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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.
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As the rate of change increases exponentially, organizations must adapt quickly to the business landscape's volatility, uncertainty, complexity, and ambiguity (VUCA). As a result, organizations must implement agile strategies and practices to ensure their responsiveness and readiness to any changes brought about by internal or external factors. With a greater number of changes, change agents are tasked with implementing various change management methodologies to ensure that change recipients accept change initiatives. This research will look at one of the methodologies used by change agents, the use of nudges from Thaler and Sunstein's Nudge Theory, which is a subtle intervention to influence an individual's decision-making with the goal of steering them towards a specific desired outcome; and analyze their effectiveness towards the change recipients when implemented. Change agents were interviewed on the application of Nudge Theory to change recipients when managing to change initiatives within their respective organizations. The results indicate that the use of nudges created by the change agents can significantly impact the level of resistance from the change recipients. If used correctly, the Nudge Theory can mitigate change resistance, and the success of a change initiative is higher. But, if change recipients are forced to comply, their resistance will be greater, affecting the organization overall.
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In recent years, the integration of Machine Learning (ML) techniques in the field of healthcare and public health has emerged as a powerful tool for improving decision-making processes [...]
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The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and th...
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The spontaneous symmetry breaking phenomena applied to Quantum Finance considers that the martingale state in the stock market corresponds to a ground (vacuum) state if we express the financial equations in the Hamiltonian form. The original analysis for this phenomena completely ignores the kinetic terms in the neighborhood of the minimal of the potential terms. This is correct in most of the cases. However, when we deal with the martingale condition, it comes out that the kinetic terms can also behave as potential terms and then reproduce a shift on the effective location of the vacuum (martingale). In this paper, we analyze the effective symmetry breaking patterns and the connected vacuum degeneracy for these special circumstances. Within the same scenario, we analyze the connection between the flow of information and the multiplicity of martingale states, providing in this way powerful tools for analyzing the dynamic of the stock markets.
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Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease.
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Human emotions can be meticulously associated with decision-making, and emotion can generate behaviours. Due to the fact that it could be bias and exhaustively complex to examine how human beings make choices, important groups of study in finance are stock traders and non-traders. The objective of this work is to analyze the connection between emotions and the decision-making process of investors and non-investors to understand how emotional arousal might dictate the process of deciding policy. As facial expressions are fleeting, neuroscience tools such as AFFDEX (Real-Time Facial Expression Analysis), Eye-Tracking, and GSR (galvanic skin response) were adopted to facilitate the experiment and its accompanying analysis process. Thirty-seven participants attended the study, ranging from 18 to 72 years old; the distribution of investors and non-investors was twenty-four and thirteen, respectively. The experiment initially disclosed a thought-provoking result between the two groups under the certainty and risk-seeking prospect theory; there were more risk-takers among non-investors at 75%, while investors were inclined toward certainty at 79.17%. The implication could be that the non-investing individuals were less complex in thought and therefore pursued higher returns besides a high probability of losing the game. In addition, the automatic emotion classification system indicates that when non-investors confronted a stock trending chart beyond their acquaintance or knowledge, they were psychologically exposed to fear, anger, sadness, and surprise. Investors, on the contrary, were detected with disgust, joy, contempt, engagement, sadness, and surprise, where sadness and surprise overlapped in both parties. Under time pressure conditions, 54.05% of investors or non-investors tend to make decisions after the peak(s) of emotional arousal. Variations were found in the deciding points of the slopes: 2.70% were decided right after the peak(s), 37.84% waited until the emotions turned stable, and 13.51% were determined as the emotional indicators started to slide downwards. Several combinations of emotional responses were associated with decisions. For example, negative emotions could induce passive decision-making, in this case, to sell the stock; nevertheless, it was also examined that as the slope slipped downwards to a particular horizontal point, the individuals became more optimistic and selected the "BUY" option. The support of physiological monitoring tools makes it possible to capture the individuals' responses and discover the science of decision-making. Future works may consider expanding the study to more significant demographic populations for further discoveries
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Projects are tactical and operational initiatives, and achieving specific outcomes through projects can help organizations achieve strategic goals. The effective use of project management tools and techniques is essential to achieve successful results, since the goal is to maximize the realization of the project's plan by effectively using the budget, time, and resources provided by the project owner to achieve the project's original purpose. The Project Management Maturity Model (PMMM) is a tool for measuring project management capabilities and is essential to improve project and portfolio performance in different industries. The main objective of this research is to analyze and characterize the maturity level and capacity of the IT industry in Macau and HengQin based on the assessment of the PMMM. The research also aims to assess and compare the maturity level in the IT industry in Macau and HengQin. An online survey was conducted and sent to IT project managers from Macau and HenqQin. A total of 34 responses were collected, divided into 3 different parts: Part I - General Information, Part II - Project Management Areas, and Part III - Perception. The results indicate that, in general, Project Managers state that their companies do not follow Project Management standards and best practices, classifying as Low and Very Low essential PM areas such as Planning and Scheduling (68%), Scope (61%) and Communications (64%). From a comparison perspective, project managers in Macau follow less formal frameworks than Hengqin in managing the triple constraints of the project. The collected data also indicate that Macau's communication management and stakeholder engagement are less mature than Hengqin's. Furthermore, the data indicate that maturity level is not necessarily related to education level, which means not higher education has a higher maturity level. Recommendations are provided for the IT industry in both areas, and specific comments are provided for each group or professionals. In conclusion, this work allows a novel characterization and a better understanding of the Project Management adoption and maturity level of the IT Industry in Macau and Hengqin
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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.
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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.
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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.
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