Classifying Diabetes Disease Using Feedforward MLP Neural Networks

Diagnosing chronic diseases is about making accurate and quick decisions based on contradictory information and constantly evolving knowledge. Hence, there has been a persistent need to help health practitioners in making correct decisions. Diabetes is a common chronic disease. It is a global healthcare threat and the eighth leading cause of death in the world. Modern artificial intelligence techniques are being used in diagnosing chronic diseases including artificial neural networks. In this chapter, a feedforward multilayer-perceptron neural network has been implemented to help health practitioners in classifying diabetes. Through the work, an algorithm was proposed in purpose of determining the number of hidden layers and neurons in a MLP. Based on the algorithm, two topologies have been introduced. Both topologies exhibited good classification accuracies with a slightly higher accuracy for the MLP with only one hidden layer. The data set was obtained from King Abdullah University Hospital in Jordan.

Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland

The main purpose of this study is to investigate the relationships between key sources of air pollutant emissions (sources of energy production, factories which are particularly harmful to the environment, the fleets of cars, environmental protection expenditure) and the main environmental air pollution (SO2, NOx, CO and PM) in Poland. Models based on MLP neural networks were used as predictive models. Global sensitivity analysis was used to demonstrate the significant impact of individual network input variables on the output variable. To verify the effectiveness of the models created, the actual data were compared with the data obtained through modelling. Projected courses of changes in the variables under study correspond with the real data, which confirms that the proposed models generalize acquired knowledge well. The high MLP network quality parameters of 0.99–0.85 indicate that the network generalizes the acquired knowledge accurately. The sensitivity analysis for NOx, CO and PM pollutants indicates the significance of all input variables. For SO2, it showed significance for four of the six variables analysed. The predictions made by the neural models are not very different from the experimental values.

Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products

Radiation-based instruments have been widely used in petrochemical and oil industries to monitor liquid products transported through the same pipeline. Different radioactive gamma-ray emitter sources are typically used as radiation generators in the instruments mentioned above. The idea at the basis of this research is to investigate the use of an X-ray tube rather than a radioisotope source as an X-ray generator: This choice brings some advantages that will be discussed. The study is performed through a Monte Carlo simulation and artificial intelligence. Here, the system is composed of an X-ray tube, a pipe including fluid, and a NaI detector. Two-by-two mixtures of four various oil products with different volume ratios were considered to model the pipe’s interface region. For each combination, the X-ray spectrum was recorded in the detector in all the simulations. The recorded spectra were used for training and testing the multilayer perceptron (MLP) models. After training, MLP neural networks could estimate each oil product’s volume ratio with a mean absolute error of 2.72 which is slightly even better than what was obtained in former studies using radioisotope sources.

Multilayer Perceptron Neural Networks for Estimating Missing Rainfall Data

<p>The quality and completeness of rainfall data have always played an important role in time series analysis and prediction for future water-related disasters. It requires to estimate missing data correctly for better results of rainfall prediction with high accuracy. In recent years, multilayer perceptron (MLP) neural networks have been applied to solve stochastic problems in data science. This study suggests a novel approach for estimating missing rainfall data with MLP neural networks. For this purpose, a mathematical model was created to analyze and predict the time series of daily rainfall data from 2003 to 2017 at six rain gauge stations in Seoul, Korea. Here, rainfall data with missing values during 20 days of time periods was considered for reconstruction of missing data at one specific rain gauge station from complete rainfall data records at five different stations. They were divided into training, validation, and testing datasets with a percentage of 70%, 15%, and 15%, respectively. This study investigates an effect of changes in data periods considered in MLP neural networks and it indicates that rainfall time series for a longer time period play a more effective role in rainfall data reconstruction.</p>