Case Study: IBM Watson Analytics Cloud Platform as Analytics-as-a-Service System for Heart Failure Early Detection

Sentiment Analysis and Emotion Understanding during the COVID-19 Pandemic in Spain and Its Impact on Digital Ecosystems

COVID-19 has changed our lives forever. The world we knew until now has been transformed and nowadays we live in a completely new scenario in a perpetual restructuring transition, in which the way we live, relate, and communicate with others has been altered permanently. Within this context, risk communication is playing a decisive role when informing, transmitting, and channeling the flow of information in society. COVID-19 has posed a real pandemic risk management challenge in terms of impact, preparedness, response, and mitigation by governments, health organizations, non-governmental organizations (NGOs), mass media, and stakeholders. In this study, we monitored the digital ecosystems during March and April 2020, and we obtained a sample of 106,261 communications through the analysis of APIs and Web Scraping techniques. This study examines how social media has affected risk communication in uncertain contexts and its impact on the emotions and sentiments derived from the semantic analysis in Spanish society during the COVID-19 pandemic.

Silva R. Artificial intelligence in medical devices and clinical decision support systems

Artificial Intelligence (AI) is a broad conceptual category of computer software that is designed to mimic, emulate, or improve human decision-making. There are two broad categories of AI: rules-based expert systems (ES), and machine-learning (ML) systems.

Using the IBM SPSS SW Tool with Wavelet Transformation for CO₂ Prediction within IoT in Smart Home Care

Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. The paper examines the possibilities of increasing the accuracy of CO₂ predictions in Smart Home Care (SHC) using the IBM SPSS software tools in the IoT to determine the occupancy times of a monitored SHC room. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods. The Radial Basis Function (RBF) method was applied to predict CO₂ levels from the measured indoor and outdoor temperatures and relative humidity. The most accurately predicted results were obtained from data processed at a daily interval. To increase the accuracy of CO₂ predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The prediction accuracy achieved in the selected experiments was greater than 95%.