European Advanced Journal for Science & Engineering (EAJSE) -p-ISSN 3050-9696 en e-ISSN 3050-970X
European Advanced Journal of Science & Engineering (EAJSE) ISSN (Print) 3050-9696 ISSN (Online) 3050-970X Publisher EAJSE Editorial Office Publication Type Gold Open Access · Peer-Reviewed · International Frequency Continuous Publication / Quarterly Issues Language English License CC BY 4.0 — Creative Commons Attribution 4.0 International Peer Review Model Double-Blind Subject Area Science · Technology · Engineering · Multidisciplinary Journal Website www.eajse.com Editor-in-Chief [Name, Affiliation, Country] Inaugural Volume [Year] About the Journal The European Advanced Journal of Science & Engineering (EAJSE) — ISSN (Print): 3050-9696 | ISSN (Online): 3050-970X — is an international, peer-reviewed, gold open-access journal dedicated to advancing high-quality original research, review studies, and innovative scholarly contributions across science, technology, and engineering disciplines. The journal serves as a rigorous and trusted scholarly platform for researchers, engineers, academics, and industry professionals worldwide to disseminate impactful findings, theoretical advancements, and applied solutions that drive scientific development and technological progress. EAJSE is indexed in Scopus (Elsevier), Web of Science (Clarivate), DOAJ, CrossRef, and other leading international abstracting and indexing databases, ensuring maximum global visibility, citation discoverability, and long-term scholarly impact for all published research. The journal publishes original research articles, systematic and scoping reviews, technical communications, short reports, and applied case studies across all areas within its defined scientific and engineering scope. EAJSE is committed to the highest standards of research integrity, editorial transparency, and equitable open knowledge — ensuring permanent, unrestricted access to published scholarship for academia, industry, policymakers, and society worldwide. 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Journal Metrics Indicator Value Scopus CiteScore Under Active Evaluation SCImago Journal Rank (SJR) Under Active Evaluation SNIP – Source Normalized Impact per Paper Under Active Evaluation Scopus Subject Classification Engineering · Computer Science · Physical Sciences · Materials Science Acceptance Rate Selective — Rigorous Double-Blind Peer Review Time to First Decision ≤ 15 Business Days Average Peer Review Duration ~50 Days Acceptance to Online Publication 60–90 Days Plagiarism Screening iThenticate / Turnitin (Pre-Review) Peer Review Model Double-Blind Post-Acceptance Indexing Within 4–6 Weeks Article Identifier DOI via CrossRef CiteScore, SJR, and SNIP values will be updated upon completion of the Scopus evaluation cycle. Authors may track current metrics via the Scopus Source List. Open Access & Publication Model EAJSE operates under a full Gold Open Access publishing model, ensuring that all accepted and published articles are freely, immediately, and permanently accessible to readers worldwide — without subscription fees, paywalls, or institutional access barriers — in full compliance with the Budapest Open Access Initiative (BOAI) and published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The CC BY 4.0 license permits unrestricted use, distribution, adaptation, translation, and reproduction in any medium or format, provided the original work is appropriately cited and any modifications are clearly indicated. Article Processing Charges (APCs) are collected solely upon formal manuscript acceptance, following the completion of rigorous peer review. No fees are charged at the point of submission or during the review process. The APC covers all costs associated with editorial management, professional typesetting and formatting, online hosting, long-term digital archiving, and indexing services. Full details on current APC rates, institutional membership pricing, and fee waiver eligibility for researchers from low- and middle-income countries are published transparently on the journal website. The EAJSE Gold Open Access model delivers: Immediate, worldwide visibility and unrestricted accessibility upon publication Enhanced readership, citation potential, and discoverability in Scopus and Web of Science Rapid and broad dissemination of science and engineering scholarship across disciplines Full compliance with major international funder open-access mandates including Plan S, the NIH Open Access Policy, and Horizon Europe requirements Equitable knowledge-sharing across academia, industry, government, and society globally Aims & Scope EAJSE publishes original research articles, systematic and scoping reviews, technical reports, short communications, and applied case studies spanning the full breadth of science, technology, and engineering disciplines. The journal strongly encourages interdisciplinary and cross-domain submissions that address contemporary scientific and engineering challenges through innovative methodologies and applied solutions. Engineering Sciences Civil, structural, and geotechnical engineering; mechanical and manufacturing engineering; electrical, electronic, and power engineering; chemical and process engineering; aerospace and automotive engineering; systems engineering and control theory; materials engineering and applied mechanics. Computer Science & Information Technology Software engineering and system architecture; artificial intelligence and machine learning applications; computer networks and distributed systems; cybersecurity and information assurance; human–computer interaction; cloud computing and edge intelligence; embedded systems and IoT engineering. Physical & Chemical Sciences