An optimized Indian-General-Elections-Based social science data prediction using multiscale dense nested parallel MobileNetV3 mantis search attention network
Bibliographic record
Abstract
Forecasting an election outcome is a challenging exercise because of the constantly varying and numerous political, social, and demographic factors. Unlike electoral data, no work explores the local structure in large-scale data since traditional approaches fail to capture complex patterns involving large sets of data, especially from diverse areas such as India. This research aims to present an improved, large-scale scale, and efficient prediction model for Indian General Elections through a Multiscale Dense Nested Parallel MobileNetV3 Mantis Search Attention Network, 3MDNPV3-SAN. The model is developed to overcome the shortcomings common to other techniques and incorporates several approaches. For data preprocessing, the novel Anisotropic Gaussian Filtering with Directionally Truncated First Derivative (AG2F2DT) is proposed to perform smoothing and eliminate noise while preserving significant directional information. To achieve the right feature selection for optimal modeling results, the proposed Group Teaching Optimization Algorithm (GTOA) is used, so as to retain and only use significant features in modeling. The main structure of the prediction process is based on two models: the 3MDNPV3-SAN model based on multiscale dense nests, the MobileNetV3 parallel framework, and a dynamic Mantis Search Attention Network for data region emphasis. The proposed model proves itself with 99.1% accuracy, 99.3% precision, 99.7% recall, 99.6% sensitivity, 0.1% error rate, 4% computation complexity, and 0.01% computational cost while outcompeting more conventional approaches due to the incorporation of multiscale interactions as well as dynamic attention. The work hence provides a scalable robust efficient model that can be used to analyze electoral phenomena and to support decisions and design in the social sciences. The methodology is in Python which means that it is practically applicable for large databases.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".