Introduction The Metascience (An International Multidisciplinary Research Journal) is a double-blind, peer-reviewed, open-access journal published quarterly in print and online. The Metascience publish research and review articles in the all fields of sciences includes chemistry, biology, botany, zoology, microbiology, biotechnology, physics, medical physics, computer science, physical health science, mathematics, engineering, economics, and statistics. Researchers with interests in science and technology are encouraged to publish their work. The Metascience is sponsored by YUKTAB Publisher. Aim & Scope of the Journal The Metascience (An International Multidisciplinary Research Journal) is a science journal that accepts contributions in English from all over the world. The Metascience aims to establish itself as a leading multidisciplinary journal and invites scientists to publish and share their work.
Risk-Tiered AI Assurance: A Policy-Ready Model for Compliance, Auditing, and Evidence
Artificial intelligence governance often fails at the point where broad principles must be converted into decisions about controls, approvals, monitoring, and evidence. A single checklist cannot govern every system because the consequences of AI vary widely. This review develops a risk-tiered assurance approach for public services and digital infrastructure. It draws on the findings of a systematic synthesis of 95 high-quality governance studies and frameworks published during 2020–mid-2025. The source evidence reveals a fragmented landscape dominated by privacy and ethics frameworks on one side and risk or compliance approaches on the other. Integrated models that combine values, technical safeguards, organisational responsibility, and auditable proof remain scarce. The article explains how five core risk domains—data privacy, algorithmic bias, transparency, operational security, and regulatory compliance can be assessed across four escalating levels: low, medium, high, and critical. It links each level to proportionate controls, approval authority, monitoring intensity, and assurance records. The review also presents a governance operating model based on principles, controls, and evidence, with lifecycle gates from initial proposal to retirement. Practical guidance is provided for risk classification, documentation, vendor oversight, internal audit, and continuous improvement. The conclusion argues that risk tiering can make AI governance both stricter and more workable by directing the strongest safeguards toward systems with the greatest potential for harm.
Participatory AI Governance for Smart Cities and Sustainable Infrastructure
Smart cities and sustainable infrastructure increasingly rely on artificial intelligence, connected sensors, digital twins, and automated decision systems. These technologies can improve energy efficiency, maintenance, mobility, and service delivery, but they also create risks related to surveillance, exclusion, cybersecurity, opacity, and concentrated institutional power. This review examines participatory AI governance as a missing element in the development of sustainable digital infrastructure. It builds on a systematic synthesis of 95 high-quality studies and governance frameworks covering the period from 2020 to mid-2025. The source analysis found that privacy, ethics, regulation, and accountability dominate the literature, while citizen participation, stakeholder engagement, and smart-city governance remain comparatively isolated. Technical and legal themes often form one cluster, ethical themes another, and participation a peripheral cluster with weak links to mainstream control design. The article argues that sustainable AI governance must combine engineering assurance with democratic involvement. It reviews participation across the AI lifecycle, from problem definition and data collection to deployment, monitoring, appeal, and retirement. It also applies the discussion to digital twins, structural health monitoring, energy management, transport, and public-service platforms. A practical participation-and-assurance model is proposed, linking stakeholder input to decision rights, technical controls, evidence, and public reporting. The review concludes that participation is not an optional communication exercise; it is a source of risk knowledge, legitimacy, and long-term system resilience.
Engaging the Crowd with Algorithms: How Machine Learning Can Boost Volunteer Participation in Citizen Science
Volunteer engagement and retention represent persistent challenges in citizen science, with many projects struggling to recruit and sustain active participants despite growing public interest in scientific participation. While citizen science has traditionally relied on manual outreach strategies—social media campaigns, community workshops, and word-of-mouth recruitment—the scalability and personalization requirements of large-scale projects demand innovative approaches. Machine learning offers a suite of tools for enhancing volunteer engagement through intelligent targeting, personalized interaction, adaptive feedback, and gamified participation. This review examines the current state and future potential of ML-driven engagement strategies in citizen science. We analyze four primary mechanisms: (1) participant targeting and recruitment through social media analysis and user profiling; (2) personalized feedback systems that enhance learning and motivation; (3) gamification and adaptive challenge systems; and (4) community building and social interaction facilitation. We synthesize evidence from projects that have implemented these approaches, including the BeeWatch project's natural language generation feedback and the potential for recommendation systems adapted from commercial platforms. We also address risks, including participant disengagement from task automation, privacy concerns with data-driven targeting, and the potential for algorithmic bias in engagement strategies. We propose a framework for ethical, effective ML-driven engagement and identify priority areas for future research and development.
