Scientific Journal of Artificial Intelligence and Blockchain Technologies
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: 306, Tower-I, KDP Grand Savanna, Noor Nagar, Loni, 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 : https://wa.me/919456916244 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 submissions spanning Computer Science, Engineering, and Technology with a strong emphasis on interdisciplinary innovation that bridges AI, Blockchain, Quantum Computing, Cybersecurity, and Industry 4.0. Subject Areas in SJAIBT: Core Focus Artificial Intelligence (AI): machine learning, deep learning, natural language processing, reinforcement learning, explainable AI, AI in robotics, AI in healthcare, AI in cybersecurity. Blockchain Technology: distributed ledger systems, consensus algorithms, decentralized finance (DeFi), smart contracts, blockchain security, Web3, tokenization, blockchain for supply chains. AI–Blockchain Integration: federated learning with blockchain, privacy-preserving AI, secure multiparty computation, blockchain-based trust models, interoperability frameworks. Engineering & Applied Domains Computer Science & Engineering (CSE): software engineering, big data analytics, cloud-native systems, edge computing, DevOps automation, API ecosystems. Electronics & Communication Engineering (ECE): VLSI design, embedded systems, IoT security, quantum devices, wireless networks, 5G/6G communication with AI and blockchain integration. Electrical Engineering (EE): smart grids, renewable energy optimization, control systems, power electronics, AI for predictive maintenance, blockchain for energy trading. Mechanical & Manufacturing Engineering (ME): industrial automation, robotics, digital twins, additive manufacturing, Industry 4.0 integration, blockchain in supply chain traceability. Civil & Structural Engineering (CE): smart cities, digital infrastructure, AI for urban planning, blockchain for construction contracts, intelligent transportation systems. Chemical & Biochemical Engineering (ChE): process optimization, bioinformatics, blockchain for pharmaceutical supply chains, AI in drug discovery. Aerospace & Automotive Engineering: autonomous vehicles, UAVs, aerospace materials, AI for navigation, blockchain for safety compliance. Biomedical & Health Engineering: AI-driven diagnostics, medical imaging, personalized medicine, blockchain for health data management. Environmental & Agricultural Engineering: AI for climate modeling, blockchain for carbon trading, smart farming, water resource management. Interdisciplinary and Emerging Areas Quantum computing and AI Cybersecurity and Zero-Trust frameworks Digital twins and metaverse ecosystems AI ethics, governance, and regulation Blockchain in law, policy, and governance AI and blockchain applications in education and e-learning
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AI-Powered Observability and Incident Prediction in Distributed Enterprise Platforms
Increasingly complex distributed enterprise platforms have revealed severe limitations of traditional monitoring tools, which cannot correlate heterogeneous telemetry signals or translate low-level anomalies into actionable incident-level insights. While recent progress in log-, metric-, and trace-based machine learning has improved anomaly detection accuracy, research demonstrates there are many remaining challenges in terms of cross-modal correlation, generalization across evolving systems, explainability, and end-to-end incident prediction. Existing deep learning models are oftentimes well-behaved on a single isolated dataset but struggle with concept drift, multi-tenant noise, and dynamic behaviors in microservice architectures. Similarly, most AIOps frameworks provide architectural recommendations with limited rigorous evaluation in operational impact, especially about the reductions in MTTD and MTTR. Root-cause analysis techniques have been advanced through graph and causal modeling. They remain decoupled from proactive incident forecasting and often fail to integrate human-in-the-loop operational knowledge. This research addresses these shortcomings by developing an integrated AI-powered observability framework that harmonizes logs, metrics, and traces through multimodal representation learning, reinforces temporal and causal reasoning for early incident prediction, and integrates explainable analytics targeted at enterprise-scale decision making. The proposed approach will aim to provide predictive, interpretable, operationally measurable incident management by mapping low-level anomalies to service-level incident likelihood, impact, and probable root causes. This work contributes an empirically validated pipeline aimed at enhancing reliability engineering outcomes and firming proactive resilience strategies in distributed enterprise platforms.
