% of participants that chose the same product in the first survey responded with the same choice of products in the second survey. The GSR peaks in video ad 1 measured a total of 60. On the other hand, video ad 2 counted a total of 55 GSR peaks. The emotions in ad1 and ad2 have similar responses, with an attention percentage of 76%. The results showed that ad1 has a higher engagement time of 11.1% and ad2 has 9.6%, but only 19 of the respondent’s conducted engagement in video ad1, and 31 showed engagement in video ad2. The results demonstrated that although ad 1 has higher engagement rates, the respondents are more attracted to video ad 2. Therefore, ad2 has better marketing power than ad 1. Overall, this study bridges the gap of no previous research on measuring tea brand advertisements with the neuroscientific method. The results provide valuable insights for marketers to develop better advertisements and marketing campaigns and understand consumer preferences by personalising and targeting advertisements based on consumers' emotional responses and behaviour of consumers' purchase intentions. Future research could explore advertisements targeting different demographics.
Number: 1

Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks - Caldas, Weslley Lioba; Do Vale Madeiro, João Paulo; Pedrosa, Roberto Coury; Gomes, João Paulo Pordeus; Du, Wencai; Marques, João Alexandre Lobo (Computer and Information Science, 2023)

Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks - Caldas, Weslley Lioba; do Vale Madeiro, João Paulo; Pedrosa, Roberto Coury; Gomes, João Paulo Pordeus; Du, Wencai; Marques, João Alexandre Lobo (Computer and Information Science, 2023)
Continuous cardiac monitoring has been increasingly adopted to prevent heart diseases, especially the case of Chagas disease, a chronic condition that can degrade the heart condition, leading to sudden cardiac death. Unfortunately, a common challenge for these systems is the low-quality and high level of noise in ECG signal collection. Also, generic techniques to assess the ECG quality can discard useful information in these so-called chagasic ECG signals. To mitigate this issue, this work proposes a 1D CNN network to assess the quality of the ECG signal for chagasic patients and compare it to the state of art techniques. Segments of 10 s were extracted from 200 1-lead ECG Holter signals. Different feature extractions were considered such as morphological fiducial points, interval duration, and statistical features, aiming to classify 400 segments into four signal quality types: Acceptable ECG, Non-ECG, Wandering Baseline (WB), and AC Interference (ACI) segments. The proposed CNN architecture achieves a $$0.90 \pm 0.02$$accuracy in the multi-classification experiment and also $$0.94 \pm 0.01$$when considering only acceptable ECG against the other three classes. Also, we presented a complementary experiment showing that, after removing noisy segments, we improved morphological recognition (based on QRS wave) by 33% of the entire ECG data. The proposed noise detector may be applied as a useful tool for pre-processing chagasic ECG signals.
Keywords: Chagas disease; Deep learning; ECG quality assessment

Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

On the probability flow in the Stock market I: The Black-Scholes case - Arraut, Ivan; Au, Alan; Tse, Alan Ching-biu; Marques, Joao Alexandre Lobo (arXiv.org, 2020)
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.
Place: Ithaca, United States Publisher: Cornell University Library, arXiv.org Section: Quantitative Finance University: Cornell University Library arXiv.org
Keywords: General Finance

Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

Prediction for Decision Support During the COVID-19 Pandemic - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

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.

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

Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System - Fong, Simon James; Marques, João Alexandre Lobo; Li, Gloria; Dey, Nilanjan; Crespo, Rubén González; Herrera-Viedma, Enrique; Gois, Francisco Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of time series under analysis from the available data. The establishment of multiple correlations and causality between the data allows modeling the variables and probabilistic distributions and subsequently obtaining also probabilistic results for time series forecasting. To improve the predictor efficiency, computational intelligence techniques are proposed, including a fuzzy inference system and an Artificial Neural Network architecture. This type of model is suitable to be considered not only for the disease monitoring and compartmental classes, but also for managerial data such as clinical resources, medical and health team allocation, and bed management, which are data related to complex decision-making challenges.
Keywords: COVID-19; Composite Monte Carlo simulation; Healthcare decision-making systems; Prediction

