TY - SLIDE TI - Finding the “BUY BUTTON”. The creation of the Laboratory of Neuroeconomics at USJ T2 - 12th International Conference on E-business, Management and Economics (ICEME 2021) A2 - Lobo Marques, J. A. CY - Beijing China DA - 07-21 PY - 07-21 M3 - Conference ER - TY - JOUR TI - Pathophysiological, cardiovascular and neuroendocrine changes in hypertensive patients during the hemodialysis session AU - Gutiérrez-Adrianzén, O. A. AU - Moraes, M. E. A. AU - Almeida, A. P. AU - Lima, J. W. O. AU - Marinho, M. F. AU - Marques, A. L. AU - Madeiro, J. P. V. AU - Nepomuceno, L. AU - da Silva Jr, J. M. S. AU - Silva Jr, G. B. AU - Daher, E. F. AU - Rodrigues Sobrinho, C. R. M. T2 - Journal of Human Hypertension AB - The pathophysiological mechanisms of arterial hypertension during hemodialysis (HD) in patients with end-stage renal disease (ESRD) are still poorly understood. The aim of this study is to investigate physiological, cardiovascular and neuroendocrine changes in patients with ESRD and its correlation with changes in blood pressure (BP) during the HD session. The present study included 21 patients with ESRD undergoing chronic HD treatment. Group A (study) consisted of patients who had BP increase and group B (control) consisted of those who had BP reduction during HD session. Echocardiograms were performed during the HD session to evaluate cardiac output (CO) and systemic vascular resistance (SVR). Before and after the HD session, blood samples were collected to measure brain natriuretic peptide (BNP), catecholamines, endothelin-1 (ET-1), nitric oxide (NO), electrolytes, hematocrit, albumin and nitrogen substances. The mean age of the studied patients was 43±4.9 years, and 54.6% were males. SVR significantly increased in group A (P<0.001). There were no differences in the values of BNP, NO, adrenalin, dopamin and noradrenalin, before and after dialysis, between the two groups. The mean value of ET-1, post HD, was 25.9 pg ml−1 in group A and 13.3 pg ml−1 in group B (P=<0.001). Patients with ESRD showed different hemodynamic patterns during the HD session, with significant BP increase in group A, caused by an increase in SVR possibly due to endothelial dysfunction, evidenced by an increase in serum ET-1 levels. DA - 2015/06// PY - 2015 DO - 10.1038/jhh.2014.93 DP - www.nature.com VL - 29 IS - 6 SP - 366 EP - 372 J2 - J Hum Hypertens LA - en SN - 1476-5527 UR - https://www.nature.com/articles/jhh201493 Y2 - 2023/10/18/01:39:00 KW - End-stage renal disease KW - Haemodialysis KW - Hypertension ER - TY - JOUR TI - A Heart Rate Variability-based Smart Approach to Analyze Frailty in Older Adults AU - Paulo do Vale Madeiro, João AU - César Cortez, Arnaldo Aires Peixoto Júnior, Paulo AU - Alexandre Lôbo Marques, João AU - Alisson Pessoa Guimarães, Antônio AU - Hebert da Silva Felix, John T2 - The Smart Computing Review AB - 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. DA - 2015/08/31/ PY - 2015 DO - 10.6029/smartcr.2015.04.002 DP - DOI.org (Crossref) J2 - SmartCR SN - 22344624 UR - http://smartcr.org/view/download.php?filename=smartcr_vol5no4p002.pdf Y2 - 2023/03/22/06:36:15 ER - TY - JOUR TI - Multi-view Convolution Neural Network with Swarm Search Based Hyperparameter Optimization for Enhancing Heart Disease and Breast Cancer Detection AU - Lan, K. AU - Li, T. AU - Fong, S. AU - Marques, J.A.L. AU - Wong, R.K. T2 - 5th International Conference on Soft Computing and Machine Intelligence, ISCMI 2018 DA - 2018/// PY - 2018 DO - 10.1109/ISCMI.2018.8703249 SP - 140 EP - 146 UR - 10.1109/ISCMI.2018.8703249 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Artificial intelligence and forensic computing AU - Lobo-Marques, J. Al T2 - Forensic Computing DA - 2018/// PY - 2018 ER - TY - CONF TI - Fast Cluster-learning with Prior Probability from Big Dataset AU - Li, Tengyue AU - Fong, Simon AU - Lobo Marques, Joao Alexandre AU - Wong, Raymond K. T2 - 2018 5th International Conference on Soft Computing & Machine Intelligence (ISCMI) AB - 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. C3 - 2018 5th International Conference on Soft Computing & Machine Intelligence (ISCMI) DA - 2018/11// PY - 2018 DO - 10.1109/ISCMI.2018.8703219 DP - IEEE Xplore SP - 60 EP - 66 KW - Association Rule Mining KW - Big Data KW - Clustering KW - Clustering algorithms KW - Data mining KW - Itemsets KW - Preprocessing KW - Prior Probability KW - Probabilistic logic KW - Probability distribution ER - TY - JOUR TI - Lead: An iOS Application to Help in the Construction of New Habits AU - de Aguiar, André Wescley Oliveira AU - Bezerra, Jagni Dasa Horta AU - Marques, João Alexandre Lobo AU - de Alexandria, Auzuir Ripardo T2 - International Journal of Information and Electronics Engineering DA - 2019/// PY - 2019 DP - Google Scholar VL - 9 IS - 4 ST - Lead UR - http://www.ijiee.org/index.php?m=content&c=index&a=show&catid=87&id=820 ER - TY - JOUR TI - Evaluating Mathematical Models for Morphological Classification of the QRS Complex AU - Do Vale Madeiro, J.P. AU - Barreto, D. AU - Marques, J.A.L. AU - Salinet, J.L. T2 - Computing in Cardiology DA - 2019/// PY - 2019 DO - 10.23919/CinC49843.2019.9005790 DP - ORCID VL - 2019-September UR - 10.23919/CinC49843.2019.9005790 Y2 - 2021/02/03/09:10:21 ER - TY - JOUR TI - Automatic Cardiotocography Diagnostic System Based on Hilbert Transform and Adaptive Threshold Technique AU - Marques, J. A. Lobo AU - Cortez, P. C. AU - Madeiro, J. P. D. V. AU - Fong, S. J. AU - Schlindwein, F. S. AU - Albuquerque, V. H. C. D. T2 - IEEE Access AB - The visual analysis of cardiotocographic examinations is a very subjective process. The accurate detection and segmentation of the fetal heart rate (FHR) features and their correlation with the uterine contractions in time allow a better diagnostic and the possibility of anticipation of many problems related to fetal distress. This paper presents a computerized diagnostic aid system based on digital signal processing techniques to detect and segment changes in the FHR and the uterine tone signals automatically. After a pre-processing phase, the FHR baseline detection is calculated. An auxiliary signal called detection line is proposed to support the detection and segmentation processes. Then, the Hilbert transform is used with an adaptive threshold for identifying fiducial points on the fetal and maternal signals. For an antepartum (before labor) database, the positive predictivity value (PPV) is 96.80% for the FHR decelerations, and 96.18% for the FHR accelerations. For an intrapartum (during labor) database, the PPV found was 91.31% for the uterine contractions, 94.01% for the FHR decelerations, and 100% for the FHR accelerations. For the whole set of exams, PPV and SE were both 100% for the identification of FHR DIP II and prolonged decelerations. DA - 2019/// PY - 2019 DO - 10.1109/ACCESS.2018.2877933 DP - IEEE Xplore VL - 7 SP - 73085 EP - 73094 SN - 2169-3536 UR - https://ieeexplore.ieee.org/abstract/document/8682138 KW - Acceleration KW - Biomedical monitoring KW - Cardiography KW - Cardiotocography (CTG) KW - Databases KW - FHR DIP II KW - FHR accelerations KW - FHR baseline detection KW - FHR decelerations KW - Fetal heart rate KW - Hilbert transform KW - Hilbert transforms KW - Monitoring KW - PPV KW - Transforms KW - accurate fetal heart rate feature detection KW - accurate fetal heart rate feature segmentation KW - adaptive threshold technique KW - antepartum database KW - automatic cardiotocography diagnostic system KW - auxiliary signal KW - cardiotocographic examinations KW - computerized diagnostic aid system KW - digital