Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

Food choice motives(i.e.,mood,health,natural content,convenience,sensory appeal,price,familiarities,ethical concerns,and weight control)have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the world.Researchers from several domains have presented several models addressing issues influencing food choice over the years.However,a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making procedure.In this paper,four Deep Learning(DL)models and one Machine Learning(ML)model are utilized to predict the weight in pounds based on food choices.The Long Short-Term Memory(LSTM)model,stacked-LSTM model,Conventional Neural Network(CNN)model,and CNN-LSTM model are the used deep learning models.While the applied ML model is the K-Nearest Neighbor(KNN)regressor.The efficiency of the proposed model was determined based on the error rate obtained from the experimental results.The findings indicated that Mean Absolute Error(MAE)is 0.0087,the Mean Square Error(MSE)is 0.00011,the Median Absolute Error(MedAE)is 0.006,the Root Mean Square Error(RMSE)is 0.011,and the Mean Absolute Percentage Error(MAPE)is 21.Therefore,the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM,CNN,CNN-LSTM,and KNN regressor.

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Wind power is one of the sustainable ways to generate renewable energy.In recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar power.To achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base models.These regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)regressor.In addition,data cleaning and data preprocessing were performed to the data.The dataset used in this study includes 4 features and 50530 instances.To accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM network.Five evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.