Assessing the Impact of ATM and POS Transactions on Currency Circulation in Nigeria: A Comparison of Artificial Neural Network and Linear Regression Models
Bibliographic record
Abstract
This work is aimed at assessing the impact of ATM and POS transactions on currency in circulation in Nigeria via the comparison of the Artificial Neural Network and Linear Regression models. The Pearson product moment correlation coefficient was used to assess the degree of relationship between currency in circulation and ATM transactions, currency in circulation and POS transactions, ATM transactions and POS transactions. In addition to this, a multiple linear regression model was also fitted on the data to in order to assess the impact of currency in circulation on ATM transactions and POS transactions. From the results obtained, it was observed that POS transactions have a significant impact on the amount of currency in circulation compared to ATM transactions because the value of POS transactions has a P-value of 1.5542E-13 which is significant at 0.05. This was further studied using the Artificial Neural Network (ANN). The Multilayer preceptor of ANN was adopted, with 70 percent of the data initially subjected to training and 30 percent for validity. Thereafter, the amount of training data was increased to 61.4% and testing data was increased to 38.6% and it was observed that there was corresponding increase in the R-square from 0.809 to 0.839. This shows that the R-square of the ANN can be improved by increasing the amount of training data and testing data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".