signal processing techniques KW - fetal distress KW - fetal heart rate (FHR) KW - fetal signals KW - maternal signals KW - medical signal detection KW - medical signal processing KW - obstetrics KW - patient monitoring KW - positive predictivity value KW - preprocessing phase KW - segmentation processes KW - uterine contractions KW - uterine contractions (UC) KW - uterine tone signals KW - visual analysis ER - TY - CONF TI - Microsoft Power BI Desktop for Data Analytics AU - NEGREIROS, J. Garrott Marques AU - MARQUES, JA Lobo C3 - Proc. Multidiscip. Acad. Conf DA - 2019/// PY - 2019 DP - Google Scholar SP - 132 ER - TY - JOUR TI - Influência da nova estrutura fiscal de impostos de Angola na gestão de escolas privadas do municipio do Lobito AU - Armando Carlos Hombo Nogueira AU - Luis Miguel Pacheco AU - Marcus Antonio Almeida Rodrigues T2 - Revista Gestao em Analise DA - 2019/06/05/ PY - 2019 DO - 10.12662/2359-618xregea.v8i2.p11-30.2019 DP - ORCID UR - http://doi.org/10.12662/2359-618xregea.v8i2.p11-30.2019 Y2 - 2021/02/03/09:10:20 ER - TY - JOUR TI - On the probability flow in the Stock market I: The Black-Scholes case AU - Arraut, Ivan AU - Au, Alan AU - Tse, Alan Ching-biu AU - Marques, Joao Alexandre Lobo T2 - arXiv.org AB - 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. DA - 2020/// PY - 2020 DP - ProQuest VL - 1 SP - 1 EP - 10 LA - English ST - On the probability flow in the Stock market I UR - https://search.proquest.com/docview/2332255379?pq-origsite=primo Y2 - 2021/02/03/07:58:09 KW - General Finance ER - TY - JOUR TI - Multi-view convolutional neural network with leader and long-tail particle swarm optimizer for enhancing heart disease and breast cancer detection AU - Lan, K. AU - Liu, L. AU - Li, T. AU - Chen, Y. AU - Fong, S. AU - Marques, J.A.L. AU - Wong, R.K. AU - Tang, R. T2 - Neural Computing and Applications DA - 2020/// PY - 2020 DO - 10.1007/s00521-020-04769-y DP - ORCID UR - 10.1007/s00521-020-04769-y Y2 - 2021/02/03/09:10:21 ER - TY - JOUR TI - Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures AU - Marques, J.A.L. AU - Cortez, P.C. AU - Madeiro, J.P.V. AU - Albuquerque, V.H.C. AU - Fong, S.J. AU - Schlindwein, F.S. T2 - Journal of Supercomputing DA - 2020/// PY - 2020 DO - 10.1007/s11227-018-2570-8 VL - 76 IS - 2 SP - 1305 EP - 1320 UR - 10.1007/s11227-018-2570-8 Y2 - 2021/02/03/09:10:20 ER - TY - JOUR TI - The Importance of Readiness for Change, a Leadership Perspective Based on a Case Study in Macau, SAR China AU - Marques, João AU - Reis, Joana AU - Phillips, Jenny O. L. AU - Diakite, Ansoumane T2 - Journal of Advanced Management Science DA - 2020/01/01/ PY - 2020 DO - 10.18178/joams.8.4.116-120 DP - ResearchGate SP - 116 EP - 120 J2 - Journal of Advanced Management Science ER - TY - JOUR TI - Evaluation of mathematical models for QRS feature extraction and QRS morphology classification in ECG signals AU - do Vale Madeiro, João Paulo AU - Lobo Marques, João Alexandre AU - Han, Tao AU - Coury Pedrosa, Roberto T2 - Measurement AB - 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. DA - 2020/05/01/ PY - 2020 DO - 10.1016/j.measurement.2020.107580 DP - ScienceDirect VL - 156 SP - 107580 J2 - Measurement LA - en SN - 0263-2241 UR - https://www.sciencedirect.com/science/article/pii/S0263224120301172 Y2 - 2022/09/21/05:15:22 KW - ECG feature extraction KW - Mathematical modeling KW - Morphology classification KW - QRS complex delineation ER - TY - JOUR TI - Health and Well-Being Education: Extending the SCARF Learning Analytics Model for Identifying the Learner Happiness Indicators AU - Li, Tengyue AU - Marques, Joao Alexandre Lobo AU - Fong, Simon T2 - International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH) AB - 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... DA - 2020/07/01/ PY - 2020 DO - 10.4018/IJEACH.2020070105 DP - www.igi-global.com VL - 2 IS - 2 SP - 42 EP - 53 J2 - IJEACH LA - en SN - 2577-4794 ST - Health and Well-Being Education UR - https://www.igi-global.com/article/health-and-well-being-education/www.igi-global.com/article/health-and-well-being-education/260728 Y2 - 2022/09/21/05:14:11 ER - TY - JOUR TI - Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living AU - Luo, Renfei AU - Marques, João Alexandre Lôbo AU - Ong, Kok-Leong AU - Fong, Simon T2 - International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH) AB - 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... DA - 2020/07/01/ PY - 2020 DO - 10.4018/IJEACH.2020070106 DP - www.igi-global.com VL - 2 IS - 2 SP - 54 EP - 64 J2 - IJEACH LA - en SN - 2577-4794 UR - https://www.igi-global.com/article/crowdsensing-based-gamification-for-collective-assistance-for-post-era-of-coronavirus-epidemic-in-community-living/www.igi-global.com/article/crowdsensing-based-gamification-for-collective-assistance-for-post-era-of-coronavirus-epidemic-in-community-living/260729 Y2 - 2022/09/21/05:14:38 ER - TY - CHAP TI - Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 69 EP - 87 PB - Springer International Publishing UR - https://doi.org/10.1007%2F978-3-030-61913-8_5 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 15 EP - 39 PB - Springer International Publishing UR - https://doi.org/10.1007%2F978-3-030-61913-8_2 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Forecasting COVID-19 Time Series Based on an Autoregressive Model AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 41 EP - 54 PB - Springer International Publishing UR - https://doi.org/10.1007%2F978-3-030-61913-8_3 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 55 EP - 68 PB - Springer International Publishing UR - https://doi.org/10.1007%2F978-3-030-61913-8_4 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 89 EP - 98 PB - Springer International Publishing ST - Predicting the Geographic Spread of the COVID-19 Pandemic UR - https://doi.org/10.1007%2F978-3-030-61913-8_6 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Prediction for Decision Support During the COVID-19 Pandemic AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 1 EP - 13 PB - Springer International Publishing UR - https://doi.org/10.1007%2F978-3-030-61913-8_1 Y2 - 2021/02/03/09:10:21 ER - TY - BOOK TI - Predictive Models for Decision Support in the COVID-19 Crisis AU - Marques, João Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, Jose AU - Fong, Simon James T2 - SpringerBriefs in Applied Sciences and Technology AB - 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. DA - 2021/// PY - 2021 LA - en PB - Springer International Publishing SN - 978-3-030-61912-1 UR - https://www.springer.com/gp/book/9783030619121 Y2 - 2021/01/29/07:53:53 ER - TY - JOUR TI - Modelling the barriers that are preventing e-commerce to thrive – a case study from Portugal, international journal of business excellence AU - Silva, Susana Costa e AU - Machado, Joana Costa AU - Martins, Carla AU - Duarte, Paulo AU - Marques, João Alexandre Lobo T2 - International Journal of Business Excellence AB - 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. DA - 2021/// PY - 2021 DP - Ciência-UCP | Universidade Católica Portuguesa SN - 1756-0047 KW - E-commerce barriers KW - Online shopping KW - Portugal KW - Retail ER - TY - JOUR TI - The Probability Flow in the Stock Market and Spontaneous Symmetry Breaking in Quantum Finance AU - Arraut, Ivan AU - Lobo Marques, João Alexandre AU - Gomes, Sergio T2 - Mathematics AB - 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. DA - 2021/01// PY - 2021 DO - 10.3390/math9212777 DP - www.mdpi.com VL - 9 IS - 21 SP - 2777 LA - en SN - 2227-7390 UR - https://www.mdpi.com/2227-7390/9/21/2777 