IoT-Based Disaster Detection Model Using Social Networks and Machine Learning
Bibliographic record
Abstract
The internet of things is the revolutionary concept of the traditional internet so that all the physical objects or devices can connect to the internet or each other for sharing information or perform specific functions through the network. Social network usage in disaster detection models can play an essential role by sharing information and update the user's status when a disaster occurs. Besides, big data has demonstrated its value as a tool to aid and mitigate any disaster by processing a massive amount of data in a short period. This paper discusses that it is essential to have a proper disaster detection system to respond quickly once disasters occur. Besides, it proposes a novel method to detect the exact location of a disaster by utilizing a Snapchat map. Moreover, IoT, social networks and big data can accelerate the disaster detection system if they use together; by using data from the social networks and data from IoT devices, we can manage, monitor, analyze and detect disaster. The main objective of this paper is to propose a new and efficient IoT-based disaster Detection model (DDM) to find the exact location of a disaster for the collected data from social networks (SN) and other IoT devices using big data (BD) and machine learning (ML).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".