Terrorism Prediction Using Artificial Neural Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main purpose of this research is to develop a hybrid computational intelligent algorithm (framework) as a decision support (DS) tool for terrorism phenomenon that has been defeated for years by governments, countries, and different multiple institutions and hence it needs multiple and integrated research from different science disciplines with a hope of being eliminated in the future. The proposed hybrid prediction algorithm based on integrated different Operations Research (OR) and Decision support tools with Data Mining (DM) techniques especially prediction and classification algorithms as well as different directions of modification and improvements in a number of recent and popular metaheuristics inspired algorithms. The proposed system has been developed, implemented, and evaluated according to different set of assessment measures. Through this study, it was found that, the proposed system is capable of predicting the terrorist group (s) responsible of terror attacks on different regions (countries). The findings of this research may serve as an alarm tool to determine the terrorist groups' networks and so minimize the terrorist attacks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it