Applied physics and photonics; quantum science and nanotechnology; computational chemistry and materials modeling; spectroscopy and analytical methods; thermodynamics and fluid mechanics; experimental and theoretical physical sciences. Materials Science & Engineering Functional and smart materials; nanomaterials and nanostructures; biomaterials and biomedical applications; composite and polymer engineering; surface science and coatings technology; materials characterization and testing methods. Environmental & Energy Engineering Renewable energy systems and clean energy technology; environmental impact assessment and remediation; sustainable engineering design; water resources engineering; waste management and circular economy applications; climate-resilient infrastructure and smart grid systems. Biomedical & Health Engineering Biomedical device design and instrumentation; clinical engineering and health technology assessment; computational biology and bioinformatics; medical imaging and diagnostic systems; tissue engineering and regenerative technologies; AI applications in healthcare engineering. Emerging & Interdisciplinary Engineering Quantum computing and quantum engineering; robotics, automation, and autonomous systems; smart manufacturing and Industry 4.0; digital twins and cyber-physical systems; space science and satellite technologies; convergent and hybrid engineering disciplines. Editorial & Publication Ethics EAJSE upholds the highest standards of editorial integrity and scholarly publication ethics, in full alignment with internationally recognized frameworks and guidelines: COPE – Committee on Publication Ethics (Core Practices & Code of Conduct) ICMJE – International Committee of Medical Journal Editors STM Association – Best Practices for Responsible Publishing WAME – World Association of Medical Editors Elsevier Publishing Ethics Standards and Guidelines All submitted manuscripts must satisfy the following conditions: Represent original, novel, and previously unpublished work in any language or medium Not be under simultaneous consideration at any other journal, conference, or preprint server (unless formally declared) Adhere to ethical standards for research involving data, human subjects, experimental models, or AI-generated content Include full and transparent disclosure of funding sources, data availability statements, conflicts of interest, and individual author contribution statements (CRediT taxonomy recommended) All manuscripts are screened using iThenticate / Turnitin prior to peer review assignment. Similarity reports are evaluated by the editorial office in strict accordance with COPE guidelines. EAJSE strictly prohibits: Plagiarism, self-plagiarism, or duplicate publication in any form Data fabrication, falsification, image manipulation, or selective reporting Undisclosed financial or non-financial conflicts of interest Ghost authorship, gift authorship, or misrepresentation of contributor roles Breach of ethical approval requirements for studies involving human participants or sensitive data Suspected violations are investigated using COPE flowcharts and may result in desk rejection, retraction, or formal notification to the author's affiliated institution and funding body. Peer Review Process EAJSE operates a fully double-blind peer review system. Author and reviewer identities remain mutually concealed throughout the entire evaluation process, ensuring impartiality, objectivity, and full protection against bias. Stage 1 — Initial Editorial Screening Every submission undergoes a thorough editorial assessment for scope compliance, structural completeness, language quality, originality, and adherence to EAJSE submission guidelines. Manuscripts that do not meet baseline standards are desk-rejected promptly, with constructive editorial feedback provided to authors. Stage 2 — Double-Blind Peer Review Manuscripts passing initial screening are assigned to a minimum of two independent expert reviewers with established subject-matter expertise relevant to the submission. Reviewers assess scientific validity, methodological soundness, novelty, clarity of presentation, and overall contribution to the discipline. Additional reviewers may be engaged for highly specialized or interdisciplinary submissions. Stage 3 — Editorial Decision Based on consolidated reviewer recommendations, the Editor-in-Chief issues one of the following formal decisions: Decision Description ✅ Accept No further revisions required — proceeds directly to production
Unraveling Structural Fragility in Global Healthcare Supply Chains
Healthcare supply chains have long been viewed as an overly complex, costly ecosystem. Visibility is limited due to data silos and lack of automation, and stringent regulatory certification hinders quality assurance. However, three key vulnerabilities have increasingly attracted attention: (a) dependence on single-source suppliers for critical products, (b) just-in-time inventory management without buffer stock, and (c) fragmented global logistics susceptible to border disruptions during crises. These vulnerabilities do not merely raise costs or reduce service levels; they directly affect patient safety and compromise health systems' resilience. All supply chains are vulnerable to disruption, whether through natural disasters, infrastructure failure, geopolitical tension or, as demonstrated in the COVID-19 pandemic, global health crises. The pharmaceutical and medical device supply chains are no exceptions. These sectors, hitherto operating under a veneer of relative stability, are now reassessing strategies in order to mitigate catastrophe — and attention is shifting to solutions that go beyond individual enterprise-level adaptations.