Data Quality in Small and Medium Enterprises: Barriers, Opportunities, and Best Practices
Small and medium enterprises face unique data quality challenges that distinguish them from larger organizations. Limited resources, technical capabilities, and specialized expertise constrain the ability of SMEs to implement comprehensive data quality management practices. Yet data quality is increasingly important for SME competitiveness, enabling customer understanding, operational efficiency, and strategic decision-making. This review examines data quality in SMEs, analyzing the barriers that limit quality management and the opportunities that quality improvement presents. We draw on evidence from studies of SMEs across multiple sectors and countries, including research on CRM adoption, business intelligence, and operational management. We identify the key barriers to data quality in SMEs, including limited resources, technical capacity, and organizational maturity. We examine the consequences of poor data quality for SME performance and competitiveness. We analyze approaches that have been effective in SME contexts, including governance frameworks adapted to resource constraints, practical tools and methodologies, and capacity building approaches. We propose a practical framework for SME data quality management that addresses the specific constraints and opportunities of these organizations.
Data Quality in Public Administration: Governance, Processes, and Outcomes
Public administration relies on high-quality data for policy development, service delivery, regulatory oversight, and accountability. However, government agencies face persistent data quality challenges that compromise their effectiveness and erode public trust. This review examines data quality in public administration, analyzing the unique challenges of government data, the governance structures that support or undermine quality, and the processes that determine quality outcomes. Drawing on literature from public administration, information systems, and government studies, we examine evidence on data quality in government settings, including studies from multiple countries and levels of government. We analyze the causes of quality problems, including organizational factors, process weaknesses, and resource constraints. We examine solutions that have been implemented, including governance reforms, capacity building, and technology investments. We propose a framework for public sector data quality management that addresses the specific characteristics of government contexts, including accountability requirements, public trust considerations, and resource constraints.
Data Quality in Customer Relationship Management: Challenges, Solutions, and Best Practices
Customer Relationship Management systems have become essential tools for organizations seeking to understand, engage, and retain customers in increasingly competitive markets. However, the effectiveness of CRM systems depends fundamentally on the quality of the data they contain. Poor data quality in CRM systems leads to flawed customer insights, ineffective marketing campaigns, wasted resources, and damaged customer relationships. This review examines data quality challenges in CRM, analyzing the dimensions of quality that are most critical for CRM success, including accuracy, completeness, consistency, timeliness, and relevance. We review evidence on the prevalence and consequences of CRM data quality problems, drawing on studies from diverse industries and organizational contexts. We analyze the causes of quality problems, including data entry errors, system integration challenges, data decay, and organizational factors. We examine solutions that have been implemented, including data governance frameworks, validation processes, data cleaning approaches, and continuous monitoring. We propose a comprehensive framework for CRM data quality management that addresses technical, organizational, and strategic dimensions.
Data Governance in the Age of Artificial Intelligence: Frameworks, Ethics, and Compliance
As artificial intelligence becomes increasingly integrated into organizational operations and decision-making processes, the importance of robust data governance has intensified significantly. This review examines the evolution of data governance frameworks in the context of AI adoption, analyzing how traditional governance structures must adapt to address the unique challenges posed by algorithmic decision-making systems. Drawing on literature from information systems, public administration, and AI ethics, we examine the core components of effective data governance, including data stewardship, quality management, compliance, and ethical oversight. We analyze the emerging regulatory landscape, including the EU AI Act, ISO/IEC 42001, and NIST's AI Risk Management Framework, and assess their implications for organizational governance practices. The review identifies key challenges in implementing AI-era data governance, including the tension between innovation and regulation, the difficulty of ensuring algorithmic accountability, and the need for continuous monitoring and adaptation. We propose a holistic governance framework that integrates technical, organizational, and ethical dimensions, providing practical guidance for practitioners and policymakers navigating the complex intersection of data governance and artificial intelligence.
Bridging the Gap: Metadata, Multimodal Data, and Hybrid Machine Learning Approaches in Citizen Science
The majority of machine learning applications in citizen science have focused on image classification, with algorithms trained to identify species or classify objects from photographs. However, many citizen science projects collect diverse types of data, including images, audio, text, and structured metadata such as location, date, and observer characteristics. These diverse data types, when integrated through multimodal machine learning approaches, can significantly enhance the accuracy and robustness of predictions, particularly for challenging classification tasks where images alone are insufficient. This review examines the integration of metadata and multimodal data in citizen science machine learning applications. We analyze the types of metadata that are commonly collected, including spatial, temporal, environmental, and observer-related information, and examine how these data can be combined with images and other unstructured data to improve model performance. We review case studies where multimodal approaches have been applied, including species identification projects that combine images with location data, acoustic monitoring that integrates environmental information, and quality assessment systems that incorporate observer expertise. We analyze the technical approaches, including early and late fusion strategies, and identify the conditions under which multimodal approaches provide significant benefits. We also address challenges, including data sparsity, heterogeneity, and the need for integrated data collection protocols. We propose best practices for designing citizen science projects that support multimodal machine learning and identify future research directions.
