Handling consumer vulnerability in e-commerce product images using machine learning

Chatrath, Sarvjeet Kaur; Batra, Gautam; Chabba, Yogesh

doi:10.1016/j.heliyon.2022.e10743

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…In light of [1] and [2], an e-commerce platform may make more informed decisions about inventory levels, pricing, and promotional timing with a sales forecast. According to [3], it is possible to learn about the e-commerce platform's development, stability, and decline via sales forecasting and the impact of short-term product objectives like promotion, price, season, and online ranking on long-term sales.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on their research efforts, both [2] and [3] have settled on a neural network method. Researchers in Stage by stage E-Ecommerce market database analysis by using machine learning models 2018 used a neural network algorithm called a nonlinear autoregressive network (NARNN), while researchers in 2019 used a neural network algorithm called a recurrent neural network (RNN) and an extended short-term memory network (LSTM).…”

Section: Literature Reviewmentioning

confidence: 99%

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Stage by stage E- Ecommerce market database analysis by using machine learning models

Ryali,

Manne,

Ravisankar

et al. 2024

EAI Endorsed Trans IoT

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In the recent era, advertising strategies are far more sophisticated than those of their predecessors. In marketing, business contacts are essential for online transactions. For that, communication needs to develop a database; this database marketing is also one of the best techniques to enhance the business and analyze the market strategies. Businesses may improve consumer experiences, streamline supply chains, and generate more income by analyzing E-Commerce market datasets using machine learning models. In the ever-changing and fiercely competitive world of e-commerce, the multi-stage strategy guarantees a thorough and efficient use of machine learning. Analyzing the database can help to understand the user's or industry's current requirements. Machine Learning models are developed to support the marketing sector. This machine learning model can efficiently operate or analyze e-commerce in different stages, i.e., systematic setup, status analysis, and model development with the implementation process. Using these models, it is possible to analyze the marketing database and create new marketing strategies for distributing marketing objects, the percentage of marketing channels, and the composition of marketing approaches based on the analysis of the marketing database. It underpins marketing theory, data collection, processing, and positive and negative control samples. It is suggested that e-commerce primarily adopt the database marketing method of the model prediction. This is done by substituting the predicted sample into the model for testing. The issue of unequal marketing item distribution may be resolved by machine learning algorithms on the one hand, and prospective customer loss can be efficiently avoided on the other. Also, a proposal for an application approach that enhances the effectiveness of existing database marketing techniques and supports model prediction is made.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Stage by stage E- Ecommerce market database analysis by using machine learning models

Ryali,

Manne,

Ravisankar

et al. 2024

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

show abstract

“…E-commerce platforms have emerged as a widely adopted option for companies, providing them with a diverse array of online purchasing and sales possibilities. These platforms enable consumers to make purchases without having to visit a physical store, unlike regular websites which are often used for information gathering [ 1 ]. One such e-commerce website, Daraz, is a popular online shopping marketplace in South Asia, including Bangladesh, Sri Lanka, Pakistan, Myanmar and Nepal.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models

Das,

Islam,

Hasan

et al. 2023

Heliyon

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Driving E-commerce Success with Advanced Machine Learning: Customer Purchase Pattern Insights

Kumar,

Venkat,

Balaji

et al. 2023

2023 International Conference on Sustainable Communication Networks and Application (ICSCNA)

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Handling consumer vulnerability in e-commerce product images using machine learning

Cited by 4 publications

References 14 publications

Stage by stage E- Ecommerce market database analysis by using machine learning models

Stage by stage E- Ecommerce market database analysis by using machine learning models

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models

Driving E-commerce Success with Advanced Machine Learning: Customer Purchase Pattern Insights

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