Background: The COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet.Objective: The purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil.Methods: This work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts.Results: In this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic.Conclusions: This study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities.

Recent advancements in Artificial Intelligence (AI), deep learning (DL), and computer vision have revolutionized various industrial processes through image classification and object detection. State-of-the-art Optical Character Recognition (OCR) and object detection (OD) technologies, such as YOLO and PaddleOCR, have emerged as powerful solutions for addressing challenges in recognizing textual and non-textual information on printed stickers. However, a well-established framework integrating these cutting-edge technologies for industrial applications still needs to be discovered. In this paper, we propose an innovative framework that combines advanced OCR and OD techniques to automate visual inspection processes in an industrial context. Our primary contribution is a comprehensive framework adept at detecting and recognizing textual and non-textual information on printed stickers within a company, harnessing the latest AI tools and technologies for sticker information recognition. Our experiments reveal an overall macro accuracy of 0.88 for sticker OCR across three distinct patterns. Furthermore, the proposed system goes beyond traditional Printed Character Recognition (PCR) by extracting supplementary information, such as barcodes and QR codes present in the image, significantly streamlining industrial workflows and minimizing manual labor demands.

The banking industry has been employing artificial intelligence (AI) technologies to enhance the quality of its services. More recently, AI algorithms, such as natural language understanding (NLU), have been integrated into chatbots to improve banking applications. These chatbots are typically designed to cater to customers’ needs. However, research in the development of troubleshooting chatbots for technical purposes remains scarce, especially in the banking sector. Although a company may possess a knowledge database, a standard methodology is essential to guiding an AI developer in building a chatbot, making the modeling of technical needs into a specialized chatbot a challenging task. This paper presents a novel methodology for developing troubleshooting chatbots. We apply this methodology to create an AI-powered chatbot capable of performing technical ATM maintenance tasks. We propose the TroubleshootingBot, an experimental protocol to obtain data for evaluating the chatbot through two scenarios. The first scenario detects user intent, and the second recognizes desired values in a user’s phrase (e.g., three beeps or two beeps). For these scenarios, we achieved accuracies of 0.93 and 0.88, respectively. This work represents a significant advancement in virtual assistants for banking applications and holds potential for other technical problem-solving applications.

Recent advancements in Artificial Intelligence (AI), Deep Learning (DL), and computer vision have revolutionized various industrial processes through image classification and object detection. State-of-the-art Optical Character Recognition (OCR) and Object Detection (OD) technologies, such as YOLO and PaddleOCR, have emerged as powerful solutions for addressing challenges in recognizing textual and non-textual information on printed stickers. However, a well-established framework integrating these cutting-edge technologies for industrial applications still needs to be discovered. In this paper, we propose an innovative framework that combines advanced OCR and OD techniques to automate visual inspection processes in an industrial context. Our primary contribution is a comprehensive framework adept at detecting and recognizing textual and non-textual information on printed stickers within a company, harnessing the latest AI tools and technologies for sticker information recognition. Our experiments reveal an overall macro accuracy of 0.88 for the sticker OCR across three distinct patterns. Furthermore, the proposed system goes beyond traditional Printed Character Recognition (PCR) by extracting supplementary information, such as barcodes and QR codes present in the image, significantly streamlining industrial workflows and minimizing manual labor demands.

Background: Voice assistants (VAs) are devices that respond to human voices and can be commanded to do a variety of tasks. Nowadays, VAs are being used to obtain health information, which has become a critical point of analysis for researchers in terms of question understanding and quality of response. Particularly, the COVID-19 pandemic has and still is severely affecting people worldwide, which demands studies on how VAs can be used as a tool to provide useful information. Objective: This work aimed to perform a quality analysis of different VAs’ responses regarding the actual and important subject of COVID-19 vaccines. We focused on this important subject since vaccines are now available and society has urged for the population to be rapidly immunized. Methods: The proposed study was based on questions that were collected from the official World Health Organization website. These questions were submitted to the 5 dominant VAs (Alexa, Bixby, Cortana, Google Assistant, and Siri), and responses were evaluated according to a rubric based on the literature. We focused this study on the Portuguese language as an additional contribution, since previous works are mainly focused on the English language, and we believe that VAs cannot be optimized to foreign languages. Results: Results showed that Google Assistant has a better overall performance, and only this VA and Samsung Bixby achieved high scores on question understanding in the Portuguese language. Regarding the obtained answers, the study also showed the best Google Assistant overall performance. Conclusions: Under the urgent context of COVID-19 vaccination, this work can help to understand how VAs must be improved to be more useful to the society and how careful people must be when considering VAs as a source of health information. VAs have been demonstrated to perform well regarding comprehension and user-friendliness. However, this work has found that they must be better integrated to their information sources to be useful as health information tools.

In state-of-art anomaly detection research, prevailing methodologies predominantly employ Generative Adversarial Networks and Autoencoders for image-based applications. Despite the efficacy demonstrated in the visual domain, there remains a notable dearth of studies showcasing the application of these architectures in anomaly detection within the sound domain. This paper introduces tailored adaptations of cutting-edge architectures for anomaly detection in audio and conducts a comprehensive comparative analysis to substantiate the viability of this novel approach. The evaluation is performed on the DCASE 2020 dataset, encompassing over 180 hours of industrial machinery sound recordings. Our results indicate superior anomaly classification, with an average Area Under the Curve (AUC) of 88.16% and partial AUC of 78.05%, surpassing the performance of established baselines. This study not only extends the applicability of advanced architectures to the audio domain but also establishes their effectiveness in the challenging context of industrial sound anomaly detection.