Core Principles and Governance Frameworks for Large Language Models in the AI Era
The rapid growth of Large Language Models (LLMs) has transformed multiple sectors, including healthcare, finance, cybersecurity, education, and supply chain management. As AI systems become increasingly integrated into critical decision-making processes, robust AI data governance frameworks are essential to ensure ethical, secure, transparent, and compliant AI operations. This article explores different AI data governance frameworks, including data-centric, policy-driven, regulatory-compliance, ethical, security-focused, industry-specific, and federated governance models. It further examines the core governance principles required for responsible LLM deployment, including data integrity and quality, fairness and ethical standards, data security and privacy, model monitoring and deployment, regulatory compliance, and data traceability. The article highlights how governance frameworks help mitigate risks related to bias, misinformation, privacy breaches, adversarial attacks, and regulatory violations while improving transparency, accountability, and trustworthiness. The study concludes that integrating comprehensive governance mechanisms into the AI lifecycle is essential for developing secure, fair, reliable, and socially responsible LLM systems capable of supporting sustainable innovation across diverse industries.
Building Reliable Systems in Practice via the Integration of Model-Based and Knowledge-Centric Systems Engineering
The increasing complexity and interconnectivity of modern software-intensive systems have significantly intensified the need for dependable engineering methodologies capable of ensuring reliability throughout the system lifecycle. Systems deployed in safety-critical and mission-critical domains, including aerospace, automotive, healthcare, transportation, and industrial automation, require rigorous engineering approaches to guarantee operational reliability, safety, and maintainability. Traditional engineering practices often struggle to manage the growing complexity of these systems, particularly when reliability considerations must be integrated with architectural design, requirements management, traceability, and quality assurance activities. In response to these challenges, this study proposes a comprehensive methodology that integrates Model-Based Systems Engineering (MBSE) and Knowledge-Centric Systems Engineering (KCSE) to support the practical development of dependable systems.
The Integration of Artificial Intelligence (AI) In Contemporary Software Ecosystems
The integration of Artificial Intelligence (AI) in contemporary software ecosystems transcends mere model correctness, necessitating the establishment and orchestration of robust, scalable backend infrastructures for seamless integration and deployment. This article delineates the essential architectural frameworks behind AI-enabled systems, emphasizing the pivotal role of backend engineering in enabling the interaction between machine learning components and production environments. It thoroughly examines significant technological challenges, including the administration of model versioning and deployment lifecycles, minimization of inference latency at scale, and preservation of data integrity inside dynamic processing pipelines. The discourse anticipates future developments, focusing on edge computing as a strategic facilitator for decentralized, low-latency AI inference, emphasizing its capacity to transform backend architecture in distributed intelligent systems.
Integration of Machine Learning and Self-Healing Mechanisms in Adaptive AI Architectures
The integration of machine learning with self-healing capabilities represents a significant advancement in the development of adaptive AI systems. These systems are designed to independently identify, analyze, and rectify operational anomalies, ensuring robustness and dependability in complex and dynamic environments. The integration of adaptive machine learning models and self-repairing mechanisms allows AI systems to promptly detect issues, such as software malfunctions or security vulnerabilities, and autonomously execute remedies without human intervention. This research investigates several approaches for integrating these elements, including reinforcement learning techniques for real-time decision-making and the implementation of predictive maintenance models that employ deep learning to anticipate faults proactively. This paper highlights the merits and weaknesses of current adaptive AI systems by meticulously analyzing diverse designs, including hybrid models that integrate rule-based reasoning with neural networks. Empirical evidence demonstrates that self-healing systems can reduce system downtime by up to 40% and improve the overall efficacy of AI applications, especially in domains such as cloud computing, cybersecurity, and the Internet of Things (IoT). The findings suggest that the use of these designs can yield more resilient AI solutions capable of continuous improvement and advancement. Furthermore, the research examines challenges such as the computing demands associated with real-time anomaly detection and the necessity for extensive datasets to train effective machine learning models. The paper concludes with recommendations for future developments in adaptive AI, emphasizing the importance of designing systems that balance responsiveness and computing efficiency.
