Journal of Data Analysis and Critical Management
The Journal of Data Analysis and Critical Management is poised to become a leading platform for researchers, practitioners, and industry professionals seeking innovative insights into data-driven decision-making and management strategies. As data continues to shape global industries, the journal's role in disseminating cutting-edge research will expand in several key areas: Advancements in Data Analytics, Interdisciplinary Research, Real-World Applications, Policy and Ethical Considerations, Integration of Emerging Technologies, Open Access, and Global Collaboration. The Journal of Data Analysis and Critical Management covers a broad spectrum of topics related to data analytics and management science, including but not limited to: Data Analytics and Business Intelligence, Critical Management Studies, AI and Machine Learning in Management, Big Data in Decision-Making –, Financial and Risk Analytics, Human Resource Analytics, Marketing and Consumer Behavior Analytics, Healthcare and Supply Chain Analytics, Ethics, Privacy, and Data Security, Sustainable and Green Data Practices By continuously evolving with technological advancements and business needs, the Journal of Data Analysis and Critical Management aims to be a cornerstone of knowledge in the rapidly growing field of data-driven management. Editor-in-Cheif: Dr. Chum Chadarin Frequency of the journal: Quarterly Year of Start: 2025 Publisher: iLearn, #7A Borey MLVILLA Preakchrey, Phnom Penh, Cambodia, editorial@ilearnkh.com Language: English ISSN: 3080-3322
Data governance and privacy-by-design frameworks (GDPR/HIPAA) in multi-cloud data pipelines
The speed at which multi-cloud architecture has permeated the enterprise data management landscape has fundamentally changed the game in scalability, resilience, and operational flexibility. But sharing information across multiple cloud services also poses challenges in governance, regulatory compliance, privacy, and secure information exchange. In this study, the data governance and privacy-by-design approaches that enable compliance with key data protection laws, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), in Multi-cloud data pipelines are explored. The study examines various governance concepts, such as data classification, metadata management, data lineage, policy enforcement, access control, encryption, auditing, and ongoing compliance monitoring. It also reviews the impact of designing privacy into the data lifecycle a minimize security risks while ensuring data integrity, availability, and accountability. Automated compliance validation, intelligent metadata management, and privacy-preserving controls are proposed in a single governance model that can be integrated into heterogeneous cloud environments. It also includes AI-enabled monitoring and cloud-native security mechanisms to enhance governance effectiveness and prepare for regulation. The results validate the benefits of governance automation, such as greater transparency, reduced compliance risks, secure data sharing, and improved operational efficiency in enterprise Multi-cloud environments. The research offers actionable advice for organizations aiming to create resilient, compliant, and sustainable cloud data management practices to meet the changing regulatory and technological needs.
Data governance, Privacy-by-design, Multi-cloud data pipelines, GDPR, HIPAA, Regulatory compliance, Metadata management, Data lineage, Cloud security, Privacy engineering.
Copyright (c) 2025 https://creativecommons.org/licenses/by-sa/4.0
Master Data Management (MDM) integration strategies in hybrid cloud environments
Organizations are dealing with an increasing amount of hybrid cloud environments, with a mix of on-premises and public and private cloud services, and Master Data Management (MDM) is rapidly emerging as a key part of enterprise data governance. Hybrid cloud architecture offers flexibility, scalability, and operational efficiency, but also poses challenges for data consistency, synchronization, governance, interoperability, and security. This study analyzes various integration approaches to facilitate effective MDM implementation in the hybrid cloud landscape. It tests architectural solutions, governance models, data integration methods, and lifecycle management solutions supporting the development and stewardship of accurate, consistent, and trusted master data. The study also delves into how automation and artificial intelligence can help improve metadata management, data quality, minimize synchronization lag times, and bolster regulatory compliance. Based on the criteria of scalability, operational efficiency, governance performance, and cost optimization, the effectiveness of the centralized, registry-based, coexistence, and transactional MDM models is assessed by comparative analysis. The results show that organizations with structured governance policies, standardized integration processes, and intelligent automation enjoy higher data reliability and enterprise agility while reducing complexity. In conclusion, the study finds that a proper hybrid cloud MDM strategy provides a secure foundation for digital transformation and enterprise decision-making, delivering long-term business value and enabling high-quality master data across a variety of computing environments.
Master Data Management (MDM), Hybrid Cloud, Data Integration, Data Governance, Data Quality, Cloud Computing, Enterprise Data Management, Artificial Intelligence, Metadata Management, Digital Transformation.
Digital Transformation and Business Resilience: How Service Now-Powered Automation Improves Organizational Performance, Risk Management, and Customer Satisfaction
In today’s complex and changing business landscape, digital transformation is not merely a buzzword; it is a necessary strategic action for organizations to build resilience, reduce operational costs and deliver strong customer-centric services. However, with today’s enterprises dealing with increasing business volatility, cybersecurity risks, regulation, and ever-changing customer expectations, intelligent automation platforms have become a key enabler for sustainable organizational performance. This paper discusses the use of ServiceNow for enterprise automation which enables enterprises to advance with digital transformation while optimizing data center resource utilization, service management process workflow, and enterprise resilience. The study delves into the functions of ServiceNow in automation of mundane tasks, system integration and exploitation of AI-powered functionalities to drive better decisions and agile operations. The platform’s role in risk management, including automated compliance monitoring, incident response, governance mechanisms, and cybersecurity tools is emphasized. This paper also explores the potential for workflow automation and AI-driven service management to boost customer satisfaction by reducing response times, delivering high-quality service, and providing personalized experiences. The results indicate the Business value ServiceNow delivers via automation are considerable — from decreasing inefficiencies to increasing organization agility, assisting in risk reduction, and enhancing customer relationships. Furthermore, intelligent automation technologies help ensure long-term business resilience by empowering firms to be proactive and agile during times of disruptions, and keep service continuity and operational excellence intact. The study finishes that this ServiceNow could be a strategic platform for digital transformation, where it can boost organization performance, risk administration ability, and customer satisfaction, and lead to sustainable competitive edge in the digital economy.