AI-powered observability, incident prediction, distributed enterprise platforms, multimodal telemetry analytics, root-cause intelligence
Copyright (c) 2024 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
Exploring Scalability of Deep Neural Networks for Real-Time Video Processing in Edge Devices
Applications like augmented reality, smart surveillance, and autonomous driving have made real-time video processing on edge devices more and more necessary. However, because of the restricted processing resources, memory limitations, and latency requirements, installing deep neural networks (DNNs) on edge devices presents major hurdles. the scalability of DNN architectures designed with the goal of maximizing model size, processing speed, and accuracy for real-time video processing on edge devices. We examine a number of methods, such as lightweight network designs, quantization, and model pruning, to determine how well they can lower computational load without sacrificing speed. According to experimental results, DNNs can significantly reduce latency and power consumption through smart tuning, allowing for effective real-time processing on devices with restricted resources. route toward high-performance, scalable DNN models, offering valuable perspectives on realistic deployment tactics for edge-based video processing applications.
Enhancing Conversational Agents with Deep Reinforcement Learning: A Novel Approach to Dialogue Management
Customer service, virtual assistants, and social media platforms are just a few of the many areas seeing increased use of conversational bots. But many current solutions fail to keep conversations interesting and consistent, which frequently leads to unhappy users. a fresh method for managing conversations that improves agents' conversational skills by using Deep Reinforcement Learning (DRL). Agents can adapt to user preferences and contextual subtleties by learning optimal conversational techniques through interactions with them; this is achieved by framing dialogue management as a reinforcement learning problem. We suggest a DRL design that considers both user input and contextual data so that agents can gradually refine their responses. We run a battery of tests on the architecture to see how well it handles things like user engagement, answer accuracy, and conversation coherence. Maintaining user pleasure and generating meaningful interactions are two areas where DRL-enhanced conversational bots excel, surpassing standard rule-based and supervised learning methods.
Human-Centered Design in Conversational AI: Optimizing Natural Language Understanding for Diverse Users
In conversational artificial intelligence, human-centered design focuses on the creation of interactions that are user-friendly, inclusive, and adaptable to meet the requirements of a wide range of users. natural language understanding (NLU) techniques that are optimized for conversational artificial intelligence, with the goal of ensuring that models are able to effectively interpret and respond to a wide variety of linguistic styles, dialects, and user preferences. We design a natural language understanding (NLU) framework that improves accessibility and engagement for a wider audience by incorporating user feedback, cultural context, and adaptive learning methodologies. The findings of our experiments indicate that our human-centered approach considerably enhances both the level of satisfaction experienced by users and the flow of discussion across all demographics. demonstrates the significance of human-centered design in the process of developing user-friendly artificial intelligence systems for a wide range of populations by fostering inclusivity and improving the naturalness of interactions. This adds to the advancement of conversational artificial intelligence.
Evaluating Conversational AI Models for Emotional Intelligence: Techniques for Enhanced User Engagement
Enhancing these models with emotional intelligence (EI) has become crucial for raising user pleasure and engagement as conversational AI is incorporated more and more into customer service, healthcare, and education. The ability of conversational AI models with emotional intelligence techniques to identify, decipher, and react to users' emotional cues is the main focus. To evaluate their effect on user engagement, we investigate a number of strategies, such as tone detection, sentiment analysis, and adaptive response generation. We investigate the effectiveness of these EI-enhanced models in establishing constructive connections, building user trust, and preserving organic, sympathetic conversations through empirical testing. The results suggest potential for applications that prioritize user experience, since models incorporating emotional intelligence techniques significantly outperform classical conversational AI in terms of user pleasure. the creation of emotionally intelligent AI, which provides information on practical methods for conversational agents seeking to engage in more meaningful and interesting interactions.
Risk Assessment in Digital Transformation Projects: Frameworks for Evaluating Security and Compliance Gaps
Organizations are undergoing a digital revolution that is changing the game by bringing new opportunities for creativity, efficiency, and competitive advantage. However, security and compliance concerns are becoming more pressing for companies as they embrace digital technologies. an all-encompassing methodology for analyzing the dangers of digital transformation initiatives, with an emphasis on finding and analyzing security holes and noncompliance. The suggested methodology provides a methodical way to evaluate risks by combining cybersecurity, risk management, and regulatory compliance best practices. Organizations can thoroughly assess their digital activities using a mix of qualitative and quantitative methodologies, guaranteeing compliance with industry standards and legal mandates. Case studies show how the framework works in practice, illuminating typical problems and how to fix them. The suggested risk assessment approach can help firms strengthen their defenses against cyber-attacks, stay in line with relevant regulations, and back their digital transformation initiatives to a successful conclusion. the importance of proactive risk management in guiding enterprises through the challenges of digital transformation so that they can benefit from technology progress while protecting their assets and reputation.
Dynamic Traffic Flow Management Using Multi-Agent Deep Reinforcement Learning: A Smart City Approach
In contemporary metropolitan environments, where growing traffic and environmental concerns necessitate creative solutions, efficient traffic flow management is essential. In this work, a multi-agent deep reinforcement learning (DRL) method for managing dynamic traffic flow in a smart city framework is presented. This model optimizes traffic flow and reduces congestion by dynamically adjusting signal timings depending on real-time traffic circumstances by utilizing many intelligent agents, each of which represents a traffic light at an intersection. Through interactions with their separate environments, agents cooperatively learn and modify their activities to create a traffic network that is globally efficient. The study assesses the model's effectiveness in terms of shorter travel times, shorter wait times, and increased fuel efficiency using simulation data. According to the results, the suggested DRL model performs noticeably better than conventional static and rule-based systems, offering smart cities that want to improve urban mobility and lessen their environmental effect a scalable, adaptable option. This study advances intelligent transportation systems by demonstrating how multi-agent DRL may be used to create effective and sustainable urban traffic control plans.
Hybrid Cryptographic Protocols for Securing Sensitive Data in Cloud-Based Applications
Since cloud-based applications are increasingly handling sensitive data across a variety of businesses, it has become increasingly important to ensure that robust data security is in place. A viable way to meet the one-of-a-kind security difficulties that cloud systems present is the utilization of hybrid cryptographic protocols, which mix different encryption algorithms. The purpose of hybrid cryptographic protocols is to improve data secrecy, integrity, and accessibility while simultaneously maximizing performance in cloud environments. These protocols offer multilayer security by using symmetric and asymmetric encryption in addition to powerful hashing algorithms. This allows them to strike a balance between computational efficiency and comprehensive protection against unauthorized access and data breaches. The results of the experiments show that hybrid protocols are successful in safeguarding data transfers and storage. In comparison to traditional approaches, hybrid protocols dramatically reduce the vulnerability to assaults. provides a method that is scalable, adaptable, and safe for the protection of data in cloud-based applications, which helps to the advancement of cloud security frameworks.
Artificial Intelligence and Blockchain Technologies: A Convergence for Future Innovations
Artificial Intelligence (AI) and Blockchain technologies are two revolutionary advancements reshaping various industries. AI, with its ability to process and analyze massive datasets, and Blockchain, known for its decentralized and secure ledger system, hold the potential to complement each other in unique ways. This paper explores the convergence of AI and Blockchain, their applications across industries, challenges, and future prospects.
