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Hybrid optimization for LSTM DO prediction

                   application  in  optimization  problem.  Soft  Comput.   29.  Chicho BT, Sallow  AB.  A  comprehensive survey of
                   2021;25:5277-5298.                                   deep learning models based on keras framework. J Soft
                   doi: 10.1007/s00500-020-05527-x                      Comput Data Min. 2021;2(2):49-62.
                25.  Nguyen QV, Miller N, Arness D, Huang W, Huang ML,      doi: 10.30880/jscdm.2021.02.02.005
                   Simoff  S.  Evaluation  on  interactive  visualization  data   30.  Karim F, Majumdar S, Darabi H. Insights into lstm fully
                   with scatterplots. Vis Informatics. 2020;4(4):1-10.  convolutional  networks  for  time  series  classification.
                   doi: 10.1016/j.visinf.2020.09.004                    IEEE Access. 2019;7:67718-67725.
                26.  Lu Z. Comparison of stock price prediction models for      doi: 10.1109/ACCESS.2019.2916828
                   linear models, random forest and LSTM. Appl Comput   31.  Liao L, Li H, Shang W, Ma L. An empirical study of the
                   Eng. 2024;54(1):226-233.                             impact of hyperparameter tuning and model optimization
                   doi: 10.54254/2755-2721/54/20241598                  on the performance properties of deep neural networks.
                27.  Gürsoy Mİ, Alkan A. Investigation of diabetes data with   ACM Trans Softw Eng Methodol. 2022;31(3):1-40.
                   permutation  feature  importance  based  deep  learning      doi: 10.1145/3506695
                   methods. Karadeniz Fen Bilim Derg. 2022;12(2):916-930.  32.  Hodson  TO, Over  TM, Foks  SS.  Mean squared
                   doi: 10.31466/kfbd.1174591                           error, deconstructed.  J  Adv Model Earth Syst.
                28.  De Smedt T, Daelemans W. Pattern for python. J Mach   2021;13(12):e2021MS002681.
                   Learn Res. 2012;13(1):2063-2067.                     doi: 10.1029/2021MS002681




























































                Volume 22 Issue 5 (2025)                        79                           doi: 10.36922/AJWEP025210165
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