Digital Transformation, Business Resilience, ServiceNow, Intelligent Automation, Organizational Performance, Risk Management, Customer Satisfaction, Workflow Automation, Artificial Intelligence, Digital Business Strategy.
Copyright (c) 2026 https://creativecommons.org/licenses/by-sa/4.0
AI-Based Automated Software Testing Model for Predicting Bug-Prone Modules in Large Software Systems
Predicting software bugs in software development life cycle is a crucial part of improving software quality. Conventional methods of testing can be slow and cannot be used in large-scale systems, necessitating intelligent and automated methods. In the current research, a hybrid model of LightGBM and GRU is suggested, which would contribute to the improvement of defect prediction. The dataset of NASA PROMISE repository JM1 is utilized, and it includes software metrics of complexity and structure of the code. Preprocessing of the dataset involves such steps as data cleaning, data outlier, feature scaling and class imbalance correction using SMOTE. LightGBM model is also useful in capturing feature interactions in structured data but the GRU model learns complex non-linear patterns. The two models are used to produce final predictions by combining the output of both models through a soft voting mechanism. Through experiments, the hybrid model is found to have high accuracy of 95.11 percent and it is superior to current models (LSTM, Random Forest). The results reveal that the suggested solution is a powerful and effective solution in predicting bug-prone modules, which finally assists to increase the reliability of the software and optimization of the testing process.
Software Fault Prediction, Hybrid Model, LightGBM, GRU, Software Metrics, Defect Prediction.
Recent Advances in Anomaly Identification for IoT Devices Using Machine and Deep Learning Models
The rapid expansion of the IoT has revolutionized data interchange in numerous industries, including smart cities, transportation, healthcare, and industry. However, it has also posed serious security problems due to the increasing number of vulnerable devices. Anomaly detection has become the primary instrument of the shield to locate abnormal behaviors in the box of the intrusion, fault, and failure of the operation. Anomaly detection in IoT systems has been the focus of recent advances in ML and DL methods. This paper assesses the efficacy, accuracy, and adaptability of supervised, unsupervised, and hybrid learning models in this context. In addition, the review emphasizes the main factors that determine detection performance, such as feature extraction, real-time detection, and multi-target learning strategies. Although ML/DL-based approaches have a strong potential, problems, for example, computational overhead, scalability limitations, and a lack of sufficient labeled data, that hinder the progress still exist. The paper provides useful information for researchers and practitioners in the choice of the most effective methods and assists in making the first steps toward future advances in IoT anomaly detection securing that it is still smart and resource-efficient
Anomaly Detection, Internet of Things (IoT), Machine Learning (ML), Deep Learning (DL), Intrusion Detection Systems (IDS), Cyber security in IoT
AI-Powered Data Analytics for Strengthening Customer Loyalty and Personalization in Retail
An essential prerequisite for the growth of client loyalty is the assessment of customer loyalty. Over the past few decades, marketing academics have created a variety of client loyalty assessment tools. Because service marketplaces are getting more competitive, businesses are having trouble bringing in and keeping clients. Consequently, there is an increasing need to encourage client loyalty. This study presents a machine learning (ML) approach to customer loyalty prediction using the Online Retail II UCI dataset, a sizable collection of actual transactions. The data set was preprocessed by addressing missing data, data outliers, and data normalization using Z-score to enhance data quality. The Multilayer Perceptron (MLP) model has been created and assessed based on the indices like R 2, RMSE, and MSE indicating the good predictive ability of the model with an R 2 value of 93.98. Additionally, the comparison research showed that the MLP outperformed other models. The results point to the efficiency of advanced methods of learning in cognizing customer behavior and data-driven decision-making in the retail field.
Customer Loyalty, Retail Industry, Machine Learning, Online Retail, Decision-Making.
A Survey on Generative AI Chatbots in Customer Service: Challenges, Benefits, and Use Cases
Generative AI chatbots have become a revolutionary technology in customer service, they have progressed way beyond the functionalities of traditional rule-based systems. Initially, chatbots were heavily reliant on execution scripts and inflexible pattern-matching techniques, they usually were not capable of understanding the complexity of natural language and customer intent to large language models, generative AI chatbots can create context-aware, adaptable, and human-like responses, thus, they are more engaging and interaction quality has improved dramatically. They comprehend communication intents, follow conversational context, learn from user history, and help companies to communicate in different languages, so they can give their customers a personalized, fast, and scalable customer-support solution. These solutions also become part of a company’s strategy to reduce costs through the elimination of the repetitive tasks of customer service where workforce is greatly reduced. Unfortunately, in the face of such advantages, there are still some challenges which include data privacy, security issues, model coherence, and the requirement of an extensive high-quality training dataset. This study explores the architecture, classification, machine-learning methods, and communication abilities of generative AI chatbots, their advantages, disadvantages, and the potential of the new use cases in customer service.