Advanced Event-Driven Cloud Architectures for Robust Enterprise Automation at Scale
Various organizations are progressively transitioning to distributed clouds, and as a result, traditional tightly connected architectures are encountering difficulties in providing the scalability, robustness, and responsiveness necessary to support current operations. This study examines event-driven cloud architectures as a strategic approach to enterprise automation. These designs are based on decoupled services, asynchronous communication, and message-oriented workflows, enabling systems to react to real-time business events without experiencing bottlenecks or cascading failures. The fundamental design principles, specifically publish-subscribe messaging, event sourcing, and eventual consistency, are examined from a systems viewpoint, illustrating their application for achieving operational resilience. The additional essential operational issues addressed in the research include observability, fault isolation, and high-throughput system governance. It offers a pragmatic and realistic framework for establishing scalability and dependability in enterprise automation platforms that can adeptly serve mission-critical workloads, detailing design principles and evaluating architectural trade-offs.
Predictive AI Proactive Customer Engagement Platform with Real-Time Friction Mitigation Utilising AI-Driven Churn Prediction
Customer engagement platforms have evolved significantly, with artificial intelligence (AI) facilitating a transition from a reactive, service-oriented model to a proactive, predictive, and personalised approach to customer engagement. Customer attrition has emerged as a critical challenge in the realm of highly competitive digital markets, impacting revenue stability and long-term growth. Traditional engagement methods are ineffective in the early identification of customer unhappiness, leading to inadequate solutions and increased churn rates. The article presents a comprehensive framework for a Predictive AI-driven Proactive Customer Engagement Platform, integrating real-time analytics, machine learning-based churn prediction, and technologies for reducing friction. The suggested architecture utilises extensive customer interaction data, including behavioural, transactional, and contextual information, to develop prediction models capable of accurately identifying churn probability. The system identifies disengagement patterns early by employing both supervised and unsupervised learning approaches, such as gradient boosting, deep neural networks, and clustering algorithms. The platform features a real-time friction reduction engine that dynamically recognises customer journey bottlenecks, such as delays, service faults, or usability issues, and addresses these instances through automated procedures. The work's primary contribution is the integration of predictive analytics with real-time orchestration systems, allowing organisations to facilitate personalised engagement across multiple channels, such as mobile applications, web platforms, and customer support systems. The platform employs reinforcement learning to continuously optimise interaction techniques based on consumer input and responses. The methodology includes data ingestion pipelines, feature engineering frameworks, model training procedures, and deployment strategies utilising cloud-native services. The system is evaluated using performance indicators, including accuracy, precision, recall, F1-score, and client retention improvement rates. Experimental data suggest that predictive AI models can substantially reduce churn rates, hence enhancing customer satisfaction and optimising operational procedures. This research contributes to the growing body of literature on AI-driven customer engagement by presenting a scalable, real-time, and intelligent platform framework. It underscores the necessity of implementing proactive engagement strategies and offers practical insights into the enterprise aiming to enhance customer experience through cutting-edge analytics and automation.
Leveraging Artificial Intelligence and Big Data Analytics for Improved Management and Research of Multimorbidity
Multimorbidity denotes the simultaneous presence of two or more chronic illnesses inside an individual. Consequently, individuals with multimorbidity possess diverse and specific treatment requirements. In reality, fulfilling these objectives is challenging due to the fact that the organisational procedures of contemporary healthcare systems are mostly designed for a singular condition. A transformative shift in the problem-solving methodology for medical research and therapy is essential to enhance clinical decision-making and patient care in multimorbidity. Alongside the conventional reductionist methodology, we advocate for participatory research bolstered by artificial intelligence (AI) and sophisticated big data analytics. This research methodology, when used with data typically gathered in healthcare environments, offers a cohesive framework for investigating multimorbidity-related research problems. This may include, for instance, prediction, correlation, and classification challenges derived from various interaction aspects. To actualise the concept of this paradigm change in multimorbidity research, it is essential to optimise, standardise, and, most critically, integrate electronic health data into a unified national and worldwide research infrastructure. Ultimately, there is a need for the integration and use of effective AI methodologies, especially deep learning, into clinical practice directly into the workflows of healthcare professionals.
