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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM19130

Search Engine with AIML Bot

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM19130

Volume:

7 |

4 |

Apr 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

BW

Bhagyashri Wakde

AU

Aakriti Upadhyay

AA

Akriti A

AS

Ansar Shaik

SEH

Syed Emaddudin Hussain

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Abstract

Our project is based on optimizing search engine results to provide relevant information to the user along with ad-free browsing experience. Main aim of this project is to develop a bot which will search in many different search engines and display top results which are more useful to the user. Later on, these results are stored in the database for further processing using Machine Learning. In the present trend, search engines are used by everyone for finding the required information on the web. Google is one of the most used search engines followed by Yahoo and Bing. For every search engine, the first few results are the sponsored results, where the website ranks because it is a paid advertise. This reduces the visibility of most organic websites with correct, accurate and more relevant information. So in this project, we collect results and display it without ads, for a better experience. Moreover, it will filter out the various irrelevant content and display only what is required. In addition to blocking ad tracking, it safeguards users from cross-site cookie tracking, bounce tracking, fingerprinting, phishing, and malware attacks. This paper contains everything we have learnt in the process.

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Cite This Article

Bhagyashri Wakde, Aakriti Upadhyay, Akriti A, Ansar Shaik, Syed Emaddudin Hussain, "Search Engine with AIML Bot," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 4, Apr 2023.

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Publication Date

4/20/2023

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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3

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The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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