We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Case-based Reasoning (CBR) Internship Placement Model

by Emmanuel Isika, Akintoba Akinwonmi, Oluyomi Akinyokun
journal cover thumbnail

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.

References
  1. Amala Deshpande, Deepika Khatri, Divya Deshpande, Prarthita Das, & Sujata Khedkar. (2016). Proposed system for resume analytics. International Journal of Engineering Research And, V5(11). https://doi.org/10.17577/ijertv5is110274
  2. Bessadok, A., Abouzinadah, E., & Rabie, O. (2021). Analyzing students’ digital behavior in an e-learning environment within the Blackboard Learning Management System. Innovative and Intelligent Technology-Based Services for Smart Environments – Smart Sensing and Artificial Intelligence, 127-133. doi:10.1201/9781003181545-19
  3. Espenakk, E., Knalstad, M. J., & Kofod-Petersen, A. (2019). Lazy learned screening for efficient recruitment. Case-Based Reasoning Research and Development, 64–78. https://doi.org/10.1007/978-3-030-29249-2_5
  4. Dhende S, Aniket S, Sushil A, Akash S, Sudhir D, (2018), Candidate Hiring Through CV Analysis. International Research Journal of Engineering and Technology (IRJET). 5(5) pp. 3148-3149
  5. Doshi B., Gandhi H., Jayadharan N., Patil M, (2019), Implementation of Online Portal for CV Analysis Using KNN Algorithm. International Journal for Research in Applied Science & Engineering Technology. 7(4): pp. 1435-1439
  6. Ferddie Quiroz Canlas, (2021), Data Mining Model for Student Internship Placement Using Modified Case-Based Reasoning.
  7. Gartner_Inc (2018) Understanding Gartner's hype cycles, Gartner. Available at: https://www.gartner.com/en/documents/3887767 (Accessed: January 6, 2023).
  8. Győrödi, C.A. et al. (2022) ‘A comparative study of mongodb and document-based MySQL for Big Data Application Data Management’, Big Data and Cognitive Computing, 6(2), p. 49. doi:10.3390/bdcc6020049.
  9. Hu, Y., & Spiro, R. J. (2021). Design for now, but with the future in mind: A “cognitive flexibility theory” perspective on online learning through the lens of moocs. Educational Technology Research and Development, 69(1), 373-378. doi:10.1007/s11423-020-09920-z
  10. Internship (2022) Wikipedia. Wikimedia Foundation. Available at: https://en.wikipedia.org/wiki/Internship (Accessed: December 20, 2022).
  11. Kumar, N., Gupta, M., Sharma, D., & Ofori, I. (2022). Technical job recommendation system using apis and web crawling. Computational Intelligence and Neuroscience, 2022, 1–11. https://doi.org/10.1155/2022/7797548
  12. Naik, Poornima & Naik, Girish. (2020). BIG DATA TOOLS WHICH, WHEN AND HOW? (Volume V) (Hands-on Sessions with Advanced MongoDB Concepts).
  13. Nasution, D., & Sitorus, Z. (2021, September 27). Enhance Web-Based Job Search Recommendation System of Hybrid-Based Recommendation | Nasution | Budapest International Research and Critics Institute-Journal (BIRCI-Journal). Enhance Web-Based Job Search Recommendation System of Hybrid-Based Recommendation | Nasution | Budapest International Research and Critics Institute-Journal (BIRCI-Journal). https://doi.org/10.33258/birci.v4i3.2579
  14. Omonijo, D.O. et al. (2019) “The review of the Student Industrial Work Experience Scheme (SIWES) in four selected countries,” Academic Journal of Interdisciplinary Studies, 8(3). Available at: https://doi.org/10.36941/ajis-2019-0014.
  15. Mayer-Schönberger, V. & Cukier, K. (2014), Big Data: A revolution that will transform how we live, work, and think. Eamon Dolan/Mariner Books, USA.
  16. Mayer-Schönberger, V. & Cukier, K. (2014). Learning with Big Data: the future of education. Boston/New York: Eamon Dolan Book.
  17. Moore, K. (2022) Beyond chatgpt: The future of AI at work, Forbes. Forbes Magazine.Available at:https://www.forbes.com/sites/karlmoore/2022/12/14/leverage-generative-ai-workplace-tools-with-semantic-research/?sh=755aa89b771e (Accessed: January 6, 2023).
  18. Pombo L., (2019), Landing on the right job: a machine learning approach to match candidates with jobs applying semantic embeddings. Master’s Thesis. Universidade Nova de Lisboa.
  19. Pradhan, R., Varshney, J., Goyal, K., & Kumari, L. (2021). Job recommendation system using content and collaborative-based filtering. Advances in Intelligent Systems and Computing, 575–583. https://doi.org/10.1007/978-981-16-2594-7_47
  20. Rea, J., & Gopalan, A. (2021). Word2Mouth - an eLearning platform catered for low-income countries. 2021 IEEE Global Engineering Education Conference (EDUCON). doi:10.1109/educon46332.2021.9454087
  21. React (JavaScript library) (2022) Wikipedia. Wikimedia Foundation. Available at: https://en.wikipedia.org/wiki/React_(JavaScript_library) (Accessed: December 19, 2022).
  22. Shalaby, W., AlAila, B. E., Korayem, M., Pournajaf, L., AlJadda, K., Quinn, S., & Zadrozny, W. (2017). Help me find a job: A graph-based approach for job recommendation at scale. 2017 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata.2017.8258088
  23. Slama, B.S. et al. (2021) Innovative and intelligent technology-based services for Smart Environments - smart sensing and Artificial Intelligence: Proceedings of the 2nd International Conference on Smart Innovation, ergonomics and applied human factors (SEAHF ’20), held online, 14-15 November 2020. Boca Raton: CRC Press.
  24. Taylor, J.A. (2018) A brief history of the internship, Taylor Research Group.Taylor Research Group Available at: https://www.taylorresearchgroup.com/news/2017/4/5/a-brief-history-of-the-internship (Accessed: December 20, 2022).
  25. Tomy, S., & Pardede, E. (2019). Map my career: Career planning tool to improve student satisfaction. IEEE Access, 7, 132950-132965. doi:10.1109/access.2019.2940986
  26. Tomy, S., & Pardede, E. (2018). Course map: A career-driven course planning tool. Computational Science and Its Applications – ICCSA 2018, 185-198. doi:10.1007/978-3-319-95165-2_13
  27. UNESCO, (2019), Artificial intelligence in education: challenges and opportunities for sustainable development.
  28. Usage statistics and market share of react for websites (no date) W3Techs. Available at: https://w3techs.com/technologies/details/js-react (Accessed: December 19, 2022).
  29. Vinay (2022) The importance of an internship: Top 5 reasons why internships are critical, Capital Placement. Available at: https://capital-placement.com/blog/the-importance-of-an-internship-top-5-reasons-why-internships-are-critical/ (Accessed: 23 July 2023).
  30. What is Artificial Intelligence (AI) ? (no date) IBM. Available at: https://www.ibm.com/topics/artificial-intelligence (Accessed: January 6, 2023).
  31. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  32. Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  33. Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  34. Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
  35. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
  36. Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
  37. Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
  38. Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender
Index Terms

Computer Science
Information Sciences
Machine Learning Case-Based Reasoning

Keywords

Machine Learning Internships Case-Based Reasoning Natural Language Processing

Powered by PhDFocusTM