Healthcare supply chain resilience,Medical supply shortages,Global health crisis preparedness,Pandemic-induced supply disruptions,Critical medical logistics,Pharmaceutical supply chain risk,Medical device availability,Just-in-time inventory failures,Global sourcing dependencies,Health system operational resilience,Emergency procurement strategies,Supply chain risk management in healthcare,Vaccine and PPE distribution challenges,Health infrastructure fragility,Crisis-driven supply chain adaptation.
Healthcare supply chain resilience, medical supply shortages, global health crisis preparedness, pandemic-induced supply disruptions, pharmaceutical supply chain risk, medical device availability, just-in-time inventory failures, global sourcing dependencies, health system operational resilience.
Supply Chain Management, Public Health, Healthcare Operations Management, Logistics, Risk Management, Healthcare Systems Engineering.
Copyright (c) 2024 European Advanced Journal for Science & Engineering (EAJSE) -p-ISSN 3050-9696 en e-ISSN 3050-970X
Adolescent Pathways to Mental Health Care During Extended Pandemic Lockdowns
Abstract Adolescent mental health has become a pressing public health concern, especially during the coronavirus disease 2019 pandemic, in light of prolonged lockdowns. A body of research has documented increased rates of depression and anxiety, and their evolution during subsequent lockdown periods. Notably, many studies indicate that few adolescents seek professional help during these distressing times. In line with health services utilization theory, these findings suggest a disconnect between expressed need and help-seeking behavior. Such research provides a wealth of information concerning the availability, accessibility, and effectiveness of mental health services during the pandemic. Perspectives on adolescents’ mental health service utilization—that is, whether and when to access services—have, however, remained marginal. By integrating information from multiple sources, these utilization patterns can be better understood. The present abstract aims to provide an overview of the determinants of adolescents’ mental health service seeking during prolonged periods of lockdown. The discussion focuses on issues within the adolescent’s life context and perceived needs, along with important influences originating from parents, the broader social environment, and the health system. Service utilization trends can shed light on whether adolescents sought care when required, and the reasons that might explain patterns. Evidence suggests an overall increase in the need for psychological care during lockdown periods, reflected in rising demand from both adolescents and their parents. However, adolescents appear to prefer communicating their emotional distress with family members or friends instead of professionals. Most adolescents experiencing risk factors for depression and anxiety do not seek help, even though those with higher levels of perceived need are likelier to do so. Help-seeking behavior is influenced by multiple factors, including age, gender, ethnicity, socio-economic status, stigma, and accessibility. Survey results indicate that stigma represents one of the main barriers to seeking help.
Adolescent mental health, service utilization, lockdown effects, telehealth, accessibility, stigma,Adolescent mental health, Mental health service utilization, Prolonged lockdowns, COVID-19 pandemic impact,Tele-mental health services, Access to mental health care, psychological distress in adolescents, Health-seeking behavior, Barriers to mental health services, Youth coping and resilience.