Generative AI Chatbots, Customer service automation, Natural language processing (NLP), Machine learning technique, conversational Agents.
Fiscal Transparency and Citizen Trust: Evaluating the Role of Budget Accountability Mechanisms in Developing Democracies
In developing democracies, the fiscal transparency and trust by the citizens are essential elements of proper governance. This paper will look at how the budget accountability (participatory budgeting, audit committees, citizen oversight, and open data platforms) have influenced the building of the trust of the people. Based on cross-country information, case studies on Nigeria, Uganda, Ghana and Kenya and emerging empirical studies, the analysis has established a strong positive relationship between transparency indicators and trustworthiness of the citizen to the government institutions. Results have shown that systems that allow active citizen involvement and real-time availability of fiscal data make a remarkable difference in increasing the level of trust, minimizing corruption risks, and enhancing perceived legitimacy in how the finance is managed by the populace. The paper highlights the need to make fiscal transparency practices and institutional reforms complementary so as to promote democratic accountability and sustainable development. The policy suggestions to the policy makers are to increase citizen participation, embrace the use of online platforms to disclose fiscal details, and establish routine and reliable reporting of budgetary performance.
Fiscal Transparency, Citizen Trust, Budget Accountability, Participatory Budgeting, Developing Democracies, Public Participation, Governance
Financial Stability in Volatile Currency Economies: Recalibrating Risk Compliance for Systemic Banking Resilience
One of the most pressing problems of the banking system in the volatile currency economies is financial stability since the exchange rate fluctuations can increase the systemic risks and jeopardize the overall economic stability. This paper examines ways of rebalancing risk compliance frameworks to increase the stability and resilience of financial institutions. Through the capital adequacy, liquidity management, and regulatory practices, the study determines how the banks can counter the negative implication of currency volatility. The results show the significance of both dynamic risk policies and adaptive stress testing; as well as organized regulatory control, as a means of protecting the banking systems against systemic shocks. The research gives some viable suggestions that can be adopted by policymakers and financial institutions to enhance the management of risks and guarantee sustainable financial stability in volatile economic conditions.
Financial stability, currency volatility, systemic risk, banking resilience, risk compliance, capital adequacy, regulatory oversight
Large Language Models (LLMs) have transformed natural language processing by demonstrating remarkable capabilities in text understanding, generation, and reasoning. Recent advances extend these models into multimodal AI, enabling the integration of multiple data modalities—such as text, images, audio, and video—into unified learning frameworks. Multimodal AI systems leverage LLMs to process and correlate information across modalities, enhancing context understanding, task flexibility, and human–computer interaction. These models find applications in image captioning, visual question answering, video summarization, conversational AI, and cross-modal retrieval. Despite their promise, challenges such as high computational requirements, alignment of heterogeneous modalities, interpretability, and ethical concerns remain. This paper explores the architecture, capabilities, applications, and limitations of LLMs and multimodal AI, highlighting their potential to enable more robust, context-aware, and interactive artificial intelligence systems.
Large Language Models, Multimodal AI, Cross-Modal Learning, Transformers, Context-Aware AI, Image-Text Models, Conversational AI, Visual Question Answering, AI Alignment, Ethical AI.
Adversarial Machine Learning and AI Security
Adversarial Machine Learning has emerged as a critical field within Artificial Intelligence (AI) security, focusing on the vulnerabilities of machine learning models to malicious manipulation and attacks. As AI systems become increasingly integrated into sensitive domains such as healthcare, finance, autonomous vehicles, and cybersecurity, ensuring their robustness and reliability is essential. Adversarial attacks exploit weaknesses in algorithms by introducing carefully crafted perturbations to input data, leading models to produce incorrect predictions or classifications without obvious changes to human observers. These attacks can occur during both training and deployment phases, including data poisoning, model inversion, evasion attacks, and backdoor insertion. The growing sophistication of adversarial techniques has raised concerns about the trustworthiness and resilience of AI systems. Attackers may manipulate image recognition systems, deceive natural language processing models, or extract sensitive information from trained models. In response, researchers have developed defense strategies such as adversarial training, robust optimization, anomaly detection, secure model architectures, and formal verification methods. Despite these efforts, achieving complete robustness remains challenging due to the evolving nature of threats and the complexity of modern deep learning systems. AI security extends beyond technical defenses to include privacy preservation, secure deployment practices, regulatory compliance, and risk assessment frameworks. Building resilient AI systems requires interdisciplinary collaboration among machine learning researchers, cybersecurity experts, policymakers, and industry stakeholders. As AI continues to power critical infrastructure and decision-making systems, strengthening adversarial robustness and security mechanisms is fundamental to ensuring safe, trustworthy, and reliable intelligent technologies.
Adversarial Machine Learning, AI Security, Adversarial Attacks, Data Poisoning, Evasion Attacks, Model Robustness, Deep Learning Security, Privacy Preservation, Cybersecurity, Secure AI Systems.
Retail with Reference to Societal Marketing and Usage of AI
Retail is undergoing a transformation shaped by societal marketing principles and artificial intelligence (AI). Societal marketing emphasizes balancing consumer needs, organizational profitability, and societal welfare, while AI provides technological tools for personalization, efficiency, and sustainability. This paper reviews existing literature to explore how AI strengthens societal marketing in retail, highlighting opportunities, challenges, and recommendations for future practice.
