Artificial intelligence and forensic computing - Lobo-Marques, J. Al (Forensic Computing, 2018)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The (non) standardized classroom - the analysis of three different cultures in the higher education systems - Angola, Brazil and Macau SAR, China - Lobo Marques (Disentangled Vision on Higher Education: Preparing the Generation Next, 2023)
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

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

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

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

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

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

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