Adolescent Mental Health; Help-Seeking Behavior; Mental Health Service Utilization; COVID-19 Pandemic; Telehealth
Public Health; Child and Adolescent Psychiatry; Clinical Psychology; Health Services Research; Behavioral Sciences
Epidemiological Fallout from Immunization Program Interruptions
Disruptions in routine immunization programs can undermine population immunity and lead to outbreaks of vaccine-preventable diseases (VPDs). In addition to the direct threat posed by COVID-19, operational and logistical challenges, disruption of supply chains, and the effect of COVID-19 on the health system, health workforce, and disease surveillance can hinder vaccination and leave children unprotected against diseases such as measles, polio, and yellow fever. Monitoring vaccination coverage, adaptation of immunization activities, and strengthening sentinel surveillance can facilitate early detection of VPD outbreaks. Recovery in routine immunization programs requires not only the resumption of normal health service delivery but also catch-up campaigns, improvement in supply chains, and intensive engagement with affected communities. Vaccination coverage is usually monitored on the basis of reported national administrative data but without formal validation, which compromises confidence in estimates of the actual number of children vaccinated, especially during wartime or severe epidemics. Coverage has fallen in many countries. Despite recommended herd immunity thresholds of at least 95%, suboptimal immunity may persist in areas with low vaccination coverage. Even in countries where high coverage is achieved at the national level, pockets of low uptake can facilitate the spread of VPDs.
Routine immunization disruption,Vaccine coverage decline,Immunization service interruption,Vaccine-preventable disease resurgence,Herd immunity gaps,Public health system resilience,Health service disruptions,Pandemic impact on immunization,Childhood vaccination delays,Missed vaccination opportunities,Health inequities in immunization,Supply chain interruptions for vaccines,Vaccine stockouts,Community-level disease outbreaks,Maternal and child health outcomes,Surveillance gaps in immunization programs,Catch-up vaccination strategies,Immunization program recovery,Morbidity and mortality from preventable diseases,Global immunization coverage trends
Epidemiology, Public Health, Immunization, Vaccine-Preventable Diseases, COVID-19 pandemic impact on health services.
Public Health, Epidemiology, Health Policy, Immunology, Global Health, Health Systems Research.
The Quiet Threat of Post-Pandemic Healthcare Worker Fatigue on Inpatient Safety Outcomes
Burnout is an occupational hazard in healthcare, often linked to reduced quality and safety. An investigation on the relationship between such problems induced by the COVID-19 pandemic and patient safety in acute-care hospitals is especially timely. Analysis of burnout trajectories suggests chronic levels of emotional exhaustion among nurses and support staff and sustained elevations in depersonalization among nurses throughout the pandemic. Evidence that higher burnout correlates with increased rates of medical errors or adverse events, a weakened safety culture, and negative reporting attitudes provides a foundation for associated decreases in patient safety. Although several mechanisms have been proposed, none have been directly tested. The elevated level of pandemic-related stressors faced by acute-care hospitals may exacerbate the usual burnout–safety relationship. Nevertheless, the available information remains useful for guiding practical action, future research, and persuasively informing policy.
Pandemic; Burnout; Patient Safety; Acute Care; Healthcare Organizations; Crisis Management;Workforce burnout;Pandemic-related stress;Patient safety outcomes;Acute care hospitals;Healthcare worker fatigue;Nurse burnout;Physician burnout;Medical errors;Quality of care;Occupational stress;Staffing shortages;Mental health of healthcare workers;Clinical performance;Safety culture;Healthcare system resilience.
Subjects Disciplines Supporting Agencies Coverage Rights Source Type Data Availability Statement
Healthcare/Medicine, Nursing, Public Health, Occupational Health, Hospital Administration/Healthcare Management
The Rise of Telehealth in Primary Practice and Its Effect on Clinical Results During COVID-19
The COVID-19 pandemic precipitated a rapid shift to virtual service delivery in the United States, with telehealth adoption soaring across clinical specialties, patient conditions, and visit types. Nevertheless, evidence supporting telehealth’s effectiveness in primary care remains limited, especially for acute and preventive services. This study tested telehealth adoption and clinical outcomes across 54 primary care clinics affiliated with a single health system in the diverse, urban, and often low-income environment of Los Angeles County. Hierarchical multivariable models assessed clinical outcomes for 27 chronic disease cohorts, along with rates of acute care visits, preventive services, and safety events. The results indicated associations between elevated telehealth use and reduced utilization of in-person services for acute conditions such as respiratory infections, without negative effects on quality or patient-reported outcomes. Findings suggest that telehealth can facilitate quality primary care for well-defined conditions despite relatively low-volume adoption, and may serve as an effective adjunct to in-person care for preventive, chronic, acute, and mental health needs. Though telehealth is not a panacea, the evidence, when viewed in aggregate, supports its nuanced deployment as a patient-centered alternative within the wider multichannel service portfolio.
