Forecasting Low-Cost Housing Demand in Johor Bahru, Malaysia Using Artificial Neural Networks (ANN)
Bibliographic record
Abstract
There is a need to fully appreciate the legacy of Malaysia urbanization on aordable housing since the proportions ofurban population to total population in Malaysia are expected to increase up to 70% in year 2020. This study focusedin Johor Bahru, Malaysia one of the highest urbanized state in the country. Monthly time-series data have been usedto forecast the demand on low-cost housing using Artificial Neural Networks approach. The dependent indicator is thelow-cost housing demand and nine independents indicators including; population growth; birth rate; mortality baby rate;inflation rate; income rate; housing stock; GDP rate; unemployment rate and poverty rate. Principal Component Analysishas been adopted to analyze the data using SPSS package. The results show that the best Neural Network is 2-22-1 with0.5 learning rate and momentum rate respectively. Validation between actual and forecasted data show only 16.44% ofMAPE value. Therefore Neural Network is capable to forecast low-cost housing demand in Johor Bahru, Malaysia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".