Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index
Bibliographic record
Abstract
In many ways, our everyday lives depend on having access to reliable data about the state of the air around us.If you can predict the air quality ahead of time, you can put in place the right warnings and safety measures to keep people from getting hurt.The Control Boards in India set up the National Air Monitoring Programme (NAMP), which checks the air in 342 locations in 240 cities.There are a few distinct categories for the Air Quality Index (AQI).The AQI in Chennai was predicted using data that was collected and preprocessed to account for missing values and eliminate duplicates.Air quality forecasting using deep learning technology is investigated using a huge dataset describing the surrounding environment.This study suggests a scheme for classifying AQI values using multi-output and (MMS) based on long short-term memory (LSTM).Increased classification precision is achieved by using Chaotic Hunger Games Search (CHGS) in the hyper-parameter tuning process.When compared to conventional methods, the AQI value provided by the proposed deep learning model is more precise and accurate for a given location within a metropolis.The suggested deep learning algorithm improves forecast accuracy, serving as a public service announcement to bring levels down to a safe level.The AQI values can be reliably predicted by the deep learning method, which aids in sustainable urban development planning.By coordinating road traffic signals and encouraging people to take public transit in strategic areas, the AQI target value can lessen pollution.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".