International Journal of Acta Informatica
INTRODUCTION International Journal of Acta Informatica (IJAI) is a fully open-access, international journal that provides academia and industry with a venue for the rapid publication of research manuscripts reporting innovative computational methods and applications that achieve major breakthroughs, practical improvements, and bold new research directions across a wide range of Computer applications. The objective of this journal is to communicate recent and projected advances in computer-based engineering techniques, with an emphasis on research and development leading to practical problem-solving. It will reflect the significant advances that are currently being made in computer science. The multidisciplinary character of this field will be typified by providing the readers with a broad range of articles. The Journal also provides an active forum for disseminating results in both research and advanced practice in computational engineering, promoting rapid communication and exchange among computer scientists, computational engineers, and software developers. The journal especially encourages papers from new, emerging, and multidisciplinary areas, as well as papers reflecting international trends in research and development, aimed at a general computer science audience seeking a comprehensive, expert overview of the latest developments in computer science. The journal aims to keep related researchers up to date on developments across a wide range of topics, reporting experiments, techniques, and ideas that advance understanding in various areas of computer science.
Regulating AI in Healthcare Policy Gaps, Ethical Challenges, and Data Protection Frameworks
The intersection of artificial intelligence, genetic information, and health research presents complex ethical dilemmas that challenge existing data protection frameworks, including the European General Data Protection Regulation. This article examines the ethical challenges arising from AI-enabled health research involving genetic data, analyzing issues of consent, privacy, discrimination, and data governance. Through systematic analysis of contemporary scholarship, the study identifies critical tensions between open science principles and individual data protection rights, complexities in applying GDPR provisions to shared genetic data, and challenges in preventing AI-driven discrimination while protecting sensitive information. Findings reveal that current regulatory frameworks inadequately address the unique characteristics of genetic data and the complexities of AI-driven analysis. The article proposes recommendations for regulatory reform, including clarification of data subject rights for biological family members, development of genetic data-specific protections, and mechanisms for balancing privacy with non-discrimination in AI-enabled health research. The GDPR provides a foundation for data protection, but its provisions require substantial refinement to address the specific ethical challenges of AI-enabled genetic research.
Copyright (c) 2026 International Journal of Acta Informatica
Governance of Artificial Intelligence in Healthcare Policy and Legislative Approaches to Balancing Innovation and Data Protection
The transformative potential of artificial intelligence in healthcare has generated substantial enthusiasm among researchers, clinicians, and policymakers. AI applications in diagnosis, treatment, monitoring, and research promise significant benefits for patient care, health outcomes, and scientific discovery. However, the realization of these benefits depends critically on the development of policy and legislative frameworks capable of balancing innovation with the protection of individual rights and public interests. Current regulatory frameworks, including the GDPR, provide foundational protections for data privacy and individual rights. However, these frameworks were not designed specifically for AI applications in healthcare and may inadequately address the unique challenges presented by AI. Gaps in legislation and policy include insufficient attention to formal regulation, limited international coordination, inadequate consumer education, and insufficient ethical-legal support for researchers. This article examines policy and legislative challenges in AI healthcare applications, analyzing current regulatory gaps and proposing recommendations for reform. The analysis draws on contemporary scholarship to identify critical policy issues and develop a comprehensive policy framework. The GDPR is considered as a foundation for data protection that must be extended and refined to address the specific challenges of AI in healthcare.
Technical and Pedagogical Challenges in AI Integration for Healthcare Systems: A Multidisciplinary Perspective
The integration of artificial intelligence into healthcare systems presents multifaceted technical and pedagogical challenges that require comprehensive multidisciplinary approaches. This article examines the practical obstacles encountered in AI implementation, encompassing technical issues such as data quality, interoperability, and regulatory compliance, alongside pedagogical challenges related to workforce training, professional development, and educational integration. Through systematic analysis of contemporary literature and case evidence, the study identifies critical challenges including data cleansing complexities, predictive variable inconsistency, instructor competency requirements, and interdisciplinary knowledge gaps. Findings reveal that successful AI integration demands collaboration across computer science, clinical practice, law, and education, with particular attention to data privacy, security, and ethical compliance. The article proposes a multidisciplinary framework for addressing technical and pedagogical challenges, emphasizing the importance of continuous refinement, interdisciplinary expertise, and adaptive educational strategies. The GDPR framework provides essential regulatory parameters that must be integrated with technical and pedagogical approaches to ensure responsible AI implementation in healthcare.
