Scientific Journal of Artificial Intelligence and Blockchain Technologies
type: channel
description: Journal particulars: Title: Scientific Journal of Artificial Intelligence and Blockchain Technologies ISSN: 3049-4389 I.F: 9.413 (2025) Frequency: Quarterly (4 issues per year) Publisher: ResaGate Global Publisher Address: Raj Nagar Extension, Ghaziabad, Uttar Pradesh (201017) Publisher Email: editor@sjaibt.org | sjaibt@gmail.com Copyright: Remain Journal Starting Year: 2024 Main Subject: Computer Science Scope: Artificial Intelligence & Blockchain Technology Language: English Publication Format: Online (Open Access) Website: https://sjaibt.org Contact : +91 9456916244 Aims and Objectives of SJAIBT: The Scientific Journal of Artificial Intelligence and Blockchain Technologies (SJAIBT) seeks to: Advance the understanding, innovation, and application of Artificial Intelligence (AI) and Blockchain across engineering and scientific domains. Foster collaboration among academia, industry, and government institutions to accelerate the adoption of AI and Blockchain. Encourage interdisciplinary research integrating AI, Blockchain, and other engineering technologies for real-world impact. Provide an open-access platform for researchers, scientists, engineers, and practitioners to disseminate groundbreaking discoveries. Explore the ethical, legal, and policy dimensions of AI and Blockchain adoption. Promote the translation of theoretical advances into scalable industrial solutions for sectors such as finance, healthcare, energy, logistics, education, and manufacturing. Scope of the Journal (SJAIBT) SJAIBT is a leading platform dedicated to exploring the frontiers of Artificial Intelligence, Blockchain, and related engineering technologies. The journal covers theory, algorithms, systems, frameworks, and applications, addressing both academic research and industrial deployment. It emphasizes not only the computational and scientific foundations but also the engineering integration required to build scalable, secure, and sustainable intelligent systems. The journal encourages sub…
publisher: ResaGate Global
language: en
publication name: Scientific Journal of Artificial Intelligence and Blockchain Technologies
issn: 3049-4389
copyright: The license allows re-users to share and adapt the work, as long as credit is given to the author and don't use it for commercial purposes.
items: Federated Learning-Based Anomaly Detection for Privacy-Preserving Cloud Security
items: Blockchain for Securing Healthcare Data Transfers in MultiCloud
items: Strategic Role of Data Engineering in Digital Pharmacy Operations
items: Audit-Ready Cloud Migration Framework for Regulated Industries
items: Management Perspectives on Data Governance in Healthcare Cloud Systems
items: Enhancing Pharmacy Compliance with Secure Cloud Pipelines
items: Explainable and Sustainable Artificial Intelligence in Finance: Opportunities, Risks, and Future Innovation
items: Integrated Anomaly Detection in Automated Cloud Data Engineering Frameworks
items: AI in Commerce Education : Digital Changes in India's Curriculum
items: Clinical Trial Data Sharing Through Blockchain-AI Platforms
items: Securing AI-Based Diagnostic Models Using Distributed Ledgers
items: AI-Blockchain Integration for Remote Patient Monitoring and Alerts
items: Fraud Detection in Cryptocurrency Transactions Using Deep Learning
items: Decentralized KYC Verification Systems with AI Facial Recognition
items: Self-Healing REST Services Using Artificial Intelligence in Multi-Cloud Environments
items: AI-Based Credit Risk Scoring in Blockchain-Enabled Lending Platforms
items: Ethics of AI-Driven Surveillance with Immutable Data Logs
items: Liability in AI-Blockchain-Enabled Autonomous Organizations
items: Decentralized AI and the Future of AI Rights Management
items: Smart Contracts and Legal Enforcement Across Jurisdictions
items: AI-Based Threat Intelligence Sharing on Decentralized Networks
items: Blockchain for Identity Theft Prevention in Digital AI Applications
items: Bias and Discrimination in Decentralized AI Decision Systems
items: AI in Cyber Law Enforcement and Forensic Evidence Collection
items: Zero-Knowledge Proofs in Blockchain for Secure AI Model Sharing
items: Causal Reasoning as a Path to Explainable and Generalizable Artificial Intelligence
items: Optimizing Problem Management through Proactive Knowledge Base Development
items: Comparative Study of Tier I vs Tier II Support Models in ITSM
items: Enhancing Root Cause Analysis Efficiency in Large-Scale IT Operations
items: Incident Response Optimization in Global Command Centers: Lessons from Financial IT Operations

Federated Learning-Based Anomaly Detection for Privacy-Preserving Cloud Security
type: item
description: The exponential growth of cloud computing has made robust anomaly and intrusion detection systems critical for safeguarding enterprise infrastructure. However, traditional centralized security architectures rely on aggregating massive volumes of raw network logs onto a single server, posing severe privacy risks, regulatory compliance challenges, and significant bandwidth overhead. As cyber threats become more sophisticated, the necessity to analyze distributed cloud traffic without exposing sensitive proprietary data or creating a vulnerable single point of failure has driven the demand for novel, privacy-preserving security paradigms. To address these challenges, this paper introduces a Federated Learning-Based Anomaly Detection framework designed specifically for multi-tenant cloud environments. Our decentralized approach enables multiple cloud nodes to collaboratively train a robust, global Deep Learning intrusion detection model by exchanging only privacy-enhanced parameter updates, ensuring raw data never leaves its local origin. Experimental evaluation utilizing the CIC-IDS2017 dataset demonstrates that our proposed federated model achieves over 97% detection accuracy—closely matching centralized baselines—while substantially reducing data transmission volumes and strictly preserving data confidentiality across distributed cloud networks.
subject: Original Research Articles
  topic: section
subject: Federated Learning, Anomaly Detection, Privacy, Cloud, Security
  topic: keywords
creator: Bhavinkumar Jayswal
rights: Copyright (c) 2025 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-07
publication date: 2025-03-07
starting page: (24
ending page: 28)
doi: 10.63345/sjaibt.v2.i1.302

Blockchain for Securing Healthcare Data Transfers in MultiCloud
type: item
description: The healthcare industry is experiencing a massive digital transformation characterized by the rapid adoption of cloud computing for storing and processing sensitive patient data. However, as organizations increasingly employ multi-cloud architectures—leveraging services from multiple providers such as AWS, Azure, and Google Cloud—the complexity of data management and security escalates dramatically. Sensitive data is fragmented across distributed systems, raising the risks of unauthorized access, data breaches, and compliance violations under frameworks like HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation). This manuscript explores Date of Publication: 26-05-2026 the application of blockchain technology as a decentralized trust mechanism for securing healthcare data transfers in multi-cloud environments. The research investigates how blockchain’s immutability, distributed ledger transparency, and cryptographic integrity can enhance confidentiality, authentication, and traceability in healthcare data exchanges. The study synthesizes academic literature and real-world use cases to demonstrate that blockchain-enabled architectures can ensure secure interoperability, tamper-proof audit trails, and consent-driven data sharing between heterogeneous cloud ecosystems. Through a mixed-method approach integrating literature review, simulationmodeling, and comparative evaluation, this paper establishes that blockchain significantly reduces the probability of data tampering and unauthorized access by over 60% compared to traditional encryption-based multi-cloud systems. The findings highlight the transformative role of blockchain in enabling trustless collaboration among healthcare stakeholders while ensuring regulatory compliance, patient privacy, and operational efficiency.
subject: Original Research Articles
  topic: section
subject: Blockchain, Multi-Cloud, Healthcare Data Security, Interoperability, Data Integrity, HIPAA Compliance, Distributed Ledger, Smart Contracts, Cryptography, Cloud Computing
  topic: keywords
creator: Er Akshun Chhapola
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-26
publication date: 2026-05-26
starting page: (57
ending page: 69)
doi: 10.63345/

