In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

Deep eutectic solvents (DESs) are considered as relativity green solvents in comparison with ionic liquids and organic solvents. DESs are used in nanotechnology applications due to their unique physiochemical properties, efficient dispersants and they can be easily prepared in high purity at low cost. Other advantages include their nontoxicity, no reactivity with water and being biodegradable. DESs have recently attracted much attention in various fields, especially in the field of nanotechnology in controlling the size, surface chemistry and morphology of the nanomaterials and in the processing of advanced functional nanomaterials. As a result, various studies have been undertaken to investigate the physicochemical characteristics of the c

This research work involves the preparation of nano activated carbonand macro activated carbon from corn seeds with a various mixing ratio ofpotassium hydroxide (1:0, 1:0.2, 1:0.4, 1:0.6, 1:0.8 and 1:1) % using thermaland micro radiation carbonization to identify the best mixing ratio. At studyto confirmed that the efficiency and effectiveness of the prepared of activatedcarbon samples increase when ratio potassium hydroxide increase with athermal and micro radiation carbonization was used. The study of samplesexternal surface area was performed via studying the adsorption of methyleneblue from their aqueous solution, also measured the internal surface area wasperformed via studying the adsorption of iodine from their aqueous solution.Measu

Polyaniline Multi wall Carbon nanotube (PANI/MWCNTs) nanocomposite thin films have been prepared by Plasma jet polymerization at low frequency on glass substrate with preliminary deposited aluminum electrodes to form Al/PANI-MWCNT/Al surface-type capacitive humidity sensors, the gap between the electrodes about 50 μm and the MWCNTs weight concentration varied between 0, 1, 2, 3, 4%. The diameter of the MWCNTs was in the range of 8-15 nm and the length 10-55 μm. The capacitance-humidity relationships of the sensors were investigated at humidity levels from 35 to 90% RH. The electrical properties showed that the capacity increased with increasing relative humidity, and that the sensitivity of the sensor increases with the increase of the

The estimation of the regular regression model requires several assumptions to be satisfied such as "linearity". One problem occurs by partitioning the regression curve into two (or more) parts and then joining them by threshold point(s). This situation is regarded as a linearity violation of regression. Therefore, the multiphase regression model is received increasing attention as an alternative approach which describes the changing of the behavior of the phenomenon through threshold point estimation. Maximum likelihood estimator "MLE" has been used in both model and threshold point estimations. However, MLE is not resistant against violations such as outliers' existence or in case of the heavy-tailed error distribution. The main goal of t

    The using of the parametric models and the subsequent estimation methods require the presence of many of the primary conditions to be met by those models to represent the population under study adequately, these prompting researchers to search for more flexible models of parametric models and these models were nonparametric models.

    In this manuscript were compared to the so-called Nadaraya-Watson estimator in two cases (use of fixed bandwidth and variable) through simulation with different models and samples sizes.  Through simulation experiments and the results showed that for the first and second models preferred NW with fixed bandwidth fo

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

In this paper, the fuzzy logic and the trapezoidal fuzzy intuitionistic number were presented, as well as some properties of the trapezoidal fuzzy intuitionistic number and semi- parametric logistic regression model when using the trapezoidal fuzzy intuitionistic number. The output variable represents the dependent variable sometimes cannot be determined in only two cases (response, non-response)or (success, failure) and more than two responses, especially in medical studies; therefore so, use a semi parametric logistic regression model with the output variable (dependent variable) representing a trapezoidal fuzzy intuitionistic number.

the model was estimated on simulati

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample