@article{4751,
  author = {Hathairat Ketmaneechairat},
  title = {Predictive AI Frameworks for Digital Inclusion, Infrastructure Maturity, Data Accessibility, and Identity System Effectiveness: A Unified Framework},
  journal = {International Journal of Web Applications},
  year = {2026},
  volume = {18},
  number = {2},
  doi = {https://doi.org/10.6025/ijwa/2026/18/2/94-118},
  url = {https://www.dline.info/ijwa/fulltext/v18n2/ijwav18n2_3.pdf},
  abstract = {Digital transformation has become a fundamental requirement for socioeconomic development, governance
modernization, and inclusive service delivery across nations. However, substantial disparities remain in
digital inclusion, infrastructure maturity, data accessibility, and the effectiveness of identity systems between
developed and developing economies. This paper presents a unified, journal-ready predictive artificial
intelligence (AI) framework that integrates machine learning, deep learning, explainable AI, federated
learning, and multi-criteria decision analysis (MCDA) techniques to enable a comprehensive assessment of
digital transformation. The proposed framework consolidates ten interconnected predictive architectures
into a coherent analytical pipeline that forecasts digital inclusion trends, infrastructure readiness, accessibility
inequalities, and the effectiveness of national digital identity. The study utilizes cross-country panel data
spanning multiple years and integrates predictive modeling techniques such as Random Forest, XGBoost,
CatBoost, Long Short-Term Memory (LSTM) networks, Temporal Fusion Transformers, Graph Neural
Networks, and federated optimization methods. In addition, the framework incorporates AHP-TOPSIS-VIKOR
decision mechanisms for global digital readiness ranking and explainable AI modules using SHAP and LIME
for transparent policy interpretation. The proposed system enables country-level forecasting, inequality
detection, infrastructure maturity classification, governance optimization, and long-term strategic policy
simulation. The paper further discusses statistical analyses, visualization strategies, geospatial mapping,
and dashboard architectures that can be generated from the framework. The integrated methodologyprovides a scalable and interpretable foundation for AI-assisted smart governance, sustainable digital
transformation, and evidence-based digital policy planning.},
}