Health Monitoring Considering Air Quality Index Prediction Using Neuro Fuzzy Inference Model: A Case Study of Lahore, Pakistan
Bibliographic record
Abstract
For many years, improving air quality has been great attention of the whole world. It has been recognized that air pollution as a hypothetically hazardous type of environmental pollution and polluted air directly affects the human health. In Asian countries, it has converged less attention of ever growing most alarming and hazardous issue of air pollution. This paper presents a case study of Lahore city of Pakistan for the prediction of Air Quality Index (AQI) using hybrid approach of Neuro Fuzzy (NF) inference system. The ambient air data of Lahore was taken from the Environmental Protection | department (EPD) working under government of the Punjab. For results evaluation, data was recorded at different station in the period from April 2007 to May 2015. The fuzzy rules have been generated according to the Pakistan Environmental Protection Agency (PAK-EPA) standard of AQI. The NF Inference Model took the air pollutants such as Particulate Matter (PM2.5), Ozone (O3), Carbon Monoxide (CO), Sulphur Dioxide (SO2) and Nitrogen Dioxide (NO2) as inputs and predicted the air quality index as good, moderate, or unhealthy air. The results showed that NF based AQI prediction model classifies the AQI proficiently, robustly, and accurately as compared to conventional method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".