PROJECT MANAGEMENT ADOPTION AND MATURITY LEVEL IN INFORMATION TECHNOLOGY INDUSTRY OF MACAU AND HENGQIN - Wong, Ka Seng (University of Saint Joseph, 2023)
Projects are tactical and operational initiatives, and achieving specific outcomes through projects can help organizations achieve strategic goals. The effective use of project management tools and techniques is essential to achieve successful results, since the goal is to maximize the realization of the project's plan by effectively using the budget, time, and resources provided by the project owner to achieve the project's original purpose. The Project Management Maturity Model (PMMM) is a tool for measuring project management capabilities and is essential to improve project and portfolio performance in different industries. The main objective of this research is to analyze and characterize the maturity level and capacity of the IT industry in Macau and HengQin based on the assessment of the PMMM. The research also aims to assess and compare the maturity level in the IT industry in Macau and HengQin. An online survey was conducted and sent to IT project managers from Macau and HenqQin. A total of 34 responses were collected, divided into 3 different parts: Part I - General Information, Part II - Project Management Areas, and Part III - Perception. The results indicate that, in general, Project Managers state that their companies do not follow Project Management standards and best practices, classifying as Low and Very Low essential PM areas such as Planning and Scheduling (68%), Scope (61%) and Communications (64%). From a comparison perspective, project managers in Macau follow less formal frameworks than Hengqin in managing the triple constraints of the project. The collected data also indicate that Macau's communication management and stakeholder engagement are less mature than Hengqin's. Furthermore, the data indicate that maturity level is not necessarily related to education level, which means not higher education has a higher maturity level. Recommendations are provided for the IT industry in both areas, and specific comments are provided for each group or professionals. In conclusion, this work allows a novel characterization and a better understanding of the Project Management adoption and maturity level of the IT Industry in Macau and Hengqin
Keywords: Theses and Dissertations Master of Business Administration (MBA); University of Saint Joseph; information technology; maturity level; project management; project management maturity model; project maturity assessment

PROJECT MANAGEMENT ADOPTION AND MATURITY LEVEL IN INFORMATION TECHNOLOGY INDUSTRY OF MACAU AND HENGQIN - Wong, Ka Seng (University of Saint Joseph, 2023)
Projects are tactical and operational initiatives, and achieving specific outcomes through projects can help organizations achieve strategic goals. The effective use of project management tools and techniques is essential to achieve successful results, since the goal is to maximize the realization of the project's plan by effectively using the budget, time, and resources provided by the project owner to achieve the project's original purpose. The Project Management Maturity Model (PMMM) is a tool for measuring project management capabilities and is essential to improve project and portfolio performance in different industries. The main objective of this research is to analyze and characterize the maturity level and capacity of the IT industry in Macau and HengQin based on the assessment of the PMMM. The research also aims to assess and compare the maturity level in the IT industry in Macau and HengQin. An online survey was conducted and sent to IT project managers from Macau and HenqQin. A total of 34 responses were collected, divided into 3 different parts: Part I - General Information, Part II - Project Management Areas, and Part III - Perception. The results indicate that, in general, Project Managers state that their companies do not follow Project Management standards and best practices, classifying as Low and Very Low essential PM areas such as Planning and Scheduling (68%), Scope (61%) and Communications (64%). From a comparison perspective, project managers in Macau follow less formal frameworks than Hengqin in managing the triple constraints of the project. The collected data also indicate that Macau's communication management and stakeholder engagement are less mature than Hengqin's. Furthermore, the data indicate that maturity level is not necessarily related to education level, which means not higher education has a higher maturity level. Recommendations are provided for the IT industry in both areas, and specific comments are provided for each group or professionals. In conclusion, this work allows a novel characterization and a better understanding of the Project Management adoption and maturity level of the IT Industry in Macau and Hengqin
Keywords: Theses and Dissertations Master of Business Administration (MBA); University of Saint Joseph; information technology; maturity level; project management; project management maturity model; project maturity assessment