Y2 - 2023/04/11/14:03:37 KW - Hermiticity KW - conservation of the information KW - degenerate vacuum KW - flow of information KW - martingale condition KW - random fluctuations KW - spontaneous symmetry breaking KW - vacuum condition ER - TY - CHAP TI - Predictive models to the COVID-19 AU - Bernardo Gois, Francisco Nauber AU - Lima, Alex AU - Santos, Khennedy AU - Oliveira, Ramses AU - Santiago, Valdir AU - Melo, Saulo AU - Costa, Rafael AU - Oliveira, Marcelo AU - Henrique, Francisco das Chagas Douglas Marques AU - Neto, José Xavier AU - Martins Rodrigues Sobrinho, Carlos Roberto AU - Lôbo Marques, João Alexandre T2 - Data Science for COVID-19 A2 - Kose, Utku A2 - Gupta, Deepak A2 - de Albuquerque, Victor Hugo C. A2 - Khanna, Ashish AB - 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. DA - 2021/01/01/ PY - 2021 SP - 1 EP - 24 LA - en PB - Academic Press SN - 978-0-12-824536-1 UR - https://www.sciencedirect.com/science/article/pii/B978012824536100023X Y2 - 2021/05/26/08:26:26 KW - COVID-19 KW - Forecast KW - Holt Winters KW - Kalman filter KW - Machine learning KW - Prophet KW - SEIR ER - TY - JOUR TI - Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning Techniques AU - Nguyen Van, Sau AU - Lobo Marques, J. A. AU - Biala, T. A. AU - Li, Ye T2 - Computer Methods and Programs in Biomedicine AB - Background and objective Intrauterine Growth Restriction (IUGR) is a condition in which a fetus does not grow to the expected weight during pregnancy. There are several well documented causes in the literature for this issue, such as maternal disorder, and genetic influences. Nevertheless, besides the risk during pregnancy and labour periods, in a long term perspective, the impact of IUGR condition during the child development is an area of research itself. The main objective of this work is to propose a machine learning solution to identify the most significant features of importance based on physiological, clinical or socioeconomic factors correlated with previous IUGR condition after 10 years of birth. Methods In this work, 41 IUGR (18 male) and 34 Non-IUGR (22 male) children were followed up 9 years after the birth, in average (9.1786 ± 0.6784 years old). A group of machine learning algorithms is proposed to classify children previously identified as born under IUGR condition based on 24-hours monitoring of ECG (Holter) and blood pressure (ABPM), and other clinical and socioeconomic attributes. In additional, an algorithm of relevance determination based on the classifier is also proposed, to determine the level of importance of the considered features. Results The proposed classification solution achieved accuracy up to 94.73%, and better performance than seven state-of-the-art machine learning algorithms. Also, relevant latent factors related to HRV and BP monitoring are proposed, such as: day-time heart rate (day-time HR), day-night systolic blood pressure (day-night SBP), 24-hour standard deviation (SD) of SBP, dropped, morning cortisol creatinine, 24-hour mean of SDs of all NN intervals for each 5 minutes segment (24-hour SDNNi), among others. Conclusion With outstanding accuracy of our proposed solutions, the classification system and the indication of relevant attributes may support medical teams on the clinical monitoring of IUGR children during their childhood development. DA - 2021/03/01/ PY - 2021 DO - 10.1016/j.cmpb.2020.105842 DP - ScienceDirect VL - 200 SP - 105842 J2 - Computer Methods and Programs in Biomedicine LA - en SN - 0169-2607 UR - https://www.sciencedirect.com/science/article/pii/S0169260720316758 Y2 - 2022/09/21/05:04:17 KW - ABPM (Ambulatory Blood Pressure Monitoring) KW - HRV (Heart Rate Variability) KW - IUGR (Intrauterine Growth Restriction) KW - Machine Learning ER - TY - CONF TI - A Prospective analysis about employee retention strategies for Macao Hotel Industry AU - Chong, Ka Yee AU - Lobo Marques, Joao A. T3 - ICEME 2021 AB - Human resources are essential to the survival, success, and long-term growth of a company. Hotel is an industry requiring a high level of human resources for delivering high-quality personal service to the hotel guests to maintain its competitiveness in the business environment. With the rapid economic growth in Macao started in 2002, all the industries have been growing fast and competing fiercely for the limited manpower in Macao. However, the Macao hotel industry has been losing its attractiveness in the Macao labor market and needs to rely on non-local workers with a limited stay in Macao. The management team of the Macao hotel industry is looking for a solution to maintain a stable workforce. Therefore, a study has been conducted on the effectiveness of its employee retention strategies. A questionnaire was designed to collect the preferences of the employees and interviews were conducted to understand the perspective of the management team toward the employee retention strategies. The study shows the employee strategies are focused on key employees’ interests such as career development and prospect. However, the communication between the management team and employees failed and led to employee turnover. C1 - New York, NY, USA C3 - The 2021 12th International Conference on E-business, Management and Economics DA - 2021/07/17/ PY - 2021 DO - 10.1145/3481127.3481251 DP - ACM Digital Library SP - 533 EP - 537 PB - Association for Computing Machinery SN - 978-1-4503-9006-4 UR - https://doi.org/10.1145/3481127.3481251 Y2 - 2022/09/20/00:00:00 KW - Employee retention KW - Employee turnover reasons KW - Macao hotel industry KW - Macao shortage of manpower ER - TY - CONF TI - Sustainable Buildings’ Projects – A Perspective from Consultants and Contractors based in Macau SAR, China AU - J.P.P. Santos, Eduardo AU - Lobo Marques, Joao AU - Phyllips, Jenny O. L. T3 - ICEME 2021 AB - China growing awareness of sustainability has brought out relevant aspects to move towards a green environment. Since its subscription in 2016, China has been committed to implementing the Paris Agreement, and the Greater Bay Area (GBA) development plan prioritizes ecology and pursuing green development. The primary purpose of this research is to perceive the companies' insights concerning the implementation of sustainable buildings’ projects in Macau. For this multi-case study analysis, primary data was gathered from interviews with two groups involved in the construction projects’ lifecycle: Consultants and Contractors, to analyze different perceptions and concerns. The interviews considered two different themes about the main topic: (1) Perception on Companies’ Experience in Sustainable Projects; (2) Key Drivers towards Sustainable Buildings’ Projects’ Implementation. In conclusion, according to the analyzed data, it is essential to notice that companies’ background and the market particularities affect their corporate performance specially connected to the green construction frameworks. The data also indicate that it is necessary to move towards regulations and policies to change corporate and people's mindset. C1 - New York, NY, USA C3 - The 2021 12th International Conference on E-business, Management and Economics DA - 2021/07/17/ PY - 2021 DO - 10.1145/3481127.3481252 DP - ACM Digital Library SP - 865 EP - 870 PB - Association for Computing Machinery SN - 978-1-4503-9006-4 UR - https://doi.org/10.1145/3481127.3481252 Y2 - 2022/09/20/00:00:00 KW - Construction industry KW - Green building KW - Sustainable Policies KW - Sustainable building ER - TY - JOUR TI - Towards an efficient prognostic model for fetal state assessment AU - Silva