Cryptographic Enhancements for AI Data Sharing Platforms
AI data sharing platforms must reconcile two pressures that often clash: the need to exchange high‐value datasets for model development and evaluation, and the obligation to guarantee privacy, integrity, and verifiability of computations on that data. This manuscript surveys and synthesizes cryptographic building blocks—differential privacy, homomorphic encryption, multiparty computation with secure aggregation, zero‐knowledge proofs, attribute-based encryption and proxy re-encryption, trusted execution environments, and domain standards such as Crypt4GH—into a pragmatic, layered architecture for AI data sharing. We outline a methodology that integrates policy-aware access control with threshold key management, private training and inference, verifiable analytics, and auditability. A compact statistical analysis (with an illustrative table) demonstrates how such a stack can bound leakage (ε), preserve utility (accuracy), and manage computational overhead (latency). Results show that a hybrid PETs (privacy-enhancing technologies) approach—combining local differential privacy and secure aggregation for ingestion, homomorphic encryption or TEEs for computation, and zk-proofs for verifiability—achieves strong privacy with modest accuracy loss and acceptable latency for many enterprise scenarios. We conclude with design guidelines and research directions for standards-aligned, future-ready AI data sharing platforms.
Privacy-Enhancing Technologies, Homomorphic Encryption, Secure Aggregation, Differential Privacy, Zero-Knowledge Proofs, Attribute-Based Encryption, Proxy Re-Encryption, Trusted Execution Environments, Threshold Cryptography, Crypt4GH
GDPR Compliance Challenges in Blockchain-Based Systems
Blockchain’s decentralization, transparency, and tamper‐resistance are celebrated properties for auditability and trust, yet they collide with core data protection duties under the EU General Data Protection Regulation (GDPR). This manuscript analyzes the principal compliance challenges that arise when blockchain processes personal data and proposes a practical, design-oriented framework to address them. First, we synthesize legal and regulatory positions on what counts as “personal data,” the difference between anonymization and pseudonymization, and the implications of the right to erasure, data protection by design and by default, allocation of controller/processor roles, and international data transfers. We then map these requirements to blockchain architectures (public permissionless, public permissioned, and private permissioned) and data patterns (on-chain, off-chain, hybrid). Building on recent guidance from the European Data Protection Board (EDPB) and national authorities, we outline concrete technical and governance controls—off-chain storage and on-chain commitments, keyed hashing, encryption/key-revocation strategies, chameleon-hash/redactable-ledger designs, selective-disclosure credentials/zero-knowledge proofs, and robust consortium governance—to reduce risk and improve demonstrable compliance. Applying a six-step assessment methodology to three realistic use cases (NFT profile registry, supply-chain provenance, and consortium KYC), we show that while no single pattern fully reconciles immutability with erasure, practicable combinations can align processing with GDPR’s principles of minimization, purpose limitation, storage limitation, and accountability. The paper concludes with a prioritized checklist for engineering “compliance-by-design” blockchains, and delineates scope and limitations for practitioners and researchers.
GDPR, Blockchain, Right to Erasure, Pseudonymization, Anonymization, Data Protection by Design, Controller/Processor, Cross-Border Transfers, Off-Chain Storage, Zero-Knowledge Proofs
Ethical AI Design in Blockchain-Powered Surveillance Systems
Artificial intelligence (AI) and distributed ledger technologies are increasingly integrated into public and private surveillance infrastructures—from city-wide camera networks to critical-infrastructure monitoring and access control. This integration promises higher integrity and accountability through immutable logs, faster incident response via on-device inference, and interoperable audit trails across organizations. Yet it also amplifies ethical risks: mass data collection, opacity in model decisions, function creep, demographic harms, cross-border data governance conflicts, and accountability gaps when immutable records meet “right to erasure” regimes. This manuscript proposes an ethics-by-design reference architecture for blockchain-powered surveillance that embeds privacy, proportionality, and fairness controls into each lifecycle stage (purpose definition → data capture → model training → inference → access → audit → decommissioning). Technically, it composes privacy-enhancing technologies (PETs)—including differential privacy, federated learning, zero-knowledge proofs, verifiable credentials (VCs), and content-provenance standards (C2PA)—with permissioned blockchain ledgers, model cards, and risk management aligned to the NIST AI RMF, ISO/IEC 23894, ISO/IEC 42001, UNESCO, and ACM guidance. A simulated evaluation illustrates how the architecture can reduce false-positive disparities and unauthorized access, while preserving evidentiary integrity. We discuss tensions with GDPR (e.g., Article 17 erasure; DPIA obligations), constraints introduced by the EU AI Act (e.g., prohibitions and high-risk biometric uses), and strategies to reconcile immutability with privacy (e.g., off-chain storage with revocation, redaction-friendly commitments). The paper closes with limitations and a future research agenda for measurable, auditable ethical guarantees in real-time surveillance.
