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Abstract

Web pattern mining in social media networks has emerged as a critical area for understanding user behavior, predicting trends, and driving data-driven decision-making. However, existing methods often face challenges in handling the vast and heterogeneous nature of social media data, resulting in suboptimal classification accuracy, scalability issues, and poor adaptability to dynamic data patterns. This research article introduces the Deep Adaptive DenseNet Algorithm (DADNA), an innovative approach that leverages adaptive deep learning mechanisms to enhance feature extraction, gradient propagation, and classification performance. By incorporating adaptive layer connectivity, DADNA dynamically adjusts to the intricacies of social media datasets, enabling precise behavioral analysis and improved classification outcomes. Experimental evaluations demonstrate that DADNA outperforms traditional algorithms such as CNN, RNN, DenseNet, SVM, Random Forest, and LSTM, achieving superior performance across diverse metrics, including G-Mean, Jaccard Index, Balanced Accuracy, Cohen's Kappa, and Fowlkes-Mallows Index. Additionally, DADNA exhibits robust scalability, handling large-scale datasets with high computational efficiency. The findings underline the potential of DADNA in transforming web pattern mining and establishing a new benchmark for behavioral analysis in social media networks.