Year: 2025
Type: article
Abstract: This thesis addresses critical challenges in privacy-preserving feature selection and classification for big data analytics. Specifically, four novel methodologies are proposed: Hierarchical Classification Feature Selection (HCFS), Privacy-Preserving Classification Selection with p-stability (PPCS), Local N-ternary Pattern combined with Modified Deep Belief Network (LNTP-MDBN), and Privacy-Preserving Cosine Similarity integrated with Multi-Manifold Deep Metric Learning (PPCS-MMDML). These approaches collectively enhance classification accuracy, optimize feature extraction from heterogeneous image sets, and robustly preserve privacy, demonstrating significant improvements in data-driven analytical applications.
Source: Informatica
Authors: Franklin Vinod D, Neha Ahlawat
Institution: SRM Institute of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410712486