Copyright (c) 2025 International Journal of Acta Informatica
Interpretability, Accountability, and Algorithmic Bias in AI-Driven Healthcare: An Ethical Analysis Under the GDPR Framework
The proliferation of artificial intelligence in healthcare has introduced transformative capabilities alongside complex ethical challenges requiring rigorous scholarly examination. This article provides a comprehensive ethical analysis of three interconnected concerns—interpretability, accountability, and algorithmic bias—within AI-driven healthcare applications, contextualized within the European General Data Protection Regulation framework. Through systematic review of contemporary literature, this study identifies critical gaps in the understanding of machine learning decision-making processes and their implications for stakeholder trust, patient safety, and regulatory compliance. Findings reveal that the opacity of AI algorithms poses significant threats to accountability, while non-representative training datasets perpetuate systemic biases that disproportionately affect marginalized populations. The GDPR provides foundational data protection mechanisms; however, its provisions require further elaboration to adequately address algorithmic transparency and fairness in clinical contexts. This article proposes a multi-layered framework combining technical explainability tools, stakeholder engagement protocols, and regulatory refinements to enhance interpretability, ensure accountability, and mitigate bias in healthcare AI applications.
Copyright (c) 2023 International Journal of Acta Informatica
Implementing Ethical AI Across Cyber-Physical Systems and Diagnostic Contexts: Transparency, Contestability, and Trust
Human-Centered AI in Healthcare: Ethical Design Principles for Diverse and Resource-Limited Settings
The deployment of artificial intelligence in healthcare settings necessitates a fundamental shift toward human-centered design principles that prioritize the needs, values, and circumstances of end-users and affected communities. This article examines the ethical dimensions of AI design and implementation in healthcare, with particular attention to resource-limited regions and diverse population groups. Through systematic analysis of contemporary scholarship, the study identifies critical ethical considerations including bias mitigation, local contextual adaptation, stakeholder engagement, and the prevention of technological monopolization. Findings reveal that AI systems developed without adequate attention to human-centered principles risk amplifying existing healthcare inequities, particularly in underserved regions of the Global South. The article proposes a comprehensive framework for human-centered AI in healthcare, encompassing participatory design methodologies, culturally sensitive implementation strategies, and governance structures that ensure equitable access and benefit distribution. The European GDPR framework provides foundational data protection standards, yet its principles must be extended to address the specific ethical challenges of AI deployment in diverse healthcare contexts.
Copyright (c) 2024 International Journal of Acta Informatica
Data Quality in the Age of Artificial Intelligence: A Comprehensive Review of Frameworks, Challenges, and Governance Strategies
Data quality has emerged as a fundamental pillar of scientific integrity, organizational performance, and evidence-based decision-making in the contemporary digital landscape. As artificial intelligence systems become increasingly integrated into data-driven processes across healthcare, business, and public administration, the importance of robust data quality frameworks has intensified significantly. This comprehensive narrative review synthesizes scientific and technical literature published between 1996 and 2022, complemented by international standards including ISO/IEC 25012 and ISO 8000, to provide an integrated overview of data quality frameworks, governance structures, and ethical considerations in the era of AI. Across diverse sectors, accuracy, completeness, consistency, timeliness, and accessibility consistently emerge as universal quality dimensions that underpin reliable data utilization. Evidence from healthcare, business, and public administration demonstrates that poor data quality leads to substantial financial losses, operational inefficiencies, and erosion of stakeholder trust. Emerging frameworks increasingly integrate FAIR principles—Findability, Accessibility, Interoperability, and Reusability—while incorporating ethical safeguards, including bias mitigation in AI systems. This review reveals that data quality is not solely a technical concern but fundamentally a socio-organizational challenge that requires robust governance structures and continuous assurance throughout the data lifecycle. Embedding quality and ethical governance into data management practices is crucial for producing trustworthy, reusable, and reproducible data that supports sound science and informed decision-making across all sectors.
Copyright (c) 2022 International Journal of Acta Informatica
Intelligent AI Data Governance Frameworks for Large Language Models: Challenges, Applications, and Future Perspectives
The rapid advancement of Large Language Models (LLMs) has significantly transformed industries such as healthcare, finance, e-commerce, travel, and cybersecurity by enabling intelligent automation, advanced analytics, and human-like communication. However, the growing dependence on LLMs has introduced critical challenges related to data misuse, hallucinations, bias, privacy violations, security threats, and ethical concerns. This article examines the importance of AI data governance frameworks in ensuring secure, ethical, transparent, and reliable LLM deployment throughout the AI lifecycle. It discusses the role of data governance in improving data quality management, model fine-tuning, privacy protection, regulatory compliance, and operational efficiency while minimizing risks associated with misinformation, adversarial attacks, and deployment failures. The study further explores how governance mechanisms support trustworthy AI systems in healthcare, finance, e-commerce, travel, and other domains. Additionally, the article highlights major governance challenges and emphasizes the need for intelligent, data-centric governance architectures capable of maintaining fairness, accountability, security, and compliance in evolving AI ecosystems. The study concludes that robust AI data governance frameworks are essential for building trustworthy and sustainable LLM-based systems across modern digital industries.
