One of the health challenges in the 21st century is to rethink approaches to non-communicable disease prevention. A solution is a smart city that implements technology to make health smarter, enables healthcare access, and contributes to all residents’ overall well-being. Thus, this paper proposes an architecture to deliver smart health. The architecture is anchored in the Internet of Things and edge computing, and it is driven by artificial intelligence to establish three foundational layers in smart care. Experimental results in a case study on glucose prediction noninvasively show that the architecture senses and acquires data that capture relevant characteristics. The study also establishes a baseline of twelve regression algorithms to assess the non-invasive glucose prediction performance regarding the mean squared error, root mean squared error, and r-squared score, and the catboost regressor outperforms the other models with 218.91 and 782.30 in MSE, 14.80 and 27.97 in RMSE, and 0.81 and 0.31 in $R^2$, respectively, on training and test sets. Future research works involve extending the performance of the algorithms with new datasets, creating and optimizing embedded AI models, deploying edge-IoT with embedded AI for wearable devices, implementing an autonomous AI cloud engine, and implementing federated learning to deliver scalable smart health in a smart city context.

Generative artificial intelligence is an emerging technology that has impacted society recently and with productivity gains in the coming years. This technology learns from existing data to generate high-quality content such as text, images, music, speech, videos, and code. As part of the creative industries, collaboration between generative algorithms and human creativity brings challenges due to producing content and artistic images in response to natural language requests. Thus, this chapter establishes a methodology based on text-to-image generative artificial intelligence for producing assisted art in contextualized cultural content to generate unique and diverse images of the historical-cultural identity of southern Brazil. The steps of the methodology that can portray elements of Brazil's biome, cultural, and historical events are dataset assembling, model training and fine-tuning, and content enhancement and post-processing. In addition, the methodology allows an immersed experience with people being transported in period clothing and within a historical or cultural setting.

Chronic non-communicable diseases (NCDs) are major public health problems and a significant financial burden on public health systems. By 2030, mortality due to NCDs, such as cardiovascular diseases, cancer, respiratory diseases, and diabetes, is predicted to increase in Brazil. A peculiar aspect of NCDs involves their long-term and integrated care management. This paper proposes a chronic disease management platform based on artificial intelligence to deliver digital health services everywhere. The proposed platform is anchored and built with healthcare 4.0 technologies, such as wearable devices, the internet of medical things, and artificial intelligence cloud-based solutions that allow the deployment of a smart healthcare system. In addition, the paper presents the feasibility of the platform in a diabetes prediction study case. For the study case, an initial dataset was established with bio-impedance, oxygen concentration, pulse rate, skin impedance, and skin temperature attributes. A baseline was implemented with ten regression models to assess the prediction performance of the mean squared error, root mean squared error, and r-squared score to compare predictive findings with capillary blood glucose measurements. Results evidence that the decision tree regressor and three ensemble methods (bagging decision tree regressor, random forest regressor, and AdaBoost regressor) yielded improvement over the other models. Moreover, a comparison among those models revealed that the decision tree regressor outperforms them and presents promissory outcomes.

Applications in Predictive Analytics: Ubiquitous Management Methodology for Predictive Maintenance in Medical Devices: 10.4018/978-1-4666-5063-3.ch003: According to the World Health Organization, Healthcare Technology (HT) is defined as the application of techniques and knowledge in the way of devices

Generative Artificial Intelligence has become pervasive in society, witnessing significant advancements in various domains. Particularly in the domain of Text-to-Image (TTI) models, Latent Diffusion Models (LDMs) showcase remarkable capabilities in generating visual content based on textual prompts. This paper addresses the potential of LDMs in representing local cultural concepts, historical figures, and endangered species. In this study, we use the cultural heritage of Rio Grande do Sul (RS), Brazil, as an illustrative case. Our objective is to contribute to the broader understanding of how generative models can help to capture and preserve the regional culture and historical identity. The article outlines the methodology, including subject selection, dataset creation, and fine-tuning process. The results showcase the picture generation alongside the challenges and feasibility of each concept. In conclusion, this work shows the power of these models to represent and preserve unique aspects of diverse regions and communities.

Generative AI has become pervasive in society, witnessing significant advancements in various domains. Particularly in the realm of Text-to-Image (TTI) models, Latent Diffusion Models (LDMs), showcase remarkable capabilities in generating visual content based on textual prompts. This paper addresses the potential of LDMs in representing local cultural concepts, historical figures, and endangered species. In this study, we use the cultural heritage of Rio Grande do Sul (RS), Brazil, as an illustrative case. Our objective is to contribute to the broader understanding of how generative models can help to capture and preserve the cultural and historical identity of regions. The paper outlines the methodology, including subject selection, dataset creation, and the fine-tuning process. The results showcase the images generated, alongside the challenges and feasibility of each concept. In conclusion, this work shows the power of these models to represent and preserve unique aspects of diverse regions and communities.