@inproceedings{li_fast_2018, title = {Fast {Cluster}-learning with {Prior} {Probability} from {Big} {Dataset}}, doi = {10.1109/ISCMI.2018.8703219}, abstract = {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.}, booktitle = {2018 5th {International} {Conference} on {Soft} {Computing} \& {Machine} {Intelligence} ({ISCMI})}, author = {Li, Tengyue and Fong, Simon and Lobo Marques, Joao Alexandre and Wong, Raymond K.}, month = nov, year = {2018}, note = {0 citations (Crossref) [2022-09-21] ISSN: 2640-0146}, keywords = {Association Rule Mining, Big Data, Clustering, Clustering algorithms, Data mining, Itemsets, Preprocessing, Prior Probability, Probabilistic logic, Probability distribution}, pages = {60--66}, } @article{do_vale_madeiro_evaluation_2020, title = {Evaluation of mathematical models for {QRS} feature extraction and {QRS} morphology classification in {ECG} signals}, volume = {156}, issn = {0263-2241}, url = {https://www.sciencedirect.com/science/article/pii/S0263224120301172}, doi = {10.1016/j.measurement.2020.107580}, abstract = {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.}, language = {en}, urldate = {2022-09-21}, journal = {Measurement}, author = {do Vale Madeiro, João Paulo and Lobo Marques, João Alexandre and Han, Tao and Coury Pedrosa, Roberto}, month = may, year = {2020}, note = {13 citations (Crossref) [2022-09-21]}, keywords = {ECG feature extraction, Mathematical modeling, Morphology classification, QRS complex delineation}, pages = {107580}, } @article{arraut_probability_2021, title = {The {Probability} {Flow} in the {Stock} {Market} and {Spontaneous} {Symmetry} {Breaking} in {Quantum} {Finance}}, volume = {9}, copyright = {http://creativecommons.org/licenses/by/3.0/}, issn = {2227-7390}, url = {https://www.mdpi.com/2227-7390/9/21/2777}, doi = {10.3390/math9212777}, abstract = {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.}, language = {en}, number = {21}, urldate = {2023-04-11}, journal = {Mathematics}, author = {Arraut, Ivan and Lobo Marques, João Alexandre and Gomes, Sergio}, month = jan, year = {2021}, note = {Number: 21 Publisher: Multidisciplinary Digital Publishing Institute}, keywords = {Hermiticity, conservation of the information, degenerate vacuum, flow of information, martingale condition, random fluctuations, spontaneous symmetry breaking, vacuum condition}, pages = {2777}, } @incollection{bernardo_gois_predictive_2021, title = {Predictive models to the {COVID}-19}, isbn = {978-0-12-824536-1}, url = {https://www.sciencedirect.com/science/article/pii/B978012824536100023X}, abstract = {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.}, language = {en}, urldate = {2021-05-26}, booktitle = {Data {Science} for {COVID}-19}, publisher = {Academic Press}, author = {Bernardo Gois, Francisco Nauber and Lima, Alex and Santos, Khennedy and Oliveira, Ramses and Santiago, Valdir and Melo, Saulo and Costa, Rafael and Oliveira, Marcelo and Henrique, Francisco das Chagas Douglas Marques and Neto, José Xavier and Martins Rodrigues Sobrinho, Carlos Roberto and Lôbo Marques, João Alexandre}, editor = {Kose, Utku and Gupta, Deepak and de Albuquerque, Victor Hugo C. and Khanna, Ashish}, month = jan, year = {2021}, doi = {10.1016/B978-0-12-824536-1.00023-X}, keywords = {COVID-19, Forecast, Holt Winters, Kalman filter, Machine learning, Prophet, SEIR}, pages = {1--24}, } @book{lobo_marques_epidemic_2022, address = {Cham, Switzerland}, title = {Epidemic analytics for decision supports in {COVID19} crisis}, isbn = {978-3-030-95281-5 978-3-030-95280-8}, url = {https://link.springer.com/book/10.1007/978-3-030-95281-5#about-this-book}, abstract = {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.