AI-Augmented Financial Services and Virtual Engagement Supervision for Contemporary Digital Support
The growing digitalisation of financial services and distant contacts has rendered artificial intelligence (AI) essential for providing assistance. This research investigates the capacity of AI-driven technologies—specifically machine learning algorithms and predictive analytics—to augment financial aid, refine risk management, and elevate user experiences within the banking sector and virtual client service contexts. By automating procedures including as credit scoring, fraud detection, virtual customer interactions, and behavioural analysis, institutions provide support systems that are more efficient, safe, and tailored. Moreover, AI-driven virtual visitor monitoring systems are becoming indispensable in telebanking and digital financial advisory, enabling stringent identity verification, compliance assessments, and real-time client interaction. This study enhances the existing literature on AI's capacity to revolutionise conventional financial and distant contact paradigms by introducing a hybrid framework that concurrently optimises financial assistance distribution and client supervision. We examine system efficacy, ethical considerations, and prospective implementation obstacles via current technical breakthroughs and illustrative case studies.
The rapid expansion of web-based platforms, digital services, and data-driven applications has significantly increased the complexity of governing online ecosystems. AI-Enabled Policy-Driven Web Governance represents an advanced governance paradigm that integrates artificial intelligence with formal policy frameworks to ensure transparency, security, compliance, and ethical operation of web environments. Traditional web governance mechanisms, which rely heavily on static rules and manual oversight, are increasingly inadequate in addressing dynamic threats, evolving regulations, and large-scale user interactions. By embedding AI technologies such as machine learning, natural language processing, and automated reasoning into governance processes, policy-driven web governance enables real-time monitoring, intelligent enforcement of rules, adaptive compliance, and proactive risk mitigation. AI systems can interpret regulatory requirements, analyze user behavior, detect policy violations, and recommend corrective actions with high accuracy and efficiency. However, the adoption of AI-enabled web governance also introduces critical challenges, including algorithmic bias, lack of transparency, data privacy concerns, cybersecurity risks, and the need for robust accountability mechanisms. This article provides an in-depth and comprehensive analysis of AI-Enabled Policy-Driven Web Governance, covering its conceptual foundations, architectural components, policy models, implementation strategies, benefits, challenges, ethical considerations, and future directions. The discussion aims to support researchers, policymakers, system architects, and organizations in designing intelligent, scalable, and policy-compliant web governance frameworks suitable for modern digital ecosystems.
AI-Driven Data Governance for Large Language Models: Ensuring Quality, Privacy, and Compliance Across Domains
The rapid advancement of large language models (LLMs) such as GPT and BERT has revolutionized multiple industries, including healthcare, finance, supply chain management, and cybersecurity. While these models demonstrate remarkable capabilities in natural language understanding, content generation, and decision support, their performance, reliability, and ethical deployment are inherently dependent on robust data governance frameworks. This paper explores the critical role of AI-driven data governance in ensuring data quality, integrity, privacy, transparency, fairness, and regulatory compliance throughout the LLM lifecycle. Key components such as ethical AI standards, data lineage, traceability, and continuous model monitoring are examined as essential pillars for mitigating risks associated with data misuse, bias, hallucinations, and security breaches. The study further highlights domain-specific applications of AI data governance in sectors like healthcare, finance, cybersecurity, and supply chain management, illustrating how governance frameworks improve operational efficiency, regulatory adherence, and ethical decision-making. Challenges in implementing governance, including scalability, data complexity, transparency, and model monitoring, are also discussed. By emphasizing the integration of structured data management, privacy-preserving techniques, and regulatory compliance, this work provides a comprehensive overview of strategies to enhance trustworthiness, reliability, and accountability of LLMs. The findings underscore the importance of proactive governance approaches to ensure responsible, fair, and secure AI deployment in modern data-driven environments.
WAFFLES – Machine Learning Toolkit: Bridging Simplicity and Advanced Analytics
WAFFLES, a comprehensive and versatile collection of cross-platform command-line tools designed to support researchers, developers, and practitioners working in the field of machine learning and data mining. The WAFFLES toolkit provides a broad and well-structured assortment of functionalities that address key tasks in machine learning workflows, including data preprocessing, model training, evaluation, and experimentation. These tools are specifically engineered to operate efficiently across multiple platforms and to integrate seamlessly with scripted automation, making them highly suitable for large-scale experiments, batch processing, and reproducible research environments. In addition to the command-line utilities, all core functionalities of WAFFLES are implemented within a robust and modular C++ class library, enabling developers to directly incorporate machine learning capabilities into custom applications and software systems. The toolkit supports a variety of learning paradigms and algorithms, facilitating both supervised and unsupervised learning tasks. Emphasis is placed on performance, flexibility, and extensibility, allowing users to adapt the toolkit to diverse research and industrial requirements. WAFFLES is distributed as open-source software under the GNU Lesser General Public License (LGPL), encouraging collaboration, transparency, and community-driven development while allowing integration into both open-source and proprietary projects. By providing a powerful yet user-friendly machine learning toolkit, WAFFLES aims to accelerate experimentation, improve reproducibility, and lower the barrier to implementing advanced machine learning techniques.