Automating Code Reviews with Context: A Retrieval-Augmented Method
Modern software development depends heavily on code review to maintain software quality, detect defects at an early stage, and ensure that coding standards are consistently followed across large and evolving projects. Although manual review remains one of the most reliable quality assurance practices, it is also time-consuming, cognitively demanding, and difficult to scale in contemporary development environments where teams continuously integrate and deploy new code. This study proposes an automated code review framework for Python 3.13 projects that combines Large Language Models with Retrieval-Augmented Generation (RAG) to generate more accurate, context-sensitive, and actionable review comments. The proposed framework first constructs a dataset from GitHub pull requests collected through the GitHub REST API (version 2022-11-28). Review comments are then organized into semantic categories using a semi-supervised Support Vector Machine (SVM) classifier. During inference, the system retrieves the most relevant historical comments from a vector database and provides them as contextual guidance for multiple open-weight language models, including DeepSeek-Coder-33B, Qwen2.5-Coder-32B, Codestral-22B, CodeLlama-13B, Mistral-Instruct-7B, and Phi-3-Mini. The performance of the framework is evaluated through a comprehensive validation methodology that combines conventional text-generation metrics such as BLEU-4, ROUGE-L, and cosine similarity with semantic evaluation using an LLM-as-a-Judge approach and expert human assessment. Experimental findings demonstrate that retrieval augmentation significantly improves review quality for larger models. DeepSeek-Coder, for instance, achieved a 17.9% improvement in alignment score when retrieval depth was set to k = 3. Smaller models such as Phi-3-Mini, however, experienced context collapse when excessive contextual information reduced the quality of generated feedback. To address this limitation, a hybrid expert-routing mechanism was developed to dynamically assign review tasks to the most suitable model according to semantic category. The proposed system achieved an overall improvement of 13.2% over zero-shot baselines while also reducing hallucinations and generating review comments that closely resemble expert human feedback.
Identity-Conscious Governance for Autonomous AI Agents: A Framework for Enterprise Authorization, Delegation, and Auditing
AI agent systems capable of autonomous operation in enterprise environments are swiftly evolving from research prototypes to production implementations. These technologies acquire ambient credentials, function non-deterministically, and lack standardised methods for associating actions with human identity, resulting in a security stance that is fundamentally incompatible with organisational governance standards. This paper introduces a thorough framework for identity-aware agent governance that addresses five significant deficiencies in existing architectures: (1) the eradication of ambient authority via identity-inherited permissions, (2) infrastructure-level policy enforcement that is independent of model compliance, (3) sequence-aware authorisation capable of identifying composition attacks within the authorised scope, (4) independent action verification that tackles the oracle problem of agents self-reporting their actions, and (5) hallucination-aware audit systems that consider the unreliability of LLM reasoning. Our analysis is based on documented vulnerabilities from production agent systems, including the ClawHavoc supply chain attack (over 824 malicious agent skills), Cisco's discovery that 26% of analysed agent skills exhibit security vulnerabilities, and an architectural examination of credential inheritance patterns in Claude Code Agent Teams, Cursor FastRender (2,000 concurrent agents), and OpenClaw (over 30,000 internet-exposed instances). We assess the relevance of current standards (OAuth 2.0, SPIFFE/SPIRE, Zero Trust Architecture, MCP) and pinpoint particular changes necessary for non-deterministic agent processes. Our primary contribution is a stratified identity model utilising cryptographic delegation tokens that disseminate human authorisation throughout multi-agent chains, facilitating per-call policy assessment at the infrastructure level.
An Integrated Cloud and Network Architecture Utilising AI and LLMs for Secure Web Applications and Financial Fraud Analysis
The swift expansion of cloud-based online applications and digital financial services has markedly heightened the intricacy of security risks and financial crime. Conventional rule-based security solutions and standalone analytics platforms are inadequate for countering advanced cyberattacks and emerging fraud trends. This study presents a cloud and network integrated architecture utilising artificial intelligence (AI) and large language models (LLMs) to improve the security of web applications and facilitate sophisticated financial fraud analytics. The architecture integrates sophisticated extract–transform–load (ETL) pipelines, network-sensitive monitoring, AI-fueled anomaly detection, and LLM-based reasoning to provide real-time and scalable analytics. The suggested approach integrates cloud infrastructure with network telemetry and financial transaction data, facilitating comprehensive visibility, adaptive threat detection, and elucidated fraud insights. Experimental assessment and application analysis reveal superior detection accuracy, diminished response time, and augmented system resilience relative to conventional security and fraud detection methods.
Metadata-Driven Privacy Classification of SQL Server Stored Procedures in Enterprise Web Applications Using Transformer-Based Models
Metadata facilitates semantic interoperability, data reutilization, and effective information retrieval across diverse systems. Nonetheless, current research exhibits disjointed methodologies and fails to provide a unified perspective on the tactics employed across many fields. This fragmentation results in a deficiency in comprehending the present state of the art and recognizing cross-domain trends and issues. This systematic study seeks to fill this vacuum by examining metadata integration strategies across many disciplines, including Health and Medicine, Smart Cities and IoT, Data Science, Geosciences, Cultural Heritage, and Library and Information Science. This evaluation adheres to the Kitchenham framework, which includes the preparation, execution, and reporting stages. A total of 81 peer-reviewed articles published from 2014 to 2023 were selected from five major databases, adhering to established inclusion and exclusion criteria and a systematic quality evaluation method. The findings indicate a majority of ontology utilization, succeeded by metadata-driven language, standards, processes, and standardized metadata schemas, alongside a burgeoning tendency towards automation via the implementation of AI-based methodologies. The recognized limitations encompass semantic heterogeneity, insufficient standardization, restricted automation, and usability concerns in existing tools and systems. We offer a thorough synthesis of current methodologies, emphasizing both domain-specific and cross-domain trends, while pinpointing research opportunities to enhance metadata integration via automation, semantic enrichment, and standardized procedures.
