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.

A Unified Multi-Signal Correlation Architecture for Proactive Detection of Azure Cloud Platform Outages

Sai Bharath Sannareddy, Suresh Sunkari — Fri, 12 Dec 2025 00:00:00 +0000

Cloud platforms constitute the operational substrate for modern digital enterprises, yet their internal health telemetry remains intrinsically opaque, delayed, and non-deterministic from the perspective of tenant-facing reliability engineering. Despite the extensive instrumentation available within Microsoft Azure—including Service Health advisories, Resource Health telemetry, and platform diagnostic exports—empirical evidence continually demonstrates structural limitations that impede timely identification of regional instabilities, control-plane disruptions, propagation inconsistencies, and multi-service correlated failures. These limitations introduce latency between fault inception and observable acknowledgement, creating blind spots that severely constrain operational response windows for high-availability systems. This paper presents a novel Unified Multi-Signal Correlation Architecture (UMSCA) designed to overcome inherent deficiencies in provider-sourced telemetry by constructing a proactive, cross-signal, time-aligned reliability intelligence layer. The proposed framework integrates four heterogeneous data modalities—Azure Service Health, Azure Resource Health, Event Hub–streamed diagnostic telemetry, and distributed synthetic endpoint instrumentation—and fuses them using (i) canonical semantic normalization, (ii) probabilistic temporal alignment, (iii) inter-signal divergence detection, and (iv) multi-source reliability inference models. A large-scale enterprise simulation comprising 40 subscriptions, 18 geo-diverse Azure regions, 1,200 heterogeneous cloud resources, and over 3.2M telemetry events demonstrates that UMSCA reduces Mean Time to Detect (MTTD) by 88%, improves multi-signal correlation accuracy to 92%, lowers false-positive escalation by 52%, and estimates cross-region blast radius with up to 93% accuracy.

Mapping Blockchain Identity Management Research: A Bibliometric Analysis (2010–2025)

Loso Judijanto — Wed, 31 Dec 2025 00:00:00 +0000

This study presents a comprehensive bibliometric analysis of blockchain identity management research published between 2010 and 2025, aiming to map its intellectual structure, thematic evolution, and global collaboration patterns. Using data retrieved from the Scopus database and analyzed with VOSviewer, the study applies network visualization, overlay visualization, density mapping, citation analysis, and co-authorship analysis to uncover dominant research streams and emerging frontiers. The results reveal that the field is conceptually centered on blockchain-based authentication and decentralized identity management systems, with increasing scholarly attention toward privacy-preserving mechanisms such as zero-knowledge proofs, anonymity, and data protection. Thematic evolution indicates a clear transition from foundational infrastructure-oriented studies to application-driven and regulatory-sensitive research domains, including e-government, IoT, healthcare, and digital governance. Collaboration analysis highlights the leading role of China and India, supported by strong transcontinental linkages with the United States and European countries, reflecting a globally interconnected yet regionally concentrated research landscape. By systematically mapping publication trends, thematic clusters, and collaboration networks, this study provides a structured knowledge base that supports future theoretical development, guides practical implementation, and informs policy formulation in blockchain-based digital identity ecosystems.

Bibliometric Analysis of Human‑Centered AI Research in Southeast Asia (2015–2025)

Loso Judijanto, Ni Desak Made Santi Diwyarthi — Wed, 31 Dec 2025 00:00:00 +0000

This study does a⁠ bibli⁠ometric analysis of human-cent⁠ered artificial intelligence (HCAI) research in Southeast Asia from 2015 to 2025,⁠ with th⁠e objective of mapping publishing trends, conceptual frameworks, and collaborative networks in the region. The investigation, utilizing the Scopus database an⁠d v⁠isual⁠ization tools l⁠ike VOSviewer and Bibliometrix, indicates that fundamental AI con⁠cepts—namely artificial inte⁠lligence, machine⁠ learning, and deep learni⁠ng—function as pivotal anc⁠hors in the literature. These technical themes increasingly converge with human-centered areas, including explain⁠able AI, user-centered design, ethic⁠al tech⁠nology, and healthcare applications. The research designates S⁠ingapore as the pr⁠eeminent center for regional and international col⁠laboration, succeeded by Malaysia, Indonesia, Vietnam, and the Philippines. Institutional networks⁠ prioritiz⁠e significant contributions from technological universities⁠ and⁠ medical research institutions. The results demonstrate a distinct transition towards in⁠tegrative and value-oriented AI r⁠esearch that incorporates transparency, user empowerment, and social accountability in technical⁠ advancemen⁠t. This study offers a comprehensive assessment of current scholarship and identifies prospects for future research, pol⁠icymaking, and international collaboration in pr⁠omoting human-centered AI throu⁠gh⁠out Southeast Asia.

Improving Cybersecurity Resilience in Indonesian Cloud Infrastructures Through AI‑Based Threat Intelligence

Manase Sahat H Simarangkir — Wed, 31 Dec 2025 00:00:00 +0000

The rapid expansion of cloud computing adoption in Indonesia has significantly increased organizational exposure to cyber threats, making cybersecurity resilience a critical strategic priority. This study examines the role of Artificial Intelligence (AI)-based threat intelligence in enhancing cybersecurity resilience within Indonesian cloud infrastructure. Using a quantitative research design, data were collected from 155 respondents consisting of IT managers, cloud engineers, and cybersecurity practitioners through a structured Likert-scale questionnaire. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3). The results indicate that AI-based threat intelligence has a significant positive effect on threat detection accuracy and response effectiveness. Both threat detection accuracy and response effectiveness also have significant positive effects on cybersecurity resilience. Furthermore, AI-based threat intelligence directly strengthens cybersecurity resilience and indirectly enhances it through the mediation of threat detection accuracy and response effectiveness. These findings confirm that AI-driven cybersecurity systems play a strategic role in improving adaptive defense capabilities, accelerating incident response, and strengthening organizational resilience in cloud environments. This study provides important implications for policymakers, cloud service providers, and organizations in designing intelligent cybersecurity frameworks to support Indonesia’s sustainable digital transformation.