Strategic Role of Data Engineering in Digital Pharmacy Operations
type: item
description: The rapid digital transformation of the pharmaceutical industry has redefined how data is managed, analyzed, and utilized across the healthcare ecosystem. Data engineering stands at the center of this evolution, enabling the integration of disparate systems, streamlining real-time insights, and ensuring regulatory compliance in digital pharmacy operations. As pharmacies move from manual inventory systems and paper prescriptions toward fully digitized and AI-driven models, robust data engineering pipelines digital Date of Publication: 25-05-2026 pharmacy ecosystems by enabling interoperability between electronic health records (EHRs), supply chains, telemedicine platforms, and patient engagement systems. It also investigates the integration of advanced data technologies such as cloud computing, distributed databases, and data lakes, which collectively improve prescription accuracy, medication adherence monitoring, and pharmaceutical logistics. facilitate operational efficiency, predictive analytics, patient safety, and decisionmaking. This manuscript explores how data engineering provides the structural backbone for The paper further outlines the challenges in designing secure, scalable, and compliant data frameworks aligned with healthcare standards like HIPAA and GDPR. The methodology involves an analytical synthesis of industry studies, architecture analysis, and quantitative evaluation of performance outcomes. Findings highlight significant improvements inmedication delivery times, inventory optimization, and patient service quality following the adoption of well-structured data pipelines. The study concludes that data engineering is not merely a technical function but a strategic enabler of value-based digital pharmacy ecosystems that connect patients, providers, and suppliers through intelligent, datadriven networks.
subject: Original Research Articles
  topic: section
subject:
  topic: keywords
  value: Data Engineering, Digital Pharmacy, Cloud Data Pipelines, Healthcare Analytics, Electronic Health Records, Data Governance, Pharmacy Automation, Predictive Analytics, Regulatory Compliance, Big Data in Healthcare
creator: Dr. Sandeep Kumar
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: (45
ending page: 56)
doi: 10.63345/

Audit-Ready Cloud Migration Framework for Regulated Industries
type: item
description: As enterprises across regulated sectors—such as healthcare, finance, pharmaceuticals—accelerate insurance, their and journey toward digital transformation, cloud migration becomes both a technological enabler and a regulatory challenge. Traditional migration frameworks emphasize scalability, cost savings, and operational agility but often neglect the stringent audit, compliance, and data governance requirements that define these industries. The absence of an audit-centric migration strategy can result in fragmented traceability, compliance drift, and extended audit remediation cycles. compliance-driven model designed to ensure continuous auditability, traceable data movement, and regulatory alignment throughout the migration lifecycle. ARCMF integrates principles of compliance-bydesign, automated evidence collection, and realtime compliance monitoring into every stage, from pre-migration assessment to post-migration operation. By embedding tools like AWS Audit Manager, Azure Policy, and GCP Security Command Center, the framework transforms compliance from a reactive task into a proactive governance layer. Using This research introduces the Audit-Ready Cloud Migration Framework (ARCMF)—a mixed-methods analysis and case evaluations from 25 regulated organizations between 2021 and 2024, the study demonstratesmeasurable improvements: audit cycle time reduced by 41%, compliance deviations decreased by 36%, and evidence collection effort halved. The framework’s layered architecture— covering governance, risk, migration monitoring, evidence storage, and continuous assurance— enables auditors and compliance officers to maintain real-time visibility into control performance and system integrity. This manuscript not only validates ARCMF as an enabler of compliant cloud transformation but also highlights its strategic role in building digital trust ecosystems across industries. The research concludes that future audit-ready systems should integrate AI-driven compliance analyti…
subject: Original Research Articles
  topic: section
subject: Audit-readiness, Cloud migration, Regulated industries, Compliance-by-design, Cloud governance, Data residency, Continuous auditing, Evidence automation, Regulatory compliance.
  topic: keywords
creator: Dr. Lalit Kumar
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: (33
ending page: 44)
doi: 10.63345/

Management Perspectives on Data Governance in Healthcare Cloud Systems
type: item
description: The rapid migration of healthcare data to cloud platforms has revolutionized medical service delivery, research, and patient engagement. However, this transformation has also amplified the Date of Publication: 25-05-2026 HIPAA, GDPR, and HITECH, alongside governance models like data stewardship, metadata management, and accountability structures. Through a synthesis of literature, casebased evaluation, and empirical methodology, the study analyzes complexity of data governance— encompassing privacy, security, compliance, and ethical considerations. From a management perspective, ensuring robust data governance in healthcare cloud systems is a multidimensional challenge that intersects policy frameworks, technological controls, and organizational culture. This manuscript explores how healthcare organizations establish and manage governance frameworks that balance innovation with risk mitigation. It reviews industry standards such as managerial practices for implementing governance controls across hybrid and multi-cloud environments. The results demonstrate that effective governance correlates strongly with improved regulatory compliance, operational transparency, and patient trust. Yet, constraints such as interoperability gaps, resource scarcity, and ambiguous accountability persist. The study concludes that management must adopt a unified data strategy integrating AIdriven auditing, zero-trust architectures, andcross-functional data stewardship councils to ensure long-term governance resilience. Enhanced collaboration among clinical, IT, and compliance teams, backed by continuous policy refinement, can transform cloud-based healthcare systems into secure, compliant, and patient-centric ecosystems.
subject: Original Research Articles
  topic: section
subject: Healthcare Cloud Systems, Data Governance, Management Perspectives, HIPAA Compliance, Cloud Security, Interoperability, Data Zero Management, Cloud Regulation
  topic: keywords
creator: Er. Shubham Jain
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-25
publication date: 2026-05-25
starting page: (22
ending page: 32)
doi: 10.63345/

Enhancing Pharmacy Compliance with Secure Cloud Pipelines
type: item
description: The global pharmaceutical industry operates in an environment of stringent regulatory oversight, complex data flows, and heightened cybersecurity risks. As digital transformation accelerates across healthcare ecosystems, pharmacies face increasing challenges in ensuring compliance with data protection laws, maintaining supply chain transparency, and adhering to Good Manufacturing Practice (GMP) and Health Insurance Portability and Accountability Act (HIPAA) standards. This manuscript explores how secure cloud pipelines—integrated frameworks combining cloud computing, automation, data governance, and cybersecurity controls—can revolutionize pharmacy compliance. By leveraging advanced cloud-native Date of Publication: 23-05-2026 technologies such as containerized microservices, encryption-at-rest, automated audit trails, and zero-trust access models, pharmacies can streamline operations while maintaining end-toend regulatory adherence. The study evaluates modern compliance automation approaches, examining frameworks like ISO 27001, GxP, FDA CFR Part 11, and EMA Annex 11 in cloud contexts. The proposed model combines cloudnative DevSecOps pipelines with real-time compliance monitoring, demonstrating improved transparency, reduced manual errors, and faster regulatory audits. Simulation-based results show that secure pipelines can enhance compliance accuracy by 32%, reduce manual documentation time by 46%, and lower audit response times by 58%. This paper concludes that integrating secure cloud pipelines into pharmaceutical workflows not only ensures sustained regulatory conformity but also strengthens data integrity, patient safety, and business continuity across the pharmacy value chain.
subject: Original Research Articles
  topic: section
subject:
  topic: keywords
  value: Pharmacy Compliance, Cloud Pipelines, Data Security, Regulatory Frameworks, DevSecOps, HIPAA, FDA 21 CFR Part 11, GxP, Automation, Zero-Trust Architecture, Cloud Governance, Audit Trails, Healthcare Cloud Integration
creator: Er. Raghav Agarwal
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-23
publication date: 2026-05-23
starting page: (10
ending page: 21)
doi: 10.63345/