Retail, Societal Marketing, Artificial Intelligence, Sustainability, Consumer Behaviour, Ethical Retailing, Digital Transformation.
Machine learning approaches to market risk forecasting
The growing riskiness and instability of financial markets have created a greater demand for sound risk forecasting methods. Machine learning (ML) methods provide an alternative solution to the classical statistical models and allow taking out complex patterns in big data related to the financial sphere and enhance predictive quality. This paper explores various types of ML algorithms such as supervised learning, deep learning, and ensemble models, as well as their application to the market risk prediction. The analysis shows that hybrid and ensemble models are always superior to single-model methods in forecasting the volatility of the market and the possible occurrence of a crisis by substantial analysis of historical market data. Moreover, feature selection and data preprocessing play a great role in improving the performance of the model, indicating the significance of the input variables of high quality. The results highlight the possible benefits of ML-guided constructs to aid in financial decision making, risk control, and law adherence, and suggest practitioners with practical recommendations on how to operate in highly unpredictable financial landscape settings.
Machine learning, Market risk, Risk forecasting, Financial volatility, Ensemble models, Deep learning, Hybrid models
Stress testing financial institutions under macroeconomic shock scenarios
The stress testing of financial institutions in macroeconomic shock situations has emerged as a major pillar of contemporary financial stability systems, allowing regulators and banks to measure resilience to the unfavourable economic situation. The paper focuses on the design, implementation, and implication of macroeconomic stress tests and how the shocks are transmitted to the credit risk, capital adequacy, and liquidity positions. The study assesses the impact of severe but realistic macroeconomic events on institutional and system-wide stability with the help of a mixture of deterministic and stochastic scenario modeling. The analysis also puts a great deal of emphasis on incorporating the effects of dynamic feedbacks, tail-risk, and structural vulnerabilities to enhance the predictive ability of stress tests. The results can be used by the policymakers, supervisors, and financial institutions to act upon and improve their risk management to protect the stability of the systemic factors and better preparedness against economic breakdowns in the future.
Stress testing, financial institutions, macroeconomic shocks, systemic risk, credit risk, capital adequacy, scenario analysis, financial stability
The Role of Management Information Systems in Organizational Decision Making
Management Information Systems have become integral to organizational effectiveness due to their significant role in supporting managerial decision making. In contemporary organizations, decision making occurs in complex environments characterized by uncertainty, rapid change, and increasing volumes of data. This article examines the role of management information systems in enhancing organizational decision making by providing timely, accurate, and relevant information to managers at different levels. The study adopts a conceptual and analytical approach to explore the relationship between management information systems and the decision-making process within organizations. It discusses how management information systems support strategic, tactical, and operational decisions by improving information quality, reducing uncertainty, and enhancing managerial performance. The article further examines the strategic importance of management information systems, as well as the challenges associated with their effective use, including data quality issues, technological constraints, and human factors. The findings highlight that when properly designed and aligned with organizational objectives, management information systems significantly improve decision quality and organizational performance. The article concludes by emphasizing the need for continuous system evaluation, managerial competence, and organizational commitment to fully realize the decision-support potential of management information systems.
Management Information Systems, Organizational Decision Making, Information Quality, Managerial Performance, Decision Support Systems, Organizational Effectiveness
Building Resilient It Infrastructures: The Role of AI and Cybersecurity in Program Management
In the current digital age, resilient IT infrastructures are critical for maintaining the continuity of business operations, protecting valuable data, and enabling organizations to adapt to rapid technological advancements. As the reliance on digital systems increases across industries, organizations are exposed to an escalating range of cyber threats that can disrupt operations, compromise sensitive data, and damage an organization’s reputation. In response to this growing concern, cybersecurity has become an integral component of IT program management. Simultaneously, artificial intelligence (AI) has emerged as a transformative technology that can greatly enhance cybersecurity and the resilience of IT infrastructures. By enabling automation, real-time threat detection, predictive risk management, and adaptive responses, AI has the potential to significantly improve both cybersecurity defenses and IT infrastructure stability. This article explores how AI can play a pivotal role in building resilient IT infrastructures by enhancing cybersecurity and facilitating the integration of AI technologies into existing IT frameworks. As organizations increasingly adopt AI-driven cybersecurity solutions, they can benefit from real-time anomaly detection, proactive threat identification, and automated incident response, which collectively improve the overall security posture of their IT systems. Traditional cybersecurity models, based primarily on signature-based systems and perimeter defense, are no longer sufficient to address the growing sophistication and complexity of modern cyber threats. As a result, organizations are turning to AI and machine learning (ML) models to enable predictive security, automate the identification of vulnerabilities, and respond faster to threats than traditional systems. While AI-driven cybersecurity solutions offer tremendous advantages, they also present several challenges. One of the most significant barriers to adopting AI in cybersecurity is the complexity of AI systems. Developing, deploying, and maintaining AI-based solutions requires significant technical expertise, substantial infrastructure investment, and continuous training and updates. AI models rely on vast amounts of data to learn and adapt, but this data must be curated carefully to avoid introducing biases and inaccuracies into AI-driven systems. Additionally, AI technologies must be integrated seamlessly with existing IT infrastructures, which can be complex and require careful planning to avoid disruptions. The data privacy concerns associated with AI-driven systems also need to be addressed, as these systems often require access to large datasets, including sensitive and personal information. Organizations must comply with data protection regulations such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) to ensure the ethical use of data while leveraging AI’s capabilities.
