A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer.

Automating sentiment analysis in texts has become an important task in recent years due to the exponential growth of user-generated content, including comments and opinions on products and services. This represents a valuable opportunity for businesses to glean insights into customer sentiment and, in turn, to refine their offerings. Motivated by this, the machine learning field has witnessed a surge of innovation, with an introduction of models and tools being introduced to streamline sentiment analysis. This paper offers a thorough review of the recent advancements in machine learning and deep learning approaches for text sentiment analysis. We propose a novel framework for studying these models, distinguishing them by their structural intricacies. Additionally, we delve into the challenges, prospects, and emerging directions in research, as illuminated by our framework. Consequently, this paper equips researchers with a detailed panorama of the cutting-edge machine learning methodologies for dissecting text sentiment, easing the way for future explorations in this vibrant field.

Monitoring air quality is very important in urban areas to alert the citizens about the risks posed by the air they breathe. However, implementing conventional monitoring networks may be unfeasible in developing countries due to its high costs. In addition, it is important for the citizen to have current and future air information in the place where he is, to avoid overexposure. In the present work, we describe a low-cost solution deployed in Lima city that is composed of low-cost IoT stations, Artificial Intelligence models, and a web application that can deliver predicted air quality information in a graphical way (pollution maps). In a series of experiments, we assessed the quality of the temporal and spatial prediction. The error levels were satisfactory when compared to reference methods. Our proposal is a cost-effective solution that can help identify high-risk areas of exposure to airborne pollutants and can be replicated in places where there are no resources to implement reference networks.