ART OF MEDICINE “Representing Communication, Empathy, and Competence in the Era of Artificial Intelligence”
Artificial Intelligence (AI) is presently incorporated into an assortment of fields, including medication. Human-made intelligence applications bring up various moral issues, mostly about the nature of care and patient-clinician connections. This article goes with two computerized photographs of art that address these thoughts narrative and outwardly, with an extraordinary accentuation on correspondence, empathy, and fitness.
From Data to Decisions: Architecting High-Performance AI Platforms for Fortune 500 Ecosystems
This systematic review examines data-driven GenAI and Go-To-Market (GTM) execution models utilized by high-growth startups and Fortune 500 companies, with the objective of identifying common strategies, critical differentiators, and performance outcomes. As enterprises increasingly depend on analytics to inform go-to-market strategies, comprehending the impact of data-driven models on market entry, customer acquisition, and revenue expansion is essential. The review consolidates findings from 58 peer-reviewed articles, case studies, and industry reports published from 2012 to 2023. The inclusion criteria targeted organizations utilizing data-driven methodologies, including predictive analytics, customer segmentation, A/B testing, sales pipeline optimization, and automated marketing technologies to implement their go-to-market strategies. The analysis uncovers multiple thematic consistencies in both startup and enterprise environments. This encompasses customer journey mapping via real-time analytics, iterative validation of product-market fit utilizing behavioral data, and multichannel attribution modeling to enhance marketing ROI. Nonetheless, significant discrepancies are present. Startups frequently utilize agile, experimental go-to-market models that capitalize on streamlined data infrastructure and swift feedback mechanisms. Conversely, Fortune 500 companies incorporate GTM models into extensive CRM and ERP systems, facilitating enhanced forecasting and personalization, albeit frequently sacrificing speed and flexibility. The synthesis emphasizes that success in data-driven go-to-market execution depends on four essential factors: organizational alignment regarding key performance indicators, cross-functional data fluency, adaptable technology stacks, and leadership dedication to experimentation. Furthermore, companies exhibiting proficiency in these domains report accelerated time-to-market, enhanced customer lifetime value, and increased marketing efficiency. This review concludes by recommending a hybrid go-to-market model that integrates startup agility with enterprise scalability, underpinned by a modular data architecture and ongoing learning cycles. It enriches the expanding dialogue on data-driven strategic implementation and provides a comparative perspective for practitioners and scholars aiming to improve go-to-market efficacy across various organizational settings.
The Next Frontier of Enterprise Transformation: A Comprehensive Analysis of Generative AI as a Catalyst for Organizational Modernization, Intelligent Automation, and Large-Scale Knowledge Acceleration Across Global Digital Ecosystems
The emergence of generative artificial intelligence (AI) technologies signifies a transformative period in industrial innovation, providing unparalleled abilities for content generation, predictive analytics, and automation. This paper explores the transformative potential of generative AI in major industrial sectors, highlighting its capacity to drive technological advancements, improve operational efficiencies, and promote sustainable practices. This study seeks to offer a thorough understanding of how generative AI is transforming the automotive, manufacturing, and energy sectors by investigating its technical attributes, developmental progression, application contexts, and critically assessing its limitations and ethical implications.
How Data Catalogs are Transforming Enterprise Data Governance: A Systematic Literature Review
Organizations must obtain insights into often fragmented and isolated data assets and eliminate data silos to properly leverage data as a strategic resource. Data catalogs are an increasingly preferred approach for achieving these objectives. Despite the recognized importance of data catalogs in reality, there is a lack of research about the design of corporate data catalogues. Current market solutions must be customized to address specific organizational needs. This article outlines a collection of operational requirements for corporate data catalogs based on thorough literature study. The criteria may be employed to define and guide more comprehensive study on data catalogs, as well as for the selection and customization of practical solutions.