Neto, Manuel Gonçalves da AU - Madeiro, João Paulo do Vale AU - Marques, João Alexandre Lobo AU - Gomes, Danielo G. T2 - Measurement AB - 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. DA - 2021/11/01/ PY - 2021 DO - 10.1016/j.measurement.2021.110034 DP - ScienceDirect VL - 185 SP - 110034 J2 - Measurement LA - en SN - 0263-2241 UR - https://www.sciencedirect.com/science/article/pii/S0263224121009568 Y2 - 2022/09/21/05:01:31 KW - Cardiotocography KW - Classification KW - Fetal state assessment KW - Prognostic model KW - System design ER - TY - JOUR TI - IoT-Based Smart Health System for Ambulatory Maternal and Fetal Monitoring AU - Marques, João Alexandre Lobo AU - Han, Tao AU - Wu, Wanqing AU - Madeiro, João Paulo do Vale AU - Neto, Aloísio Vieira Lira AU - Gravina, Raffaele AU - Fortino, Giancarlo AU - de Albuquerque, Victor Hugo C. T2 - IEEE Internet of Things Journal AB - 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. DA - 2021/12// PY - 2021 DO - 10.1109/JIOT.2020.3037759 DP - IEEE Xplore VL - 8 IS - 23 SP - 16814 EP - 16824 SN - 2327-4662 KW - Artificial intelligence (AI) KW - Biomedical monitoring KW - Cloud computing KW - Feature extraction KW - Fetal heart rate KW - Internet of Things KW - Medical diagnostic imaging KW - Monitoring KW - convolutional neural networks (CNNs) KW - feature extraction KW - fetal monitoring KW - health analytics KW - maternal monitoring ER - TY - CHAP TI - A Quantum Field Formulation for a Pandemic Propagation AU - Arraut, Ivan AU - Marques, João Alexandre Lobo AU - Fong, Simon James AU - Li, Gloria AU - Gois, Francisco Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 141 EP - 158 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_6 Y2 - 2022/09/21/02:32:32 KW - COVID-19 KW - Mathematical modeling KW - Nonlinear analysis KW - Quantum field theory ER - TY - CHAP TI - Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models AU - Fong, Simon James AU - Marques, João Alexandre Lobo AU - Li, G. AU - Dey, N. AU - Crespo, Rubén G. AU - Herrera-Viedma, E. AU - Gois, F. Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 17 EP - 64 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_2 Y2 - 2022/09/21/02:33:52 KW - Adaptive SEAIRD model KW - Adaptive SVEAIRD model KW - Asymptomatic cases KW - Prediction models ER - TY - CHAP TI - The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak AU - Fong, Simon James AU - Marques, João Alexandre Lobo AU - Li, G. AU - Dey, N. AU - Crespo, Rubén G. AU - Herrera-Viedma, E. AU - Gois, F. Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 65 EP - 81 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_3 Y2 - 2022/09/21/02:36:15 KW - Artificial neural networks KW - Epidemiology KW - Machine learning KW - Prediction models ER - TY - CHAP TI - Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System AU - Fong, Simon James AU - Marques, João Alexandre Lobo AU - Li, Gloria AU - Dey, Nilanjan AU - Crespo, Rubén González AU - Herrera-Viedma, Enrique AU - Gois, Francisco Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 83 EP - 102 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_4 Y2 - 2022/09/21/02:35:35 KW - COVID-19 KW - Composite Monte Carlo simulation KW - Healthcare decision-making systems KW - Prediction ER - TY - CHAP TI - The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic AU - Fong, Simon James AU - Marques, João Alexandre Lobo AU - Li, Gloria AU - Dey, Nilanjan AU - Crespo, Rubén González AU - Herrera-Viedma, Enrique AU - Gois, Francisco Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 103 EP - 139 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_5 Y2 - 2022/09/21/02:33:30 KW - ANN predictor KW - COVID19 KW - Epidemiology KW - Fuzzy predictor KW - PID control KW - SEAIRD ER - TY - SLIDE TI - Neuroeconomics Applications Based on Multiple Monitoring Tools. T2 - 13th International Conference on E-business, Management and Economics (ICEME 2022) A2 - Lobo Marques, J. A. CY - Beijing China DA - 2022/07// PY - 2022 M3 - Conference ER - TY - BOOK TI - Epidemic analytics for decision supports in COVID19 crisis A3 - Lobo Marques, Joao Alexandre A3 - Fong, Simon AB - 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. CY - Cham, Switzerland DA - 2022/// PY - 2022 DP - K10plus ISBN SP - 158 LA - eng PB - Springer SN - 978-3-030-95281-5 978-3-030-95280-8 UR - https://link.springer.com/book/10.1007/978-3-030-95281-5#about-this-book ER - TY - CHAP TI - Research and Technology Development Achievements During the COVID-19 Pandemic—An Overview AU - Marques, João Alexandre Lobo AU - Fong, Simon James AU - Li, G. AU - Arraut, Ivan AU - Gois, F. Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 1 EP - 15 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_1 Y2 - 2022/09/21/02:35:32 KW - COVID-19 KW - Research cooperation KW - Research ethics KW - Scientific research KW - Technology development ER - TY - SLIDE TI - Automatic Classification System for Subjects Exposed to Short-Term Stress Based on Facial Expression Analysis and ElectroDermal Activity T2 - XXVIII Brazilian Congress of Biomedical Engineering CBEB 2022 A2 - Motta, P. A2 - Silva, B. A2 - Furtado, F. A2 - Lobo Marques, J. A. CY - Florianópolis DA - 2022/// PY - 2022 M3 - Conference ER - TY - JOUR TI - Quality of life during the pandemic: a cross sectional study about attitude, individual perspective and behavior change affecting general population in daily life AU - Khatoon, F. AU - Kumar, M. AU - Khalid, A. A. AU - Alshammari, A. D. AU - Khan, F. AU - Alshammari, R. D. AU - Balouch, Z. AU - Verma, D. AU - Mishra, P. AU - Abotaleb, M. AU - Makarovskikh, T. AU - El-kenawy, E. M. AU - Dutta, P. K. AU - Marques, J. A. AB - Quality of life in general population before and during pandemic is topic need to be address by researcher in terms of mobility, self-care, usual activities, pain/discomfort and anxiety/depression. The study was carried out among Saudi population. Data were collected from general population using questionnaire during the period from 22 August 2021 to 10th January 2022. As a result, total 214 participants have included in this study. Among them prevalent age group include 40 years (n= 63, 29.4%) shadowed by the age group 25-35 (n= 61, 28.5%) while above 60 years group were least frequent (n= 1, 0.5%). On questioning the applicants whether they were satisfied with their health and how would they rate their quality of life, their answers were as follows: yes, or satisfied (n= 86, 40.2%), very Satisfied (n= 102, 47.7%) Dissatisfied (n= 11, 5.1%) and neither satisfied nor dissatisfied (n= 15, 7%). Due to pandemic, they were rate quality of life very good (n= 94, 43.9%), good (n= 63, 29.4 %) poor (n= 5, 2.3 %) and neither good and nor poor (n= 52, 24.3 %). During pandemic 96 participants feel no change in their weight but 110 participants respond that there is increase in coffee intake during the pandemic. Similarly increased in smoking habits and decrease rate in social activities (n=119,41.4%). The psychosomatic well-being of people has been interrupted by disturbing their social activities during pandemic. DA - 2022/01/01/ PY - 2022 DO - 10.1049/icp.2023.0596 DP - digital-library.theiet.org SP - 379 EP - 383 LA - en ST - Quality of life during the pandemic UR - https://digital-library.theiet.org/content/conferences/10.1049/icp.2023.0596 Y2 - 2023/10/10/04:32:51 ER - TY - CHAP TI - AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions AU - Lôbo Marques, João Alexandre AU - Bernardo Gois, Francisco Nauber AU - Nunes da Silveira, Jarbas Aryel AU - Li, Tengyue AU - Fong, Simon James T2 - Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data A2 - Bhoi, Akash Kumar A2 - de Albuquerque, Victor Hugo C. A2 - Srinivasu, Parvathaneni Naga A2 - Marques, Gonçalo T3 - Intelligent Data-Centric Systems AB - 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. DA - 2022/01/01/ PY - 2022 DP - ScienceDirect SP - 101 EP - 121 LA - en PB - Academic Press SN - 978-0-323-85751-2 UR - https://www.sciencedirect.com/science/article/pii/B9780323857512000013 Y2 - 2022/09/21/04:34:03 KW - Artificial intelligence KW - Clinical decisions KW - Computer-aided diagnostic systems KW - Deep learning KW - Patient-centric data ER - TY - CHAP TI - Artificial neural network-based approaches for computer-aided disease diagnosis and treatment AU - Marques, João Alexandre Lôbo AU - Gois, Francisco Nauber Bernardo AU - Madeiro, João Paulo do Vale AU - Li, Tengyue AU - Fong, Simon James T2 - Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data A2 - Bhoi, Akash Kumar A2 - de Albuquerque, Victor Hugo C. A2 - Srinivasu, Parvathaneni Naga A2 - Marques, Gonçalo T3 - Intelligent Data-Centric Systems AB - 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. DA - 2022/01/01/ PY - 2022 DP - ScienceDirect SP - 79 EP - 99 LA - en PB - Academic Press SN - 978-0-323-85751-2 UR - https://www.sciencedirect.com/science/article/pii/B9780323857512000086 Y2 - 2022/09/21/02:36:30 KW - Artificial intelligence KW - Computer-aided diagnosis and treatment KW - Deep learning KW - Medical imaging KW - Neural networks ER - TY - GEN TI - Neuromarketing and Global Branding Reaction Analysis Based on Real-Time Monitoring of Multiple Consumer's Biosignals and Emotions AU - Goncalves, Marcus V. AU - Marques, Joao Alexandre Lobo AU - Silva, Bruno Riccelli Santos AU - Luther, Valorie AU - Hayes, Sydney AB - 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. CY - Rochester, NY DA - 2022/03/31/ PY - 2022 DO - 10.2139/ssrn.4071297 DP - Social Science Research Network LA - en UR - https://papers.ssrn.com/abstract=4071297 Y2 - 2023/03/22/06:30:39 KW - Branding Reaction KW - Consumer Biosignal KW - Galvanic Skin Response KW - consumer decision-making KW - eye-tracking KW - neuromarketing KW - neurosciences ER - TY - CONF TI - Intersectoral Linkages in Booming Sector Economies AU - Gomes, Sérgio Ricardo Santos AU - Marques, Joao Alexandre Lobo AU - Reis, Ricardo Ferreira T2 - ICEME 2022: 2022 13th International Conference on E-business, Management and Economics C1 - Beijing China C3 - 2022 13th International Conference on E-business, Management and Economics DA - 2022/07/16/ PY - 2022 DO - 10.1145/3556089.3556111 DP - DOI.org (Crossref) SP - 225 EP - 232 LA - en PB - ACM SN - 978-1-4503-9639-4 UR - https://dl.acm.org/doi/10.1145/3556089.3556111 Y2 - 2023/03/22/04:58:49 ER - TY - SLIDE TI - IoT-based smart health system for ambulatory maternal and fetal monitoring T2 - Symposium on Intelligent Manufacturing and Artificial Intelligence Technologies (ISIMAIT 2022) A2 - Lobo Marques, J. A. CY - Zhejiang, China DA - 2022/08/19/20 PY - 2022 M3 - Symposium ER - TY - SLIDE TI - Developing Neuromarketing Innovative Research T2 - Neuromarathon 2022 A2 - Lobo Marques, J. A. DA - 2022/10/25/ PY - 2022 M3 - Conference ER - TY - CONF TI - Investigation on citizen trust towards e-government services in GBA – A study on WeChat and Alipay government service mini-programs AU - Lai, Chimeng AU - Joao, Alexandre Lobo Marques T3 - ICEME 2022 AB - 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. C1 - New York, NY, USA C3 - 2022 13th International Conference on E-business, Management and Economics DA - 2022/11/30/ PY - 2022 DO - 10.1145/3556089.3556110 DP - ACM Digital Library SP - 588 EP - 594 PB - Association for Computing Machinery SN - 978-1-4503-9639-4 UR - https://doi.org/10.1145/3556089.3556110 Y2 - 2023/03/14/00:00:00 ER - TY - CONF TI - The future of Cosmetics Advertisement Strategy: A Neuromarketing Study using Electrodermal Activity (EDA) as a measure of Emotional Arousal AU - Neto, Andreia AU - Lobo Marques, Joao Alexandre T3 - ICEME 2022 AB - 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. C1 - New York, NY, USA C3 - 2022 13th International Conference on E-business, Management and Economics DA - 2022/11/30/ PY - 2022 DO - 10.1145/3556089.3556126 DP - ACM Digital Library SP - 81 EP - 86 PB - Association for Computing Machinery SN - 978-1-4503-9639-4 ST - The future of Cosmetics Advertisement Strategy UR - https://dl.acm.org/doi/10.1145/3556089.3556126 Y2 - 2023/03/21/00:00:00 KW - advertisement KW - consumer behavior KW - consumer neurosciences KW - cosmetics KW - marketing KW - neuromarketing ER - TY - CONF TI - The Application of Nudge Theory in Ensuring Change Acceptance in the Hospitality-Gaming Industry – A Case Analysis from Macau SAR, China AU - Tagulao, Thea Clarice Saavedra AU - Marques, Joao Alexandre Lobo T3 - ICEME 2022 AB - 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. C1 - New York, NY, USA C3 - 2022 13th International Conference on E-business, Management and Economics DA - 2022/11/30/ PY - 2022 DO - 10.1145/3556089.3556121 DP - ACM Digital Library SP - 533 EP - 539 PB - Association for Computing Machinery SN - 978-1-4503-9639-4 UR - https://doi.org/10.1145/3556089.3556121 Y2 - 2023/03/14/00:00:00 KW - Change Initiatives KW - Change Resistance KW - Nudge Theory KW - Organizational Behavior KW - Organizational Change Management ER - TY - CHAP TI - Segmentation of CT-Scan Images Using UNet Network for Patients Diagnosed with COVID-19 AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 29 EP - 44 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_3 Y2 - 2023/10/10/04:37:03 ER - TY - CHAP TI - Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre AU - Fong, Simon James T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 79 EP - 97 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_6 Y2 - 2023/10/10/04:37:10 ER - TY - CHAP TI - TPOT Automated Machine Learning Approach for Multiple Diagnostic Classification of Lung Radiography and Feature Extraction AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre AU - Fong, Simon James T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 117 EP - 135 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_8 Y2 - 2023/10/10/04:37:12 ER - TY - CHAP TI - Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks AU - Caldas, Weslley Lioba AU - do Vale Madeiro, João Paulo AU - Pedrosa, Roberto Coury AU - Gomes, João Paulo Pordeus AU - Du, Wencai AU - Marques, João Alexandre Lobo T2 - Computer and Information Science A2 - Lee, Roger T3 - Studies in Computational Intelligence AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 117 EP - 129 LA - en PB - Springer International Publishing SN - 978-3-031-12127-2 UR - https://doi.org/10.1007/978-3-031-12127-2_8 Y2 - 2023/08/01/12:50:19 KW - Chagas disease KW - Deep learning KW - ECG quality assessment ER - TY - JOUR TI - Sudden cardiac death multiparametric classification system for Chagas heart disease's patients based on clinical data and 24-hours ECG monitoring AU - Cavalcante, Carlos H. L. AU - Primo, Pedro E. O. AU - Sales, Carlos A. F. AU - Caldas, Weslley L. AU - Silva, João H. M. AU - Souza, Amauri H. AU - Marinho, Emmanuel S. AU - Pedrosa, Roberto C. AU - Marques, João A. L. AU - Santos, Hélcio S. AU - Madeiro, João P. V. AU - Cavalcante, Carlos H. L. AU - Primo, Pedro E. O. AU - Sales, Carlos A. F. AU - Caldas, Weslley L. AU - Silva, João H. M. AU - Souza, Amauri H. AU - Marinho, Emmanuel S. AU - Pedrosa, Roberto C. AU - Marques, João A. L. AU - Santos, Hélcio S. AU - Madeiro, João P. V. T2 - Mathematical Biosciences and Engineering AB -

