The comparison between extreme learning machine and artificial neural network-back propagation for predicting the dengue incidences number in DKI Jakarta
Bibliographic record
Abstract
Abstract The existence of COVID-19 in Indonesia is not the only disease which we must be aware of. The Health Ministry has said that Dengue Hemorrhagic Fever is as dangerous as COVID-19 and must also be treated with caution. Based on data, until July 2020, there are 71,633 dengue cases in Indonesia and DKI Jakarta has the sixth-highest dengue incidence number. One of the factors that affects the spread of dengue vector is weather. It is necessary to predict the number of dengue incidences so that the dengue handling and prevention efforts can be done optimally. In this study, the number of dengue incidences will be predicted by involving weather factors (rainfall, temperature, and humidity) using Extreme Learning Machine and Artificial Neural Network-Back Propagation and also comparing the both of their performance. The result shows that Extreme Learning Machine can give the dengue incidence prediction in DKI Jakarta with the best RMSE testing result of 0.04584, which is more accurate than the dengue incidence prediction that is given by using Artificial Neural Network-Back Propagation with 100 epochs. Moreover, Extreme Learning Machine can do the training process faster than Artificial Neural Network-Back Propagation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".