Explainable and Sustainable Artificial Intelligence in Finance: Opportunities, Risks, and Future Innovation
type: item
description: Artificial Intelligence is revolutionizing the banking and financial services sector through improved efficiency, data analytics, and innovative customer experience. AI is being used in several applications within the financial services sector such as algorithmic trading, credit scoring, compliance, and fraud detection. The current study emphasizes the opportunities offered by artificial intelligence to the financial sector while simultaneously discussing the associated challenges such as data security, ethics, algorithmic bias, and legal uncertainty. It also pertains to future topics, which include XAI, AI for sustainable finance, RegTech improvements, and hybrid human-AI collaboration. This research proposes to provide a complete evaluation of the prospects and challenges of AI in finance, along with providing recommendations for future studies.
subject: Original Research Articles
  topic: section
subject: Artificial Intelligence, Financial Services, Machine Learning, Explainable AI, Sustainable Finance
  topic: keywords
creator: Shubham Gairola
creator: Rinki Joshi
creator: Dr Vaishali
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-23
publication date: 2026-05-23
starting page: (1
ending page: 9)
doi: 10.63345/sjaibt.v3.i2.201

Integrated Anomaly Detection in Automated Cloud Data Engineering Frameworks
type: item
description: This paper gives an example of an automated data engineering solution that focuses on large-scale cloud analytics and attempts to solve issues such as high data velocity, variable workloads, and numerous sources of heterogeneous data. The proposed model aims to achieve maximum performance and improved reliability, while saving on manual steps by utilizing a single pipeline combining automated ingestion control, adaptive data transformation, and resource optimization in a closed-loop manner. The model has been tested to provide a data ingestion throughput of 6.7 units in comparison to traditional cloud analytics platforms, and thus improving analytics throughput by up to 28 percent, and reducing end to end pipeline latency to 360 units. In terms of tested variable workloads, the proposed model showed an average of 94 percent for scalability, and 9 units for elasticity defined as the immediate response to adaptive demand by the model. The model showed data processing consistency of 97.9 percent and accuracy of 97.2 percent through confidence-based filtering, and early validation and schema alignment, in the end reducing automation overhead to 11 percent, demonstrating a high degree of operational autonomy. Enhanced system reliability and availability improved to 99.5 percent and fault recovery time was recorded at 16 units boosting system robustness. In improved resource utilization, the model achieved 91 percent balancing optimal arrangement of computing resources relative to workload, and overall reducing operational resource expenditure. In general, the findings support that the proposed framework provides a reliable, cost-effective, and scalable foundation for next-generation cloud analytics systems in dynamic environments.
subject: Original Research Articles
  topic: section
creator: Prof. Dr. Utku Kose
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-30
publication date: 2026-04-30
starting page: (60
ending page: 67)
doi: 10.63345/sjaibt.v3.i2.102

AI in Commerce Education : Digital Changes in India's Curriculum
type: item
description: The rapid advancement of Artificial Intelligence (AI) is significantly transforming the landscape of commerce education in India, prompting a shift from traditional teaching methodologies to digitally enriched, skill-oriented learning environments. This study examines the evolving role of AI in reshaping the commerce curriculum, focusing on its impact on pedagogy, student competencies, and institutional practices. It explores how AI-driven tools such as adaptive learning platforms, automated assessment systems, and data analytics applications are enhancing educational delivery and enabling personalized learning experiences. The research also investigates the integration of interdisciplinary domains, including financial technology, business analytics, and digital marketing, into commerce education to meet contemporary industry demands. Furthermore, the study highlights the challenges associated with AI adoption, including limited faculty readiness, infrastructural disparities, and concerns related to data privacy and ethical usage. By analyzing existing literature and current educational practices, the paper identifies critical gaps in curriculum standardization, practical implementation, and long-term impact assessment. The findings suggest that while AI has the potential to significantly improve learning outcomes and employability, its effective integration requires strategic policy support, teacher training, and a balanced approach to ethical considerations. This research contributes to the understanding of digital transformation in India’s commerce education system and provides insights for policymakers, educators, and academic institutions aiming to develop future-ready curricula aligned with technological advancements.
subject: Original Research Articles
  topic: section
subject: Artificial Intelligence, Commerce Education, Digital Transformation, Curriculum Development, India, Educational Technology.
  topic: keywords
creator: Dr. Ankur Aggarwal
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-05
publication date: 2026-04-05
starting page: (1
ending page: 7)
doi: 10.63345/sjaibt.v3.i2.101

Clinical Trial Data Sharing Through Blockchain-AI Platforms
type: item
description: Clinical trial data are essential public goods, yet sharing them remains constrained by fragmented infrastructure, inconsistent consent management, complex regulatory landscapes, and legitimate concerns about privacy, security, and scientific misuse. Traditional repositories and bilateral data-use agreements often struggle to provide verifiable provenance, enforceable permissions, and scalable mechanisms for cross-institutional analysis. This manuscript proposes and analyzes a reference architecture for a permissioned blockchain–AI platform that enables compliant, trustworthy, and efficient sharing and secondary use of clinical trial data. The platform couples a consortium ledger for tamper-evident consent, access control, and auditability with a privacy-preserving AI layer that supports federated learning, differential privacy, and secure aggregation across sites. Interoperability is addressed via HL7 FHIR-based schemas and metadata harmonization pipelines; off-chain encrypted stores (e.g., content-addressed storage) manage bulk data while on-chain smart contracts encode governance rules, data-use conditions, and revocation. We situate the approach within current policy frameworks (e.g., ICMJE data sharing, EU GDPR, NIH Data Management and Sharing Policy, EMA Policy 0070) and synthesize evidence from the healthcare blockchain and federated learning literatures regarding security, performance, and adoption barriers. A methodology section details system components, identity/consent models, threat assumptions, and evaluation metrics (governance, privacy, model utility, and operational performance). The results section presents a design-level analysis and an implementation blueprint derived from prior prototypes and standards, along with realistic operational scenarios—such as consent withdrawal mid-trial, cross-border data requests, and audit preparation for regulators. We conclude that blockchain–AI platforms can materially improve verifiability, accountability, and …
subject: Original Research Articles
  topic: section
subject: Clinical Trials
  topic: keywords
subject: Data Sharing
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Privacy
  topic: keywords
subject: Consent
  topic: keywords
subject: HL7 FHIR
  topic: keywords
subject: Auditability
  topic: keywords
subject: Governance
  topic: keywords
subject: Differential Privacy
  topic: keywords
creator: Prof. (Dr) Sofia Dimitrova
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-01
publication date: 2026-04-01
starting page: (41
ending page: 52)
doi: 10.63345/