Cloud Storage Security in Government Agencies: Protecting National Data from Cyber Threats
Cloud storage security in government agencies is critical to safeguarding national data from cyber threats, as the increasing reliance on cloud computing introduces new vulnerabilities that adversaries seek to exploit. Government agencies store vast amounts of sensitive information, including classified intelligence, citizen records, and operational data, necessitating robust security measures to prevent breaches, unauthorized access, and cyberattacks. Ensuring the confidentiality, integrity, and availability of this data requires a multi-layered approach, incorporating encryption, identity and access management (IAM), zero-trust architecture, and continuous monitoring. Advanced encryption protocols, both at rest and in transit, mitigate risks associated with data interception and unauthorized access, while IAM frameworks enforce strict authentication and authorization policies. Zero-trust models, which assume that no entity inside or outside the network can be fully trusted, further enhance security by continuously verifying users and devices. Moreover, compliance with regulatory frameworks such as the Federal Risk and Authorization Management Program (FedRAMP), the National Institute of Standards and Technology (NIST) guidelines, and agency-specific security policies is essential to maintaining secure cloud environments. Government agencies must also address the risks of insider threats and supply chain vulnerabilities, ensuring that third-party cloud service providers adhere to strict security standards. The integration of artificial intelligence (AI) and machine learning (ML) enhances threat detection capabilities by identifying anomalies and potential breaches in real time. Additionally, adopting a hybrid cloud strategy with secure on-premises storage for highly classified data while leveraging the cloud for scalable operations provides a balanced approach to security and efficiency. Regular security audits, penetration testing, and incident response planning further fortify cloud storage defenses against evolving cyber threats. As cyber adversaries continuously develop sophisticated attack techniques, government agencies must proactively update security policies, implement cutting-edge technologies, and foster collaboration with cybersecurity experts and industry leaders. The future of cloud storage security in government agencies will depend on a dynamic and adaptive security posture, ensuring the resilience of national data against cyber threats while enabling the efficient and secure use of cloud technologies for mission-critical operations.
Machine Learning Models for Predicting Ransomware Attacks on Critical Public Health Infrastructure: A Cross-National Study
Machine learning (ML) models have emerged as powerful tools in cybersecurity, offering proactive threat detection and risk mitigation capabilities. This study examines the effectiveness of ML models in predicting ransomware attacks on critical public health infrastructure across multiple countries. Ransomware attacks pose a significant threat to hospitals, laboratories, and emergency response systems, disrupting essential healthcare services and jeopardizing patient safety. Our research employs a cross-national dataset comprising attack patterns, network vulnerabilities, socio-economic indicators, and geopolitical risk factors to develop predictive models for early threat detection. We utilize supervised learning techniques, including decision trees, random forests, support vector machines, and deep learning architectures, to assess their predictive accuracy in identifying ransomware threats. The study incorporates feature engineering methods to extract key predictors, such as anomalous network traffic, phishing email indicators, and system configuration weaknesses. Additionally, we evaluate the role of external variables, including cyber hygiene policies, national cybersecurity readiness, and health sector digitalization levels, in shaping ransomware susceptibility. Model performance is benchmarked using precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) to ensure robustness and generalizability across diverse healthcare environments. Findings suggest that ensemble-based models, particularly random forests and gradient boosting techniques, outperform traditional classifiers by capturing complex attack patterns and reducing false positives. Cross-national comparisons reveal significant variations in ransomware vulnerability, influenced by policy frameworks, technological preparedness, and cybercrime enforcement mechanisms. The study highlights the need for integrating AI-driven cybersecurity solutions with existing healthcare IT infrastructures to enhance resilience against ransomware threats. Furthermore, it underscores the importance of international collaboration in threat intelligence sharing and policy harmonization to counteract evolving cyber threats. The insights from this research provide valuable contributions to public health security, guiding policymakers, cybersecurity professionals, and healthcare administrators in implementing ML-driven preventive measures. Future work will explore federated learning approaches to improve privacy-preserving threat detection and assess adversarial attacks on ML models to enhance their robustness in real-world applications.
This research offers a comprehensive review of Enterprise Resource Planning (ERP) literature in both journals and conferences, specifically concentrating on Higher Education Institutions (HEIs). This research aims to delineate and evaluate the pertinent intellectual landscape of ERP systems in higher education, with the objective of updating the current status of research, formulating research questions, and subsequently enhancing the knowledge base. The overview encompasses the period from 2009 to till date, organized by issues addressed in contemporary writing. The study aims to achieve three objectives. Initially, to investigate the types of inquiries that emerge inside the ERP area. This paper will serve as a valuable resource for future research. Finally, it will furnish a comprehensive bibliography of the articles for the specified period.