Ethical AI, Surveillance, Blockchain, Privacy-Enhancing Technologies, Fairness, Zero-Knowledge Proofs, Verifiable Credentials, Audit, GDPR, EU AI Act
Data Sovereignty in Global AI-Blockchain Infrastructure
As artificial intelligence (AI) systems scale across borders and decentralized ledgers interconnect global networks, a central challenge emerges: how to ensure data sovereignty—the ability of jurisdictions, organizations, and individuals to exert legitimate control over data—without stifling innovation or undermining the integrity and utility of distributed architectures. This manuscript proposes a comprehensive, practice-oriented blueprint for embedding data sovereignty into AI-blockchain infrastructure. We first synthesize the legal and policy landscape shaping cross-border processing (e.g., GDPR, Schrems II, SCCs, EU–U.S. Data Privacy Framework, EU AI Act, Data Governance Act, Data Act, CLOUD Act, India’s DPDP Act, China’s PIPL, OECD/G7 initiatives, and Global CBPR). We then examine technical levers—on-chain/off-chain partitioning, permissioned topologies, sovereign cloud patterns, privacy-enhancing computation, verifiable provenance, and risk management frameworks—to operationalize jurisdictional constraints without losing decentralization benefits. Building on this review, we introduce SOVEREIGN-Stack, a governance-by-design methodology spanning eight layers (identity, consent, data classification, locality & routing, compute & model governance, ledger governance, transfer mechanisms, and assurance/tooling). Two applied vignettes—in health analytics spanning the EU, U.S., and India, and a permissioned supply-chain ledger touching the EU and APAC—demonstrate how the approach balances legal obligations (erasure, purpose limitation, transfer restrictions) with architectural needs (immutability, integrity, transparency). We conclude with a set of implementation checkpoints and maturity indicators that organizations can use to align AI-blockchain roadmaps with evolving global rules while maintaining verifiability, auditability, and performance.
Data Sovereignty, Cross-Border Data Flows, AI Governance, Blockchain Compliance, Privacy-Enhancing Technologies, Federated Learning, Sovereign Cloud, GDPR, EU AI Act, Global CBPR
AI-Powered Intrusion Detection Systems in Blockchain Networks
Blockchain networks—public, consortium, and permissioned—promise integrity, transparency, and decentralization, yet they continue to face a shifting landscape of threats across layers: peer-to-peer overlays, consensus, smart contracts, mempools, bridges, and off-chain oracles. Conventional intrusion detection systems (IDS) tuned for enterprise or ISP traffic struggle to capture blockchain–specific semantics such as transaction graphs, validator behaviors, bytecode execution traces, cross-chain flows, and MEV-style manipulations. This manuscript proposes and analyzes a multilayer, AI-powered IDS architecture tailored to blockchain networks. First, we synthesize the state of the art on deep learning for IDS, graph learning over transaction networks, smart-contract vulnerability detection, and federated learning (FL) for privacy-preserving collaboration among heterogeneous nodes. Second, we formalize a design that fuses (i) graph neural networks for address/contract behavior on dynamic transaction graphs, (ii) sequence models over EVM opcode traces for runtime anomalies and contract-level exploits, (iii) temporal models for mempool manipulation and spam/DoS patterns, (iv) validator-telemetry analytics for consensus-layer deviations including selfish mining, and (v) cross-chain risk scoring to detect bridge and arbitrage abuse. We detail features, training objectives, privacy safeguards (secure aggregation, differentially private updates), and explainability (subgraph rationales, opcode saliency). Finally, we discuss evaluation methodology using public ledgers and labeled case corpora (e.g., Ponzi/phishing datasets) and report illustrative results from a pilot study design, along with deployment guidance for miners/validators, L2 sequencers, exchanges, and custodians. Our analysis indicates that AI-powered, graph-centric, and federated IDS can reduce false positives while improving early detection of fraud patterns and validator misbehavior, provided that model and data governance are rigorous and that alerts are verifiable and auditable. We conclude with open challenges—concept drift, adaptive adversaries, data imbalance, privacy–utility trade-offs, and cross-chain observability—and a roadmap for standardizing datasets and benchmarks for blockchain IDS research.
