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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 6

Published on: June 2026

SMART CAREER NAVIGATOR: AI-POWERED PLACEMENT FORECASTING AND CAREER PLANNING FOR MODERN CAMPUS ENVIRONMENTS

Sumitha.S

A. B. Hajira Be

Department of Computer Applications, Karpaga Vinayaga College of Engineeringand Technology, Chinna Kolambakkam, Maduranthagam Taluk, Chengalpattu District, Tamil Nadu – 603308

Article Status

Plagiarism Passed Peer Reviewed Open Access

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Abstract

Campus placement is a critical milestone in students' academic and professional journeys. Traditional career guidance methods often rely on manual assessment and generalized recommendations, which may not accurately reflect individual capabilities and industry requirements. This paper presents "Smart Career Navigator," an Artificial Intelligence (AI)-powered system designed to forecast placement opportunities and provide personalized career planning for students in modern campus environments. The proposed system utilizes machine learning algorithms to analyze academic performance, technical skills, aptitude scores, certifications, and extracurricular activities. Based on predictive analytics, the system estimates placement probabilities and recommends suitable career paths, skill-development strategies, and training programs. Experimental results demonstrate improved prediction accuracy and enhanced student preparedness, enabling institutions to optimize placement support services. The proposed solution contributes to data-driven career decision-making and efficient talent development.

Keywords:Artificial Intelligence, Placement Prediction, Career Planning, Machine Learning, Predictive Analytics, Student Employability, Campus Recruitment.

How to Cite this Paper

Sumitha.S, (2026). Smart Career Navigator: AI-Powered Placement Forecasting and Career Planning for Modern Campus Environments. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.215

Sumitha.S, . "Smart Career Navigator: AI-Powered Placement Forecasting and Career Planning for Modern Campus Environments." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.215.

Sumitha.S, . "Smart Career Navigator: AI-Powered Placement Forecasting and Career Planning for Modern Campus Environments." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.215.

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References


  1. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110,2004.

  2. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015.

  3. W. Picard, Affective Computing, MIT Press, Cambridge, MA, USA, 1997.

  4. Poria, E. Cambria, D. Hazarika, and P. Vij, “A deeper look into sarcastic tweets using deep convolutional neural networks,” COLING, pp. 1601–1612, 2016.

  5. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang, “A survey of affect recognition methods: Audio, visual, and spontaneous expressions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 1, pp. 39–58,2009.

  6. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105, 2012.

  7. Deng et al., “The FER-2013 facial expression recognition dataset,” ICMLWorkshop on Challenges in Representation Learning, 2013.

  8. Schuller et al., “Speech emotion recognition: Two decades in a nutshell, benchmarks, and ongoing trends,” Communications of the ACM, vol. 61, no. 5, pp. 90–99, 2018.

  9. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv:1301.3781, 2013.

  10. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” NAACL-HLT, pp. 4171–4186, 2019.

  11. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

  12. Cambria and B. White, “Jumping NLP curves: A review of natural language processing research,” IEEE Computational Intelligence Magazine, vol. 9, no. 2, pp. 48–57, 2014.

  13. Ekman and W. V. Friesen, “Facial action coding system: A technique for the measurement of facial movement,” Consulting Psychologists Press, 1978.

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  • Peer Review Type: Double-Blind Peer Review
  • Published on: Jun 17 2026
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