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<p>Big data classification categorizes unstructured or structured data depending on file contents, types, and other metadata. The issues faced by big data classification are poor generalization, inaccurate predictions, security vulnerabilities, and a lack of big data scientists and data specialists. Hence, an effective fractional hunter osprey optimization-based parallel convolutional neural network (FHOO_PCNN) is proposed for big data classification. The process begins with collecting the input big data and partitioning it using deep embedded clustering (DEC) for efficient structuring. Then, big data classification is implemented using the MapReduce framework, involving mapper and reducer stages. Data normalization using logarithmic scaling is executed in the mapper stage to standardize the dataset. Then, normalized data is passed to feature weighting utilizing the proposed hunter osprey optimization (HOO), which incorporates the honey badger algorithm (HBA) and osprey optimization algorithm (OOA). The reducer phase merges the weighted features, after which classification is achieved using a PCNN optimized through FHOO, formulated by integrating fractional calculus and HOO. Moreover, FHOO_PCNN yields 91.765% accuracy, 94.765% true positive rate (TPR), 93.368% F1-score, and 90.766% true negative rate (TNR).</p>

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FHOO: feature weighting and PCNN for big data classification using MapReduce framework medical data

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • G. Keerthana,
  • L. Sherly Puspha Annabel

摘要

Big data classification categorizes unstructured or structured data depending on file contents, types, and other metadata. The issues faced by big data classification are poor generalization, inaccurate predictions, security vulnerabilities, and a lack of big data scientists and data specialists. Hence, an effective fractional hunter osprey optimization-based parallel convolutional neural network (FHOO_PCNN) is proposed for big data classification. The process begins with collecting the input big data and partitioning it using deep embedded clustering (DEC) for efficient structuring. Then, big data classification is implemented using the MapReduce framework, involving mapper and reducer stages. Data normalization using logarithmic scaling is executed in the mapper stage to standardize the dataset. Then, normalized data is passed to feature weighting utilizing the proposed hunter osprey optimization (HOO), which incorporates the honey badger algorithm (HBA) and osprey optimization algorithm (OOA). The reducer phase merges the weighted features, after which classification is achieved using a PCNN optimized through FHOO, formulated by integrating fractional calculus and HOO. Moreover, FHOO_PCNN yields 91.765% accuracy, 94.765% true positive rate (TPR), 93.368% F1-score, and 90.766% true negative rate (TNR).