Chatbots and Virtual Assistants May be Used to Examine Conversational AI in the Banking Sector
The use of conversational AI has enhanced service efficiency within the worldwide baking sector. This research seeks to examine the role of AI-powered virtual assistants in enhancing customer service efficiency and satisfaction within the banking industry. AI-powered chatbots have been shown to provide personalised services to users in the banking sector. The use of secondary data and theme analysis has proven advantageous in addressing the requirements of the study goals. Thematic study has elucidated the prospective advantages and drawbacks of using conversational AI in the banking sector.
AI-Optimized Full-Stack Governance: An Integrated Artificial Intelligence Framework for End-to-End Organizational Oversight, Compliance, and Decision-Making
Artificial Intelligence (AI) has emerged as a transformative force reshaping governance frameworks across modern organizations. AI-Optimized Full-Stack Governance represents an advanced, integrated governance model that embeds AI-driven intelligence across all layers of the organizational stack, including infrastructure, data, applications, security, compliance, and strategic decision-making. Unlike traditional governance models that operate in silos and rely heavily on manual oversight, this approach enables real-time monitoring, predictive risk assessment, automated policy enforcement, and continuous performance optimization. The growing complexity of digital ecosystems, cloud-native architectures, big data environments, and global regulatory demands has made conventional governance increasingly insufficient. AI technologies such as machine learning, natural language processing, and advanced analytics enhance governance by interpreting regulations, detecting anomalies, managing compliance, and supporting ethical and transparent decision-making. However, AI-driven governance also introduces critical challenges, including algorithmic bias, explainability, data privacy risks, cybersecurity threats, and the necessity of human oversight. This article provides a comprehensive and in-depth analysis of AI-Optimized Full-Stack Governance, covering its conceptual foundations, architectural design, key components, implementation strategies, benefits, risks, real-world applications, and future directions. The discussion aims to support researchers, policymakers, and organizations in adopting governance frameworks that are intelligent, adaptive, ethical, and resilient in the era of AI-driven digital transformation.
Leveraging Intelligent Master Data Management to Strengthen Real-Time Data Governance and Business Value
In the era of rapid digital transformation, where organizations increasingly rely on data-driven strategies, the ability to manage and synchronize enterprise data in real time has become a critical strategic requirement. Traditional Master Data Management (MDM) systems are progressively proving inadequate in addressing the complexity of modern data environments, particularly those spanning multi-cloud and hybrid infrastructures. This paper explores the integration of Artificial Intelligence (AI) into MDM systems to enable real-time data synchronization and maximize enterprise value through intelligent data governance. AI-enabled MDM platforms facilitate advanced governance capabilities, including dynamic anomaly detection, automated data cleansing, and adaptive metadata management, ensuring consistent and high-quality master data across organizational data silos. By leveraging machine learning, natural language processing, and real-time stream analytics, enterprises can significantly improve operational efficiency, enhance decision-making accuracy, and support scalable data operations. The article examines contemporary AI-driven MDM architectures, real-world enterprise use cases, and key implementation challenges, while also addressing practical considerations related to adoption and system integration. Additionally, it presents a conceptual framework to guide organizations seeking to deploy AI-powered MDM solutions effectively. Ultimately, this study underscores the transformative potential of AI in reshaping enterprise data strategies and enabling sustained competitive advantage in an increasingly data-centric economy.
Artificial Intelligence–Driven Metadata Management for Comprehensive Data Governance
As data ecosystems become increasingly complex, organizations are compelled to implement strong data governance mechanisms to safeguard data accuracy, confidentiality, and regulatory adherence. Within this landscape, metadata management serves as a fundamental component by enabling the systematic organization, classification, and retrieval of data across diverse platforms. However, conventional metadata management techniques often fall short in terms of scalability, flexibility, and responsiveness to real-time data demands. This study presents an AI-driven metadata management framework that employs machine learning (ML) and natural language processing (NLP) to automate metadata categorization, contextual interpretation, and adaptive policy enforcement. By integrating AI capabilities, the proposed approach enhances the precision, performance, and scalability of metadata governance within heterogeneous data environments. Experimental findings indicate notable improvements in data discoverability, efficient lineage tracing, and sustained compliance with regulatory requirements. The proposed framework offers a comprehensive solution for modern enterprise data governance by transforming metadata management into an intelligent, adaptive, and proactive function. Ultimately, the study highlights the transformative role of AI in strengthening decision-making processes, improving data security, and ensuring continuous regulatory compliance within evolving organizational data infrastructures.
