Journal of Advances in Developmental Research

E-ISSN: 0976-4844   •   Impact Factor: 9.71

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 17 Issue 1 January-June 2026 Submit your research before last 3 days of June to publish your research paper in the issue of January-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 17 Issue 1
2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

E-Commerce Fraud Detection Based on Machine Learning

Author(s) N. Shravani, N. Renuka, M. Pavan Naik, B Sree Theja, S. Siva Sankar
Country India
Abstract Nowadays, there are so many applications available on internet because of that user cannot always get correct or true reviews about the product on internet. In this project, we propose the system by developing web application which help to detect fraud apps using sentiment comments and data mining. We can check for user’s sentimental comments on multiple application. The reviews may be fake or genuine. But after comparing reviews of admin as well as user’s, we can get more clear idea. Hence, we can get higher probability of getting real reviews. So we are proposing a system to develop a web application that will take reviews from registered users for single product, and analyse them for positive negative rating. For every users reviews and comments will be fetched separately and analysed for positive negative rating. Then their rating/comments will be judged by the admin and it would be easy for admin to predict the application as Genuine or Fraud. In Review Based Evidences, besides ratings, most of the App stores also allow users to write some textual comments as App reviews. Such reviews can reflect the personal perceptions and usage experiences of existing users for particular mobile Apps. Indeed, review manipulation is one of the most important perspective of App ranking fraud.
Keywords E-commerce fraud, machine learning, fraud detection, data mining, Random Forest, Decision Tree.
Field Engineering
Published In Volume 17, Issue 1, January-June 2026
Published On 2026-04-04
Cite This E-Commerce Fraud Detection Based on Machine Learning - N. Shravani, N. Renuka, M. Pavan Naik, B Sree Theja, S. Siva Sankar - IJAIDR Volume 17, Issue 1, January-June 2026.
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJAIDR DOI prefix is
10.71097/IJAIDR

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 17 Isu 1Cover Page Vol 16 Isu 2Cover Page Vol 16 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJAIDR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijaidr.com