Authors : Nomishan, Iyorrumun; Wayo, Iorkyaa

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/263cmjr2

DOI : https://doi.org/10.38124/ijisrt/25may2239

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In the era of data-driven governance, institutions like Akperan Orshi Polytechnic Yandev are increasingly relying on digital systems to streamline operations and enhance decision-making. However, hidden anomalies or outliers in institutional data—such as irregular staff attendance, student performance, or financial transactions—can distort analyses and lead to suboptimal decisions. This study presents the design and implementation of an Outlier Detection System tailored for Akperan Orshi Polytechnic Yandev. The system employs statistical and machine learning techniques to automatically identify and flag unusual patterns across various administrative and academic datasets. By integrating this system into the Polytechnic's existing data infrastructure, stakeholders can proactively detect inconsistencies, improve data integrity, and make more informed and timely decisions. The results demonstrate the system’s effectiveness in revealing hidden anomalies, thereby supporting strategic planning and policy formulation across departments.

Keywords : Outlier Detection, Decision-Making, Data Integrity, Machine Learning, Anomaly Detection, Polytechnic Administration, Akperan Orshi Polytechnic Yandev, Institutional Data Analysis.

References :

1. Acuna, E., & Rodriguez, C. (2004). A meta analysis study of outlier detection methods in classification. Technical paper, Department of Mathematics, University of Puerto Rico at Mayaguez, 1(25), 14.
2. Adams, J., Hayunga, D., Mansi, S., Reeb, D., Verardi, V. (2019). Identifying and treating outliers in finance. Financial Management, 48, 345–384. doi:10.1111/fima.12269
3. Anusha, P. V., Anuradha, C., Murty, P. C., & Kiran, C.S. (2019). Detecting outliers in high dimensional data sets using z-score methodology. International Journal of Innovative Technology and Exploring Engineering, 9(1), 48-53. doi: 10.3594 0/ijitee.A3910.119119
4. Atsa’am, D.D., Gbaden, T., & Wario, R. (2024). DrugApp: A simulation of drug suspects and offenders classification. International Journal of Simulation and Process Modelling, 21(3), 147-154. doi: 10.1504/IJSPM.2024.10066647
5. Bell, D. (2023, June 25). An introduction to the unified modeling language. https://developer.ib m.com/articles/an-introduction-to-uml/
6. Ben-Gal, I. (2005). Outlier Detection. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_7
7. Bumblauskas, D., Nold, H., Bumblauskas, P., and Igou, A. (2017). Big data analytics: transforming data to     action. Business Process Management Journal, 703-720.
8. Chikodili, N. B., Abdulmalik, M. D., Abisoye, O. A., Bashir, S. A. (2021). Outlier detection in multivariate time series data using a fusion of k-medoid, standardized euclidean distance and z-score. In: Misra, S., Muhammad-Bello, B. (eds) Information and Communication Technology and Applications (ICTA 2020). Communications in Computer and Information Science, 1350. Springer, Cham. doi:10.1007/978-3-030-69143-1_21
9. Consul, J. I., & Ndiwari, G. (2018). The use of outliers in the detection of suspicious examination malpractices in the scores of students in a Nigerian university. International Journal of Education and Research, 6(3), 65-74.
10. Duraj, A., Szczepaniak, P. S. (2020). Outlier detection in data streams — A comparative study of selected methods. Procedia Computer Science, 192,  2769-2778. doi:10.1016/j.procs.2021.09.047.
11. Gimpel, H., Hosseini, S., Huber, R. X. R., Probst, L., Röglinger, M. and Faisst, U. (2018). Structuring Digital Transformation: A Framework of Action Fields and its Application at ZEISS. Journal of Information Technology, Theory and Application, 1(19).
12. Hodge, V. J. & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22 (2), 85-126.
13. Krstic, J., Jovanov, G., Radovanovic, R., Ljusic, M. and Nikolic, M. (2016). Process of Business Reengineering from the Aspect of E-Business. Journal of Textile Science & Engineering, 272(6).
14. OECD and the United Kingdom Department for Business, Energy and Industrial Strategy (BEIS). (2018). Implications of the Digital Transformation for the Business. http://www.oecd.org/sti/ind/digital-         transformation-business-sector-summary.pdf.
15. Oyelere, S. S., Atsa’am, D. D., Ayuba, H. M., Olawumi, O. Suhonen, J., & Joy, M. (2018). TerrorWatch: A prototype mobile app to combat terror in terror-prone nations. In F.J. Mtenzi (Ed) Mobile technologies and socio-economic development in emerging nations (pp 203-233) Hershey, PA: IGI Global. doi:10.4018/978-1-5225-4029-8.ch010
16. Rudnick, M., Riezebos, J., Powell, D. J. and Hauptvogel, A. (2020). Effective after-sales services through the lean servitization canvas. International Journal of Lean Six Sigma, 5(11), 943-956.
17. Schwertner, K. (2017). Digital transformation of business. Trakia Journal of Sciences, 15(1), 388-393.
18. Sethi, S., Malhotra, D., and Verma, N. (2016). Data mining: current applications & trends. International           Journal of Innovations in Engineering and Technology, 6(4), 586- 589.
19. Singh, K., & Upadhyaya, K. (2012). Outlier Detection: Applications and techniques. International Journal of Computer Science Issues, 9(1),307-323.
20. Ski, M. C. (2024). An overview of outlier detection methods. Lond Journal of Engineering Research, 24(2), 37-79.
21. Smiti, A. (2020). A critical overview of outlier detection methods. Computer Science Review, 38, 100306. doi: 10.1016/j.cosrev.2020.100306
22. ur Rehman, A., & Belhaouari, S. B. (2021). Unsupervised outlier detection in multidimensional data. Journal of Big Data, 8(80), 1-27. (2021). doi:10.1186/s40537-021-00469-z
23. West, D. M. and Allen, J. R. (2018). How artificial intelligence is transforming the world. Report, Brookings Institution.
24. Won, M. (2020). Outlier analysis to improve the performance of an incident duration estimation and incident management system. Transportation Research Record, 2674(5), 486-497. doi: 10.1177/0361198120916472
25. Yaro, A. S., Maly, F,. & Prazak, P. (2023). Outlier detection in time-series receive signal strength observation using z-score method with S_n scale estimator for indoor localization. Applied Sciences, 13, 3900. doi:10.3390/app13063900
26. Yu, T. W. (2009). The moderation of liberal studies school based assessment scores: How to ensure fairness and reliability? International Education Studies, 2(4), 91-98.