Metadata-Driven Approaches to AI-Ready Data: Improving Data Discovery and Usability
As organizations increasingly leverage Artificial Intelligence (AI) to foster innovation and improve operational performance, the availability of high-quality, well-documented, and easily accessible data has become a decisive factor for success. This paper investigates the pivotal role of metadata—comprising descriptive, structural, and administrative information that contextualizes data assets—in enabling AI-ready data environments. By examining implementation experiences from the financial services, healthcare, and retail sectors, this study illustrates how comprehensive metadata frameworks enhance data discoverability, contextual understanding, reliability, and reusability across enterprise systems. The analysis demonstrates how metadata underpins the entire AI lifecycle, from data acquisition and preparation to model development, evaluation, deployment, and governance. Drawing on real-world practices, the paper highlights the contribution of metadata to reducing time-to-insight, enabling automated data lineage tracking, and ensuring compliance with governance and ethical AI standards. Furthermore, it presents architectural best practices for integrating metadata frameworks—such as data catalogs, knowledge graphs, and semantic layers—into modern data ecosystems. By recognizing metadata as a strategic enterprise asset rather than a secondary operational concern, organizations can substantially improve data quality and usability, accelerate AI adoption, and generate greater business value. The paper concludes with a practical blueprint for metadata-driven AI readiness, offering actionable guidance for data leaders, architects, and AI practitioners seeking to modernize their data landscapes.
Standards and Global Initiatives Shaping Modern Data Mining
Data mining has become a fundamental component of knowledge discovery across diverse domains such as healthcare, finance, scientific research, and business intelligence. As data volumes, sources, and analytical techniques continue to expand, the need for well-defined standards and coordinated initiatives has grown significantly. Standards in data mining provide common frameworks, methodologies, and terminologies that ensure consistency, interoperability, reproducibility, and quality across tools, platforms, and research outcomes. Without such standards, the effective sharing, validation, and reuse of data mining models and results would be severely limited. This abstract examines the role of data mining standards and international initiatives in establishing structured and reliable practices for data analysis. Key standards address areas such as data representation, preprocessing, model description, evaluation metrics, and deployment formats, enabling seamless integration between heterogeneous systems. In parallel, global initiatives led by academic institutions, industry consortia, and standardization bodies aim to promote open data, benchmark datasets, ethical data usage, and collaborative research. These initiatives support transparency, accelerate innovation, and reduce redundancy in data mining efforts. Furthermore, the abstract highlights how standards and initiatives contribute to responsible and trustworthy data mining by incorporating guidelines for data privacy, security, and fairness. As machine learning and artificial intelligence increasingly overlap with data mining, these coordinated efforts play a crucial role in aligning technical progress with regulatory and societal expectations. Overall, data mining standards and initiatives form the backbone of scalable, interoperable, and ethically grounded analytics ecosystems in the data-driven world.
Machine Learning–Driven Strategies for Effective Pileup Suppression
Pileup is a critical challenge in modern high-energy physics experiments, particularly at high-luminosity colliders where multiple particle interactions occur simultaneously within a single detector readout. These overlapping events introduce significant noise and ambiguity, complicating the accurate reconstruction of physics objects such as jets, missing transverse energy, and vertex positions. Traditional pileup mitigation techniques rely on rule-based algorithms and handcrafted features derived from detector geometry and event-level statistics. While effective to a degree, these approaches often struggle to scale with increasing collision rates and detector complexity. Recent advances in machine learning (ML) have opened new avenues for addressing pileup by enabling data-driven, adaptive, and highly granular mitigation strategies. Machine learning models, including supervised learning algorithms, deep neural networks, and graph-based architectures, can learn complex, non-linear correlations between detector signals and underlying physical interactions. By leveraging low-level detector information, such as tracking data and calorimeter deposits, ML-based methods can more precisely distinguish between particles originating from the primary interaction and those produced by secondary pileup events. This abstract explores the application of machine learning techniques to pileup suppression, highlighting their advantages over conventional methods in terms of accuracy, robustness, and scalability. It also discusses the integration of ML models into real-time and offline data processing pipelines, along with challenges related to training data quality, model interpretability, and computational efficiency. Overall, machine learning–driven pileup mitigation represents a transformative approach that enhances signal purity and enables more precise measurements in next-generation particle physics experiments.
Where are the Professors in this Systematic Study of AI's Potential Uses in Universities?
