Experimental study of neuro-fuzzy-genetic framework for oil spillage risk management
Bibliographic record
Abstract
This paper reports the findings from the experimental study of an intelligent system driven by Neural Network (NN), Fuzzy Logic (FL) and Genetic Algorithm (GA) for knowledge discovery and oil spillage risk management. Application software was developed in an environment characterized by 11Ants Analytics, Matrix Laboratory (MatLab), Microsoft Excel, SPSS and GraphPadInstat as frontend engines; Microsoft Access Database Management System as backend engine and Microsoft Windows as platform. 11Ants Analytics served as a tool for oil spillage indicators rank analysis and predictive model building. Matlab served as a tool for the extraction of patterns from 11Ants Analytics Model of oil spillage. Microsoft Excel serves as an interface between 11Ants Analytics and Matlab. Microsoft Excel, SPSS and GraphPadInstat serve as tools for the generation of relevant statistics. Indicators of oil spillage risks serve as input to the NN. GA is used to provide optimal set of parameters for NN training while FL used for modelling imprecise knowledge and provision of membership functions for the GA and NN. Data on Oil Spill incidences associated with oil exploration activities in the Niger Delta Region of Nigeria were collected from National Oil Spill Detection and Response Agency (NOSDRA) and used to assess and evaluate the practical function of the intelligent system. Adaptive Neuro-Fuzzy Inference System (ANFIS) driven by Mamdani’s inference mechanism was used to predict and estimate oil spillage risks. The findings from the experimental study are presented.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".