About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.

DA - 2023/// PY - 2023 DO - 10.3934/mbe.2023402 DP - www.aimspress.com VL - 20 IS - 5 SP - 9159 EP - 9178 J2 - MBE LA - en SN - 1551-0018 UR - http://www.aimspress.com/rticle/doi/10.3934/mbe.2023402 Y2 - 2023/03/21/16:22:54 ER - TY - CHAP TI - Covid-19 Detection Based on Chest X-Ray Images Using Multiple Transfer Learning CNN Models AU - dos Santos Silva, Bruno Riccelli AU - Cesar Cortez, Paulo AU - Crosara Motta, Pedro AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 45 EP - 63 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_4 Y2 - 2023/10/10/04:37:05 ER - TY - CHAP TI - Lung Segmentation of Chest X-Rays Using Unet Convolutional Networks AU - dos Santos Silva, Bruno Riccelli AU - Cesar Cortez, Paulo AU - Gomes Aguiar, Rafael AU - Rodrigues Ribeiro, Tulio AU - Pereira Teixeira, Alexandre AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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%. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 15 EP - 28 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_2 Y2 - 2023/10/10/04:41:11 ER - TY - CHAP TI - X-Ray Machine Learning Classification with VGG-16 for Feature Extraction AU - dos Santos Silva, Bruno Riccelli AU - Cortez, Paulo Cesar AU - da Silva Neto, Manuel Gonçalves AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 65 EP - 78 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_5 Y2 - 2023/10/10/04:37:07 ER - TY - CHAP TI - Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures AU - Gois, Francisco Nauber Bernardo AU - Marques, João Alexandre Lobo AU - de Oliveira Dantas, Allberson Bruno AU - Santos, Márcio Costa AU - Neto, José Valdir Santiago AU - de Macêdo, José Antônio Fernandes AU - Du, Wencai AU - Li, Ye T2 - Computer and Information Science A2 - Lee, Roger T3 - Studies in Computational Intelligence AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 197 EP - 212 LA - en PB - Springer International Publishing SN - 978-3-031-12127-2 UR - https://doi.org/10.1007/978-3-031-12127-2_14 Y2 - 2023/03/22/06:27:01 ER - TY - CHAP TI - The (non) standardized classroom - the analysis of three different cultures in the higher education systems - Angola, Brazil and Macau SAR, China AU - Lobo Marques T2 - Disentangled Vision on Higher Education: Preparing the Generation Next AB - This book offers an objective and dispassionate analysis of modern educational architecture allowing us to notice gaps. The fundamental question addressed is whether our education system will embrace knowledge-based society and have the foresight to better prepare future generations. If educators around the world step back for a moment, it is not difficult to notice that unanswered questions about education are looming everywhere. The existent academic literature on education is abundant and embracing. In consequence, one can ask why is this book necessary? Indeed, this book is the result of senior university professors sharing their learnings and anticipating the pivotal issues facing all education professionals. According to the United Nations, by 2050, 68% of the world’s population will be living in urban areas. This fact cannot be ignored as it is one of the drivers of the profile of the future students. The reasons to organize this publication are many, but among them three stand out which also function as the driving forces behind this project: (1) University professors teach future generations based on models grounded on knowledge advanced by past experiences; (2) The decisive requirement to understand the needs of the new generations of university millennial students; and (3) What are the critical challenges of global societies? "This book problematizes the issues concerning education, and its main contribution is to answer the need to rethink education, face contemporary challenges, and reorganize the way public policies address education. It critically analyses the challenges of global societies in a decentralized perspective, not only reflecting a western perspective of education and knowledge production. The project's originality comes from the contemporaneity of the topics covered, from the interdisciplinary perspective, and from the specific attention given to trends around education." —Cátia Miriam Costa, Researcher and Invited Assistant Professor, Centre for International Studies, Perfil Ciência CY - New York DA - 2023/// PY - 2023 SP - 329 EP - 351 LA - English PB - Peter Lang SN - 978-1-4331-8594-6 ST - The (non) standardized classroom UR - https://www.peterlang.com/document/1266828 ER - TY - BOOK TI - Computerized Systems for Diagnosis and Treatment of COVID-19 A3 - Lobo Marques, Joao Alexandre A3 - Fong, Simon James CY - Cham DA - 2023/// PY - 2023 DP - DOI.org (Crossref) LA - en PB - Springer International Publishing SN - 978-3-031-30787-4 978-3-031-30788-1 UR - https://link.springer.com/10.1007/978-3-031-30788-1 Y2 - 2023/10/10/04:35:42 KW - Artificial Intelligence KW - Biofeedback KW - Computerized Diagnostic Support KW - Covid-19 KW - Signal and Image Processing ER - TY - CHAP TI - Technology Developments to Face the COVID-19 Pandemic: Advances, Challenges, and Trends AU - Lobo Marques, Joao Alexandre AU - Fong, Simon James T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 1 EP - 13 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 ST - Technology Developments to Face the COVID-19 Pandemic UR - https://doi.org/10.1007/978-3-031-30788-1_1 Y2 - 2023/10/10/04:37:00 ER - TY - CHAP TI - Exploratory Data Analysis on Clinical and Emotional Parameters of Pregnant Women with COVID-19 Symptoms AU - Lobo Marques, Joao Alexandre AU - Macedo, Danielle S. AU - Motta, Pedro AU - dos Santos Silva, Bruno Riccelli AU - Carvalho, Francisco Herlanio Costa AU - Kehdi, Renata Castro AU - Cavalcante, Letícia Régia Lima AU - da Silva Viana, Marylane AU - Lós, Deniele AU - Fiorenza, Natália Gindri T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 179 EP - 209 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_11 Y2 - 2023/10/10/04:37:22 ER - TY - CHAP TI - COVID-19 Classification Using CT Scans with Convolutional Neural Networks AU - Motta, Pedro Crosara AU - Cesar Cortez, Paulo AU - Lobo Marques, Jao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 99 EP - 116 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_7 Y2 - 2023/10/11/10:42:48 ER - TY - CONF TI - Training Strategies for Covid-19 Severity Classification AU - Pordeus, Daniel AU - Ribeiro, Pedro AU - Zacarias, Laíla AU - de Oliveira, Adriel AU - Marques, João Alexandre Lobo AU - Rodrigues, Pedro Miguel AU - Leite, Camila AU - Neto, Manoel Alves AU - Peixoto, Arnaldo Aires AU - do Vale Madeiro, João Paulo A2 - Rojas, Ignacio A2 - Valenzuela, Olga A2 - Rojas Ruiz, Fernando A2 - Herrera, Luis Javier A2 - Ortuño, Francisco T3 - Lecture Notes in Computer Science AB - 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. C1 - Cham C3 - Bioinformatics and Biomedical Engineering DA - 2023/// PY - 2023 DO - 10.1007/978-3-031-34953-9_40 DP - Springer Link SP - 514 EP - 527 LA - en PB - Springer Nature Switzerland SN - 978-3-031-34953-9 KW - COVID-19 KW - Electrocardiogram (ECG) KW - Heart Rate Variability (HRV) KW - disease severity classification KW - signal processing ER - TY - CHAP TI - Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability AU - Pordeus, Daniel AU - Ribeiro, Pedro AU - Zacarias, Laíla AU - Paulo Madeiro, João AU - Lobo Marques, Joao Alexandre AU - Miguel Rodrigues, Pedro AU - Leite, Camila AU - Alves Neto, Manoel AU - Aires Peixoto Jr, Arnaldo AU - de