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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.

Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.

Neuromarketing lies at the intersection of three main disciplines: psychology, neuroscience, and marketing, and it has been a successful neuroscientific approach for the study of real-life choices such as consumer behavior [1]. A current gap in the cosmetics field is the lack of published research studies, considering the marketing investment done yearly in this category. With the rapid economic expansion and the rise of social media in China, consumers' interest in beauty is growing. Even though the Chinese cosmetics sector is rapidly expanding, no studies have been done with Chinese consumers. This study aims to employ the same approach as previously done in consumer neuroscience studies to evaluate cosmetic brands' marketing strategy to understand better if immediate emotional responses can be measured using Electrodermal Activity (EDA). Here, we focus on cosmetics products advertisement as a model to understand consumer preference formation and choice. Eighteen Chinese female consumers were recruited between 19 and 37 years old. From the results obtained, it was understood that none of the participants have voted for the product advertisement for which they showed higher emotional arousal. However, it appears that the participants' preference is for the products for which the brand awareness is stronger since the product advertisements with more votes are the ones for the Korean brand used. The product advertisements with Asian faces were the ones with more votes, suggesting that Asian faces have engaged consumer preference. However, the product advertisements for the Brazilian brands, unknown to the Chinese public, were the ones with fewer votes, although, those product advertisements were the ones with more emotional arousal per minute. Those advertisements were also those with non-Asian faces, suggesting that this feature influenced voting decisions. From this study, it has been observed that Electrodermal Activity is a measure of emotional arousal that by itself cannot be translated into consumer engagement. Therefore, it is also proposed to evaluate brand awareness in future studies related to product advertisements. The physical features of the people included in the advertisements is also suggested to be further evaluated in future studies since a different cultural background seems to influence the consumers' engagement. Furthermore, using EDA to complement other neurophysiological tools like facial expression analysis is also suggested for future studies to have evidence about the nature of the emotions raised.

The classification of emotions based on facial expressions have been a new topic of research in recent years, especially in marketing and consumer behavior areas. However, there is lack of studies to understand how the research topic is developed in terms of bibliometric data. Therefore, the purpose of this work is to provide a bibliometric analysis of the research on the analysis of facial expressions for marketing and consumer behavior, identifying the state of the art, the latest research direction, and other indicators. We extracted data from Web of Science (WOS) platform, considering its core database, resulting in a total of 117 articles. The software Vosviewer was used to analyze the data and graphically visualize the results. This study indicates some of the most influential authors citations and coupling analysis in this specific field, identifies journals with the most published articles, and provide trends of the research area based on the analysis of keywords and corresponding number of articles per year. The results shows that 11 articles (9.4%) were cited more than 100 times, and the two most prolific authors published 5 articles, and the two most influential authors are Bouaziz Sofien and Pauly mark(270 citations) in this field. Of the 117 articles retrieved by WOS, more than 70% were published in high impact journals. The bibliometric analysis of the existing work in this study provides a valuable and reliable reference for researchers in this field and makes a reasonable prediction of the research direction trends.

Stock price prediction has always been challenging due to its volatility and unpredictability. This paper performs a preliminary exploratory comparison that utilizes Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms to forecast the stock market in Hong Kong. It considers a public dataset publicly available and uses feature engineering to extract relevant features. Then, LSTM and SVM algorithms are applied to predict stock prices. Our results show that the proposed machine learning techniques can predict stock prices in Hong Kong's share market with the error metrics presented, and, for this purpose, LSTM achieved better results than SVM, with MSE = 0.0026, RMSE = 0.0508, MAE = 0.0406, and MAPE = 1.325.

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.

This research aims to evaluate a Macau tea brand's social media advertising effectiveness with neuromarketing tools, including physiological monitoring that can measure emotional arousal. This research bridges the gap of social media marketing on Instagram for brands through the neuromarketing method. Data from 40 respondents were collected with iMotions software using neuroscientific tools. This research uses the stimuli of Guanding Teahouse, a newly established Macau tea brand, to evaluate social media advertising effectiveness. The neuroscientific tools – Galvanic Skin Response (GSR) sensors, Eye-tracking, Facial Expression Analysis (FEA) and emotion analysis are used to do the experiment. The data analysis was drawn from one representative respondent to measure the emotions and attention on the Instagram advertisements. Video 1 recorded 9 GSR peaks and Video 2 recorded 12 GSR peaks, both videos attention is ranging between 96-98 indexes. Results show that advertising videos should focus more on the products than the model. Moreover, the participant is more interested in Video 2, but the effectiveness of advertising is showing a lower focus on the brand and the tea. Future studies should consider comparing the video advertising effectiveness of Instagram stories and Instagram reels to prevent disruption of video on the stories ad.