Research and Technology Development Achievements During the COVID-19 Pandemic—An Overview - Marques, João Alexandre Lobo; Fong, Simon James; Li, G.; Arraut, Ivan; Gois, F. Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
At the beginning of 2020, the World Health Organization (WHO) started a coordinated global effort to counterattack the potential exponential spread of the SARS-Cov2 virus, responsible for the coronavirus disease, officially named COVID-19. This comprehensive initiative included a research roadmap published in March 2020, including nine dimensions, from epidemiological research to diagnostic tools and vaccine development. With an unprecedented case, the areas of study related to the pandemic received funds and strong attention from different research communities (universities, government, industry, etc.), resulting in an exponential increase in the number of publications and results achieved in such a small window of time. Outstanding research cooperation projects were implemented during the outbreak, and innovative technologies were developed and improved significantly. Clinical and laboratory processes were improved, while managerial personnel were supported by a countless number of models and computational tools for the decision-making process. This chapter aims to introduce an overview of this favorable scenario and highlight a necessary discussion about ethical issues in research related to the COVID-19 and the challenge of low-quality research, focusing only on the publication of techniques and approaches with limited scientific evidence or even practical application. A legacy of lessons learned from this unique period of human history should influence and guide the scientific and industrial communities for the future.
Keywords: COVID-19; Research cooperation; Research ethics; Scientific research; Technology development

Segmentation of CT-Scan Images Using UNet Network for Patients Diagnosed with COVID-19 - Bernardo Gois, Francisco Nauber; Lobo Marques, Joao Alexandre (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
The use of computational tools for medical image processing are promising tools to effectively detect COVID-19 as an alternative to expensive and time-consuming RT-PCR tests. For this specific task, CXR (Chest X-Ray) and CCT (Chest CT Scans) are the most common examinations to support diagnosis through radiology analysis. With these images, it is possible to support diagnosis and determine the disease’s severity stage. Computerized COVID-19 quantification and evaluation require an efficient segmentation process. Essential tasks for automatic segmentation tools are precisely identifying the lungs, lobes, bronchopulmonary segments, and infected regions or lesions. Segmented areas can provide handcrafted or self-learned diagnostic criteria for various applications. This Chapter presents different techniques applied for Chest CT Scans segmentation, considering the state of the art of UNet networks to segment COVID-19 CT scans and a segmentation experiment for network evaluation. Along 200 epochs, a dice coefficient of 0.83 was obtained.

Stock Market Prediction Using Artificial Intelligence: A Systematic Review of Systematic Reviews - Lin, Coka Chinyang; Marques, Joao Alexandre Lobo (2023)
There are many systematic reviews on predicting stock. However, each of them reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review and conclude the systematic reviews on AI and stock to provide particularly useful predictions for making future strategies for stock markets. Keywords that would fall under the broad headings of AI and stock prediction were looked up in two databases, Scopus and Web of Science. We screened 69 titles and read 43 systematic reviews which include more than 379 studies before retaining 10 of them.
Keywords: Deep Learning; Long Short-Term Memory (LSTM); Machine Learning; Neural Networks (NN); Support Vector Machines (SVM)

Sustainable Practices in Hotel Chains—A Comparative Analysis of Official Annual Hospitality Sustainable Reports from Listed Companies in Macau SAR, China - Santos, Ana Sofia Kong; Marques, João Alexandre Lobo (New Perspectives and Paradigms in Applied Economics and Business, 2023)
In the last few years, the tourism industry has experienced rapid expansion and diversification, making it one of the fastest-growing financial industries in the world. Consequently, the hotel industry has significantly affected the environment's long-term viability. Many hotels have begun voluntarily implementing environmentally sustainable practices as they become more aware of their ecological footprint. There has been a great deal of discussion about the effects of hotel operations on the environment and tourism sustainability in Macau. It is because of these negative impacts that hoteliers have adopted green practices in an attempt to minimize them. By developing sustainability reports, hotels can set goals, measure performance, and manage change, resulting in better sustainability. It could also be viewed as a strategy to enhance the company’s sustainability reporting to ensure stakeholders know what the company does. The objective of this study is twofold based on the analysis of the official sustainability reports of four major hotel chains. Firstly, seven categories of sustainable practices effectively adopted by these chain hotels are identified and clusterized. Second, it is presented in which areas some hotels performed more efficiently than others, considering the UN Sustainable Development Goals (SDGs) as a reference. The results allow a comprehensive clusterized analysis of the industry in a highly developed gaming and entertainment area of South China and create a clear comparison between relevant players and their concerns about sustainability practices.
Keywords: Environmentally sustainable practices; Macau hotels; Sustainable development goals (SDGs); Sustainable reports; Tourism industry