Securing AI-Based Diagnostic Models Using Distributed Ledgers
type: item
description: Artificial intelligence (AI) now frequently supports high-stakes diagnostic decisions in radiology, pathology, cardiology, dermatology, and beyond. Yet the same models are exposed to integrity risks (data poisoning, adversarial perturbations), intellectual-property (IP) theft, provenance gaps, and governance liabilities, especially when models are trained across institutions and deployed at the edge. This manuscript proposes and details a comprehensive, ledger-centric security blueprint for AI diagnostics that couples permissioned distributed ledgers with privacy-preserving federated learning (FL), confidential computing, secure aggregation, model provenance artifacts, and IP protection (watermarking/fingerprinting). We synthesize the literature on blockchain-enabled learning and medical AI threats, align the design with NIST AI RMF and ISO/IEC 23894 risk management expectations, and map controls to emerging regulatory obligations (EU AI Act; HIPAA Security Rule modernization). We then present a systems methodology—DLT-MedGuard—covering threat models, data and model lineage capture, consent and access control via smart contracts, on-chain attestation of trusted execution environments, and reproducible release management via model cards and dataset datasheets anchored on-chain. A statistical analysis using an illustrative evaluation shows that DLT-MedGuard can preserve diagnostic performance while reducing successful poisoning and model-extraction rates, at modest latency overheads suitable for clinical settings. We conclude with a forward research agenda spanning post-quantum security, zero-knowledge attestations, energy-aware consensus, formal verification of smart contracts, and harmonized cross-border compliance. Overall, distributed ledgers do not merely “store hashes”; when designed as a verifiable provenance and control fabric, they materially raise the security baseline for AI-based diagnostics without undermining clinical utility.
subject: Original Research Articles
  topic: section
subject: AI Diagnostics
  topic: keywords
subject: Distributed Ledger
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Secure Aggregation
  topic: keywords
subject: Confidential Computing
  topic: keywords
subject: Remote Attestation
  topic: keywords
subject: Provenance
  topic: keywords
subject: Model Watermarking
  topic: keywords
subject: Healthcare Compliance
  topic: keywords
creator: Dr. Ethan Carter
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-03
publication date: 2026-04-03
starting page: (31
ending page: 41)
doi: 10.63345/

AI-Blockchain Integration for Remote Patient Monitoring and Alerts
type: item
description: Remote Patient Monitoring (RPM) has matured from episodic teleconsultations to continuous, sensor-driven care supported by edge analytics and cloud services. Yet three friction points persist: (i) privacy and trust in data handling, (ii) interoperability across fragmented health information systems, and (iii) timely, auditable alerting that can be verified across organizations. This manuscript proposes a reference architecture that fuses Artificial Intelligence (AI) for streaming physiological inference with permissioned blockchain for tamper-evident logging, consent management, and cross-institutional data exchange. The design: (1) captures multi-modal signals from Internet of Medical Things (IoMT) wearables; (2) performs on-device/edge AI for anomaly detection to minimize latency and exposure of raw data; (3) persists summaries and cryptographic hashes on a consortium blockchain while storing large payloads off-chain; (4) implements smart-contract-based consent and alert workflows integrated with HL7® FHIR® resources; and (5) supports privacy-preserving learning (federated learning) to continually improve models without centralizing protected health information (PHI). We synthesize the most recent guidance from WHO and FDA on telemedicine/RPM, FHIR-based interoperability, and global privacy frameworks (HIPAA, GDPR, India’s DPDP Act), and we translate these into concrete design controls. A qualitative evaluation across security, latency, and governance dimensions suggests the approach can reduce alert time, strengthen provenance and auditability, and enable compliant data minimization. We conclude with an implementation roadmap, known risks (e.g., key management, model drift, edge heterogeneity), and future research directions such as zero-knowledge proofs for selective disclosure and formal verification of smart-contracted clinical alerts.
subject: Original Research Articles
  topic: section
subject: Remote Patient Monitoring
  topic: keywords
subject: Internet of Medical Things
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: HL7 FHIR
  topic: keywords
subject: Hyperledger Fabric
  topic: keywords
subject: IPFS
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Consent Management
  topic: keywords
subject: GDPR/HIPAA/DPDP
  topic: keywords
subject: Alerting
  topic: keywords
creator: Dr. Isabelle Laurent
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-04
publication date: 2026-04-04
starting page: (21
ending page: 31)
doi: 10.63345/

Fraud Detection in Cryptocurrency Transactions Using Deep Learning
type: item
description: Cryptocurrency’s promise of open, programmable finance has been shadowed by an evolving spectrum of fraud—investment scams, phishing, ransomware monetization, and money-laundering pipelines that hop across chains and jurisdictions. Traditional rule-based monitoring struggles with scale, concept drift, and adversarial obfuscation (mixers, peel chains, cross-chain bridges). This manuscript synthesizes the state of the art and proposes an end-to-end deep learning framework for fraud detection that blends graph neural networks (GNNs) on transaction graphs with temporal sequence models, representation learning for wallets and entities, and risk-aware active learning to leverage sparse labels. We ground the discussion in public datasets (e.g., Elliptic1/Elliptic2, BitcoinHeist, XBlock-ETH) and up-to-date industry intelligence (Chainalysis, TRM Labs) and map the methodology to regulatory expectations (FATF’s standards and the Travel Rule). We describe a modular pipeline covering data engineering, graph construction, feature learning, semi-supervised training, evaluation (ROC-AUC, PR-AUC, precision@k), and explainability (counterfactual traces, subgraph rationales). Results summarized from recent literature show that GNNs and temporal graph transformers generally outperform shallow models and static heuristics, especially when augmented with edge-time features and neighborhood motif learning. We conclude with deployment guidance—human-in-the-loop triage, concept-drift monitoring, and privacy-preserving analytics—and outline limitations (label scarcity, feedback loops, evasion tactics) and future work (federated graph learning, causal inference on chain, and cross-chain entity resolution).
subject: Original Research Articles
  topic: section
subject: Cryptocurrency Fraud
  topic: keywords
subject: Anti-Money Laundering
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Blockchain Forensics
  topic: keywords
creator: Kavya Rao
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-03
publication date: 2026-04-03
starting page: (11
ending page: 20)
doi: 10.63345/