Bibliometric Mapping of Digital Transformation in Government Services

Loso Judijanto — Wed, 31 Dec 2025 00:00:00 +0000

The rapid advancement of digital technologies has fundamentally reshaped the way governments design, deliver, and manage public services, positioning digital transformation as a strategic priority in contemporary public administration. Alongside this transformation, scholarly interest in digital government services has expanded rapidly, resulting in a fragmented and diverse body of literature. This study aims to systematically map the intellectual structure, thematic evolution, and collaborative patterns of research on digital transformation in government services through a bibliometric approach. Using publication data retrieved from the Scopus database covering the period 2003–2023, this study employs VOSviewer to conduct keyword co-occurrence analysis, co-authorship analysis, institutional collaboration mapping, and country-level collaboration analysis. The findings reveal that digital transformation serves as the central integrative concept connecting governance reform, public service delivery, decision-making, and advanced technologies such as artificial intelligence. Temporal analysis indicates a clear shift from early technology-oriented themes toward service-centric, data-driven, and intelligent governance paradigms. Collaboration networks further demonstrate the globalization of digital government research, with strong contributions from North America, Europe, and rapidly growing participation from Asia and emerging economies. Overall, this study provides a comprehensive overview of the knowledge landscape, highlights emerging research frontiers, and offers insights to guide future academic inquiry and policy development in digital government services.

An Integrated Production Pipeline for 2D Animation in Cultural Heritage Visualization

Dadan Zaliluddin, Tri Ferga Prasetyo, Maulana Ibrahim — Wed, 31 Dec 2025 00:00:00 +0000

The visualization of cultural heritage through digital media has become an effective approach to preserving and disseminating historical narratives to a wider audience. However, the production of 2D animation for cultural heritage visualization often faces challenges related to inconsistent workflows, inefficiencies in production stages, and the lack of structured integration between storytelling and technical animation processes. This study aims to design and implement an integrated production pipeline for 2D animation that supports systematic, efficient, and reproducible development of cultural heritage visualization.

The proposed pipeline is structured into three main stages: pre-production, production, and post-production, incorporating storytelling design, visual asset development, animation principles, and compositing techniques. The research adopts a design-based research approach, using a local cultural heritage case study as the implementation context. Data were collected through observation, documentation, and iterative development of animation assets, followed by qualitative evaluation of workflow effectiveness and production consistency.

The results demonstrate that the integrated pipeline improves production efficiency, enhances visual coherence, and supports accurate representation of cultural narratives. The proposed framework provides a practical reference for animators, educators, and researchers in developing 2D animation-based cultural heritage visualization. This study contributes to the field of animation production systems by offering a structured pipeline model that bridges technical animation processes with cultural storytelling requirements.

Analysis of the Moral Obligations of AI Developers Thru the Principle of Explainability in the Perspective of Kantian Deontological Ethics: A Qualitative Study

Rizma Fauziyah, Agung Winarno, Subagyo Subagyo — Wed, 31 Dec 2025 00:00:00 +0000

The proliferation of "Black Box" Artificial Intelligence systems creates a significant ethical void regarding accountability and user autonomy, fundamentally challenging the right of individuals to understand decisions affecting their lives. This study aims to analyze the moral obligations of AI developers to implement Explainability (XAI) using the rigorous normative framework of Kantian Deontological Ethics. Employing a qualitative research design with conceptual analysis, the study utilizes secondary data from Kant's foundational texts and contemporary literature on algorithmic transparency, applying the Categorical Imperative as the primary lens. The findings conclude that the deployment of non-explainable AI constitutes a direct violation of Kant’s Formula of Humanity, as it reduces users merely to means for achieving computational goals rather than treating them as autonomous, rational agents. Furthermore, the practice fails the Universal Law test, which prohibits the universalization of opacity in decision-making processes. Consequently, the study asserts that Explainability is a non-negotiable moral duty for developers, establishing that predictive accuracy cannot ethically justify the erosion of human autonomy, thereby demanding a paradigm shift from utilitarian efficiency to deontological adherence in AI development.

Cybersecurity in ERP-Integrated Supply Chains: Risks and Mitigation Strategies

Ravindra Khokrale — Wed, 31 Dec 2025 00:00:00 +0000

Cybersecurity risks have emerged as a burning issue as global supply chains increasingly use Enterprise Resource Planning (ERP) systems to integrate official systems into their supply chains. ERP systems unite different stakeholders, including suppliers, logistics, and finance teams, making it possible to exchange real-time information and streamline it. However, there is a possibility of cyberattacks in these systems, particularly when integrating with third-party systems, having poor access control, and using outdated software. The emergence of high-profile attacks such as the 2017 NotPetya has underscored the dramatic financial and operational loss factors because of ERP breaches and outlined the importance of firm protection against cyberattacks. This paper discusses the most significant cybersecurity threats to ERP-integrated supply chains and voices the successful mitigation measures. Major risks observed are the vulnerability of third parties, weak access control, and the use of old ERP systems. Such measures as multi-factor authentication, continuous monitoring, and vendor risk management are also evaluated as the best practices of the study. The study provides effective suggestions that can be implemented in organizations to ensure that their ERP-based supply chains are secured, and the chances of data breaches and disruptions in operations are reduced. With the digitalization of supply chains, the future is seen to utilize the new capabilities to use new technologies, including artificial intelligence and blockchain, to further improve the security and information integrity of ERP.