Implementation of an intelligent clustering methodology for classification of terrorist acts
Bibliographic record
Abstract
Terrorist acts have elevated the level of violence, intimidation and pose a threat to life/property, peace and security in the world today. Deployed solutions to curb the occurrence of terrorism prove to be of insignificant value, hence there is the need for more solutions. The research aims at implementing an intelligent clustering methodology for classification of the acts of terrorism in Nigeria. Three experiments were carried out. In the first experiment, the qualitative terrorists data attributes were converted to quantitative attributes using an existing One-of-N (OoN) method and the processed data supplied to Adaptive Neuro-Fuzzy Inference System (ANFIS) (OoN-ANFIS) for training. The second experiment converted the qualitative data attributes to quantitative attributes using the formulated Rank-Frequency-Based (RFB) model before the data was supplied to ANFIS (RFB-ANFIS) for training. In the third experiment, which constitutes the current study, the RFB-processed data was used by Fuzzy C Means (FCM) to generate initial membership values for each point in the data set and then supplied to ANFIS (RFB-FCMANFIS). The results show that RFB-FCMANFIS model generated the least Root Mean Square Error (RMSE), Mean Absolute Error (MAE), training error and checking error of 0.002887, 0.004598, 0.0000713 and 0.0056155 respectively with the highest correlation coefficient of 0.99954, therefore indicating a superior classification capability using the RFB-FCMANFIS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".