Integrated Multi-Model Learning Framework for Structuredâ€“ Textualâ€“Temporal Data: A Descriptive Analysis
Puttakul Puttawattanakul, Hathairat Ketmaneechairat
Journal of Intelligent Computing, Vol. 17, No. 2, 2026.
DOI: https://doi.org/10.6025/jic/2026/17/2/76-92
Link: https://www.dline.info/jic/fulltext/v17n2/jicv17n2_3.pdf
Abstract:
Real-world datasets increasingly encompass heterogeneous modalities, yet conventional fusion techniques
often struggle with varying data reliability, missing inputs, and dynamic cross-modal interactions. To address
these limitations, this study proposes an integrated multi-model learning framework that synergistically
combines transformer, tree, and sequential based architectures. Central to this approach is the
AdaptiveFusion module, which dynamically estimates modality reliability, facilitates cross modal attention,
and aggregates features adaptively via a learnable gating mechanism. The framework operationalizes this
architecture by integrating BERT for deep textual semantics, XGBoost for structured numerical temporal
prediction, and LSTM for sequential dependency modeling. Empirical validation is conducted on a curated
dataset tracking the chronological evolution of conversational AI systems, comprising categorical, temporal,
numerical, and textual attributes. Experimental results demonstrate that each component effectively captures
its designated data dimension: BERT achieves rapid semantic convergence, XGBoost delivers robust structured
classification with high F1-scores (>0.80), and LSTM successfully identifies latent temporal trajectories.
When integrated, these paradigms overcome individual limitations, yielding a holistic analytical system
that enhances both predictive accuracy and interpretability. Despite constraints related to dataset scale
and class imbalance, the proposed framework establishes a scalable, context-aware methodology for
multimodal data fusion. This work underscores the critical value of hybrid modeling strategies in extracting
comprehensive insights from complex, real-world information ecosystems.