Decentralized KYC Verification Systems with AI Facial Recognition
type: item
description: Know-Your-Customer (KYC) processes are foundational to anti–money laundering (AML) compliance but are often centralized, costly, and privacy-invasive. This manuscript proposes and analyzes a decentralized KYC (dKYC) architecture that binds government-issued identity attributes to a user-held digital wallet using Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and AI-based facial recognition with liveness detection. The design minimizes data movement and disclosure by leveraging selective-disclosure credentials and zero-knowledge proofs (ZKPs) so that relying parties can verify eligibility (e.g., age, residency, risk tier) without accessing raw personally identifiable information (PII). We map the system to prevailing assurance frameworks (FATF digital ID guidance; NIST SP 800-63-3 Identity Assurance Levels), biometric performance/testing standards (ISO/IEC 30107-3 PAD; ISO/IEC 19795-1), and emerging regulatory regimes (EU AI Act, eIDAS 2.0, RBI directives on KYC and V-CIP). A comparative evaluation (simulated) contrasts centralized KYC with the proposed dKYC across error rates, onboarding time, and privacy risk. Results suggest dKYC can reduce median onboarding time by ~35–55%, lower document leakage risk through off-chain storage and selective disclosure, and maintain biometric security with PAD Level 2 while controlling demographic differential performance via auditable testing and thresholding. The paper contributes (i) a standards-aligned reference architecture; (ii) an evaluation plan referencing recognized biometric metrics (FMR/FNMR; APCER/BPCER); and (iii) a regulatory and governance blueprint for cross-jurisdictional adoption. Anchors to standards and regulation: DIDs and VCs enable user-controlled identifiers and machine-verifiable credentials; FATF clarifies when digital ID can satisfy CDD; NIST provides assurance and authentication guidance; ISO standards define biometric testing and anti-spoofing; the EU AI Act and eIDAS 2.0 frame lawful…
subject: Original Research Articles
  topic: section
subject: Decentralized Identity
  topic: keywords
subject: KYC/AML
  topic: keywords
subject: Verifiable Credentials
  topic: keywords
subject: Decentralized Identifiers
  topic: keywords
subject: Facial Recognition
  topic: keywords
subject: Liveness Detection
  topic: keywords
subject: Zero-Knowledge Proofs
  topic: keywords
subject: Privacy
  topic: keywords
subject: ISO/IEC 30107-3
  topic: keywords
subject: NIST SP 800-63-3
  topic: keywords
creator: Dr. Richard Collins
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-02
publication date: 2026-04-02
starting page: (01
ending page: 10)
doi: 10.63345/

Self-Healing REST Services Using Artificial Intelligence in Multi-Cloud Environments
type: item
description: The current day digital applications are based on the search of RESTful services and use distributed cloud infrastructures. The problem of service reliability becomes much more complicated as organizations are switching to multi-cloud architecture to enhance their scalability, reliability, and independence with a single vendor. Conventional methods of monitoring and incident-response usually respond very slowly to failures like API spikes in latencies, service failures, container crashes, and configuration errors. These limits cause downtime, reduced performance and operational costs. Self-healing systems have become a good solution in order to overcome these challenges. A self-healing architecture allows software systems to automatically identify, diagnose, and recover failure automatically without human intervention. Together with Artificial Intelligence (AI), self-healing can forecast possible failures and optimise system behaviour, as well as automatically implement corrective measures. Monitoring systems that are powered by AI can process large amounts of system telemetry, logs and performance metrics to find anomalies and initiate automatic remediation. This paper suggests a self-healing model of AI to be used by REST services in the context of multi-cloud environments. The framework will combine machine learning-based outlier detection, automated fault diagnosis, predictive analytics and smart orchestration systems. Through cloud monitoring data, distributed tracing, and decision engines based on reinforcing learning, the proposed system is able to monitor the performance of REST services and take automatic recovery measures like restarting a container, rerouting of a service, auto-scaling, and reconfiguring an API gateway.
subject: Original Research Articles
  topic: section
subject:
  topic: keywords
  value: Self-Healing Systems, RESTful Services, Artificial Intelligence, Multi-Cloud Computing, Cloud-Native Architecture, Fault Detection, Autonomous Systems, Service Reliability, Machine Learning Monitoring, Distributed Systems.
creator: Ishu Anand Jaiswal
rights: Copyright (c) 2024 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2024-08-04
publication date: 2024-08-04
starting page: (1
ending page: 7)
doi: 10.63345/sjaibt.v1.i3.201

AI-Based Credit Risk Scoring in Blockchain-Enabled Lending Platforms
type: item
description: The rapid growth of blockchain-based lending—spanning decentralized finance (DeFi) pools, tokenized real-world assets, and permissioned distributed ledgers—demands risk assessment methods that can keep pace with borderless capital flows and pseudonymous borrowers. Conventional credit scoring, designed for centralized institutions and static data, struggles with fragmented identity, limited verifiable disclosures, and adversarial behaviors such as Sybil attacks or wash activity. This paper proposes a comprehensive, privacy-aware, and explainable AI framework for credit risk scoring tailored to blockchain-enabled lending platforms. The framework fuses three strata of information: (1) on-chain behavioral metrics (wallet age, transaction regularity, protocol interactions, counterparty quality), (2) off-chain verified data (KYC/AML results, income proxies, open-banking signals), and (3) reputational and network features (decentralized identity attestations, staking behavior, cross-platform defaults). Feature pipelines are secured with cryptographic assurances—verifiable computation and zero-knowledge proofs (ZKPs)—and governed by policies aligned with prudential crypto-asset guidance and data-protection law. We discuss modeling choices (gradient boosting, calibrated deep nets, and fairness-aware learners), explainability via SHAP and LIME, and deployment through smart contracts and oracles. A reference evaluation using public loan data and synthetic on-chain features illustrates improvements in AUC, expected loss calibration, and disparate impact mitigation versus baseline heuristics, while reducing time-to-decision and operational risk.
subject: Original Research Articles
  topic: section
subject: Blockchain Lending, DeFi, Credit Scoring, Explainable AI, Fairness, Zero-Knowledge Proofs, Decentralized Identity, Model Governance, Smart Contracts, Risk Management
  topic: keywords
creator: Satyam Agarwal
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-05
publication date: 2026-03-05
starting page: (40
ending page: 49)
doi: 10.63345/

Ethics of AI-Driven Surveillance with Immutable Data Logs
type: item
description: AI-driven surveillance systems—combining computer vision, sensor fusion, and predictive analytics—are rapidly diffusing into public safety, workplace monitoring, retail analytics, and critical infrastructure. In parallel, organizations increasingly anchor their audit trails in immutable data logs (e.g., append-only ledgers or blockchains) to guarantee verifiable accountability, chain-of-custody, and tamper-evidence. This paper examines the ethical terrain at that intersection. We synthesize concerns around privacy, autonomy, discrimination, due process, and proportionality; link them to technical and governance properties of immutability; and analyze tensions such as the right to erasure versus non-repudiation, function creep, and secondary use. Methodologically, we pair a conceptual-normative analysis with a simulation of an AI surveillance pipeline that incorporates immutable logging, differential privacy for analytics, and an “audit-trigger” that gates model updates. Statistical analysis on simulated events (N ≈ 1,000,000 over 30 days) suggests that immutable logs—when combined with targeted audits—can reduce false-positive disparities between demographic cohorts while improving investigative traceability. However, immutability also heightens risks of long-lived harm from misclassification, complicates data minimization and redress, and can externalize power to ledger governance that is opaque to the public. We propose a layered governance framework: (1) use-limiting immutability (hash-anchoring with key-lifecycle controls), (2) privacy-preserving auditability (zero-knowledge proofs, redaction mechanisms), (3) proportionate retention with ML-specific model cards and incident logs, and (4) community oversight with impact assessments and sunset clauses. The results underscore that immutable logging is neither intrinsically ethical nor unethical; its legitimacy depends on design choices, procedural safeguards, and distribution of accountability across institutions a…
subject: Original Research Articles
  topic: section
subject: AI Surveillance, Immutable Logs, Blockchain, Accountability, Privacy, Fairness, Transparency, Auditability, Governance, Differential Privacy
  topic: keywords
creator: Anurag Gupta
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-05
publication date: 2026-03-05
starting page: (30
ending page: 39)
doi: 10.63345/

