Demand Forecasting for Food Production Using Machine Learning Algorithms: A Case Study of University Refectory

Accurate food demand forecasting is one of the critical aspects of successfully managing restaurants, cafeterias, canteens, and refectories. This paper aims to develop demand forecasting models for a university refectory. Our study focused on the development of Machine Learning-based forecasting models which take into account the calendar effect and meal ingredients to predict the heavy demand for food within a limited timeframe (e.g., lunch) and without pre-booking. We have developed eighteen prediction models gathered under five main techniques. Three Artificial Neural Network models (i.e., Feed Forward, Function Fitting, and Cascade Forward), four Gauss Process Regression models (i.e., Rational Quadratic, Squared Exponential, Matern 5/2, and Exponential), six Support Vector Regression models (i.e., Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian), three Regression Tree models (i.e., Fine, Medium, and Coarse), two Ensemble Decision Tree (EDT) models (i.e., Boosted and Bagged) and one Linear Regression model were applied. When evaluated in terms of method diversity, prediction performance, and application area, to the best of our knowledge, this study offers a different contribution from previous studies. The EDT Boosted model obtained the best prediction performance (i.e., Mean Squared Error = 0,51, Mean Absolute Erro = 0,50, and R = 0,96)

Flexural performance of reinforced concrete beams strengthened with prestressed near-surface-mounted FRP reinforcements

YesA numerical method for estimating the curvature, deflection and moment capacity of reinforced concrete beams strengthened with prestressed near-surface-mounted (NSM) FRP bars/strips is presented. A sectional analysis is carried out to predict the moment–curvature relationship from which beam deflections and moment capacity are then calculated. Based on the amount of FRP bars, different failure modes were identified, namely tensile rupture of prestressed FRP bars and concrete crushing before or after yielding of steel reinforcement. Comparisons between experimental results available in the literature and predicted curvature, moment capacity and deflection of reinforced concrete beams with prestressed NSM FRP reinforcements show good agreement. A parametric study concluded that higher prestressing levels improved the cracking and yielding loads, but decreased the beam ductility compared with beams strengthened with nonprestressed NSM FRP bars/strips

Reducing simulation duration of carbon nanotube using support vector regression method

WOS: 000412800400022Density Functional Theory (DFT) is one of the most important application of Carbon Nanotubes (CNTs). Because of the chemical and physical characteristics of carbon, CNTs play an important role in the field of nanotechnology. The most difficult part of CNT simulations is DFT calculations that take hours or even days. In this study, a Support Vector Regression (SVR) model that reduces the atomic coordinate calculation of CNT simulation duration has been proposed. u, v, and w coordinates which obtained from CNT simulations are predicted with high accuracy using the SVR method within minutes. A dataset containing 10721 samples was created using CASTEP software for the prediction model. The dataset consists of the atomic coordinates and chiral vectors. To evaluate the accuracy of the proposed model, Mean Square Error (MSE), Mean Absolute Error (MAE), Standard Error of the Estimate (SEE) and Correlation Coefficient (R) metrics were used. The dataset is studied separately with and without using 10-fold cross-validation. The results obtained from this study can be used in two ways: 1) The atomic coordinates can be predicted with a low-error without using a simulation program, 2) The estimated results can be used as an initial value of simulation software for reducing duration of the atomic coordinate simulation seriously