Technology Developments to Face the COVID-19 Pandemic: Advances, Challenges, and Trends - Lobo Marques, Joao Alexandre; Fong, Simon James (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
The global pandemic triggered by the Corona Virus Disease firstly detected in 2019 (COVID-19), entered the fourth year with many unknown aspects that need to be continuously studied by the medical and academic communities. According to the World Health Organization (WHO), until January 2023, more than 650 million cases were officially accounted (with probably much more non tested cases) with 6,656,601 deaths officially linked to the COVID-19 as plausible root cause. In this Chapter, an overview of some relevant technical aspects related to the COVID-19 pandemic is presented, divided in three parts. First, the advances are highlighted, including the development of new technologies in different areas such as medical devices, vaccines, and computerized system for medical support. Second, the focus is on relevant challenges, including the discussion on how computerized diagnostic supporting systems based on Artificial Intelligence are in fact ready to effectively help on clinical processes, from the perspective of the model proposed by NASA, Technology Readiness Levels (TRL). Finally, two trends are presented with increased necessity of computerized systems to deal with the Long Covid and the interest on Precision Medicine digital tools. Analyzing these three aspects (advances, challenges, and trends) may provide a broader understanding of the impact of the COVID-19 pandemic on the development of Computerized Diagnostic Support Systems.

The Application of Nudge Theory in Ensuring Change Acceptance in the Hospitality-Gaming Industry – A Case Analysis from Macau SAR, China - Tagulao, Thea Clarice Saavedra; Marques, Joao Alexandre Lobo (2022 13th International Conference on E-business, Management and Economics, 2022)
As the rate of change increases exponentially, organizations must adapt quickly to the business landscape's volatility, uncertainty, complexity, and ambiguity (VUCA). As a result, organizations must implement agile strategies and practices to ensure their responsiveness and readiness to any changes brought about by internal or external factors. With a greater number of changes, change agents are tasked with implementing various change management methodologies to ensure that change recipients accept change initiatives. This research will look at one of the methodologies used by change agents, the use of nudges from Thaler and Sunstein's Nudge Theory, which is a subtle intervention to influence an individual's decision-making with the goal of steering them towards a specific desired outcome; and analyze their effectiveness towards the change recipients when implemented. Change agents were interviewed on the application of Nudge Theory to change recipients when managing to change initiatives within their respective organizations. The results indicate that the use of nudges created by the change agents can significantly impact the level of resistance from the change recipients. If used correctly, the Nudge Theory can mitigate change resistance, and the success of a change initiative is higher. But, if change recipients are forced to comply, their resistance will be greater, affecting the organization overall.
0 citations (Crossref) [2023-03-15]
Keywords: Change Initiatives; Change Resistance; Nudge Theory; Organizational Behavior; Organizational Change Management

The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic - Fong, Simon James; Marques, João Alexandre Lobo; Li, Gloria; Dey, Nilanjan; Crespo, Rubén González; Herrera-Viedma, Enrique; Gois, Francisco Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
The application of different tools for predicting COVID19 cases spreading has been widely considered during the pandemic. Comparing different approaches is essential to analyze performance and the practical support they can provide for the current pandemic management. This work proposes using the susceptible-exposed-asymptomatic but infectious-symptomatic and infectious-recovered-deceased (SEAIRD) model for different learning models. The first analysis considers an unsupervised prediction, based directly on the epidemiologic compartmental model. After that, two supervised learning models are considered integrating computational intelligence techniques and control engineering: the fuzzy-PID and the wavelet-ANN-PID models. The purpose is to compare different predictor strategies to validate a viable predictive control system for the COVID19 relevant epidemiologic time series. For each model, after setting the initial conditions for each parameter, the prediction performance is calculated based on the presented data. The use of PID controllers is justified to avoid divergence in the system when the learning process is conducted. The wavelet neural network solution is considered here because of its rapid convergence rate. The proposed solutions are dynamic and can be adjusted and corrected in real time, according to the output error. The results are presented in each subsection of the chapter.
Keywords: ANN predictor; COVID19; Epidemiology; Fuzzy predictor; PID control; SEAIRD

The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak - Fong, Simon James; Marques, João Alexandre Lobo; Li, G.; Dey, N.; Crespo, Rubén G.; Herrera-Viedma, E.; Gois, F. Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in history, and the most recent one has unique characteristics, which are tightly connected to the current society’s lifestyle and beliefs, creating an environment of uncertainty. Because of that, the development of mathematical/computational models to forecast the pandemic behavior since its beginning, i.e., with a restricted amount of data collected, is necessary. This chapter focuses on the analysis of different data mining techniques to allow the pandemic prediction with a small amount of data. A case study is presented considering the data from Wuhan, the Chinese city where the virus was first detected, and the place where the major outbreak occurred. The PNN + CF method (Polynomial Neural Network with Corrective Feedback) is presented as the technique with the best prediction performance. This is a promising method that might be considered in future eventual waves of the current pandemic or event to have a suitable model for future epidemic outbreaks around the world.
Keywords: Artificial neural networks; Epidemiology; Machine learning; Prediction models

The future of Cosmetics Advertisement Strategy: A Neuromarketing Study using Electrodermal Activity (EDA) as a measure of Emotional Arousal - Neto, Andreia; Lobo Marques, Joao Alexandre (2022 13th International Conference on E-business, Management and Economics, 2022)
Neuromarketing lies at the intersection of three main disciplines: psychology, neuroscience, and marketing, and it has been a successful neuroscientific approach for the study of real-life choices such as consumer behavior [1]. A current gap in the cosmetics field is the lack of published research studies, considering the marketing investment done yearly in this category. With the rapid economic expansion and the rise of social media in China, consumers' interest in beauty is growing. Even though the Chinese cosmetics sector is rapidly expanding, no studies have been done with Chinese consumers. This study aims to employ the same approach as previously done in consumer neuroscience studies to evaluate cosmetic brands' marketing strategy to understand better if immediate emotional responses can be measured using Electrodermal Activity (EDA). Here, we focus on cosmetics products advertisement as a model to understand consumer preference formation and choice. Eighteen Chinese female consumers were recruited between 19 and 37 years old. From the results obtained, it was understood that none of the participants have voted for the product advertisement for which they showed higher emotional arousal. However, it appears that the participants' preference is for the products for which the brand awareness is stronger since the product advertisements with more votes are the ones for the Korean brand used. The product advertisements with Asian faces were the ones with more votes, suggesting that Asian faces have engaged consumer preference. However, the product advertisements for the Brazilian brands, unknown to the Chinese public, were the ones with fewer votes, although, those product advertisements were the ones with more emotional arousal per minute. Those advertisements were also those with non-Asian faces, suggesting that this feature influenced voting decisions. From this study, it has been observed that Electrodermal Activity is a measure of emotional arousal that by itself cannot be translated into consumer engagement. Therefore, it is also proposed to evaluate brand awareness in future studies related to product advertisements. The physical features of the people included in the advertisements is also suggested to be further evaluated in future studies since a different cultural background seems to influence the consumers' engagement. Furthermore, using EDA to complement other neurophysiological tools like facial expression analysis is also suggested for future studies to have evidence about the nature of the emotions raised.
Keywords: advertisement; consumer behavior; consumer neurosciences; cosmetics; marketing; neuromarketing

The Future of Cosmetics Marketing Strategy: An Exploratory Study into How Neuromarketing Can Deepen its Understanding - Neto, Andreia Da Conceição (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

The Influence of Online Payment Methods on Consumers’ Online Purchase Behavior: The Case of Young People in Macau - Martins, Maria Cordes Aniceto (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