AI-driven cybersecurity, resilient IT infrastructure, program management, artificial intelligence, machine learning, deep learning, risk management, cyber threat detection, incident response automation, predictive security, data privacy, IT infrastructure resilience, infrastructure stability, adversarial attacks, cybersecurity frameworks, AI integration, security automation, proactive risk mitigation, cloud security, data protection regulations, compliance, AI model security, AI vulnerabilities, security operations management, business continuity, data protection laws, compliance regulations, AI for infrastructure management, automation in cybersecurity, network security, cybersecurity challenges, AI model failures, AI-enhanced threat prediction, security risk assessment, system stability, IT program strategies, AI model transparency, federated learning.
Biochar-Enhanced Filtration Media For Multi-Pollutant Industrial Runoff
Industrial stormwater runoff represents a complex environmental challenge due to the simultaneous presence of heavy metals, per- and polyfluoroalkyl substances (PFAS), and excess nutrients. Conventional stormwater treatment systems are often designed to target single contaminant classes, limiting their effectiveness under mixed-pollutant conditions. This article examines the emerging role of engineered biochar as a multifunctional filtration medium capable of addressing diverse pollutant groups within industrial runoff. By tailoring feedstock selection, surface chemistry, and activation methods, biochar demonstrates high adsorption capacity for metals, strong affinity for PFAS compounds, and significant nutrient retention potential. The mechanisms governing these interactions, system design considerations, and performance implications for industrial stormwater management are critically evaluated. The findings highlight biochar-enhanced filtration systems as a scalable and sustainable solution for improving runoff quality and regulatory compliance in industrial environments.
Biochar filtration, industrial stormwater, PFAS removal, heavy metals adsorption, nutrient control, runoff treatment
Probabilistic Generative Models for Synthesizing Privacy-Preserving Big Data with Statistical Fidelity Guarantees
The increasing demand for large-scale data sharing in data-driven research and industry has intensified concerns surrounding individual privacy and data confidentiality. Conventional privacy-preserving techniques such as anonymization, suppression, and heuristic perturbation have proven insufficient, particularly for high-dimensional big data, where linkage and inference attacks remain feasible. Synthetic data generation has therefore emerged as a promising alternative, enabling data dissemination while reducing direct exposure of sensitive records. Nonetheless, achieving rigorous privacy guarantees without sacrificing statistical fidelity and analytical utility remains a fundamental challenge. This paper investigates probabilistic generative models as a principled solution for synthesizing privacy-preserving big data with formal guarantees. A unified framework is presented that integrates probabilistic generative modeling with differential privacy mechanisms to provide quantifiable protection against information leakage. The study examines Bayesian networks, variational autoencoders, and generative adversarial networks, incorporating advanced privacy accounting techniques such as Rényi differential privacy and moments-based analysis. Privacy budgets are carefully allocated, and noise is calibrated to data sensitivity during model training to balance privacy and utility. Comprehensive experiments are conducted on benchmark tabular datasets to evaluate privacy protection, statistical fidelity, and downstream task performance. Results show that differentially private probabilistic generative models can preserve marginal distributions, correlation structures, and predictive accuracy under strict privacy constraints. Moreover, the generated synthetic datasets demonstrate strong resistance to membership inference attacks, indicating robustness against common adversarial threats. Overall, this work provides a systematic and empirically grounded foundation for trustworthy synthetic data generation, offering practical guidance for secure data sharing in sensitive domains and governance contexts.
Probabilistic generative models, Synthetic data, Differential privacy, Big data analytics, Statistical fidelity, Privacy preservation.
Fair Reinforcement Learning for Equitable Health Resource Allocation
Inequitable distribution of health resources remains a persistent challenge in many healthcare systems, disproportionately affecting underserved communities, rural populations, and socioeconomically disadvantaged groups. Traditional decision-support tools often optimize for efficiency or overall population benefit, but they rarely account for structural biases that contribute to unequal access. Reinforcement Learning (RL) has emerged as a powerful approach for optimizing dynamic resource allocation; however, conventional RL models risk reinforcing existing disparities when rewards are based solely on aggregate performance metrics. This study introduces a fairness-aware reinforcement learning framework designed to promote equitable allocation of critical health resources including hospital beds, vaccines, and diagnostic equipment across diverse demographic groups. The proposed approach incorporates explicit fairness constraints, such as counterfactual fairness and group-level equity metrics, directly into the RL reward function and training process. By combining fairness penalties with outcome-based rewards, the model balances efficiency with equity, ensuring that improved system performance does not come at the expense of vulnerable populations. Simulation results demonstrate that the fairness-aware RL approach significantly reduces disparities in resource allocation while maintaining competitive performance compared to unconstrained RL baselines. The findings contribute to the growing field of equitable AI in healthcare by offering a scalable, adaptable framework that can be applied across various health system contexts. Overall, this work highlights the potential of fairness-constrained RL to support more just, transparent, and inclusive healthcare delivery systems.
Assessing Workforce Competency and Skills Readiness for AI-Assisted Public Governance
The rapid integration of artificial intelligence (AI) into people’s governance requires a competent workforce capable of utilizing new technologies efficiently. This work evaluates employees’ current competencies and preparedness for AI-assisted governance in the public sector, identifying essential gaps that could complicate successful implementation. The study measures the technical, analytical, and decision-making abilities required to adopt AI using a mixed-methods approach, a survey, and a competency assessment. The results point to substantial differences in AI literacy and practical preparation, underscoring the need for specialized training programs and policy interventions. The paper will offer practical suggestions to increase workforce readiness, enabling public institutions to use AI to deliver efficient, transparent, and responsive governance.