}, language = {eng}, publisher = {Springer}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon}, year = {2022}, } @incollection{lobo_marques_ai_2022, series = {Intelligent {Data}-{Centric} {Systems}}, title = {{AI} and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions}, isbn = {978-0-323-85751-2}, url = {https://www.sciencedirect.com/science/article/pii/B9780323857512000013}, abstract = {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.}, language = {en}, urldate = {2022-09-21}, booktitle = {Cognitive and {Soft} {Computing} {Techniques} for the {Analysis} of {Healthcare} {Data}}, publisher = {Academic Press}, author = {Lôbo Marques, João Alexandre and Bernardo Gois, Francisco Nauber and Nunes da Silveira, Jarbas Aryel and Li, Tengyue and Fong, Simon James}, editor = {Bhoi, Akash Kumar and de Albuquerque, Victor Hugo C. and Srinivasu, Parvathaneni Naga and Marques, Gonçalo}, month = jan, year = {2022}, doi = {10.1016/B978-0-323-85751-2.00001-3}, keywords = {Artificial intelligence, Clinical decisions, Computer-aided diagnostic systems, Deep learning, Patient-centric data}, pages = {101--121}, } @inproceedings{neto_future_2022, address = {New York, NY, USA}, series = {{ICEME} 2022}, title = {The future of {Cosmetics} {Advertisement} {Strategy}: {A} {Neuromarketing} {Study} using {Electrodermal} {Activity} ({EDA}) as a measure of {Emotional} {Arousal}}, isbn = {978-1-4503-9639-4}, shorttitle = {The future of {Cosmetics} {Advertisement} {Strategy}}, url = {https://dl.acm.org/doi/10.1145/3556089.3556126}, doi = {10.1145/3556089.3556126}, abstract = {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.}, urldate = {2023-03-21}, booktitle = {2022 13th {International} {Conference} on {E}-business, {Management} and {Economics}}, publisher = {Association for Computing Machinery}, author = {Neto, Andreia and Lobo Marques, Joao Alexandre}, month = nov, year = {2022}, keywords = {advertisement, consumer behavior, consumer neurosciences, cosmetics, marketing, neuromarketing}, pages = {81--86}, } @incollection{bernardo_gois_segmentation_2023, address = {Cham}, title = {Segmentation of {CT}-{Scan} {Images} {Using} {UNet} {Network} for {Patients} {Diagnosed} with {COVID}-19}, isbn = {978-3-031-30788-1}, url = {https://doi.org/10.1007/978-3-031-30788-1_3}, abstract = {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.}, language = {en}, urldate = {2023-10-10}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {Bernardo Gois, Francisco Nauber and Lobo Marques, Joao Alexandre}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_3}, pages = {29--44}, } @incollection{bernardo_gois_classification_2023, address = {Cham}, title = {Classification of {COVID}-19 {CT} {Scans} {Using} {Convolutional} {Neural} {Networks} and {Transformers}}, isbn = {978-3-031-30788-1}, url = {https://doi.org/10.1007/978-3-031-30788-1_6}, abstract = {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.}, language = {en}, urldate = {2023-10-10}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {Bernardo Gois, Francisco Nauber and Lobo Marques, Joao Alexandre and Fong, Simon James}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_6}, pages = {79--97}, } @incollection{bernardo_gois_tpot_2023, address = {Cham}, title = {{TPOT} {Automated} {Machine} {Learning} {Approach} for {Multiple} {Diagnostic} {Classification} of {Lung} {Radiography} and {Feature} {Extraction}}, isbn = {978-3-031-30788-1}, url = {https://doi.org/10.1007/978-3-031-30788-1_8}, abstract = {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.}, language = {en}, urldate = {2023-10-10}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {Bernardo Gois, Francisco Nauber and Lobo Marques, Joao Alexandre and Fong, Simon James}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_8}, pages = {117--135}, } @incollection{dos_santos_silva_covid-19_2023, address = {Cham}, title = {Covid-19 {Detection} {Based} on {Chest} {X}-{Ray} {Images} {Using} {Multiple} {Transfer} {Learning} {CNN} {Models}}, isbn = {978-3-031-30788-1}, url = {https://doi.org/10.1007/978-3-031-30788-1_4}, abstract = {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.