A number of recent studies have identified Artificial Intelligence in Education (AIEd) as a promising new area of educational technology. Although it has been available for over 30 years, educators still don't completely understand how to use it for pedagogical purposes and how it might significantly affect higher education teaching and learning. This paper's overarching goal is to provide a thorough evaluation of the literature on artificial intelligence (AI) and its potential uses in academic settings. The final synthesis includes 146 articles out of 2656 papers initially found for the period between 2007 and 2018, based on specific inclusion and exclusion criteria. According to the descriptive statistics, the majority of the fields represented in AIEd publications are STEM (science, technology, engineering, and mathematics), and quantitative approaches were most commonly employed in empirical investigations. In terms of academic support services and administrative and institutional services, the findings synthesis identifies four areas of AIEd applications: 1. learning, 2. evaluation and assessment, 3. personalization and adaptive systems, and 4. AI-powered instructional platforms. The findings highlight the following issues: the dearth of critical analysis of AIEd's hazards and difficulties; the tenuous relationship to theoretical pedagogical viewpoints; and the necessity for more investigation into educational and ethical methods pertaining to AIEd's implementation in universities.
Intelligent Data Flow Tagging Across Layered Web Application Architectures: A Privacy-by-Design Approach for NET Enterprise Systems
Blockchain technology is rapidly gaining prominence globally. Blockchain, characterized by its decentralized, transparent, and secure attributes, has emerged as a transformative technology for the forthcoming generation of various industrial applications. One such concept is the Cloud of Things, facilitated by the integration of cloud computing and the Internet of Things. In this context, blockchain offers unique ways to tackle difficulties in the Cloud of Things regarding decentralization, data privacy, and network security, while the Cloud of Things provides elasticity and scalability features to enhance the efficiency of blockchain operations. Consequently, a new paradigm of blockchain and Cloud of Things integration, termed BCoT, is increasingly recognized as a promising facilitator for various application scenarios. This paper offers a comprehensive study of BCoT integration, aimed at providing general readers with an overview of BCoT, encompassing basic information, motivation, and integrated architecture. Specifically, we offer a comprehensive analysis of BCoT applications across various use-case domains, including smart healthcare, smart cities, smart transportation, and smart industry. Subsequently, we examine the latest advancements in BCoT alongside the burgeoning blockchain and cloud platforms, applications, and research initiatives. Ultimately, significant research problems and prospective approaches are emphasized to stimulate additional inquiry in this intriguing field.
Data Discovery and Security: Protecting Sensitive Information
The growth of contemporary computer hardware and the availability of soft-computing tools have penetrated the market and influenced prospective research across nearly all areas of education. Management education is not exempt from the growing trends examined by experts recently. Nonetheless, it is a truth that the bulk of research, innovation, and development predominantly concentrate on the fundamental disciplines of science and engineering, either for parametric analysis or for addressing real-world problems. There is significant opportunity for study utilizing empirical methodologies in Management Education. In the context of effective governance, particularly within Public Administration where citizen-centric choices are made, opportunities for prospective study were observed. Consequently, a study is conducted to test the integration of developing technologies and to illustrate its potential connection to practical applications in decision-making systems. The research outcomes, both contemporary and historical, have predominantly concentrated on parametric studies, and solutions derived from study for real-world practical issues are infrequent. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) are a developing computational paradigm inspired by the human brain's functionality, generating significant interest in modeling complex behavioral issues in recent times. This effort attempts to establish a fresh connection between AI and many specific administrative applications. The primary impetus for researchers to examine, study, and employ the knowledge acquisition approach known as neural computing is the accessibility of high-speed digital computers, robust software/languages, and contemporary ideas of machine learning and brain processing. Current and historical literature indicates significant potential for the advancement of modern techniques, such as artificial neural networks, which may address complicated real-world issues that are otherwise challenging and costly to represent analytically or by direct computing. This research aims to address several complicated administrative and management issues that have not been explored inside the realm of AI. Practical examples are chosen from the extensive range of governance systems.
Why Real-Time Data Discovery is a Game Changer for Enterprises
Real-time data processing has emerged as a crucial tool for contemporary enterprises, allowing instantaneous analysis and response to perpetually created data. This study examines the significant influence of real-time data processing on corporate decision-making, emphasizing its capacity to improve responsiveness, operational efficiency, competitive advantage, and risk management. We examine the development and importance of real-time data processing, essential supporting technologies such as Apache Kafka and Apache Flink, as well as solutions like Google Cloud Dataflow and Spark Streaming. We illustrate, via comprehensive case studies in the retail, financial, and healthcare industries, how real-time data processing optimizes inventory management, boosts fraud detection, and improves patient care. Implementing real-time data processing presents challenges, including technological difficulties such as data integration and scalability, concerns over data quality and security, and organizational obstacles including resistance to change and the necessity for specific skills. We examine forthcoming trends and breakthroughs, encompassing the integration of AI and machine learning, progress in edge computing and IoT, and the emergence of applications across diverse sectors. The report finishes with detailed strategic advice for firms contemplating real-time data processing, offering a path for success and underscoring the necessity of remaining informed about breakthroughs to sustain a competitive advantage in technology. By utilizing real-time data processing, firms may facilitate prompt, data-informed decision-making, optimize operations, and foster tailored customer experiences, therefore promoting growth and innovation in a dynamic market landscape.
