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A Case-based Reasoning (CBR) Internship Placement Model

by Emmanuel Isika, Akintoba Akinwonmi, Oluyomi Akinyokun
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 14
Year of Publication: 2024
Authors: Emmanuel Isika, Akintoba Akinwonmi, Oluyomi Akinyokun
10.5120/ijca2024923486

Emmanuel Isika, Akintoba Akinwonmi, Oluyomi Akinyokun . A Case-based Reasoning (CBR) Internship Placement Model. International Journal of Computer Applications. 186, 14 ( Mar 2024), 9-14. DOI=10.5120/ijca2024923486

@article{ 10.5120/ijca2024923486,
author = { Emmanuel Isika, Akintoba Akinwonmi, Oluyomi Akinyokun },
title = { A Case-based Reasoning (CBR) Internship Placement Model },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2024 },
volume = { 186 },
number = { 14 },
month = { Mar },
year = { 2024 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number14/a-case-based-reasoning-cbr-internship-placement-model/ },
doi = { 10.5120/ijca2024923486 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-03-27T00:44:45.403890+05:30
%A Emmanuel Isika
%A Akintoba Akinwonmi
%A Oluyomi Akinyokun
%T A Case-based Reasoning (CBR) Internship Placement Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 14
%P 9-14
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Candidates are faced with numerous challenges when seeking internship especially in IT-based firms, the challenges include elongated time-frame resulting from the conventional search of placement among others. This research presents a platform through the design of a case-based reasoning (CBR) model which mitigates the challenges and facilitates internship placements for candidates. The aim is to alleviate intern-employer mapping dilemma. The research applies supervised machine learning techniques including data pre-processing, feature extraction, document similarity metrics, and knowledge-intensive CBR pattern matching to optimize matching between intern candidate vectors and employer criteria vectors. The system resultantly introduce an ML based personalized and efficient matching platform with real-time support, potentially improving outcomes for interns and companies within the same ecosystem.

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Index Terms

Computer Science
Information Sciences
Machine Learning Case-Based Reasoning

Keywords

Machine Learning Internships Case-Based Reasoning Natural Language Processing

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