}, language = {en}, urldate = {2023-10-10}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {dos Santos Silva, Bruno Riccelli and Cesar Cortez, Paulo and Crosara Motta, Pedro and Lobo Marques, Joao Alexandre}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_4}, pages = {45--63}, } @incollection{dos_santos_silva_lung_2023, address = {Cham}, title = {Lung {Segmentation} of {Chest} {X}-{Rays} {Using} {Unet} {Convolutional} {Networks}}, isbn = {978-3-031-30788-1}, url = {https://doi.org/10.1007/978-3-031-30788-1_2}, abstract = {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\%.}, language = {en}, urldate = {2023-10-10}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {dos Santos Silva, Bruno Riccelli and Cesar Cortez, Paulo and Gomes Aguiar, Rafael and Rodrigues Ribeiro, Tulio and Pereira Teixeira, Alexandre and Bernardo Gois, Francisco Nauber and Lobo Marques, Joao Alexandre}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_2}, pages = {15--28}, } @incollection{dos_santos_silva_x-ray_2023, address = {Cham}, title = {X-{Ray} {Machine} {Learning} {Classification} with {VGG}-16 for {Feature} {Extraction}}, isbn = {978-3-031-30788-1}, url = {https://doi.org/10.1007/978-3-031-30788-1_5}, abstract = {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.}, language = {en}, urldate = {2023-10-10}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {dos Santos Silva, Bruno Riccelli and Cortez, Paulo Cesar and da Silva Neto, Manuel Gonçalves and Lobo Marques, Joao Alexandre}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_5}, pages = {65--78}, } @book{lobo_marques_computerized_2023, address = {Cham}, title = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, isbn = {978-3-031-30787-4 978-3-031-30788-1}, url = {https://link.springer.com/10.1007/978-3-031-30788-1}, language = {en}, urldate = {2023-10-10}, publisher = {Springer International Publishing}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1}, keywords = {Artificial Intelligence, Biofeedback, Computerized Diagnostic Support, Covid-19, Signal and Image Processing}, } @incollection{lobo_marques_technology_2023, address = {Cham}, title = {Technology {Developments} to {Face} the {COVID}-19 {Pandemic}: {Advances}, {Challenges}, and {Trends}}, isbn = {978-3-031-30788-1}, shorttitle = {Technology {Developments} to {Face} the {COVID}-19 {Pandemic}}, url = {https://doi.org/10.1007/978-3-031-30788-1_1}, abstract = {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.}, language = {en}, urldate = {2023-10-10}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {Lobo Marques, Joao Alexandre and Fong, Simon James}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_1}, pages = {1--13}, } @incollection{lobo_marques_exploratory_2023, address = {Cham}, title = {Exploratory {Data} {Analysis} on {Clinical} and {Emotional} {Parameters} of {Pregnant} {Women} with {COVID}-19 {Symptoms}}, isbn = {978-3-031-30788-1}, url = {https://doi.org/10.1007/978-3-031-30788-1_11}, abstract = {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.}, language = {en}, urldate = {2023-10-10}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {Lobo Marques, Joao Alexandre and Macedo, Danielle S. and Motta, Pedro and dos Santos Silva, Bruno Riccelli and Carvalho, Francisco Herlanio Costa and Kehdi, Renata Castro and Cavalcante, Letícia Régia Lima and da Silva Viana, Marylane and Lós, Deniele and Fiorenza, Natália Gindri}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_11}, pages = {179--209}, } @incollection{motta_covid-19_2023, address = {Cham}, title = {{COVID}-19 {Classification} {Using} {CT} {Scans} with {Convolutional} {Neural} {Networks}}, isbn = {978-3-031-30788-1}, url = {https://doi.org/10.1007/978-3-031-30788-1_7}, abstract = {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.