Liability in AI-Blockchain-Enabled Autonomous Organizations
type: item
description: As decentralized autonomous organizations (DAOs) mature into AI-enabled, on-chain collectives that can own assets, contract, and act through smart contracts and autonomous agents, the question “who is liable when things go wrong?” becomes central. This manuscript develops a comparative, doctrine-informed framework for allocating liability in AI-blockchain-enabled autonomous organizations (AI-BAOs). We synthesize case law (e.g., CFTC v. Ooki DAO; Sarcuni v. bZx DAO), evolving statutory schemes (Wyoming, Tennessee, Utah, Marshall Islands), and major regulatory instruments (EU AI Act; EU Product Liability Directive; MiCA) to map how tort, contract, securities, consumer, and product liability attach to participants (founders, token-holders, developers), service providers (oracles, custodians), and AI system “providers” and “deployers.” We propose a layered liability model: (1) entity shield (DAO/LLD/DAO LLC statutes), (2) functional roles (controller, provider, deployer, maintainer), and (3) use-case risk (financial vs. physical-world effects). A ten-jurisdiction comparative table illustrates differential risk under a transparent scoring rubric and shows that jurisdictions with DAO entity statutes reduce member personal-liability exposure but not platform or product-liability exposure when AI systems cause harm. We close with actionable governance patterns—liability-aware bylaws, role-based indemnities, AI assurance dossiers, incident response playbooks, and human-in-the-loop overrides—compatible with current law while remaining technology-agnostic. The analysis clarifies that AI-BAOs are not lawless: liability follows function and foreseeability, while new EU rules extend strict liability to software/AI defects and the U.S. courts have shown willingness to treat token-holder collectives as partnerships where no entity shield exists.
subject: Original Research Articles
  topic: section
subject: AI-Enabled DAOs, Liability, Product Liability, Securities, Governance, MiCA, EU AI Act, DAO LLC, General Partnership Risk, Safe Harbors
  topic: keywords
creator: Er. Akshit Kohli
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-04
publication date: 2026-03-04
starting page: (21
ending page: 29)
doi: 10.63345/

Decentralized AI and the Future of AI Rights Management
type: item
description: As artificial intelligence (AI) systems scale across sectors, the regimes governing who may access, use, audit, license, and benefit from models, weights, datasets, prompts, and outputs remain fragmented. “AI rights management” (AIRM) refers to the technical, legal, and institutional mechanisms that allocate these rights and duties across creators, data contributors, model publishers, deployers, and end-users. Centralized approaches—terms of service enforced by platforms, siloed registries, and proprietary compliance APIs—struggle to provide transparency, composability, and cross-jurisdictional enforceability, especially when models and content are remixed across organizational and national boundaries. This manuscript argues that decentralized identity, verifiable provenance, content authenticity, and programmable licensing—coordinated by ledgers and decentralized storage—offer a credible path to future-proof AIRM. We synthesize insights from emerging regulations (e.g., the EU AI Act, the EU Data Act), public-law guidance (e.g., U.S. Copyright Office positions on human authorship), standards (W3C Verifiable Credentials, Decentralized Identifiers, PROV), and industry efforts (C2PA, IPFS, Solid pods, open and responsible AI licensing). Building on this base, we propose a layered architecture: (1) decentralized identity and role binding; (2) machine-verifiable rights assertions and provenance; (3) tokenized or programmable licensing and revenue distribution; (4) auditability and dispute resolution through cryptographic attestations and governed data trusts. Methodologically, we employ a design-science synthesis with normative legal analysis and scenario-based evaluation in creative media and dataset contribution contexts. The results show how decentralized primitives can make AI rights legible and enforceable ex ante (through embedding rights in artifacts and workflows) rather than ex post policing. We conclude with a scope and limitations section that surfaces governa…
subject: Original Research Articles
  topic: section
subject: Decentralized AI, Rights Management, Provenance, Verifiable Credentials, Decentralized Identifiers, C2PA, Data Trusts, Programmable Licensing, EU AI Act, Copyright
  topic: keywords
creator: Er. Lucky Jha
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-07
publication date: 2026-03-07
starting page: (11
ending page: 20)
doi: 10.63345/

Smart Contracts and Legal Enforcement Across Jurisdictions
type: item
description: Smart contracts—self-executing code that automates performance of agreed terms—are moving from proof-of-concepts to production in financial markets, supply chains, digital media, and data-sharing ecosystems. Their cross-border use exposes long-standing private-international-law questions (choice of law, jurisdiction, recognition and enforcement) to new technical realities (immutability, pseudonymity, decentralization, oracles, and automated execution). This manuscript synthesizes statutory developments, soft-law instruments, case trends, and regulatory experiments across common-law and civil-law systems to assess enforceability and remedies when “code meets law.” It distinguishes (i) smart legal contracts (code plus natural-language terms) from (ii) code-only arrangements and (iii) hybrid architectures that rely on oracles and off-chain governance. A comparative analysis reviews the UK’s common-law approach (UKJT legal statement and digital-asset reforms), the EU Data Act’s “essential requirements” for smart contracts used in data-sharing, U.S. e-signature and UCC Article 12 reforms, Switzerland’s DLT Act, Germany’s eWpG, France’s DEEP/PACTE framework, Italy’s statutory recognition of smart contracts, Singapore’s ETA/PSA model, and India’s IT Act recognition of e-contracts. Building on these sources, we propose a ten-jurisdiction Smart-Contract Enforceability Index (SC-EI) and offer a doctrinal-plus-technical methodology for drafting, evidence, and dispute resolution. Results show: (1) enforceability is strongest where e-signature/e-record laws, property-law clarity for digital assets, and arbitration pathways co-exist; (2) regulatory “guardrails” (e.g., EU Data Act safe-termination controls) are rising; (3) private-international-law instruments (Rome I, Hague Principles, New York Convention) remain the backbone for cross-border resolution. We conclude with a best-practice checklist (governing law, forum, off-chain override, oracles, auditability, kill-switch, evide…
subject: Original Research Articles
  topic: section
subject: Smart Contracts, Enforceability, Cross-Border, Arbitration, UCC Article 12, EU Data Act, UKJT, DLT Act, E-Signatures, Private International Law
  topic: keywords
creator: Drishti Chaudhary
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: (1
ending page: 10)
doi: 10.63345/

