Paper Title
A Survey of AI-ML Based Placement Recommendation System
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Registration ID: IJNRD_303610
Published ID: IJNRD2501266
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Keywords
Machine Learning, NLP, Decision Tree, Logistic Regression, Database
Abstract
This paper deep dives into an extensive outline of different work suggestion frameworks and their applications in optimizing the enrolment handle, upgrading career improvement, and moving forward instructive results. The proposed adaptable work proposal framework utilizes ideal transport hypothesis to play down blockage in work application pipelines by adjusting work request and supply whereas considering territorial and industry-specific components. The think about too compares real-time and bunch preparing strategies, highlighting their focal points and confinements in energetic enrolment scenarios. Besides, the paper presents a career suggestion framework that recognizes aptitude holes and proposes custom-made instructive ways to adjust to changing work advertise requests. The framework leverages machine learning to anticipate patterns and important competencies, guaranteeing nimbleness in career advancement. Furthermore, the creators propose a strategy for suggesting course instructors to understudies utilizing a progressed weighted bipartite chart and the Incline One calculation, considering both instructor traits and understudy inclinations. The paper moreover investigates a moment vitality planning suggestion (IESR) framework for optimizing vitality utilization in keen homes utilizing IoT gadgets and real-time information analytics. Besides, the study paper audits strategies and innovations utilized to distinguish aptitudes from online work promotions, emphasizing the significance of aptitude distinguishing proof for different partners. The creators moreover examine procedures for identifying online enrolment extortion within the Australian work advertise, centering on relevant highlights to improve the precision of extortion location instruments. At long last, the inquire about utilizes information charts and reasonable AI (XAI) to upgrade predictions of employee turnover, giving interpretable bits of knowledge into turnover hazard components and enabling organizations to execute focused on worker maintenance procedures
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How To Cite (APA)
Matrika Dhamala, Mohammed Ayan, Mohamed Rayyan Ahmed, Mohammed Irfan Sufiyan, & Tahir Naquash (January-2025). A Survey of AI-ML Based Placement Recommendation System . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(1), c560-c568. https://ijnrd.org/papers/IJNRD2501266.pdf
Issue
Volume 10 Issue 1, January-2025
Pages : c560-c568
Other Publication Details
Paper Reg. ID: IJNRD_303610
Published Paper Id: IJNRD2501266
Research Area: Science and Technology
Author Type: Indian Author
Country: Bengaluru, Karnataka, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2501266.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2501266
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