arbiter
JournalsPricingAccount

Big data-based predictive model for attendance rate of reserve forces training

Jungmok Ma

International Journal of Services Operations and Informatics2025https://doi.org/10.1504/ijsoi.2025.152324article
AJG 1
Weight
0.50

What the paper says

While reserve forces are strategically important to deter war in the Republic of Korea (ROK), the training of the reservists is a challenge since they are civilians and difficult to control. In order to tackle the difficulty of predicting the attendance rate of reserve training, this paper proposes a predictive model using Big Data. The current prediction method in the military uses the last year's attendance rate, and one previous study suggests utilising daily weather information without a systematic analysis. This paper aims to test the significance of the predictor variables in the daily attendance rate. Next, to improve the prediction accuracy of the current method, a predictive model with the volume of web search data is proposed. In the case study, statistically significant predictor variables are identified, and the proposed Big Data-based predictive model improves the prediction performance in comparison to the current method with real reserve training data.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1504/ijsoi.2025.152324

Or copy a formatted citation

@article{jungmok2025,
  title        = {{Big data-based predictive model for attendance rate of reserve forces training}},
  author       = {Jungmok Ma},
  journal      = {International Journal of Services Operations and Informatics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijsoi.2025.152324},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Big data-based predictive model for attendance rate of reserve forces training

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.