AI-assisted governance, workforce competency, skills readiness, public sector, AI literacy, capacity building
Copyright (c) 2025 Owolabi Babatunde Akinsanya (Author) https://creativecommons.org/licenses/by-sa/4.0
Healthcare Supply Chain Analytics: Smart Supply Chain AI Powered Inventory Optimization in Healthcare
The healthcare supply chains have been confronted with the same problems of shortages in inventory,wastages, expired items, and the lack of visibility of stock levels. These business problems have an impact on patient outcomes, higher costs, and efficiency of a system. This study focuses on artificial intelligence driven inventory optimization as an intelligent supply chain solution to hospitals and medical providers. The paper suggests an architecture of data driven which integrates predictive analytics, real time observatory and smart reorder scheduling to enhance the precision of medical consumable demand forecasting. The machine learning algorithms were trained on historical data of hospital inventory including drug usage rates, lead time fluctuations, emergency demand fluctuations, and supply variability. The findings indicate that AI predictive models are more accurate compared to traditional manual planning, which results in fewer instances of stock out, lesser inventory carrying costs, and the availability of supplies to necessary care services. The results are evidence that AI facilitated systems can help with smarter procurement choices and visibility of constant supplies. The study can be useful in modern digital transformation initiatives in healthcare operations as it will show that artificial intelligence can improve supply chain analytics and reinstate hospital resource planning.
Copyright (c) 2025 Bertrand L. Dias (Author) https://creativecommons.org/licenses/by-sa/4.0
Salesforce Security Architecture for Zero-Trust, Encryption & Compliance
Salesforce has become one of the most popular cloud platforms, so the security architecture has become critical in ensuring the security of sensitive data that belongs to an enterprise. The paper will analyze the ways Salesforce has implemented Zero-Trust, state-of-the-art encryption, and international regulatory frameworks to protect multi-tenant environments. It compares Salesforce identity-first security model, multi-layered access control, and continuous authentication designs, which aim to remove implicit trust. The paper also discusses Shield Platform Encryption, key management practices, and in-transit protection that all make data confidential and intact. Also, the study assesses monitoring and threat-detection of Salesforce, its compliance with regulatory requirements like GDPR, HIPAA, PCI DSS, and FedRAMP. Results show how Salesforce has been fully moving towards its defense-in-depth model without overstating the shared-responsibility paradigm between the platform provider and the customer. The paper concludes that the dynamic Salesforce security architecture offers a stable platform to the contemporary business environment that pursues Zero-Trust, encryptionfocused, and compliance-based data security.
Salesforce security, Zero-Trust architecture and data encryption, compliance, cloud security, identity management, threat detection.
Automated Incident Containment Using SOAR Platforms in Large-Scale Enterprises
Large-scale enterprises are experiencing rapid growth in cyber incidents due to expanded attack surfaces, increased cloud integration, and the speed at which modern threats evolve. Traditional Security Operations Centers rely on manual processes that result in slow containment, inconsistent decision making, and high levels of alert fatigue. This study examines the effectiveness of automated incident containment using Security Orchestration, Automation, and Response platforms within large enterprise environments. A qualitative synthesis of twenty peer reviewed sources, NIST and ISO security frameworks, and empirical studies was conducted to evaluate containment speed, analyst workload, incident classification accuracy, and zero trust alignment. The findings demonstrate a significant improvement in containment performance when SOAR is deployed. Results indicate that automated workflows reduce average containment time from multiple hours to several minutes, lower analyst workload by up to sixty percent, and enhance the overall consistency of zero trust enforcement. Two performance comparison tables and two visual graphs support these findings by illustrating measurable gains in alert triage quality, containment efficiency, and operational accuracy. AI enabled capabilities such as predictive triage, dynamic playbook recommendation, and automated host isolation further strengthen containment processes across distributed enterprise networks. The study concludes that SOAR driven containment provides a scalable, accurate, and highly efficient response model for large enterprises. However, successful implementation requires strong governance, high quality SIEM data, and strategies to mitigate automation bias. These insights provide valuable guidance for enterprises seeking to modernize their cybersecurity posture and transition toward automated, intelligence driven response operations.
SOAR automation, incident containment, enterprise cybersecurity, zero trust security, security operations centers, AI enabled incident response, automated playbooks.
High-dimensional survival analysis using penalized hazard models to integrate genomic, environmental, and socioeconomic variables for precision public health decisions
High-dimensional survival data have become increasingly common in modern public health research, especially with the rapid growth of genomic sequencing technologies, satellite-based environmental monitoring, and detailed socioeconomic profiling. These multidomain datasets offer enormous potential for understanding population-level health risks, but they also introduce significant analytical challenges, including overfitting, multicollinearity, and the difficulty of selecting meaningful predictors from thousands of correlated variables. To address these challenges, this study applies penalized hazard model specifically the LASSO-Cox and elastic-net Cox approaches which are well-suited for variable selection and robust risk prediction in high-dimensional settings. Unlike traditional Cox models, penalized methods can efficiently shrink irrelevant coefficients toward zero while identifying a small, interpretable subset of influential predictors across genomic, environmental, and socioeconomic domains. Because real-world multidomain datasets are often inaccessible or restricted, this research uses a carefully constructed simulated dataset that mimics realistic public health conditions. The simulated cohort incorporates hundreds of genomic markers, multiple environmental exposures such as PM2.5 and temperature variability, and socioeconomic indicators reflecting income and neighborhood disadvantage. By applying penalized survival models to these simulated data, the study demonstrates how key predictors can be identified and how model performance metrics such as the concordance index, time-dependent AUC, and calibration quality can be evaluated. Overall, the abstract presents a rigorous, results-based framework that illustrates how penalized hazard models can support precision public health by integrating complex, high-dimensional data into actionable survival predictions.
