Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

The goal of the present study was to evaluate techniques for modeling the physiological responses, rectal temperature, and respiratory rate of black and white Holstein dairy cows. Data from the literature (792 data points) and obtained experimentally (5884 data points) were used to fit and validate the models. Each datum included dry bulb air temperature, relative humidity, rectal temperature, and respiratory rate. Two models based on artificial intelligence—artificial neural networks and neurofuzzy networks—and one based on regression were evaluated for each response variable. The adjusted models predict rectal temperature and respiratory rate as a function of dry-bulb air temperature and relative humidity. These models were compared using statistical indices. The model based on artificial neural networks showed the best performance, followed by the models based on neurofuzzy networks and regression; the last two performed similarly.

The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

Due to a number of factors involving the thermal environment of a broiler cutting installation and its interaction with the physiological and productive responses of birds, artificial intelligence has been shown to be an interesting methodology to assist in the decision-making process. For this reason, the main aim of this work was to develop an artificial neural network (ANN) to predict feed conversion (FC), water consumption (Cwater), and cloacal temperature (tclo) of broilers submitted to different air dry-bulb temperatures (24, 27, 30, and 33ºC) and durations (1, 2, 3, and 4 days) of thermal stress in the second week of the production cycle. Relative humidity and wind speed were fixed at 60% and 0.2 ms-1 , respectively. The experimental data were used for the development of an ANN with supervised training using the Levenberg-Marquardt backpropagation algorithm. The ANN consisted of three input layers one hidden, and three output with sigmoidal tangent transfer functions with values between-1 and 1. The developed ANN has adequate predictive capacity, with coefficients of determination (R 2) for tclo, FC, and Cwater of 0.79, 0.87, and 0.97, respectively. In this way, the proposed ANN can be used as a support for decision-making to trigger poultry heating systems for broiler breeding.

Each phytoplankton species presents a different behavior and tolerance to the cryopreservation process. Therefore, in a species-specific protocol, it is essential to ensure both growth and post-thawing cell viability. In this study, we explored the effect of cryopreservation of Scenedesmus sp. with two cryoprotectants, dimethyl sulfoxide (DMSO) and methanol (MET), at 5% and 10% inclusion for each. In the control treatment, the microalgae were not exposed to cryoprotective agents (Control). Three post-thawing cell viability criteria were used: no cell damage (NCD), cell damage (CD), and marked lesions (LM), and mitochondrial and cell membrane damage was evaluated by flow cytometry. The study was a 2 × 2 factorial design, with five replications by treatments, population growth, and cell damage evaluated from the fifth day after thawing. On the fifth day, the highest percentage of NCD was observed when the microalgae were cryopreserved with DMSO 5% (50%); Regarding the control group, it showed 0% NCD. Flow cytometry analysis reveals minor damage at the membrane and mitochondria (9–10.7%) when DMSO is used at both inclusion percentages (5–10%) after thawing. In the exponential phase, the highest growth rates, doubling time, and yield was observed in cryopreserved cells with MET 5%. The results suggest that DMSO 5% is an ideal treatment for cryopreserving microalgae Scenedesmus sp.