National Cybersecurity Frameworks for Critical Infrastructure: Lessons from Governmental Cyber Resilience Initiatives
Governments worldwide recognize the increasing threats posed by cyberattacks on critical infrastructure, which encompasses sectors such as energy, healthcare, finance, and transportation. To mitigate risks and enhance resilience, national cybersecurity frameworks have emerged as strategic tools that establish policies, standards, and best practices for protecting essential systems from cyber threats. These frameworks, such as the U.S. National Institute of Standards and Technology (NIST) Cybersecurity Framework, the European Union’s NIS2 Directive, and Australia’s Critical Infrastructure Risk Management Program (CIRMP), provide structured approaches for risk assessment, incident response, and resilience-building. This study examines lessons learned from governmental cyber resilience initiatives, highlighting key success factors and challenges in their implementation. A crucial lesson is the importance of public-private collaboration, as critical infrastructure is often owned and operated by private entities that must align with governmental regulations and threat intelligence-sharing mechanisms. Furthermore, regulatory adaptability is essential, given the rapid evolution of cyber threats, necessitating periodic updates to cybersecurity policies to address emerging risks such as supply chain vulnerabilities, ransomware, and nation-state attacks. Another critical insight is the need for robust incident response and recovery mechanisms, as seen in frameworks that mandate regular cyber drills, penetration testing, and real-time monitoring of critical systems. Countries that have successfully implemented cybersecurity frameworks emphasize capacity building through workforce development, cybersecurity education, and investment in research and development to foster innovation in threat detection and mitigation. However, challenges persist, including compliance burdens on small and medium-sized enterprises (SMEs), the difficulty of enforcing regulations across diverse industry sectors, and the need for international cooperation in combating cybercrime. The study underscores that while national cybersecurity frameworks provide a foundation for resilience, their effectiveness depends on continuous evaluation, stakeholder engagement, and the integration of cutting-edge technologies such as artificial intelligence and zero-trust security models. By analysing governmental cyber resilience initiatives, policymakers can derive actionable insights to enhance national cybersecurity strategies, ensuring that critical infrastructure remains safeguarded against evolving cyber threats. Ultimately, the success of these frameworks lies in their ability to foster a proactive cybersecurity culture, facilitate knowledge-sharing between public and private entities, and maintain regulatory agility to counter emerging digital risks. This research contributes to the ongoing discourse on national cybersecurity policies, offering strategic recommendations to strengthen cyber resilience and protect critical infrastructure from sophisticated cyber adversaries.
Resilient IT Strategies for Governmental Disaster Response and Crisis Management
Governments worldwide face increasing challenges in responding to disasters and crises, necessitating resilient IT strategies that ensure continuity, adaptability, and efficiency. A robust IT framework for disaster response and crisis management integrates cloud computing, artificial intelligence, and cybersecurity measures to enhance decision-making, coordination, and service delivery. Cloud-based infrastructures offer scalability and remote accessibility, enabling real-time data sharing across agencies, while AI-driven analytics facilitate predictive modeling, early warnings, and resource optimization. Cybersecurity safeguards critical systems from cyber threats that often escalate during crises. Additionally, redundancy measures, such as backup data centers and failover mechanisms, enhance operational continuity. Interoperability between governmental and non-governmental entities is crucial, requiring standardized communication protocols and integrated platforms for seamless information exchange. The incorporation of Geographic Information Systems (GIS) aids in mapping affected areas, optimizing resource deployment, and enhancing situational awareness. Furthermore, resilient IT strategies prioritize mobile and edge computing solutions to extend connectivity to field responders in remote or infrastructure-compromised regions. Blockchain technology also contributes by ensuring secure and tamper-proof records for relief distribution and accountability. Governments must adopt agile and adaptive IT governance models, incorporating regular stress testing, training programs, and stakeholder collaboration to strengthen system resilience. Policy frameworks should mandate compliance with international IT resilience standards, emphasizing proactive risk assessments and continuous improvement. Public-private partnerships play a vital role in leveraging technological advancements, ensuring rapid deployment of innovative solutions. Resilient IT strategies also encompass digital inclusion, ensuring equitable access to critical information and services, particularly for vulnerable populations. Social media and crowdsourced data further enhance crisis management by providing real-time citizen reports and facilitating two-way communication. The integration of automated response systems, chatbots, and virtual assistants streamlines public inquiries, reducing response bottlenecks. Additionally, machine learning algorithms can analyze vast datasets to identify emerging threats and inform strategic planning. Governments must institutionalize post-crisis IT assessments to refine their digital response strategies based on lessons learned. By fostering a culture of technological preparedness, collaboration, and innovation, resilient IT strategies empower governments to mitigate the impact of disasters, accelerate recovery, and safeguard national stability. The future of governmental disaster response lies in the continuous evolution of IT capabilities, ensuring that technology remains a cornerstone of crisis resilience and adaptive governance.
