In the computer field, data encryption has always been a hot research topic, and TreScriptic algorithm has become the object of attention and research because of its simple parameters, clear classification and other advantages. This paper mainly introduces the web server which is built based on different models and has good performance, does not affect the system performance, has strong portability, and can be widely used in most occasions. This paper proposes a genetic NSBP neural network based on evolutionary algorithm, which is over fitting multi-dimensional and has global optimization characteristics. In this algorithm, parameters are regarded as input variables rather than directly interacting with output samples. Then this paper designs the Dropout optimization algorithm process and tests the performance. The test results show that the performance of the Dropout optimization algorithm based on machine learning to prevent over fitting takes about 5 seconds to process data, and the time to prevent over fitting is at least 3 seconds. This method can improve the biological signal processing effect of the over fitting.

Xiuming Wu. A Dropout Optimization Algorithm to Prevent Overfitting in Machine Learning. Machine Learning Theory and Practice (2023), Vol. 4, Issue 1: 18-26. https://doi.org/10.38007/ML.2023.040103.

[1] An F P. Pedestrian Re-Recognition Algorithm Based on Optimization Deep Learning-Sequence Memory Model. Complexity, 2019, 2019(4):1-16.

[2] Mathew J,Emmanuel M. Optimized deep CNN based Prediction model for Injury Severity Risk factor in Automobile Crashes. International Journal of Advanced Science and Technology, 2020, 29(5):5128-5136.

[3] Khodayari H,Pazooki F,Khodayari A R. Motion optimization algorithm designing for swarm quadrotors in application of grasping objects. Proceedings of the Institution of Mechanical Engineers, 2019, 233(11):3938-3951.

[4] Abdel-Basset M,El-Shahat D,Sangaiah A K. A modified nature inspired meta-heuristic whale optimization algorithm for solving 0-1 knapsack problem. International journal of machine learning and cybernetics, 2019, 10(3):495-514.

[5] Bhaskar G, MD Reddy,Thatikonda M. A Review on Secure Data Transmission for Banking Application using Machine Learning. International Journal of Engineering and Advanced Technology, 2021, 10(5):182-186.

[6] Dogra A K,Kaur J. Moving towards smart transportation with machine learning and Internet of Things (IoT): a review. Journal of Smart Environments and Green Computing, 2022, 2(1):3-18. 

[7] Lu H. Application of wireless network and machine learning algorithm in entrepreneurship education of remote intelligent classroom. Journal of Intelligent and Fuzzy Systems, 2021, 40(2):2133-2144.

[8] Kultin N,Kultin D,Bauer R. Application of machine learning technology to analyze the probability of winning a tender for a project. Proceedings of the Institute for System Programming of RAS, 2020, 32(2):29-36.

[9] Trung N D,Tang N,Xuan S H. Interpretation of Machine Learning Models for Medical Diagnosis. Advances in Science Technology and Engineering Systems Journal, 2020, 5(5):469-477.

[10] Peji A,Molcer P S. Predictive Machine Learning Approach for Complex Problem Solving Process Data Mining. Acta Polytechnica Hungarica, 2021, 18(1):45-63.

[11] Li Z. Research on the expression of new visual intelligence system based on machine learning technology. Journal of Intelligent and Fuzzy Systems, 2021(8-9):1-9.

[12] Cheng L C,Hu H W,Wu C C. Spammer Group Detection Using Machine Learning Technology for Observation of New Spammer Behavioral Features. Journal of Global Information Management, 2021, 29(2):61-76.

[13] Zhuang M E,Pan W T. Information Security Assessment Based On Machine Learning Technology-Fuzzy-Gra-Ahp. International Journal of Research -GRANTHAALAYAH, 2019, 7(6):230-240. 

[14] Verma V,Sinha A. Manipulation of Email Data Using Machine Learning and Data Visualization. International Journal of Advanced Science and Technology, 2020, 29(4):9743-9761. 

[15] Kumar G,Kumar S,Prakash A A. Credit Card Fraud Detection using Machine Learning. International Journal of Engineering and Advanced Technology, 2021, 10(4):124-126.

[16] Rana M E,Wang W. A Machine Learning based Software Project Schedule Management Solution. Test Engineering and Management, 2020, 83(1):307-321. 

[17] Mimoun J. Technology Focus: Well Testing (February 2021). Journal of Petroleum Technology, 2021, 73(2):51-51.

[18] Jayamini W,Weerasinghe K. Decision Support for Diagnosing Thyroid Diseases using Machine Learning. International Journal of Scientific and Research Publications (IJSRP), 2021, 11(6):1-5.