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-sca...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.展开更多
文摘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.