Government service mini-programs (GSMPs) in mobile payment have become integral to the eGovernment in China’s Greater Bay Area (GBA). The ubiquitous nature of WeChat and Alipay provides excellent flexibility for accessing public e-services. Yet, the determinants and mechanisms of adoption have not been identified. A convenience sample was collected from GBA core cities for statistical and SEM analysis. The findings suggest that service quality, trust in eGovernment, ubiquity, and social influence constitute the determinants. A structural model grounded on Self-Determination and Motivation theory is verified, where perceived value and intention contribute a high explanatory power. Benevolence, integrity, and competence are crucial indicators of trust, while social influence amplifies risk perception. Surprisingly, government support negatively moderates the impact of determinants on intention, indicating that over-intervention leads to inhibition. The mechanism illustrates the beneficial impact of GSMPs as the smart government channel and provides insights into addressing service homogeneity and policy applicability. Relevant theoretical and managerial implications are instructive to policymakers and practitioners of smart city innovation and in-depth integration in GBA.

There are many systematic reviews on predicting stock. However, each reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review the existing systematic reviews on Artificial Intelligence (AI) models applied to stock market prediction to provide valuable inputs for the development of strategies in stock market investments. Keywords that would fall under the broad headings of AI and stock prediction were looked up in Scopus and Web of Science databases. We screened 69 titles and read 43 systematic reviews, including more than 379 studies, before retaining 10 for the final dataset. This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction. In addition, the time series of historical closing stock prices are the most commonly used data source, and accuracy is the most employed performance metric of the predictive models. We also identified several research gaps and directions for future studies. Specifically, we indicate that future research could benefit from exploring different data sources and combinations, while we also suggest comparing different AI methods and techniques, as each may have specific advantages and applicable scenarios. Lastly, we recommend better evaluating different prediction indicators and standards to reflect prediction models’ actual value and impact.

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.

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.

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.

The potential of blockchain technology extends beyond cryptocurrencies and has the power to transform various sectors, including accounting and auditing. Its integration into auditing practices presents opportunities and challenges, and auditors must navigate new standards and engage with clients effectively. Blockchain technology provides tamper-proof record-keeping and fraud prevention, enhancing efficiency, transparency, and security in domains such as finance, insurance, healthcare, education, e-voting, and supply chain management. This paper conducts a bibliometric analysis of blockchain technology literature to gain insights into the current state and future directions of blockchain technology in auditing. The study identifies significant research themes and trends using keyword and citation analysis. The Vosviewer software was used to analyze the data and visualize the results. Findings reveal significant growth in blockchain research, particularly from 2021 onwards, with China emerging as a leading contributor, followed by the USA, India, and the UK. This study provides valuable insights into current trends, key contributors, and global patterns in blockchain technology research within auditing practices, and future research may explore thematic areas in greater depth.

Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This paper presents an empirical study that evaluates four existing deep learning models—VGG16, DenseNet, ResNet50, and GoogLeNet—utilizing the Facial Expression Recognition 2013 (FER2013) dataset. The dataset contains seven distinct emotional expressions: angry, disgust, fear, happy, neutral, sad, and surprise. Each model underwent rigorous assessment based on metrics including test accuracy, training duration, and weight file size to test their effectiveness in FER tasks. ResNet50 emerged as the top performer with a test accuracy of 69.46%, leveraging its residual learning architecture to effectively address challenges inherent in training deep neural networks. Conversely, GoogLeNet exhibited the lowest test accuracy among the models, suggesting potential architectural constraints in FER applications. VGG16, while competitive in accuracy, demonstrated lengthier training times and a larger weight file size (512MB), highlighting the inherent balance between model complexity and computational efficiency.

In the wave of digital transformation, Chinese banks have prioritized digital banking services as key strategic goals, aiming to revolutionize the mobile banking experience. This study aims to assess the factors influencing the willingness to use the various financial and contextual services offered through digital banking. Specifically, it is proposed a model based on users' perceptions of mobile banking scenarios and examines how the development of digital banking services influences users' willingness to use them. The study involved qualitative in-depth interviews with 12 mobile banking users, with the interview content analyzed using Nvivo qualitative analysis software. The data analysis identified 9 core coding categories: Financial Professionalism, Security, Marketing Stimulation, Innovative Products, Use Experience, Strong Relationship, Trust, Perceived Usefulness, and Willingness to Use. These categories were further refined to construct a theoretical model of user willingness in digital banking services, drawing from the optimized Technology Acceptance Model (TAM). The findings provide valuable insights for the banking industry in Macau, aiding in understanding customer needs and supporting the positive development of mobile finance and contextual digital banking services in the region.

Construction projects are complex endeavours, with potential obstacles that can cause delays which can have particularly profound implications potentially impacting on company's financial health, business continuity and reputation. It is becoming increasingly recognised that delays are context-specific and multifaceted, requiring more industry-oriented perceptions. This work proposes the exploratory use of Machine Learning based on Classification and Regression Trees (CART) Decision Trees (DT) to assess the predictive analysis of these approaches, considering surveys (primary data) collected from 100 specialists with different backgrounds and experiences in the construction industry. Survey responses are discussed, followed by the CART DTs, which are used as predictor for clarifying underneath relationship among different variables in a project environment. The major issue presented is related to Project Design, with "The firm is not allowed to apply for an extension of contract period", with two possible predictors, firstly, as the main factor it is found "Mistakes, inconsistencies, and ambiguities in specification and drawing", while other aspect highlights "Poor site supervision and management by the contractor". The results indicate that the correct use of Artificial Intelligence techniques with relevant data are potential tools to support the analysis of scenarios and avoidance of project delays in Project Management.