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Record W3025908742 · doi:10.3233/jifs-179813

Intelligence computing approach for solving second order system of Emden–Fowler model

2020· article· en· W3025908742 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Intelligent & Fuzzy Systems · 2020
Typearticle
Languageen
FieldMathematics
TopicFractional Differential Equations Solutions
Canadian institutionsKinectrics (Canada)
Fundersnot available
KeywordsCorrectnessSequential quadratic programmingComputer scienceVariance (accounting)Artificial neural networkConvergence (economics)Consistency (knowledge bases)Scheme (mathematics)AlgorithmQuadratic equationMean squared errorGenetic algorithmStandard deviationMathematical optimizationMathematicsQuadratic programmingArtificial intelligenceMachine learningStatistics

Abstract

fetched live from OpenAlex

In this research study, an advance computational intelligence paradigm is used for solving second order Emden-Fowler system (EFS) based on artificial neural network, genetic algorithm (GA) which is a famous global search method, sequential quadratic programming (SQP) known as rapid local refinement and the hybrid of GA-SQP. The proficiency of the designed scheme is inspected by solving the three examples of EFS to check the efficiency, consistency, precision and exactness of the technique. The numerical outcomes of the purposed scheme are compared with the exact solution that shows the significance of the scheme based on accuracy, correctness and convergence. Moreover, statistical explorations have been executed to verify the precision and accuracy of the outcomes based on performance measures of mean absolute deviation, root mean squared error and variance account for.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.144
GPT teacher head0.335
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it