Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

作     者：Ahmed M.Elshewey Mahmoud Y.Shams Zahraa Tarek Mohamed Megahed El-Sayed M.El-kenawy Mohamed A.El-dosuky 

作者机构：Faculty of Computers and InformationComputer Science DepartmentSuez UniversitySuezEgypt Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and InformationComputer Science DepartmentMansoura University35561Egypt Faculty of Physical EducationTrack and Field Competitions DepartmentArish UniversityEgypt Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer ScienceArab East CollegesRiyadh13544Saudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第7期

页      面：765-781页

核心收录：

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 0303[法学-社会学] 0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Weight prediction machine learning deep learning LSTM CNN KNN 

摘      要：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 *** from several domains have presented several models addressing issues influencing food choice over the ***,a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making *** this paper,four Deep Learning(DL)models and one Machine Learning(ML)model are utilized to predict the weight in pounds based on food *** Long Short-Term Memory(LSTM)model,stacked-LSTM model,Conventional Neural Network(CNN)model,and CNN-LSTM model are the used deep learning *** the applied ML model is the K-Nearest Neighbor(KNN)*** efficiency of the proposed model was determined based on the error rate obtained from the experimental *** 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 ***,the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM,CNN,CNN-LSTM,and KNN regressor.