ERP Systems in Higher Education Institutions: Adoption, Challenges, and Future Trends
Enterprise Resource Planning (ERP) systems have become essential for higher education institutions (HEIs), facilitating centralized management of academic and administrative functions. As HEIs face increasing operational complexities, ERP solutions integrate various institutional processes, including student lifecycle management, finance, human capital management, academic scheduling, and course administration. The adoption of ERP in universities is driven by the need for streamlined data management, scalability, regulatory compliance, and technological advancements such as cloud computing and artificial intelligence. Despite its advantages, ERP implementation in HEIs presents significant challenges, including high costs, resistance to change, interoperability concerns, and data migration complexities. Successful implementation requires strategic planning, stakeholder engagement, continuous monitoring, and effective integration with legacy systems. Institutions must carefully evaluate ERP solutions to ensure alignment with academic and administrative goals while balancing customization and standardization to optimize operational efficiency. Modern ERP systems offer comprehensive functionalities, including Student Information Systems (SIS), Learning Management Systems (LMS), Financial Aid (FA), Human Capital Management (HCM), and performance assessment tools. Cloud-based ERP solutions such as PeopleSoft Campus Solutions and Workday Student provide institutions with greater flexibility, scalability, and real-time data accessibility. Additionally, data analytics and reporting capabilities in ERP systems enable institutions to make informed decisions, improve student services, and enhance governance and compliance. This paper explores the evolution of ERP systems in higher education, the driving factors for their adoption, key implementation challenges, evaluation frameworks, and emerging trends. The study provides insights into best practices for ERP deployment in HEIs, emphasizing the role of integrated workflow automation, AI-driven analytics, and seamless system interoperability. As universities continue to embrace digital transformation, ERP solutions will play a pivotal role in enhancing institutional effectiveness and ensuring a more efficient, data-driven educational environment.
Data analytics in higher education offers distinct potential to analyze, comprehend, and model educational processes. As a result, the approaches and procedures supporting data analytics in higher education have resulted in distinct, highly correlated terminologies such as Learning Analytics (LA), Academic Analytics (AA), and Educational Data Mining (EDM), where the results of one may serve as the input for another. This study aims to provide IS educators and researchers with an overview of the present state of research and theoretical perspectives on educational data analytics. The document presents a comprehensive set of concepts and a cohesive framework for data analytics within higher education. By examining the framework, scholars may uncover novel settings and domains of investigation. The Gestalt-like process of integrating the framework (total) with the articulation of data analytics (parts) may prove beneficial for educational stakeholders in making decisions about individual students, student groups, curricula, schools, and educational systems.
Secure Software Design Through IoT Risk Modeling and Regional Compliance
This paper introduces an enhanced, risk-aware software design framework tailored for Internet of Things (IoT) applications, emphasizing the integration of region-specific compliance mandates directly into the development lifecycle. The proposed framework builds upon the foundational policy analysis conducted by Dalal et al. [1], which underscored key cybersecurity challenges faced by IoT systems in the United States, Canada, and the European Union. Our model advances their work by incorporating compliance-based threat modeling, quantitative risk scoring, and strategically placed security validation checkpoints throughout the software lifecycle. The framework was evaluated on a smart home IoT prototype, demonstrating measurable improvements in security design robustness, a significant reduction in compliance gaps, and faster preparedness for region-specific regulatory approval. These results reinforce the ongoing relevance and foundational impact of Dalal et al. [1] work in guiding the development of secure and regulation-aligned IoT software solutions.
Leveraging Automation in Software Quality Assurance: Enhancing Efficiency and Reducing Defects
As the complexity of modern software systems continues to escalate, organizations face increasing pressure to enhance the efficiency, reliability, and speed of their software quality assurance (SQA) processes. In this dynamic and rapidly evolving landscape, automation has emerged as a transformative tool, providing robust solutions to streamline workflows, minimize manual intervention, and elevate overall software quality. This paper delves into the expanding role of automation in SQA, exploring a comprehensive range of automated testing tools, frameworks, and methodologies specifically designed to mitigate human error, accelerate testing cycles, and yield more reliable software products that meet stringent quality standards. The discussion highlights several key benefits of automation, including scalability, which allows organizations to execute thousands of test cases simultaneously, thus significantly reducing the time required for comprehensive testing. Additionally, repeatability ensures consistent test execution across multiple environments, which is essential for maintaining software integrity during frequent updates and releases. Enhanced accuracy minimizes the risk of human error, which is particularly critical in high-stakes industries such as finance, healthcare, and cybersecurity. However, the paper does not shy away from addressing the significant challenges associated with implementing automation in SQA. High initial setup costs, complexities in tool integration, and the ongoing maintenance of automation scripts pose substantial barriers for many organizations. These challenges necessitate careful planning and resource allocation, as well as the development of specialized skills within QA teams to manage and optimize automated testing processes effectively. Furthermore, the paper outlines effective strategies for incorporating automation into existing QA processes, emphasizing a phased approach that considers the unique context, needs, and maturity levels of each organization. Best practices for fostering continuous improvement and defect reduction throughout the software development lifecycle are recommended, including the seamless integration of automated tests within agile and DevOps frameworks.