Behavioral Analytics for Detecting Insider Threats in Governmental Organizations: A Human-Centric Approach
Behavioral analytics for detecting insider threats in governmental organizations represents a critical area of research focused on identifying malicious or negligent activities from within an organization, often by individuals with authorized access. Traditional security measures, such as firewalls and network monitoring, are insufficient on their own, as they do not capture the complex patterns of human behavior that are often indicative of insider threats. This abstract explores a human-centric approach to behavioral analytics, emphasizing the importance of analyzing user activities, interactions, and behavioral anomalies to detect potential threats. By incorporating machine learning algorithms and advanced data analytics techniques, organizations can establish baseline behavior patterns for individuals, allowing for real-time detection of deviations that could indicate malicious intent or accidental violations. This approach focuses not only on system access logs but also on psychological and behavioral indicators, such as unusual working hours, changes in communication patterns, and access to sensitive data beyond normal job requirements. The goal is to develop a more nuanced understanding of risk by integrating both technological and human-centric factors, recognizing that insider threats often emerge from a blend of personal, social, and professional dynamics. Moreover, such analytics can be used proactively to identify potential vulnerabilities and address them before a serious security breach occurs. This human-centric model, while primarily data-driven, incorporates feedback loops from security teams and human resource departments, allowing for a more comprehensive, multidisciplinary perspective on potential risks. It also seeks to balance security with privacy concerns, ensuring that behavioral analytics are implemented in a way that respects individual rights while protecting sensitive governmental data. Ultimately, the proposed approach offers a more adaptive, dynamic, and effective strategy for mitigating insider threats within governmental organizations, creating a security framework that evolves in tandem with the changing landscape of digital and organizational behaviors.
Next-Generation Threat Intelligence: Securing Governmental Healthcare Networks Against Evolving Risks
As global healthcare systems encounter difficulties including rising prices, restricted access, and increasing demand for personalised treatment, artificial intelligence (AI) is emerging as a pivotal agent of transformation. This assessment is driven by the pressing necessity to leverage AI's capabilities to address these challenges and seeks to critically evaluate AI's incorporation across several healthcare sectors. We examine how AI enhances clinical decision-making, streamlines hospital operations and administration, improves medical image processing, and transforms patient care and monitoring via AI-enabled wearables. We examine several case studies to illustrate how AI has revolutionised particular healthcare sectors, while also addressing the persistent difficulties and potential remedies. Furthermore, we will examine approaches for evaluating AI healthcare solutions, the ethical dilemmas associated with AI implementation, and the significance of data protection and bias reduction for the responsible application of technology. This review provides a critical evaluation of AI's transformational potential, enhancing academics' comprehension of AI's present and future effects on healthcare. It promotes multidisciplinary discourse among academics, physicians, and engineers to address the intricacies of AI deployment, facilitating the creation of AI-driven solutions that emphasise ethical norms, equity, and a patient-centered methodology.
Utilizing APEX Applications in Analytics
In an era of digital transformation, as companies strive to adapt to changing advances and demands, the importance of Customer Relationship Management (CRM) systems increases business relevance. In any firm, the importance of sustaining customer relationships, refining interactions, and improving performance requires superior CRM solutions. Salesforce is considered one of the most suitable platforms for customization and integration. The complexities of business environments result in customer needs for customized solutions that current standard technology cannot provide. This paper will explore the potential of employing Salesforce's own programming language, Apex, to facilitate distinctive integrations in complex business environments. This paper's case studies will demonstrate methods to enhance Apex's performance through the implementation of configuration options, facilitation of scalability, and automation of business procedures. Ultimately, the best practices for Apex development for security, maintenance, and system administration will be integrated to provide customized solutions for managing CRM functionalities.
Role of Data-Driven Decision Making in Enhancing Higher Education Performance: A Comprehensive Analysis of Analytics in Institutional Management
Higher education institutions (HEIs) are increasingly leveraging data analytics to enhance decision-making processes and improve student outcomes. As academic institutions face mounting challenges such as fluctuating enrollment rates, financial constraints, and the demand for improved student retention strategies, data-driven decision-making (DDD) emerges as a critical solution. This paper explores the role of data analytics in higher education management by examining key domains such as student retention analytics, learning analytics, enrollment management, academic performance analysis, financial analytics, and student success metrics. Additionally, it discusses the impact of institutional effectiveness, data integration, data visualization dashboards, student demographic analysis, retention strategies, faculty performance and productivity, student engagement analytics, benchmarking, data-driven curriculum development, alumni analytics, admission analytics, learning pathways and personalization, institutional reporting, student behavior analysis, cloud-based analytics, data governance, data security and privacy, workforce readiness, and competency-based education (CBE) analytics. By employing a combination of theoretical frameworks and case studies, this research identifies best practices for implementing analytics-driven strategies in HEIs. The findings underscore the necessity of integrating data analytics into institutional decision-making to optimize student success, faculty performance, and financial sustainability, ensuring a competitive edge in the evolving academic landscape.