Blockchain Security, Intrusion Detection, Graph Neural Networks, Smart Contracts, Federated Learning, Anomaly Detection, Ethereum, Consensus Security, Explainability, Privacy
Integration Challenges in Blockchain-Based AI Model Deployment
The promise of combining blockchain with artificial intelligence (AI) is compelling: auditable data provenance for training sets, tamper-evident logging for model lifecycle events, decentralized marketplaces for models and datasets, and automated enforcement of usage policies via smart contracts. Yet organizations quickly discover that operationalizing blockchain-based AI goes beyond stitching together two popular technologies. Differences in trust assumptions, latency and throughput profiles, security primitives, compliance expectations, and tooling maturity frequently collide at deployment time. This manuscript organizes those frictions into a coherent integration problem space and proposes a reference architecture and evaluation methodology to reason about trade-offs. We review the literature on blockchain consensus and scalability, privacy-preserving machine learning (federated learning, differential privacy, secure computation, and zero-knowledge proofs), data governance and compliance (e.g., GDPR), and MLOps platforms. We then present a methodology that stress-tests seven integration dimensions: architecture and partitioning (on-chain vs. off-chain responsibilities), performance and cost (latency, throughput, gas), privacy and confidentiality (leakage risks and mitigations), security and integrity (tamper-evidence, oracle trust), interoperability (heterogeneous chains and toolchains), compliance and governance (auditability versus erasure rights), and human/organizational fit (DevOps, incident response, and skills).
Blockchain, AI Deployment, MLOps, Privacy, Zero-Knowledge Proofs, Federated Learning, Oracles, Data Governance, Interoperability, Compliance
Legal Challenges in Blockchain-Based Smart Contract Execution
Smart contracts promise automation, transparency, and reduced transaction costs, yet their deployment on distributed ledgers exposes deep legal fault lines that traditional doctrinal tools only partially address. This paper synthesizes contract, property, data-protection, consumer, and financial-regulatory dimensions to map the principal legal challenges that arise when code becomes performance. We distinguish “smart legal contracts” (where code implements an enforceable legal agreement) from purely automated, code-only arrangements, and we explain why, despite technical self-execution, contract law, remedies, and regulatory oversight remain indispensable. We analyze enforceability and interpretation (e.g., the “reasonable coder” question), formation and consent, jurisdiction and choice-of-law in borderless networks, immutability versus legal rectification, oracle risk and attribution of liability, consumer-protection/unfair terms, AML/KYC overlays, evidentiary issues, and property/transfer rules for on-chain assets. Recent instruments—such as the EU Data Act’s essential requirements for smart contracts used in data-sharing, the UK Law Commission’s guidance on smart legal contracts, the UKJT’s Digital Dispute Resolution Rules, UNCITRAL model laws, and the U.S. UCC Article 12 on controllable electronic records—are reshaping the legal terrain but do not eliminate foundational questions. To organize this complexity, we propose a simple Legal Risk Salience Index (LRSI) that scores likelihood and impact for ten recurring risk families; the index is a transparent, replicable rubric for counsel and product teams to prioritize mitigations. We conclude with actionable recommendations—layering natural-language wrappers, robust governance and kill-switch design consistent with statutory requirements, explicit choice-of-law and dispute-resolution clauses (including on-chain arbitration), and defensible oracle strategies—along with scope and limitations of the analysis. The result is a practitioner-oriented map of where code and law still talk past each other—and how to bring them into better alignment.