}, language = {en}, urldate = {2023-10-11}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {Motta, Pedro Crosara and Cesar Cortez, Paulo and Lobo Marques, Jao Alexandre}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_7}, pages = {99--116}, } @incollection{pordeus_classification_2023, address = {Cham}, title = {Classification of {Severity} of {COVID}-19 {Patients} {Based} on the {Heart} {Rate} {Variability}}, isbn = {978-3-031-30788-1}, url = {https://doi.org/10.1007/978-3-031-30788-1_10}, abstract = {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.}, language = {en}, urldate = {2023-10-10}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {Pordeus, Daniel and Ribeiro, Pedro and Zacarias, Laíla and Paulo Madeiro, João and Lobo Marques, Joao Alexandre and Miguel Rodrigues, Pedro and Leite, Camila and Alves Neto, Manoel and Aires Peixoto Jr, Arnaldo and de Oliveira, Adriel}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_10}, keywords = {COVID-19, Electrocardiogram (ECG) signal, Heart Rate Variability (HRV) indices, Severity, Signal processing}, pages = {155--177}, } @incollection{ribeiro_evaluation_2023, address = {Cham}, title = {Evaluation of {ECG} {Non}-linear {Features} in {Time}-{Frequency} {Domain} for the {Discrimination} of {COVID}-19 {Severity} {Stages}}, isbn = {978-3-031-30788-1}, url = {https://doi.org/10.1007/978-3-031-30788-1_9}, abstract = {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 {\textless} 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 {\textless} 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 {\textless} 0.05, All vs. All—Correlation Dimension p-value = 0.0185 {\textless} 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.}, language = {en}, urldate = {2023-10-10}, booktitle = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, publisher = {Springer International Publishing}, author = {Ribeiro, Pedro and Pordeus, Daniel and Zacarias, Laíla and Leite, Camila and Alves Neto, Manoel and Aires Peixoto Jr, Arnaldo and de Oliveira, Adriel and Paulo Madeiro, João and Lobo Marques, Joao Alexandre and Miguel Rodrigues, Pedro}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1_9}, keywords = {COVID-19, ECG signals, Non-linear analysis, Statistical analysis}, pages = {137--154}, } @incollection{singh_medical_2023, title = {Medical {Information} {Extraction} of {Clinical} {Notes} and {Pictorial} {Visualisation} of {Electronic} {Medical} {Records} {Summary} {Interface}}, isbn = {978-1-00-325411-9}, url = {https://www.routledge.com/Smart-Distributed-Embedded-Systems-for-Healthcare-Applications/Nagrath-Alzubi-Singla-Rodrigues-Verma/p/book/9781032183473}, booktitle = {Smart {Distributed} {Embedded} {Systems} for {Healthcare} {Applications}}, publisher = {CRC Press}, author = {Singh, Praveen and Chaudhary, Gopal and Lobo Marques, Joao Alexandre}, year = {2023}, pages = {29--40}, } @article{wong_exploratory_2023, title = {Exploratory {Analysis} of {Project} {Management} {Adoption} and {Maturity} {Level} of {IT} {Companies}–{A} {Comparison} between {Macao} and {Hengqin}}, volume = {11}, issn = {28109740}, url = {http://www.joams.com/show-106-594-1.html}, doi = {10.18178/joams.11.3.124-129}, number = {2}, urldate = {2024-01-14}, journal = {Journal of Advanced Management Science}, author = {Wong, Ka Seng and Lobo Marques, João Alexandre}, year = {2023}, pages = {124--129}, } @article{goncalves_neuromarketing_2023, title = {Neuromarketing and {Global} {Branding} {Reaction} {Analysis} {Based} on {Real}-{Time} {Monitoring} of {Multiple} {Consumer}'s {Biosignals} and {Emotions}}, volume = {6}, issn = {2616-4655, 2616-5163}, url = {https://rpajournals.com/jibm-2023-04-5912/}, doi = {10.37227/JIBM-2023-04-5912}, abstract = {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.