Telehealth; primary care; COVID-19; outcomes; telemedicine; video visits; telephonic visits; chronic disease management; preventive care; safety; quality; acute care; patient-centeredness; equity; technology use.
Telehealth; primary care; COVID-19; telemedicine; chronic disease management; preventive care; acute care; patient safety; quality of care; health equity; technology use (drawn directly from the paper's keywords).
Medicine (primary care, internal medicine, family medicine, pediatrics, obstetrics-gynecology, psychiatry), public health, health informatics, and health policy.
Copyright (c) 2023 European Advanced Journal for Science & Engineering (EAJSE) -p-ISSN 3050-9696 en e-ISSN 3050-970X
Machine Learning–Driven Compliance Intelligence Framework for Automated Vulnerability Mitigation and Continuous Audit Preparedness
Automation of security compliance is limited by the need for continuous monitoring and remediation of vulnerabilities, the foundation of almost all security-related policies. By explicitly linking vulnerability management and policy documentation, a machine-learning model enables not just vulnerability-closure workflows, but also automatic generation of audit-related artifacts—such as evidence of compliance, demonstrable signature of key decision-making, and policy-enforcement operation logs—that together facilitate continuous audit readiness. Orchestration of remediation efforts using risk scores derived from machine-learning-based security analyses allows justification of prioritization decisions based on changes in policy context. Persistence of security risks and resource constraints often lead organizations to prioritize remediation of vulnerabilities with known exploits over true risk, leaving the most dangerous unpatched. Integrating alerting and remediation capabilities into one artifact provides a crucial foundation to support automated triage and remediation orchestration, ensuring that even vulnerabilities without obvious management precedence receive timely remediation without draining resources. The ability to generate remediation is augmented with a second component: support for the production of evidence and process-mapping artifacts needed for audit readiness. Audit preparation and compliance validation are two of the biggest operational burdens organizations face, yet both can be partly automated through continuous monitoring of infrastructure changes and of knowledge repositories, such as audit logs and change approval records.
Compliance Automation, Continuous Monitoring, Vulnerability Management, Audit Readiness, Machine Learning, Security and Compliance Explainability
Compliance Automation, Vulnerability Management, Continuous Monitoring, Audit Readiness, Machine Learning, Security Explainability (as stated in the Keywords).
Cybersecurity, Information Systems, Machine Learning / Artificial Intelligence, Software Engineering.
Copyright (c) 2025 European Advanced Journal for Science & Engineering (EAJSE) -p-ISSN 3050-9696 en e-ISSN 3050-970X
Next-Generation Database Hardening and Threat Visibility Framework for Hybrid AWS-Azure Clouds with Wiz Analytics
Cloud services allow enterprises to establish hybrid, multi-cloud environments. Such proliferation increases complexity and the attack surface, leading to needed security-, privacy-, and accessibility-compliance controls. Existing tools underutilize cloud providers’ compliance tools. Wiz automates asset inventory, vulnerability management, compliance drift detection, risk insights, and identity and access management. Cloud-integrated database hardening uses Wiz insights and guidance to automate hardening and compliance in heterogeneous clouds. Assets—data stores, storage accounts, and databases—proliferate in Azure and AWS, often within a single logical entity. A framework automates security posture, improving cloud-integrated database hardening in hybrid AWS–Azure environments. Data collection and telemetry follow Wiz-driven insights. Controls satisfy technical risks for hybrid asset-sharing scenarios. Attack surfaces and risks in heterogeneous AWS–Azure environments are specified and mitigated. Security controls are mapped to family and subfamily participants. Identity and access management splits responsibilities between Azure and AWS security flaws. Database service encryption meets Azure Secrets store and KMS risk categories. The threat landscape of hybrid solutions is broader than that of single- or multi-cloud deployments. Data-exfiltration-database-leaking-risk scenarios for Azure are addressed by Wiz identity and access management recommendations and Microsoft and AWS database-service data-in-transit-and-at-rest-encryption controls. These recommendations automate security-compliance attestation. Azure data resource characteristics and Wiz approach are combined with risk categories for remaining clouds. A simple security-architecture pattern for control-expression mapping creates the required hardening.
