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Empirical Analysis of Machine Learning in Enhancing the E-Business Through Structural Equation Modeling

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Mobile Radio Communications and 5G Networks (MRCN 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 915))

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Abstract

The research’s main goal is to identify the crucial elements of putting machine learning techniques into practice in order to improve electronic commerce (E-business) inside the company. The data gathered will be analyzed to achieve this. In order to improve e-business and achieve sustainable development, researchers have concentrated their efforts on taking into account significant machine learning determinants that affect demand forecasting, the application of ML in purchase behavior, improving customer engagement, and support in overall cross-selling of the products. The major objective of this essay is to provide an in-depth and critical understanding of how machine learning technology may be used to enhance the performance of online businesses. As a result of the dynamic nature of the current environment in which businesses operate, enhance the value of their brands. Because of this, having an understanding of the machine learning approaches that the owners of these websites have used is more significant in this research. This is because management needs to be able to support their E-business and create the most money feasible for the owners of these websites. Because they use this kind of website approach, these commercial websites are better able to withstand the intense competition that exists in their field. In this brief message, we present these cutting-edge websites that make use of deep learning-driven machine learning and artificial intelligence. These websites are dynamic and cutting-edge at the same time.

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Authors and Affiliations

  1. Department of Information Technology, Sanjivani College of Engineering, Kopargaon, India

    P. William

  2. Department of Pharmacognosy, Smt. Sharadchandrika Suresh Patil College of Pharmacy, Chopda, Maharashtra, India

    Md. Rageeb

  3. Independent Researcher, Morrisville, NC, USA

    Md. Usman Roja Boina

  4. Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, 500075, India

    T. R. Vijaya Lakshmi

  5. Department of Computer Engineering and Applications, GLA University, Mathura, India

    Ashish Sharma

  6. Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India

    Nikhil Kumar Marriwala

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  1. P. William
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  2. Md. Rageeb
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  3. Md. Usman Roja Boina
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  4. T. R. Vijaya Lakshmi
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  5. Ashish Sharma
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  6. Nikhil Kumar Marriwala
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Corresponding author

Correspondence to T. R. Vijaya Lakshmi .

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Editors and Affiliations

  1. University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India

    Nikhil Kumar Marriwala

  2. University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India

    Sunil Dhingra

  3. Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Shruti Jain

  4. Electrical and Computer System Engineering, RMIT University, Melbourne, VIC, Australia

    Dinesh Kumar

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William, P., Rageeb, M., Boina, M.R., Lakshmi, T.R.V., Sharma, A., Marriwala, N.K. (2024). Empirical Analysis of Machine Learning in Enhancing the E-Business Through Structural Equation Modeling. In: Marriwala, N.K., Dhingra, S., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. MRCN 2023. Lecture Notes in Networks and Systems, vol 915. Springer, Singapore. https://doi.org/10.1007/978-981-97-0700-3_45

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  • DOI: https://doi.org/10.1007/978-981-97-0700-3_45

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  • Online ISBN: 978-981-97-0700-3

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