Oliveira, Adriel T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 155 EP - 177 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_10 Y2 - 2023/10/10/04:37:17 KW - COVID-19 KW - Electrocardiogram (ECG) signal KW - Heart Rate Variability (HRV) indices KW - Severity KW - Signal processing ER - TY - CHAP TI - Evaluation of ECG Non-linear Features in Time-Frequency Domain for the Discrimination of COVID-19 Severity Stages AU - Ribeiro, Pedro AU - Pordeus, Daniel AU - Zacarias, Laíla AU - Leite, Camila AU - Alves Neto, Manoel AU - Aires Peixoto Jr, Arnaldo AU - de Oliveira, Adriel AU - Paulo Madeiro, João AU - Lobo Marques, Joao Alexandre AU - Miguel Rodrigues, Pedro T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 137 EP - 154 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_9 Y2 - 2023/10/10/04:41:02 KW - COVID-19 KW - ECG signals KW - Non-linear analysis KW - Statistical analysis ER - TY - CONF TI - Sustainable Practices in Hotel Chains—A Comparative Analysis of Official Annual Hospitality Sustainable Reports from Listed Companies in Macau SAR, China AU - Santos, Ana Sofia Kong AU - Marques, João Alexandre Lobo A2 - Gartner, William C. T3 - Springer Proceedings in Business and Economics AB - 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. C1 - Cham C3 - New Perspectives and Paradigms in Applied Economics and Business DA - 2023/// PY - 2023 DO - 10.1007/978-3-031-23844-4_23 DP - Springer Link SP - 319 EP - 338 LA - en PB - Springer International Publishing SN - 978-3-031-23844-4 KW - Environmentally sustainable practices KW - Macau hotels KW - Sustainable development goals (SDGs) KW - Sustainable reports KW - Tourism industry ER - TY - CHAP TI - Medical Information Extraction of Clinical Notes and Pictorial Visualisation of Electronic Medical Records Summary Interface AU - Singh, Praveen AU - Chaudhary, Gopal AU - Lobo Marques, Joao Alexandre T2 - Smart Distributed Embedded Systems for Healthcare Applications DA - 2023/// PY - 2023 SP - 29 EP - 40 PB - CRC Press SN - 978-1-00-325411-9 UR - https://www.routledge.com/Smart-Distributed-Embedded-Systems-for-Healthcare-Applications/Nagrath-Alzubi-Singla-Rodrigues-Verma/p/book/9781032183473 ER - TY - JOUR TI - Exploratory Analysis of Project Management Adoption and Maturity Level of IT Companies–A Comparison between Macao and Hengqin AU - Wong, Ka Seng AU - Lobo Marques, João Alexandre T2 - Journal of Advanced Management Science DA - 2023/// PY - 2023 DO - 10.18178/joams.11.3.124-129 DP - DOI.org (Crossref) VL - 11 IS - 2 SP - 124 EP - 129 J2 - JOAMS SN - 28109740 UR - http://www.joams.com/show-106-594-1.html Y2 - 2024/01/14/15:50:47 ER - TY - JOUR TI - A review on multimodal machine learning in medical diagnostics AU - Yan, Keyue AU - Li, Tengyue AU - Marques, João Alexandre Lobo AU - Gao, Juntao AU - Fong, Simon James AU - Yan, Keyue AU - Li, Tengyue AU - Marques, João Alexandre Lobo AU - Gao, Juntao AU - Fong, Simon James T2 - Mathematical Biosciences and Engineering AB - 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. DA - 2023/// PY - 2023 DO - 10.3934/mbe.2023382 DP - www.aimspress.com VL - 20 IS - 5 SP - 8708 EP - 8726 J2 - MBE LA - en SN - 1551-0018 UR - http://www.aimspress.com/rticle/doi/10.3934/mbe.2023382 Y2 - 2023/03/21/16:22:08 ER - TY - GEN TI - Stock Market Prediction Using Artificial Intelligence: A Systematic Review of Systematic Reviews AU - Lin, Coka Chinyang AU - Marques, Joao Alexandre Lobo AB - 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. CY - Rochester, NY DA - 2023/01/29/ PY - 2023 DO - 10.2139/ssrn.4341351 DP - Social Science Research Network LA - en ST - Stock Market Prediction Using Artificial Intelligence UR - https://papers.ssrn.com/abstract=4341351 Y2 - 2023/03/22/06:33:51 KW - Deep Learning KW - Long Short-Term Memory (LSTM) KW - Machine Learning KW - Neural Networks (NN) KW - Support Vector Machines (SVM) ER - TY - JOUR TI - COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review AU - Ribeiro, Pedro AU - Marques, João Alexandre Lobo AU - Rodrigues, Pedro Miguel T2 - Bioengineering AB - 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. DA - 2023/02// PY - 2023 DO - 10.3390/bioengineering10020198 DP - www.mdpi.com VL - 10 IS - 2 SP - 198 LA - en SN - 2306-5354 ST - COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems UR - https://www.mdpi.com/2306-5354/10/2/198 Y2 - 2023/03/21/16:20:13 KW - COVID-19 KW - artificial intelligence KW - computerized diagnostic systems KW - image processing KW - signal processing ER - TY - JOUR TI - EFFECTIVENESS ANALYSIS OF WATERFALL AND AGILE PROJECT MANAGEMENT METHODOLOGIES – A CASE STUDY FROM MACAU'S CONSTRUCTION INDUSTRY AU - Marques, Joao Alexandre Lobo AU - Morais, João José Bragança dos Reis AU - Alves, José AU - Gonçalves, Marcus T2 - Revista Gestão em Análise AB - 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. DA - 2023/02/24/ PY - 2023 DO - 10.12662/2359-618xregea.v12i1.p23-38.2023 DP - periodicos.unichristus.edu.br VL - 12 IS - 1 SP - 23 EP - 38 LA - en SN - 2359-618X UR - https://periodicos.unichristus.edu.br/gestao/article/view/2508 AN - Interview and Survey Y2 - 2023/03/22/06:40:23 KW - PMBOK KW - agile KW - construction industry KW - project management methodologies ER - TY - JOUR TI - Neuromarketing and Global Branding Reaction Analysis Based on Real-Time Monitoring of Multiple Consumer's Biosignals and Emotions AU - Goncalves, Marcus AU - Lobo Marques, Joao Alexandre AU - Silva, Bruno Riccelli Santos AU - Luther, Valorie AU - Hayes, Sydney T2 - Journal of International Business and Management AB - 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. DA - 2023/04/26/ PY - 2023 DO - 10.37227/JIBM-2023-04-5912 DP - DOI.org (Crossref) VL - 6 IS - 5 SP - 01 EP - 32 J2 - JIBM LA - en SN - 2616-4655, 2616-5163 UR - https://rpajournals.com/jibm-2023-04-5912/ Y2 - 2023/05/29/02:15:18 ER - TY - SLIDE TI - Intelligent Data Fusion System for Assessing and Classifying the Long-Term Effects of Exposure to COVID-19 in Pregnancy (Long Covid): Associated Neurophysiological and Epigenetic Mechanisms and Consequences for Infant Development T2 - 2023 7th International Conference on Data Mining, Communications and Information Technology (DMCIT & CSMO 2023) A2 - Lobo Marques, J. A. CY - Chongqin, China DA - 2023/05//26th to 28th PY - 2023 M3 - Conference ER - TY - JOUR TI - Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices AU - Rodrigues, Pedro Miguel AU - Madeiro, João Paulo AU - Marques, João Alexandre Lobo T2 - Bioengineering AB - 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 [...] DA - 2023/07// PY - 2023 DO - 10.3390/bioengineering10070792 DP - www.mdpi.com VL - 10 IS - 7 SP - 792 LA - en SN - 2306-5354 ST - Enhancing Health and Public Health through Machine Learning UR - https://www.mdpi.com/2306-5354/10/7/792 Y2 - 2023/08/01/12:50:11 KW - n/a ER - TY - JOUR TI - Neurofinance: Exploratory Analysis Stock Trader's Decision-Making Process by Real-Time Monitoring of Emotional Reactions AU - Hsu, Hsin-Tzu AU - Marques, João Alexandre Lobo T2 - European Conference on Management Leadership and Governance AB - 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. DA - 2023/11/13/ PY - 2023 DO - 10.34190/ecmlg.19.1.1692 DP - papers.academic-conferences.org VL - 19 IS - 1 SP - 147 EP - 155 J2 - ECMLG LA - en SN - 2048-903X ST - Neurofinance UR - https://papers.academic-conferences.org/index.php/ecmlg/article/view/1692 Y2 - 2023/12/18/03:07:03 ER - TY - JOUR TI - Leadership and Neurosciences - The analysis of emotional arousal during decision-making processes with decision-makers exposed to acute stress AU - Marques, Joao Alexandre Lobo T2 - European Conference on Management Leadership and Governance AB - 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. DA - 2023/11/13/ PY - 2023 DO - 10.34190/ecmlg.19.1.1950 DP - papers.academic-conferences.org VL - 19 IS - 1 SP - 232 EP - 239 J2 - ECMLG LA - en SN - 2048-903X UR - https://papers.academic-conferences.org/index.php/ecmlg/article/view/1950 Y2 - 2023/12/18/03:07:07 ER - TY - JOUR TI - Neuromarketing: Evaluating Consumer Emotions and Preferences to Improve Business Marketing Management AU - Zeng, Ian Mei AU - Marques, João Alexandre Lobo T2 - European Conference on Management Leadership and Governance AB - 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. DA - 2023/11/13/ PY - 2023 DO - 10.34190/ecmlg.19.1.1876 DP - papers.academic-conferences.org VL - 19 IS - 1 SP - 436 EP - 444 J2 - ECMLG LA - en SN - 2048-903X ST - Neuromarketing UR - https://papers.academic-conferences.org/index.php/ecmlg/article/view/1876 Y2 - 2023/12/18/03:06:57 ER - TY - CONF TI - Exploring EEG Signal Features for Predicting Post Cardiac Arrest Prognosis AU - Guilherme Cunha Santos, Antonio AU - Alexandre Lobo Marques, Joao AU - Rigo Jr., Luis AU - Paulo Madeiro, João T2 - 2023 Computing in Cardiology Conference DA - 2023/11/26/ PY - 2023 DO - 10.22489/CinC.2023.312 DP - DOI.org (Crossref) UR - https://www.cinc.org/archives/2023/pdf/CinC2023-312.pdf Y2 - 2024/01/14/15:55:19 ER - TY - SLIDE TI - Connecting Neuromarketing and AI - a path to new insights and analysis T2 - Neuromarathon A2 - Marques, J. A. L. CY - Italian Association of Neuromarketing DA - 2023/12// PY - 2023 M3 - CONFERENCE INVITED SPEECH ER - TY - JOUR TI - Categorization of Foreign Aid Donors: A Critical Review of the Criteria in Light of China's Reemergence as a Donor AU - Diakite, Ansoumane Douty AU - Marques, João Alexandre Lobo T2 - Journal of Global South Studies AB - 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. DA - 2023/12/11/ PY - 2023 DP - journals.upress.ufl.edu VL - 40 IS - 2 LA - en SN - 2476-1419 ST - Categorization of Foreign Aid Donors UR - https://journals.upress.ufl.edu/JGSS/article/view/2381 Y2 - 2023/12/18/08:58:13 KW - China and development assistance committee donors KW - aid policies KW - categorization of donors KW - sustainable development goal KW - traditional and emerging donors ER - TY - CONF TI - 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 AU - Ip, Chi Hang AU - Lobo Marques, Joao Alexandre AU - Dos Santos Silva, Bruno Riccelli AU - Cortez, Paulo Cesar AU - Barbosa, Alvaro T3 - ICEME '23 AB - 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. C1 - New York, NY, USA C3 - Proceedings of the 2023 14th International Conference on E-business, Management and Economics DA - 2023/12/15/ PY - 2023 DO - 10.1145/3616712.3616786 DP - ACM Digital Library SP - 417 EP - 422 PB - Association for Computing Machinery SN - 9798400708022 UR - https://dl.acm.org/doi/10.1145/3616712.3616786 Y2 - 2024/01/01/00:00:00 KW - Absorptive Capacity KW - Bibliometric analysis KW - Open innovation KW - SMEs KW - VOSviewer ER - TY - CONF TI - An Exploratory Comparison of Stock Prices Prediction using Multiple Machine Learning Approaches based on Hong Kong Share Market AU - Lin, Chinyang AU - Lobo Marques, João Alexandre T3 - ICEME '23 AB - 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. C1 - New York, NY, USA C3 - Proceedings of the 2023 14th International Conference on E-business, Management and Economics DA - 2023/12/15/ PY - 2023 DO - 10.1145/3616712.3616762 DP - ACM Digital Library SP - 311 EP - 316 PB - Association for Computing Machinery SN - 9798400708022 UR - https://doi.org/10.1145/3616712.3616762 Y2 - 2024/01/14/00:00:00 KW - Long Short-Term Memory (LSTM) KW - Machine Learning KW - Stock price prediction KW - Support Vector Machine (SVM) KW - Time-series analysis ER - TY - CONF TI - Facial expression for marketing and consumer behavior: A Bibliometric analysis from 2013-2023 using Data Visualization Tools AU - Qian, Cheng AU - Lobo Marques, Joao Alexandre T3 - ICEME '23 AB - 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. C1 - New York, NY, USA C3 - Proceedings of the 2023 14th International Conference on E-business, Management and Economics DA - 2023/12/15/ PY - 2023 DO - 10.1145/3616712.3616717 DP - ACM Digital Library SP - 270 EP - 275 PB - Association for Computing Machinery SN - 9798400708022 ST - Facial expression for marketing and consumer behavior UR - https://doi.org/10.1145/3616712.3616717 Y2 - 2024/01/14/00:00:00 KW - Bibliometric analysis KW - Consumer KW - Facial expression KW - Marketing KW - Research KW - VOSviewer ER - TY - CONF TI - Neuromarketing As A Tool To Measure And Evaluate The Consumer Behaviour Of Guanding Teahouse's Social Media Advertisement AU - Zeng, Ian Mei AU - Lobo Marques, Joao Alexandre T3 - ICEME '23 AB - 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. C1 - New York, NY, USA C3 - Proceedings of the 2023 14th International Conference on E-business, Management and Economics DA - 2023/12/15/ PY - 2023 DO - 10.1145/3616712.3616787 DP - ACM Digital Library SP - 63 EP - 69 PB - Association for Computing Machinery SN - 9798400708022 UR - https://doi.org/10.1145/3616712.3616787 Y2 - 2024/01/14/00:00:00 ER - TY - JOUR TI - Aspects that constitute citizens’ trust in e-government - A review and framework development AU - Lai, Chimeng AU - Marques, Alexandre Joao Lobo T2 - Multidisciplinary Reviews AB - The extent of citizens' trust in government determines the success or failure of e-government initiatives. Nevertheless, the idiosyncrasies of the concept and the broad spectrum of its approach still present relevant challenges. This work presents a systematic literature review on e-government trust while elaborating and summarizing a conceptual analysis of trust, introducing evaluation methods for government trust, and compiling relevant research on e-government trust and intentional behavior. A total of 26 key factors that constitute trust have been identified and classified into six categories: Government trust, Trust in Internet and technology (TiIT), Trust in e-government (TiEG), Personal Beliefs, Trustworthiness, and Trust of intermediary (ToI). The value added of this work consists of developing a conceptual framework of TiEG to provide a significant reference for future in-depth studies and research on e-government trust. DA - 2024/// PY - 2024 DO - 10.31893/multirev.2024023 DP - malque.pub VL - 7 IS - 2 SP - 2024023 EP - 2024023 LA - en SN - 2595-3982 UR - https://malque.pub/ojs/index.php/mr/article/view/1591 Y2 - 2024/01/14/15:54:06 KW - Systematic Literature Review (SLR) KW - framework KW - trust KW - trust in e-government ER - TY - JOUR TI - Machine learning-based cardiac activity non-linear analysis for discriminating COVID-19 patients with different degrees of severity AU - Ribeiro, Pedro AU - Marques, João Alexandre Lobo AU - Pordeus, Daniel AU - Zacarias, Laíla AU - Leite, Camila Ferreira AU - Sobreira-Neto, Manoel Alves AU - Peixoto, Arnaldo Aires AU - de Oliveira, Adriel AU - Madeiro, João Paulo do Vale AU - Rodrigues, Pedro Miguel T2 - Biomedical Signal Processing and Control AB - 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. DA - 2024/01/01/ PY - 2024 DO - 10.1016/j.bspc.2023.105558 DP - ScienceDirect VL - 87 SP - 105558 J2 - Biomedical Signal Processing and Control SN - 1746-8094 UR - https://www.sciencedirect.com/science/article/pii/S1746809423009916 Y2 - 2024/01/14/15:54:09 KW - Accuracy KW - COVID-19 KW - ECG signals KW - Machine learning classifiers KW - Non-linear analysis KW - – ER -