Survival analysis; penalized hazard models; genomic data; environmental exposures; socioeconomic factors; precision public health.
Multimodal AI Systems for Strategic Business Forecasting and Risk Management
The idea of multimodal artificial intelligence has become a disruptive solution towards improved strategic business forecasting and risk management systems through the combination of various types of data like text, numerical data, images, sensor streams, and market indicators. This paper investigates the role of multimodal AI systems in helping organizations gain a more valuable insight, enhance predictive accuracy, and make business more resilient in dynamic business settings. The multimodal AI enables the integrated study of financial patterns, customer patterns, operational risk, and macroeconomic conditions by using complex fusion architectures e.g. transformer-based cross-modal models together with classical forecasting models. It focuses on the importance of multimodal learning in enhancing uncertainty estimation, better prediction of disruptive events and informed decision-making in strategic planning. Results indicate that these systems are better than unimodal strategies in terms of predictive power, anomaly identification, and simulation of scenarios, which can be of great importance to companies aiming to gain a competitive edge. The paper presents a basis to apply multimodal AI to real-world scenarios such as supply chain optimization, credit risk assessment, market trend forecasting, and crisis management.
Multimodal AI, Business Forecasting, Risk Management, Data Fusion, Strategic Decision-Making, Predictive Analytics, Transformer Models.
Forecasting the Fiscal Sustainability of U.S. Medicare: Scenario-Based Projections Through 2035
This paper is an analysis on the fiscal sustainability of the U.S. Medicare by considering past spending patterns between the years 2010 and 2023 and in the future to 2035 based on a scenario spanning model by quantifying the patterns of spending. Based on the information provided by the Centers of Medicare and Medicaid Services (CMS), and the Federal Reserve Economic Database, the research incorporates regression analysis, descriptive statistics, and projections of a scenario to determine the dependence of Medicare expenditure, the real GDP, demographics, and medical prices increase. The findings indicate that the actual Medicare expenditure has been steadily increasing over the years, which is mainly caused by an increased number of the aging population, economic growth and increasing the cost of medical services. Regression models show a good overall explanatory power (R 2 = = 0.98), which proves that these macroeconomic variables together explain most of the changes in Medicare spending. The results of forecasts of the outcome in three alternative scenarios- Baseline, High-Cost, and Low-Cost- show that the real Medicare spending will proceed to increase to become between 850 billion and 1 trillion (in 2010 USD) by 2035. The fiscal footprint of the program will grow to an average of 4.2-5.0% of the real GDP as compared to 3.7 in 2023. The results highlight the fact that the fiscal course of Medicare presents a sustainability issue in the long term. Unless the policy interventions are directed at cost containment, efficiency improvements, and payment reforms, the Medicare spending will exceed the rate of economic growth, making federal finances strained. The paper suggests reinforcing value-based care programs, improving the price control, and refining the Medicare Advantage compensation to achieve financial balance and healthcare effectiveness.
Medicare, Fiscal Sustainability, Forecasting, Scenario Analysis, Health Expenditure, Aging Population, Real GDP, Medical Inflation, Cost Containment, U.S. Healthcare Policy.
Inclusion Through Ensemble: Simple Orff Arrangements for Mixed-Ability Groups
This article discusses the use of simplified Orff-based ensemble arrangements as a framework for inclusion in the mixed-ability music classroom. The challenge of creating meaningful opportunities for participation in ensemble music-making for learners with a wide range of cognitive, physical, and emotional needs remains a central focus for classroom music educators. The Orff Schulwerk, with its emphasis on creativity, movement, rhythm, and improvisation, offers a flexible model for inclusive group participation, in which all learners can play a valued and contributing role. In this qualitative, literature-based synthesis, we review the evidence from scholarly and professional literature to identify the pedagogical principles, ensemble strategies, and classroom practices that can help educators create inclusive communities of music-making. We highlight approaches to adaptation and arrangement that feature simplified rhythmic and melodic materials, layered percussion and pentatonic ostinati, and peer-assisted group collaboration. These features provide all learners with entry points for participation while preserving the integrity and expressiveness of ensemble playing. Our findings suggest that inclusive Orff ensembles support social interaction, empathy, and collaborative learning, and can thus enhance both musical and social growth. Students develop a sense of confidence and agency through participation in rhythm circles, movement activities, and collaborative improvisations that are intentionally designed to be accessible to a range of abilities. Teachers also benefit from the flexibility and adaptability of the Orff-based methods, which support differentiation without stigmatizing or singling out individual learners. The study concludes that simple Orff arrangements can help create an inclusive music classroom community in which all differences are seen as a source of richness and creativity. We recommend that teacher education and professional development should include a focus on adaptive arranging skills, differentiated instruction, and ensemble facilitation to support ongoing inclusive practices. Overall, the Orff approach shows great promise for promoting a blend of accessibility, artistry, and social connection in the 21st-century music classroom.
inclusion, Orff Schulwerk, ensemble learning, mixed-ability groups, differentiated instruction, peer collaboration, music education.
Points, Levels, and Streaks in Primary EFL: Effects on Vocabulary Growth and Engagement
Gamification is a learning approach with effective outcomes on primary learners' involvement and learning. The purpose of this study is to evaluate the differences between the three types of gamification, namely points, levels, and streaks, in order to assess their potential impacts on the vocabulary learning progress and engagement of the primary English as a Foreign Language (EFL) learners. Ninety EFL learners aged 9-11 were assigned to one of the three gamified conditions and took part in the four-week classroom experiment, during which they practiced their vocabulary by using an EFL vocabulary app based on the retrieve-practice-learn principle and gamified with the three mechanics. Vocabulary achievement tests and engagement and motivation questionnaires, together with observations, were conducted to track the vocabulary performance and learning behavior of the participants. The outcome of the study indicated the benefits of all three gamified conditions, compared to the baseline data collected in the preliminary stage. As for the effect on engagement, students who were in the streak condition exhibited the highest rates of participation over the four-week period. In contrast, the level condition was found to have brought about the most progress in vocabulary performance. On the other hand, while the points condition demonstrated a potential for initiating short-term motivation, it may become redundant in the long run. In general, these three gamified components showed positive effects on the vocabulary progress and engagement of the primary EFL students and could therefore be taken into consideration when gamifying language learning. The findings of the study also suggest that a combination of points, levels, and streaks might serve as the best intervention strategy for sustaining the engagement and learning of primary learners. This study thus concludes that gamification can be effectively incorporated into primary EFL teaching, especially with the aid of technology. Further research might be conducted in the area of long- and short-term vocabulary retention, as well as age and task-specific gamification. In addition, the application of digital data analysis could be useful in this case.
Gamification, Points, Levels, Streaks, Vocabulary Learning, Learner Engagement, Primary EFL, Motivation.
Explainable Machine Learning Pipelines for Customer Risk Scoring in Anti-Money Laundering: A Management and Governance Perspective
The rising application of machine learning (ML) in the context of the Anti-Money Laundering (AML) systems has improved the ability to identify suspicious activities by customers, but the obscurity of most ML models is a cause to question the issue of transparency, accountability, and regulatory adherence. The study suggests a customer risk scoring explainable machine learning pipeline that incorporates explainable artificial intelligence (XAI) methods and effective management and governance structures. Based on socio-technical and responsible AI governance lenses, the analyzing paper forms a conceptual and empirical framework that consists of a model performance and explainability measurements, applicable to the compliance officers and regulators. The proposed pipeline utilizes interpretable modeling techniques like SHAP and LIME to identify high-risk customers using the AML data that can be observed in real life and give a clear and audited explanation of model decisions. Results indicate that explainable pipelines enhance accuracy in detection as well as build stakeholder trust, justification of decisions, and conformity with emerging regulatory issues like the EU AI Act, and the financial model risk management policies. The research is theoretically and practically important in that it provides a governance-based framework of deploying credible, interpretable ML systems in AML settings, which eventually provides a solution to the discrepancy between interpretability of technical models and managerial responsibility.
Explainable AI (XAI), Machine Learning Pipelines, Anti-Money Laundering (AML), Customer Risk Scoring, Governance, Compliance, Responsible AI, Model Transparency.
Copyright (c) 2025 Pristly Turjo Mazumder (Author) https://creativecommons.org/licenses/by-sa/4.0
Digital Forensics and Incident Response (DFIR) Automation: Leveraging AI to Accelerate Breach Investigation, Evidence Collection, and Cyberattack Mitigation
The rapid escalation of cyber threats in both frequency and sophistication has outpaced the capacity of traditional Digital Forensics and Incident Response (DFIR) practices. Conventional manual investigation methods such as log examination, evidence extraction, and threat correlation are often too time-consuming and labor-intensive to meet the demands of real-time incident management. Consequently, organizations are increasingly turning to artificial intelligence (AI) and automation to enhance the speed, accuracy, and scalability of DFIR operations. This paper explores how AI-driven models and automation frameworks can transform digital forensics and incident response, enabling faster detection, investigation, and containment of cyberattacks. It examines the integration of machine learning, natural language processing (NLP), and robotic process automation (RPA) into DFIR workflows to automate evidence collection, pattern recognition, and anomaly detection. Moreover, the study discusses how AI-enabled SOAR (Security Orchestration, Automation, and Response) platforms streamline the decision-making process by automatically correlating multi-source data and executing predefined containment actions. The paper also highlights practical applications across enterprise and national defense contexts, showcasing how predictive forensics and adaptive response mechanisms reduce investigation time and operational fatigue. Despite these advancements, several challenges persist, including AI model bias, data imbalance, interpretability issues, and legal admissibility of AI-generated evidence. To address these concerns, the study emphasizes the need for explainable AI frameworks, standardized forensic data models, and cross-disciplinary training for DFIR professionals. Ultimately, AI and automation do not aim to replace human expertise but to augment it enhancing investigative precision, improving incident readiness, and fostering a new generation of intelligent, resilient cyber defense systems.
Copyright (c) 2025 John Kuforiji (Author) https://creativecommons.org/licenses/by-sa/4.0