}, language = {en}, number = {5}, urldate = {2023-05-29}, journal = {Journal of International Business and Management}, author = {Goncalves, Marcus and Lobo Marques, Joao Alexandre and Silva, Bruno Riccelli Santos and Luther, Valorie and Hayes, Sydney}, month = apr, year = {2023}, pages = {01--32}, } @inproceedings{ip_review_2023, address = {New York, NY, USA}, series = {{ICEME} '23}, title = {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}}, isbn = {9798400708022}, url = {https://dl.acm.org/doi/10.1145/3616712.3616786}, doi = {10.1145/3616712.3616786}, abstract = {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.}, urldate = {2024-01-01}, booktitle = {Proceedings of the 2023 14th {International} {Conference} on {E}-business, {Management} and {Economics}}, publisher = {Association for Computing Machinery}, author = {Ip, Chi Hang and Lobo Marques, Joao Alexandre and Dos Santos Silva, Bruno Riccelli and Cortez, Paulo Cesar and Barbosa, Alvaro}, month = dec, year = {2023}, keywords = {Absorptive Capacity, Bibliometric analysis, Open innovation, SMEs, VOSviewer}, pages = {417--422}, } @inproceedings{lin_exploratory_2023, address = {New York, NY, USA}, series = {{ICEME} '23}, title = {An {Exploratory} {Comparison} of {Stock} {Prices} {Prediction} using {Multiple} {Machine} {Learning} {Approaches} based on {Hong} {Kong} {Share} {Market}}, isbn = {9798400708022}, url = {https://doi.org/10.1145/3616712.3616762}, doi = {10.1145/3616712.3616762}, abstract = {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.}, urldate = {2024-01-14}, booktitle = {Proceedings of the 2023 14th {International} {Conference} on {E}-business, {Management} and {Economics}}, publisher = {Association for Computing Machinery}, author = {Lin, Chinyang and Lobo Marques, João Alexandre}, month = dec, year = {2023}, keywords = {Long Short-Term Memory (LSTM), Machine Learning, Stock price prediction, Support Vector Machine (SVM), Time-series analysis}, pages = {311--316}, } @inproceedings{qian_facial_2023, address = {New York, NY, USA}, series = {{ICEME} '23}, title = {Facial expression for marketing and consumer behavior: {A} {Bibliometric} analysis from 2013-2023 using {Data} {Visualization} {Tools}}, isbn = {9798400708022}, shorttitle = {Facial expression for marketing and consumer behavior}, url = {https://doi.org/10.1145/3616712.3616717}, doi = {10.1145/3616712.3616717}, abstract = {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.}, urldate = {2024-01-14}, booktitle = {Proceedings of the 2023 14th {International} {Conference} on {E}-business, {Management} and {Economics}}, publisher = {Association for Computing Machinery}, author = {Qian, Cheng and Lobo Marques, Joao Alexandre}, month = dec, year = {2023}, keywords = {Bibliometric analysis, Consumer, Facial expression, Marketing, Research, VOSviewer}, pages = {270--275}, } @inproceedings{zeng_neuromarketing_2023, address = {New York, NY, USA}, series = {{ICEME} '23}, title = {Neuromarketing {As} {A} {Tool} {To} {Measure} {And} {Evaluate} {The} {Consumer} {Behaviour} {Of} {Guanding} {Teahouse}'s {Social} {Media} {Advertisement}}, isbn = {9798400708022}, url = {https://doi.org/10.1145/3616712.3616787}, doi = {10.1145/3616712.3616787}, abstract = {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.}, urldate = {2024-01-14}, booktitle = {Proceedings of the 2023 14th {International} {Conference} on {E}-business, {Management} and {Economics}}, publisher = {Association for Computing Machinery}, author = {Zeng, Ian Mei and Lobo Marques, Joao Alexandre}, month = dec, year = {2023}, pages = {63--69}, }