IoT and AI based Forest Fire Prediction and Animal Monitoring System
Bibliographic record
Abstract
Forest fires, caused by high air temperatures, are a common occurrence during the summer months in areas with Mediterranean climates, including Turkey. The country’s constitution requires the reforestation of fire-affected zones, which can be achieved using remote sensing techniques for identification and rehabilitation. Regarding animal health, current methods for monitoring require assessments and diagnoses from veterinary professionals, leading to delayed treatment and a decline in health. To address this issue, we propose a system that tracks animal health and enables primary diagnosis by the animal’s owner. The system includes sensors for blood pressure, ECG, temperature, heart rate, and breathing rate, installed on the animal’s body to collect physiological data. Furthermore, the spread of crop destruction by dangerous wild animals has become a societal issue, impacting communities and regions, and causing problems such as roadkill and village appearance. Therefore, it is crucial to develop a plan for reducing the number of dangerous animals and maintaining a healthy population. This can be achieved through big data analysis algorithms for dangerous animals, real-time wired/wireless data processing, and performance monitoring research, resulting in an IoT and AI-based intelligent system for harmful wild animal extermination that can be applied in farmhouses, orchards, airports, and military boundaries.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".