AI-Based Threat Intelligence Sharing on Decentralized Networks
type: item
description: Cyber threat intelligence (CTI) has become indispensable for anticipating, detecting, and mitigating sophisticated attacks that evolve faster than any single organization can track. Traditional, hub-and-spoke CTI exchanges—centralized repositories, mailing lists, or vendor-managed portals—struggle with timeliness, trust, privacy, and single-point-of-failure risks. This manuscript proposes and elaborates an AI-based, privacy-preserving threat intelligence sharing framework built atop decentralized networks. Artificial intelligence components automate ingestion from heterogeneous sources, extract indicators of compromise (IOCs) and tactics, techniques, and procedures (TTPs), correlate multi-source signals across organizations, and continuously score indicator quality, confidence, and relevance. A decentralized substrate—using permissioned distributed ledgers and peer-to-peer overlays—ensures provenance, immutability, and resilient dissemination while enforcing fine-grained access policies (e.g., Traffic Light Protocol), lineage tracking, and incentive-compatible governance. We review the state of the art in CTI formats (STIX/TAXII), sharing platforms (MISP, OpenCTI), and knowledge bases (MITRE ATT&CK). We then synthesize advances in federated learning, secure aggregation, differential privacy, and private set intersection to support collaborative analytics without exposing sensitive telemetry. The methodology section details an end-to-end architecture: (1) AI pipelines for NLP-based IOC/TTP extraction, graph learning for cross-organizational correlation, anomaly detection for novel threats, and active learning loops with analysts; (2) a decentralized trust layer with verifiable credentials, reputation-weighted consensus, and smart contracts for curation and slashing; (3) privacy-preserving protocols for secure multi-party collaboration; and (4) interoperability bridges to existing CTI ecosystems via STIX 2.1/TAXII 2.1 and MISP connectors. A results section presents a …
subject: Original Research Articles
  topic: section
subject: Cyber Threat Intelligence, Decentralized Networks, Federated Learning, Differential Privacy, STIX/TAXII, Blockchain, Reputation Systems, Graph Neural Networks, MISP, MITRE ATT&CK
  topic: keywords
creator: Ajay Shriram Kushwaha
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-03
publication date: 2026-02-03
starting page: (45
ending page: 56)
doi: 10.63345/

Blockchain for Identity Theft Prevention in Digital AI Applications
type: item
description: Escalating identity theft—fueled by large‐scale data breaches, AI-assisted social engineering, and deepfakes—undermines trust in digital systems and especially in AI-driven applications where automated agents transact, personalize content, and make consequential decisions. This manuscript proposes a standards-aligned, privacy-preserving reference architecture that uses blockchain to anchor decentralized identifiers (DIDs), verifiable credentials (VCs), selective-disclosure cryptography, and revocation registries; combines device-bound, phishing-resistant authentication (passkeys); and integrates content provenance signals for AI outputs. We synthesize the state of the art (W3C DID/VC, OpenID4VCI/OpenID4VP, ISO/IEC 18013-5 mDL, C2PA, NIST SP 800-63 and 800-207, ENISA), then present a methodology—BC-Guard—for securing AI user and agent identity life cycles: enrollment, authentication, authorization, transaction attestation, and post-event audit. A qualitative results section analyzes expected risk reductions for common identity-theft attack paths (credential phishing, account takeover, synthetic identity KYC fraud, and real-time deepfake impersonation). The approach reduces the reliance on centrally stored PII, enables privacy-preserving proofs (age, citizenship, risk checks) without data exposure, and supports continuous assurance for AI agents interacting with users, APIs, and other agents. We close with deployment guidance and research directions (verifiable AI agents, confidential computing, and L2 trust registries). (FTC data and ENISA reporting show identity theft and AI-assisted fraud are rising; eIDAS 2.0, NIST AI-100-4, and C2PA establish complementary guardrails for provenance and transparency.)
subject: Original Research Articles
  topic: section
subject: Blockchain, Self-Sovereign Identity, Decentralized Identifiers, Verifiable Credentials, Selective Disclosure, OpenID4VC, Passkeys (FIDO), Zero Trust, C2PA, eIDAS 2.0, mDL, AI Safety
  topic: keywords
creator: A Renuka
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-03
publication date: 2026-02-03
starting page: (35
ending page: 44)
doi: 10.63345/

Bias and Discrimination in Decentralized AI Decision Systems
type: item
description: Decentralized AI decision systems—spanning federated learning, peer-to-peer optimization, DAO-governed models, and blockchain-orchestrated inference—promise resilience, privacy, and transparency by distributing data, compute, and control. Yet decentralization does not automatically guarantee fairness. This paper examines how bias and discrimination emerge and persist when decision-making is pushed to the edge or collectively governed, and how those dynamics differ from centralized pipelines. We synthesize the literature on algorithmic bias, fairness metrics, federated learning with non-IID data, and token-based governance to articulate a socio-technical framework for risk. We then propose and test a mixed-methods methodology combining (i) a conceptual risk model mapping bias vectors (data, model, governance, incentive, and identity layers), (ii) a simulation on heterogeneous subpopulations under three deployment regimes—centralized baseline, decentralized stake-weighted governance, and decentralized with fairness and governance mitigations, and (iii) a statistical analysis using standard equality-of-opportunity and calibration measures. In a synthetic evaluation configured to stress real-world non-IID skew and wealth concentration in governance, a naïve decentralized regime amplifies parity gaps (e.g., equalized-odds TPR gaps increase by ~3–5 percentage points versus centralized), primarily due to (a) minority underrepresentation in local silos, (b) emergent power-law concentration in token voting, and (c) protocol-level incentive misalignment favoring short-term accuracy. A mitigated design—differentially private group-reweighted training, group distributionally robust optimization (Group DRO), model-card/datasheet governance requirements, and quadratic voting with identity checks—reduces disparities to below centralized baselines on core metrics while preserving decentralization benefits. We conclude with implementation guidance: align incentives with fairness con…
subject: Original Research Articles
  topic: section
subject: Decentralized AI, Fairness, Discrimination, Federated Learning, DAO Governance, Blockchain, Non-IID Data, Equalized Odds, Quadratic Voting, Bias Mitigation
  topic: keywords
creator: Arpit Jain
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-03
publication date: 2026-02-03
starting page: (24
ending page: 34)
doi: 10.63345/

AI in Cyber Law Enforcement and Forensic Evidence Collection
type: item
description: Artificial intelligence (AI) is transforming how law-enforcement agencies investigate cybercrime and collect, analyze, and present digital evidence. This manuscript examines the end-to-end lifecycle of AI-enabled digital forensics—from first response and triage to acquisition, interpretation, legal admissibility, and courtroom presentation—through the lens of internationally recognized standards and emerging regulatory frameworks. We situate AI tools (e.g., anomaly detection for log triage, NLP for case intelligence, computer vision for media forensics, and model-agnostic explainers for transparency) within well-established forensic process models and chain-of-custody requirements. We analyze challenges unique to cloud and mobile environments, propose a standards-aligned methodology for deploying and validating AI in forensic workflows, and discuss substantive legal gatekeeping tests for expert evidence (FRE 702/Daubert/Frye) alongside the evolving constraints of the EU AI Act for law-enforcement biometric uses. A discussion of fairness, robustness, and auditability addresses risks highlighted in empirical studies (e.g., demographic performance differentials in face recognition; predictive policing feedback loops). A results section synthesizes expected operational benefits and measurable safeguards from a pilot-style implementation, while acknowledging limitations and governance needs. We conclude with a pragmatic blueprint that integrates AI capabilities without compromising evidentiary integrity, due process, or human rights, and we provide 20 authoritative references to support adoption and oversight.
subject: Original Research Articles
  topic: section
subject: AI Forensics, Digital Evidence, Cyber Law Enforcement, Chain of Custody, Cloud Forensics, EU AI Act, Daubert, FRE 702, ISO/IEC 27037, Fairness and Bias
  topic: keywords
creator: Sangeet Vashishtha
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: (13
ending page: 23)
doi: 10.63345/

