Software engineering implementation and e-commerce model design based on neural digestive system monitoring and rapid prediction of amylase

Pei Fan; Yezhi Yuan; Jie Zhang; Yuting Huang; Xiaobo Wu; Liangyu Li; Yi Qin

doi:10.54097/n2fb9b65

Authors

Pei Fan
Yezhi Yuan
Jie Zhang
Yuting Huang
Xiaobo Wu
Liangyu Li
Yi Qin

DOI:

https://doi.org/10.54097/n2fb9b65

Keywords:

Neural network, Computer, Pharmacology, E-commerce, Data, Device.

Abstract

Convolutional neural networks can be used to predict amylase levels in patients with acute abdomen. Based on this algorithm, we can reduce the error rate in hospital emergency work. As a biomedical engineering study, this study processed a series of continuous data such as patient age, symptoms, pain index, and life risk exposure factors. This software project has the ability to perform regression analysis based on the above characteristics, it can be used to determine whether the blood amylase level is normal, which provides assistance in the clinical diagnosis of pancreatitis, a disease that directly affects the 24-hour survival of patients. We believe that this software engineering can serve as a purchasing option for e-commerce systems and propose a solution for this situation.

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References

Silver Ike & Small Deborah A. (2022). On the wisdom and utility of (under)sociality: A consumer psychology perspective. Journal of Consumer Psychology (1). doi: 10.1002/JCPY. 1333.

Feng Jinfeng. (2022). Optimal Control Strategy Model of Marketing Management Based on Consumer Psychology. Mathematical Problems in Engineering. doi: 10.1155/2022/8689244.

Schulze Schwering Dorothee, Isabell Sonntag Winnie & Kühl Sarah. (2022). Agricultural E-commerce: Attitude segmentation of farmers. Computers and Electronics in Agriculture. doi: 10.1016/J.COMPAG. 2022. 106942.

Esmeli Ramazan, Bader-El-Den Mohamed & Abdullahi Hassana. (2022). An analysis of the effect of using contextual and loyalty features on early purchase prediction of shoppers in e-commerce domain. Journal of Business Research. doi: 10.1016/J. JBUSRES. 2022. 04. 012.

Ayala Daniel, Hernández Inma, Ruiz David & Rahm Erhard. (2022). Corrigendum to multi-source dataset of e-commerce products with attributes for property matching [Data in Brief 41 (2022) 107884]. Data in Brief. doi: 10.1016/J. DIB. 2022. 108134.

Ma Guanhua, Ma Junhua, Li Hao, Wang Yiming, Wang Zhaohua & Zhang Bin. (2022). Customer behavior in purchasing energy-saving products: Big data analytics from online reviews of e-commerce. Energy Policy. doi: 10. 1016/J. ENPOL. 2022. 112960.

Pathrose Eugin Prakash & Verma Priyanka. (2022). E-Commerce and Teleworking: Business Opportunities in the COVID-19 Era. Electrochemical Society Transactions (1). doi: 10.1149/10701. 12585ECST.

Pathrose Eugin Prakash & Shetty Bharati. (2022). Managing Business in COVID-19 Era: Opportunities in E-Commerce and Teleworking Sector. Electrochemical Society Transactions (1). doi: 10. 1149/10701. 4163ECST.

Patro S Gopal Krishna, Mishra Brojo Kishore, Panda Sanjaya Kumar & Hota Amruta Shree. (2022). Hybrid Action-Allied Recommender Mechanism: An Unhackneyed Attribute for E-Commerce. Electrochemical Society Transactions (1). doi: 10. 1149/10701. 4537ECST.