Image classification is a well known of the significant tools used to recognize and examine most sharp information in satellite images. In any remote sensing research, the decision-making way mainly rely on the efficiency of the classification process. There are disparate classification algorithms on the large satellite imagery: Multilayer perceptron back propagation neural network (MLP BPNN), ...

The objective of this study is to build a fuzzy linguistic prediction model (FLPM) for analyzing the actuation duration of acute hyperglycemia to sinoatrial node field potential. The field potential was recorded using microelectrode arrays (MEA). The experimental data were analyzed using partial least squares (PLS), support vector machine (SVM), back propagation neural network (BPNN) and the pr...

In order to reduce the risk of enterprise management, financial early warning methods listed companies are mainly studied. The characteristics analysed. With help rough set theory, indicators selected, and index system is established. model constructed by using back propagation neural network (BPNN) algorithm based on deep learning. Finally, accuracy feasibility verified. results show that theo...

Horizontal displacement of Hoa Binh dam in operation phase is analyzed and then forecasted by using three methods: the multi-regression model (MTR), Seasonal Integrated Auto-regressive Moving Average (SARIMA) Back-propagation Neural Network (BPNN) model. The monitoring data Dam 137 periods, including horizontal displacement, time, reservoir water level air temperature, are used for experiments....

In this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. This aims maximise the output from solar grids when panels are connected with boost converter under different variable load conditions. BPNN-DL enables prediction of reference voltage at weather conditions for severing various ensures stable voltage. The propose...

Biomass energy represents a huge supplement for meeting current energy demands. A hyperspectral imaging system covering the spectral range of 874-1734 nm was used to determine the pH value of anaerobic digestion liquid produced by water hyacinth and rice straw mixtures used for methane production. Wavelet transform (WT) was used to reduce noises of the spectral data. Successive projections algo...

Simultaneous spectrophotometric estimation of Fluoxetine and Sertraline in tablets were performed using UV–Vis spectroscopic and Artificial Neural Networks (ANN). Absorption spectra of two components were recorded in 200–300 (nm) wavelengths region with an interval of 1 nm. The calibration models were thoroughly evaluated at several concentration levels using the spectra of synthetic binary mix...

Simultaneous spectrophotometric estimation of Fluoxetine and Sertraline in tablets were performed using UV–Vis spectroscopic and Artificial Neural Networks (ANN). Absorption spectra of two components were recorded in 200–300 (nm) wavelengths region with an interval of 1 nm. The calibration models were thoroughly evaluated at several concentration levels using the spectra of synthetic binary mix...

This paper utilizes artificial neural networks for temperature forecasting. Our study based on back propagation neural network which is trained and tested based on dataset provided. In formulating the ANN-based predictive model; three-layer network has been constructed. Suitable air temperature predictions can provide farmers and producers with valuable information when they face decisions rega...

The authentication of people using Iris based recognition system is the most reliable biometric traits due to its stability, invariant and distinctive features for personal identification. Iris recognition consists of localization of the Iris region, extracting Iris features, generation of data set of Iris images and then Iris pattern recognition. This paper presents Iris recognition system bas...