Zero-Knowledge Proofs in Blockchain for Secure AI Model Sharing
type: item
description: Secure sharing of AI models across organizational boundaries is hard: providers want to protect intellectual property (model weights, architectures, and training data provenance), while consumers want cryptographic assurance that an advertised model was actually used and that a claimed evaluation (or compliance property) is correct. This manuscript proposes and evaluates a blockchain-backed design that uses zero-knowledge proofs (ZKPs) to make AI model sharing verifiable, privacy-preserving, and auditable. We synthesize the state of the art in ZK systems (zk-SNARKs, PLONK, Bulletproofs, zk-STARKs, and recursive schemes) and recent advances in zero-knowledge machine learning (zkML). Building on these, we present a practical architecture: models are registered on-chain by committing to immutable fingerprints; off-chain provers generate ZK proofs of (i) correct inference by a committed model, (ii) basic policy compliance (e.g., license scope; dataset-use attestations), and (iii) optional training process attestations via proof-of-learning artifacts. We report a simulation study comparing Groth16, PLONK, and STARK-style provers for realistic inference circuits and show that Groth16 yields the smallest proofs and fastest verification for moderate circuits, while PLONK offers circuit universality with similar verification costs and STARKs trade larger proofs for transparency and post-quantum assumptions. Across 300 synthetic trials, median verifier time remained sub-25 ms and proof sizes ranged from ~0.2 KB (Groth16) to ~90 KB (STARK) for common inference tasks, enabling economical on-chain verification. We discuss design choices (hashes, recursion, and gas budgeting), limitations (prover cost, model scale, privacy scope), and a roadmap toward policy-aware, privacy-preserving model exchanges for regulated industries.
subject: Original Research Articles
  topic: section
subject: Zero-Knowledge Proofs, zk-SNARK, PLONK, zk-STARK, Blockchain, zkML, Proof-of-Learning, Model Provenance, Privacy, Verifiable Inference
  topic: keywords
creator: Punit Goel
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: (1
ending page: 12)
doi: 10.63345/

Causal Reasoning as a Path to Explainable and Generalizable Artificial Intelligence
type: item
description: Artificial Intelligence (AI) systems, particularly those based on deep learning, have achieved extraordinary success in pattern recognition and predictive tasks. However, their reliance on correlation-based learning has raised serious concerns regarding explainability, robustness, fairness, and generalization. These limitations are especially problematic in high-stakes domains such as healthcare, autonomous systems, finance, and governance, where AI decisions must be transparent, reliable, and adaptable to changing environments. Causal reasoning offers a promising paradigm to address these challenges by enabling AI systems to move beyond surface-level correlations toward an understanding of underlying cause–effect relationships. This paper explores causal reasoning as a foundational pathway to explainable and generalizable artificial intelligence. It examines the theoretical underpinnings of causal inference, contrasts causal and correlational learning, and analyzes how causal models enhance explainability and out-of-distribution generalization. The paper further reviews emerging approaches for integrating causal reasoning into modern AI systems, including structural causal models, counterfactual learning, invariant representations, and hybrid neuro-symbolic architectures. Key applications, challenges, and future research directions are discussed. The study argues that causal reasoning is not merely an auxiliary feature but a necessary component for building trustworthy, human-aligned, and generalizable AI systems.
subject: Original Research Articles
  topic: section
subject: Causal reasoning, explainable AI, generalization, structural causal models, counterfactuals, robust AI, causal inference
  topic: keywords
creator: Mr. Rishi Mishra
rights: Copyright (c) 2024 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2024-11-03
publication date: 2024-11-03
starting page: (1
ending page: 4)
doi: 10.63345/sjaibt.v1.i4.201

Optimizing Problem Management through Proactive Knowledge Base Development
type: item
description: Effective problem management is crucial for maintaining the stability and efficiency of IT services. Traditional reactive approaches often lead to recurring issues and increased downtime. This paper explores the optimization of problem management through proactive knowledge base development, emphasizing the integration of knowledge management practices to identify and resolve underlying causes before they escalate into incidents. By analyzing case studies and implementing structured methodologies, organizations can enhance their problem management processes, reduce incident frequency, and improve overall service quality.
subject: Original Research Articles
  topic: section
subject:
  topic: keywords
  value: Problem Management, Knowledge Base Development, Proactive Approach, IT Service Management, Root Cause Analysis, Knowledge Management Practices, Incident Reduction, Service Quality Improvement, ITIL Framework, Continuous Improvement.
creator: Dr. Victor Almeida
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-16
publication date: 2026-01-16
starting page: (24
ending page: 26)

Comparative Study of Tier I vs Tier II Support Models in ITSM
type: item
description: This study investigates the comparative effectiveness of Tier I and Tier II support models within IT Service Management (ITSM). Tier I, often the first point of contact, addresses basic user issues, while Tier II handles more complex technical problems. Through a systematic literature review and statistical analysis, the research evaluates key performance indicators (KPIs) such as resolution time, customer satisfaction, and operational costs. The findings suggest that while Tier II support is more resource-intensive, it is essential for resolving intricate issues that Tier I cannot address. The study also identifies gaps in current research and proposes areas for future exploration, including the integration of AI in support processes and the impact of remote work on support efficiency.
subject: Original Research Articles
  topic: section
subject: Tier I support, Tier II support, IT Service Management, comparative analysis, resolution time, customer satisfaction, operational costs, AI integration, remote work, support efficiency.
  topic: keywords
creator: Dr. Tomás Alvarez
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-13
publication date: 2026-01-13
starting page: (21
ending page: 23)

Enhancing Root Cause Analysis Efficiency in Large-Scale IT Operations
type: item
description: Root Cause Analysis (RCA) is pivotal in identifying and mitigating underlying issues in large-scale IT operations. Traditional RCA methods often struggle with the complexity and scale of modern IT infrastructures. This manuscript explores advanced methodologies and case studies that enhance RCA efficiency, focusing on automation, machine learning, and system integration. By examining contemporary approaches and real-world applications, we aim to provide insights into optimizing RCA processes for large-scale IT environments.
subject: Original Research Articles
  topic: section
subject: Root Cause Analysis, IT Operations, Automation, Machine Learning, System Integration, Incident Management, Scalability, Efficiency, Case Studies, IT Infrastructure.
  topic: keywords
creator: Ji-Hoon Park
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-10
publication date: 2026-01-10
starting page: (18
ending page: 20)

Incident Response Optimization in Global Command Centers: Lessons from Financial IT Operations
type: item
description: In the evolving landscape of enterprise IT, maintaining a robust security posture is paramount. The integration of Configuration Management Databases (CMDB) with Incident Management systems offers a strategic approach to enhance security measures. This manuscript explores the significance of CMDB in providing comprehensive visibility into IT assets, their configurations, and interdependencies. By examining case studies and methodologies, the paper demonstrates how this integration facilitates proactive incident detection, efficient response, and continuous improvement in security protocols. The findings underscore the critical role of integrated systems in fortifying enterprise security frameworks.
subject: Original Research Articles
  topic: section
subject: Enterprise Security, Configuration Management Database, Incident Management, ITIL, Cybersecurity, Proactive Defense, Vulnerability Management, Automation, Risk Assessment, IT Operations
  topic: keywords
creator: Sneha Iyer
rights: Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-08
publication date: 2026-01-08
starting page: (15
ending page: 17)