Big Data Classification Using Hybrid Squeeze‐ <scp>EfficientNet</scp> With Feature Fusion in Spark Architecture

ABSTRACT In this paper, the big data classification is done using the hybrid Squeeze‐EfficientNet with Feature fusion in the spark framework. At first, the partitioning of big data is executed by employing Bayesian Fuzzy Clustering (BFC). Later, big data classification is accomplished in the spark architecture. At the slave node, the subsequent process is accomplished and the partitioned data are applied to the slaves, where they are pre‐processed by Quantile normalization. Further, the feature fusion is carried out based on the Deep Kronecker Network (DKN) and Matusita Distance measure. At the Master node, all the features from the slave nodes are fused and the big data are classified by employing the Hybrid Squeeze‐EfficientNet. The Hybrid Squeeze‐EfficientNet is generated by integrating SqueezeNet and EfficientNet. The evaluation results show that the Squeeze‐EfficientNet attained an accuracy of 0.904, a sensitivity of 0.916, and a specificity of 0.925. The high performance obtained by the devised model improves the big data classification task in real‐time scenarios like targeted marketing, agriculture, smart transportation, efficient resource allocation, and personalized health monitoring systems. Thus, the integration of big data classification within daily life provides more informed decisions thereby improving the life quality.