: Cloud-integrated database security,Hybrid cloud security architecture,AWS–Azure hybrid environments,Database hardening framework,Cloud security posture management (CSPM),Automated security posture assessment,Wiz security insights,Multi-cloud risk visibility,Cloud-native database protection,Zero trust data security,Security posture automation,Misconfiguration detection in cloud databases,Compliance monitoring in hybrid clouds,Continuous security assurance,Cloud attack surface management,Identity and access management for databases,Infrastructure-as-code security validation,Threat modeling for hybrid databases,Cross-cloud governance and controls,DevSecOps-driven database security
Cloud security, database hardening, hybrid cloud environments, threat visibility, identity and access management, compliance monitoring
Computer Science / Cybersecurity / Cloud Computing / Information Systems
IntelliChain Automotive: Embedding Machine Cognition into Dispersed Supply Logistics Networks within Industry 4.0 Operational Boundaries
Digital transformation in automotive supply networks—typically characterized by Just-in-Time principles and high-risk vulnerability to shocks—enables predictive logistics: a predictive-prescriptive-optimization-enhanced real-time decision-support paradigm spanning demand forecasting, inventory optimization, transportation planning, and capacity network design. Supply networks supporting Industry 4.0 detect, analyze, and respond rapidly to changes, disruptions, and stresses, in the process minimizing total cost of ownership while improving lead-time and service-level targets predicted with a digital twin. Data ecosystems capture, aggregate, analyze, and disseminate data streams generated by a network’s partners employing standardized discrete-event, agent-based, and machine-learning models, deployed within ultra-responsive edge cloud and fog architectures. Despite long-standing research interest, relatively few predictive-logistics applications exist, signaling a gap that demands investigation. Evidence presented here addresses an objective defined through literature synthesis: Explore Industry 4.0 drivers and constraints while assessing the impact of emerging Industry 4.0 capabilities on predictive logistics for distributed automotive supply networks. Key findings illustrate the performance benefits of predictive-logistics in two empirical cases describing automotive semiconductor supply chains and electric vehicle battery and component logistics.
Artificial Intelligence, Predictive Logistics, Supply Chain Management, Distributed Automotive Networks, Industry 4.0, Edge Computing.
Artificial Intelligence, Predictive Logistics, Supply Chain Management, Distributed Automotive Networks, Industry 4.0, and Edge Computing.
The paper spans supply chain engineering, logistics, operations research, and computer science — specifically covering demand forecasting, inventory optimization, transportation planning, and capacity network design within an Industry 4.0 context.
Beyond the Pipeline Embedding Generative AI Risk Intelligence into Cloud-Native Financial Transaction Architectures
The increasing frequency, sophistication, and financial impact of cyberattacks against financial institutions demand a proactive instead of a reactive approach. One current direction consists of the adoption of Cloud-Native Architecture principles supported by DevOps practices driven by Automation and AIOps tools supplied as-a-Service by cloud providers. Taking into consideration the property of security for the deployment of Payment Systems is paramount, it is proposed a Security-as-a-Service solution using Generative Artificial Intelligence techniques to induce Risk Models. This approach applied within the Design-and-Build phases of the DevOps Automation might link the application code with the risk/security posture of the Payment System and support effective decision-making regarding the acceptance/rejection of the software deployment or update. Generative Artificial Intelligence techniques have been used for textual completion since the introduction of the Transformer architecture. It can be used to induce Risk Models since usually a Risk Model can be represented in a tabular structure summarized by its three fundamental components: Asset, Threat, and Vulnerability. Recent advances in confidential computing and secure enclaves enable the processing of sensitive information without exposing the input data.
DevOps, financial services, payments, security, cloud, microservices, infrastructure, continuous delivery, automation, generative AI.
The paper does not list a formal "Subjects" field. However, based on the keywords provided: DevOps, financial services, payments, security, cloud, microservices, infrastructure, continuous delivery, automation, and generative AI.