The Integration of Blockchain Technology in Accounting and Auditing - Almeida, Ermelinda Nazaré António de (University of Saint Joseph, 2024)
The emergence of blockchain technology in 2008 marked a significant milestone in the evolution of digital currencies, paving the way for the emergence of cryptocurrencies such as Bitcoin. Since then, blockchain has undergone four generations of development, expanding its applications across various sectors. In particular, the integration of blockchain into accounting and auditing practices has garnered significant attention due to its potential to transform traditional methods. However, there's a lack of clear understanding of how blockchain impacts traditional auditing practices and finance recordkeeping and the implications for audit quality. Significant challenges and uncertainties hinder its widespread adoption, including technical hurdles, regulatory complexities, and practical barriers. This dissertation aims to determine the transformative impact of blockchain technology on auditing practices and finance recordkeeping. In order to fully understand the impact of blockchain on auditing practices and finance recordkeeping, the dissertation utilizes a mixed sequential research approach that is divided into three phases. The first approach involves gathering qualitative data through interviews with blockchain experts. The second approach involves collecting secondary qualitative data through a systematic literature review to determine the changes that blockchain has brought to traditional auditing practices and finance recordkeeping. This is followed by a bibliometric analysis to identify current trends in blockchain research related to auditing practices and finance recordkeeping. The third approach involves gathering data through an online-focused survey distributed to finance and other industry professionals to determine the challenges organizations face in implementing blockchain technology in auditing practices and finance recordkeeping. Additionally, in phase three, case studies will be conducted based on the survey responses to examine the hindrances and challenges faced by organizations in implementing blockchain and its impact on auditing practices in different regions and among different demographic groups. As the findings indicate, Integrating blockchain technology into accounting and auditing practices can bring about significant improvements in transparency, efficiency, and fraud prevention. However, there are several challenges that must be overcome for successful implementation, such as technical difficulties, regulatory uncertainties, and privacy concerns. To overcome these hurdles, it is necessary to establish clear regulatory frameworks and innovative solutions. Although smart contracts offer automation, they also pose security risks that need to be addressed. Despite these challenges, blockchain has the potential to revolutionize auditing by enabling real-time auditing and enhancing integrity verification. To ensure audit quality, auditors must adapt to new responsibilities and stay up-to-date with emerging trends. Collaboration among stakeholders and continuous education and training programs are key to driving the successful adoption of blockchain technology
Keywords: Accounting; Audit Quality; Auditing Practices; Blockchain Technology; Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

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

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

TPOT Automated Machine Learning Approach for Multiple Diagnostic Classification of Lung Radiography and Feature Extraction - Bernardo Gois, Francisco Nauber; Lobo Marques, Joao Alexandre; Fong, Simon James (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity.

Training Strategies for Covid-19 Severity Classification - Pordeus, Daniel; Ribeiro, Pedro; Zacarias, Laíla; de Oliveira, Adriel; Marques, João Alexandre Lobo; Rodrigues, Pedro Miguel; Leite, Camila; Neto, Manoel Alves; Peixoto, Arnaldo Aires; do Vale Madeiro, João Paulo (Bioinformatics and Biomedical Engineering, 2023)
The COVID-19 pandemic has posed a significant public health challenge on a global scale. It is imperative that we continue to undertake research in order to identify early markers of disease progression, enhance patient care through prompt diagnosis, identification of high-risk patients, early prevention, and efficient allocation of medical resources. In this particular study, we obtained 100 5-min electrocardiograms (ECGs) from 50 COVID-19 volunteers in two different positions, namely upright and supine, who were categorized as either moderately or critically ill. We used classification algorithms to analyze heart rate variability (HRV) metrics derived from the ECGs of the volunteers with the goal of predicting the severity of illness. Our study choose a configuration pro SVC that achieved 76% of accuracy, and 0.84 on F1 Score in predicting the severity of Covid-19 based on HRV metrics.
Keywords: COVID-19; Electrocardiogram (ECG); Heart Rate Variability (HRV); disease severity classification; signal processing

X-Ray Machine Learning Classification with VGG-16 for Feature Extraction - dos Santos Silva, Bruno Riccelli; Cortez, Paulo Cesar; da Silva Neto, Manuel Gonçalves; Lobo Marques, Joao Alexandre (Computerized Systems for Diagnosis and Treatment of COVID-19, 2023)
The Covid-19 pandemic evidenced the need Computer Aided Diagnostic Systems to analyze medical images, such as CT and MRI scans and X-rays, to assist specialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the spread of the virus. There are some types of CAD systems, such as Machine and Deep Learning-based and Transfer learning-based. This chapter proposes a pipeline for feature extraction and classification of Covid-19 in X-ray images using transfer learning for feature extraction with VGG-16 CNN and machine learning classifiers. Five classifiers were evaluated: Accuracy, Specificity, Sensitivity, Geometric mean, and Area under the curve. The SVM Classifier presented the best performance metrics for Covid-19 classification, achieving 90% accuracy, 97.5% of Specificity, 82.5% of Sensitivity, 89.6% of Geometric mean, and 90% for the AUC metric. On the other hand, the Nearest Centroid (NC) classifier presented poor sensitivity and geometric mean results, achieving 33.9% and 54.07%, respectively.