Smart cities can use artificial neural networks to provide more accurate information about public transportation schedules, and thus help the population plan their day to day activities. In this context, this paper describes the essential steps for the acquisition and processing of data, and the creation of a neural network model capable of predicting possible delays or advances on bus lines in the city of Curitiba, Paraná. The neural network considers traffic data, climate, time and history of a public transport line. The article details all phases of collection and treatment, as well as how informatio n is inserted into the network and what are the obtained results.

Pressure injuries (LPP) are one of the biggest adverse events foundin health services and consist of damage to the body tissues ofbedridden patients, resulting from prolonged pressure on the skin.This situation impacts on the quality of life of people who developthe condition, causing physical and emotional damage to the bedridden,in addition to increasing the time and costs of hospitalization.Based on this problem, software was developed that shows thepoints of greatest pressure between the body of a bedridden patientand the bed in which he is. This software receives information fromhardware, under development, built specifically for this project.The points of greatest pressure are made available on the screenof a monitoring application, in an organized and intuitive manner.For each person, a pressure map image is generated with the valuesread and decubitus change times are suggested through alarms. Inaddition, this image can be analyzed by a health professional whocan take steps to relieve pressure points and prevent the appearanceof LPP. As a result, in tests carried out during the research, the systembuilt showed the information successfully and the objectiveswere achieved.