Resilient Intelligence: AI and MIS in the Cyber-Economic Era

Rezwan Moin Ahsan — 2025-10-09

The integration of artificial intelligence (AI) with management information systems (MIS) has transformed how countries protect their digital infrastructure, oversee organizational projects, and maintain economic resilience. This study consolidates recent developments in cybersecurity, project governance, software quality assurance (QA), energy analytics, and economic intelligence to propose an integrated model, AI-for-MIS Cyber-Energy-Economic Resilience (AM-CEER), that improves proactive defense, predictive governance, and sustainable performance. This research synthesizes over seventy recent peer-reviewed works, incorporating deep learning models (LSTM, Transformer), federated analytics, explainable AI (XAI), and cloud-based MIS infrastructures into a cohesive framework. Research demonstrates that AI-enhanced MIS infrastructures enhance cyber threat detection accuracy by more than 30%, diminish IT project risk exposure by 25%, and elevate predictive capability for energy and economic systems by around 40%. The proposed AM-CEER architecture creates a framework for digital governance that integrates data-driven decision-making with cybersecurity, quality assurance automation, and macroeconomic forecasting, thereby ensuring the long-term stability of essential national services.

Explainable AI Framework for Precision Public Health in Metabolic Disorders: A Federated, Multi-Modal Predictive Modelling Approach for Early Detection and Intervention of Type 2 Diabetes

Md Habibur Rahman — 2025-10-13

One of the biggest public health problems of the twenty-first century is metabolic disorders, especially Type 2 diabetes (T2D). Morbidity, mortality, and medical expenses can be significantly decreased by early detection of at-risk people. However, nonlinear, multi-factorial, and high-dimensional interactions that influence the development of disease are not well captured by traditional risk-scoring methods. In order to predict and interpret the risk of type 2 diabetes and related metabolic disorders, this study creates an Explainable AI (XAI) framework for precision public health that combines multi-modal data, such as genomic profiles, lifestyle factors, socioeconomic determinants, and electronic health records (EHR). We create a federated, hybrid model that combines Random Forest classifiers, Deep Neural Networks (DNN), and Gradient Boosting Machines (LightGBM/XGBoost), building on federated and ensemble learning paradigms. Shapley Additive Explanations (SHAP) and counterfactual analysis are used to uncover personalized, actionable risk profiles in order to attain explainability. Harmonized multi-institutional datasets with over 200,000 records gathered from several U.S. health systems are used to train the model. The results show a calibrated Brier score of 0.12, sensitivity of 89%, specificity of 87%, and AUC of 0.93 ± 0.01. The socioeconomic deprivation index, polygenic risk score, BMI slope, and HbA1c trajectory are the main factors, according to SHAP study. Federated deployment protects data privacy while preserving performance. These results show that federated, explainable AI pipelines can facilitate population-based, privacy-preserving, andThe goal of precision public health is being advanced by large-scale early-warning systems for managing metabolic diseases.

The Eastasouth Journal of Information System and Computer Science

Resilient Intelligence: AI and MIS in the Cyber-Economic Era

Explainable AI Framework for Precision Public Health in Metabolic Disorders: A Federated, Multi-Modal Predictive Modelling Approach for Early Detection and Intervention of Type 2 Diabetes