---
title: Objectionable Web Content Filtering System
url: https://www.ml-quant.com/papers/repec/bjf-journl-v-9-y-2024-i-11-p-51-60/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:bjf:journl:v:9:y:2024:i:11:p:51-60
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.rsisinternational.org%2Fjournals%2Fijrias%2Fdigital-library%2Fvolume-9-issue-11%2F51-60.pdf%3Bh%3Drepec%3Abjf%3Ajournl%3Av%3A9%3Ay%3A2024%3Ai%3A11%3Ap%3A51-60
featured: 2024-12-12
citations: unknown
topic: ML & AI Methods
---


# Objectionable Web Content Filtering System

The research is focused on developing a machine learning system to block inappropriate web content and alert parents when children encounter such content.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.rsisinternational.org%2Fjournals%2Fijrias%2Fdigital-library%2Fvolume-9-issue-11%2F51-60.pdf%3Bh%3Drepec%3Abjf%3Ajournl%3Av%3A9%3Ay%3A2024%3Ai%3A11%3Ap%3A51-60
- Identifier: RePEc:bjf:journl:v:9:y:2024:i:11:p:51-60
- Released: 2024-12-12
- First featured: Quant Letter No. 78 (2024-12-12): https://www.ml-quant.com/issues/2024-12-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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