The Eastasouth Journal of Information System and Computer Science

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

Rezwan Moin Ahsan, Borhan Uddin, Tawhid Hossen, Sachin Das — Thu, 09 Oct 2025 00:00:00 +0000

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, Md Nazibullah Khan, Sachin Das, Borhan Uddin — Mon, 13 Oct 2025 00:00:00 +0000

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.

AI-Powered Quality Assurance and MIS Analytics: Building Resilient and Intelligent Digital Economies

Shakila Sarker, Mashur Bin Mahmud Nihat — Fri, 24 Oct 2025 00:00:00 +0000

Artificial intelligence (AI), predictive analytics, and management information systems (MIS) are all converging to remake U.S. companies into smart, adaptive ecosystems that can sustain economic resilience, cybersecurity, and software quality assurance (QA). This study synthesizes the empirical and conceptual findings of 20 peer-reviewed articles published between 2023 and 2025 to establish an integrated AI–MIS–QA Resilience Framework (AMQRF) that synthesizes automation, analytics, and governance in critical sectors such as IT, health, energy, and supply-chain infrastructure. The meta-synthesis reveals predictive QA with AI reduces software defect rates by 25–45%, MIS-based analytics increase operational visibility levels by 30–35%, and AI-driven cybersecurity models improve the accuracy of threat detection by up to 40%. All combined these flips enterprise resilience as an enterprise function of interconnected digital smartness and organizational learning. The study concludes by recommending a governance-aware architecture in which predictive QA, business analytics, and MIS co-evolve to facilitate sustainable competitiveness and national digital security.