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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - CHAP TI - Ritual and the Sacred in Peter Shaffer's Theatre AU - Phillips, Jenny O. L. T2 - Sacred and the Everyday: Comparative approaches to literature, religious and secular CY - Macao DA - 2021/// PY - 2021 SP - 137 EP - 150 PB - University of Saint Joseph 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 - CHAP TI - The Road to Service Export Diversification: Gambling and Convention in Macao AU - Lei, Weng Chi T2 - Economics and Finance Readings: Selected Papers from Asia-Pacific Conference on Economics & Finance, 2019 A2 - Lau, Evan A2 - Simonetti, Biagio A2 - Trinugroho, Irwan A2 - Tan, Lee Ming AB - This book is a compilation of the best papers presented at the APEF 2019 conference which was held on 25th and 26th July 2019 at the Grand Copthorne Waterfront in Singapore. With a great number of submissions, it presents the latest research findings in economics and finance and discusses relevant issues in today's world. The book is a useful resource for readers who want access to economics, finance and business research focusing on the Asia-Pacific region. DA - 2020/// PY - 2020 DP - www.springer.com LA - en PB - Springer Singapore SN - 9789811529054 UR - https://www.springer.com/gp/book/9789811529054 Y2 - 2021/02/04/03:38:42 ER - TY - CHAP TI - Obediência militar, liberdade e consciência, Ética Aplicada: Política, Edições 70, AU - Carreiras, H. AU - Leandro, F. J. DA - 2018/// PY - 2018 SP - 339 EP - 364 ER - TY - CHAP TI - The OBOR Global Geopolitical Drive: The Chinese Access Security Strategy - AU - Leandro, F. J T2 - Law, Economics, and Politics A2 - Julien Chaisse and Jędrzej Górski DA - 2018/// PY - 2018 SP - 83 EP - 106 PB - Brill UR - https://brill.com/view/ti-tle/38740 ER - TY - CHAP TI - The OBOR geopolitical drive: the Chinese access security Strategy AU - Leandro, Francisco J. B. S. T2 - The Belt and Road Initiative Law, Economics, and Politics A2 - Nottage, Julien Chaisse and Luke CY - Leiden DA - 2018/// PY - 2018 PB - Brill ER - TY - CHAP TI - Artificial intelligence and forensic computing AU - Lobo-Marques, J. Al T2 - Forensic Computing DA - 2018/// PY - 2018 ER - TY - CHAP TI - Macau, Quality of Life AU - Davey, Gareth AU - Rato, Ricardo T2 - Encyclopedia of Quality of Life and Well-Being Research A2 - Michalos, Alex C. CY - Dordrecht DA - 2014/// PY - 2014 DP - Springer Link SP - 3737 EP - 3740 LA - en PB - Springer Netherlands SN - 978-94-007-0753-5 UR - https://doi.org/10.1007/978-94-007-0753-5_3953 Y2 - 2022/02/12/04:43:42 ER - TY - CHAP TI - "Going through the mist": early career transitions of Chinese Millennial returnees AU - Porschitz, Emily T. AU - Guo, Chun AU - Alves, José T2 - Managing the New Workforce C2 - Ng, Eddy C2 - Lyons, Sean C2 - Schweitzer, Linda DA - 2012/// PY - 2012 DP - DOI.org (Crossref) SP - 86 EP - 106 LA - en PB - Edward Elgar Publishing SN - 978-0-85793-301-0 ST - "Going through the mist" UR - http://www.elgaronline.com/view/9780857933003.00010.xml Y2 - 2023/04/12/03:56:56 ER - TY - CHAP TI - Quality of Work Life in Macau AU - Rato, Ricardo AU - Davey, Gareth T2 - Happiness Across Cultures: Views of Happiness and Quality of Life in Non-Western Cultures A2 - Selin, Helaine A2 - Davey, Gareth T3 - Science Across Cultures: the History of Non-Western Science AB - This chapter explores Quality of Work Life (QWL) in Macau. We investigate the meanings and importance of QWL and its implications in terms of happiness and business performance. Although QWL is central to people’s lives, research on this topic is still in its infancy in Macau. Our interviews revealed three salient themes of QWL: Work context, the perceived benefits and demands of the job; Organization, mainly work environment and factors within the organizational context mediating QWL; and the implications of QWL on overall living and happiness. CY - Dordrecht DA - 2012/// PY - 2012 DP - Springer Link SP - 95 EP - 106 LA - en PB - Springer Netherlands SN - 978-94-007-2700-7 UR - https://doi.org/10.1007/978-94-007-2700-7_7 Y2 - 2023/04/12/03:25:50 KW - Gaming Industry KW - Gaming Revenue KW - Gaming Section KW - International Wellbeing Index KW - Work Context ER - TY - CHAP TI - Aid to Infrastructure Development in Sub Saharan Africa: An Assessment of China Financing Approaches AU - Diakité, A.D. T2 - China-Africa: New Types of Exchange, Cultural Identity and Emerging Relations in a Globalized World CY - Macau DA - 2010/// PY - 2010 SP - 205 EP - 227 PB - Saint Joseph Academic Press ER -