Interpretable AI and ML Models for Transparent Clinical Decision-Making
Artificial intelligence has the potential to augment clinical decision making. By learning patterns of risk and disease directly from empirical data, AI methods offer one solution to the difficulty health care professionals face in considering ever-increasing amounts of information. Clinicians making a medical decision for a patient want not only an accurate estimate of the risks associated with their patient's disease or treatment options but also an understanding of the reasoning behind these risks. This desire for explanation drives the growing interest in explainability in AI, particularly in AI for health care. Explainable Artificial Intelligence (XAI) is defined as methods that generate new AI models for which the behaviour can be understood, directly or indirectly, by humans. The concept of human understanding encompasses three different levels – transparency, interpretability and accountability. The heart of the concern for transparency in AI is the incomprehensibility of the learned representations, the “black box” nature of the complex function learned from the training data.
Artificial Intelligence in Healthcare, Clinical Decision Support Systems, Explainable Artificial Intelligence (XAI), Model Transparency in Medicine, Interpretability of Machine Learning Models, Accountability in Clinical AI, Risk Prediction and Prognostic Modeling, Black-Box Model Limitations, Human-Centered AI Design, Trustworthy Medical AI, Ethical AI in Healthcare, Model Explainability Frameworks, Data-Driven Clinical Risk Assessment, AI Governance in Health Systems.
The paper covers Explainable AI (XAI), clinical decision support systems, machine learning interpretability, and transparent medical AI.
Artificial Intelligence, Healthcare/Medicine, Biomedical Informatics, and Data Science.
Copyright (c) 2026 European Advanced Journal for Science & Engineering (EAJSE) -p-ISSN 3050-9696 en e-ISSN 3050-970X
Cloud Computing Architectures Enabled by AI for Predictive Financial Market Insights
The abstract summarizes the research background, methodology, and results. Given the stochastic nature of financial markets, AI models, particularly recurrent neural networks (RNNs) and LSTM networks, have gained traction. Cloud architectures and services can facilitate consumption and offer new paradigms for predictive financial analytics. Past literature has primarily focused on the predictive accuracy and robustness of AI models with economic considerations having a peripheral role. Past voting models suffer from overfitting. The curvilinear relationship between portfolio risk and concentration has not been exploited in signal creation or return prediction. Time series forecasting models, K-Nearest Neighbour classifiers, and supervised regression approaches have offered mixed results. AI-driven investment strategies can be economically valuable, generating excess returns after accounting for transaction costs. Major capabilities include Infrastructure as a Service, Platform as a Service, and Software as a Service. Performance, scalability, latency, and potential for real-time processing are the key factors shaping industry adoption and determining system architecture. The study proposes a distributed framework for signal generation and portfolio strategy backtesting to assess the economic value of AI-driven investment strategies.
Cloud computing paradigms; Predictive analytics; Time series forecasting; Deep learning methods; Infrastructure, platform, and software as a service; Scalability; Latency; Real-time processing; Risk management; Model risk; Model interpretability; Data privacy; Privacy-preserving analytics; Compliance; AI-enabled systems; Equity markets; Fixed income and derivatives; Data availability and quality issues; Computational costs; Energy efficiency.
Big Data Governance Frameworks in the Cloud for Digital Public Service Platforms
Governments are often viewed by citizens and businesses as the natural owners of public data that are generated or collected in their jurisdictions. It is expected that such data assets, if governed properly, can facilitate the provision of better services, enable data-driven policymaking, foster innovation, and drive new economic opportunities. However, big data present new and complicated challenges. Recent studies indicate data governance has become one of the most puzzling issues for governments pursuing digital transformation, which necessitates the establishment of a governance framework. Indeed, evidence-based guidance to inform the establishment of a big data governance framework for digital public service platforms on cloud infrastructure using a governance-as-a-service model is needed.
Big Data Governance, Digital Public Services, Cloud-Based Governance, Data Governance-as-a-Service, Government Data Platforms, Public Data Sharing, Smart City Data Governance, Data-Driven Policymaking, Public Sector Digital Transformation, Cloud Data Ecosystems, Data Security and Reuse, Government Data Exchange Frameworks.
Big Data Governance, Cloud Computing, Digital Public Services, Data Security, Public Sector Digital Transformation, Smart City Data Governance, Data-Driven Policymaking.
Information Systems, Public Administration, Computer Science, Data Science, Government Policy and Management.