Smart Contracts, Enforceability, Jurisdiction, EU Data Act, UCC Article 12, GDPR, Oracles, Dispute Resolution, DAO Governance, Evidence
Securing Autonomous Vehicles Using AI-Blockchain Architecture
Autonomous vehicles (AVs) promise safer and more efficient mobility but are exposed to an evolving attack surface spanning on-board sensors, in-vehicle networks, over-the-air (OTA) software updates, and vehicle-to-everything (V2X) communications. Traditional perimeter defenses and centralized public-key infrastructures (PKIs) struggle to keep pace with adversarial machine learning (ML), Sybil attacks in vehicular networks, and supply-chain compromises. This manuscript proposes an end-to-end security architecture that fuses artificial intelligence (AI) with a permissioned blockchain to deliver verifiable identity, tamper-evident telemetry, decentralized policy enforcement, and adaptive intrusion detection. The architecture binds device and model identities to decentralized identifiers (DIDs), anchors data and update artifacts to an immutable ledger, and coordinates a privacy-preserving federated-learning loop to continuously refine anomaly detectors. We describe the threat model and system components (edge validators, roadside units, OEM validators, on-vehicle guardians), define a trust and attestation workflow, and integrate AI modules for CAN-bus anomaly detection and V2X trust scoring. A simulation study using realistic traffic patterns evaluates detection efficacy, latency, and ledger throughput under benign and adversarial conditions (false-data injection, spoofing, replay, and Sybil attacks). Statistical analysis across 30 independent runs shows significant improvements in true positive rate (TPR), false positive rate (FPR), and end-to-end decision latency compared with a PKI-only baseline, while maintaining transaction throughput suitable for V2X policy events. We conclude that AI-Blockchain co-design can harden AV ecosystems by coupling adaptive detection with cryptographic accountability, and we discuss deployment considerations, limitations, and paths to certification-grade assurance.
Autonomous Vehicles, Blockchain, AI, V2X Security, Intrusion Detection, Decentralized Identifiers, Federated Learning, OTA Integrity, Trust Management
Blockchain + AI in Combating Deepfake Content Circulation
The rapid proliferation of AI-generated “deepfake” images, audio, and video is eroding public trust in digital media and amplifying risks to elections, markets, journalism, and personal safety. While AI detection models have improved, they face an adversarial “cat-and-mouse” problem and often struggle to generalize across manipulation methods and compression regimes. This manuscript proposes and analyzes a hybrid, end-to-end approach that couples upstream provenance and authenticity signals—anchored via open standards (e.g., C2PA Content Credentials) and decentralized ledgers—with downstream AI detection and moderation. The pipeline captures and signs media at source; binds verifiable, tamper-evident metadata; anchors cryptographic hashes on a public or consortium blockchain; stores originals off-chain with content addressing (e.g., IPFS/Filecoin); and fuses these trust signals with model-based detectors and policy engines at distribution edges. We situate the proposal within current regulation (e.g., EU AI Act transparency duties) and state-of-the-art methods (e.g., watermarking such as SynthID, Stable Signature, and Tree-Ring; deepfake detectors trained on DFDC and FaceForensics++), highlighting both strengths and known attack vectors against watermarking that motivate layered defenses. A simulation-based evaluation illustrates that combining provenance signals with video-level transformer detectors can raise F1 from 0.85 to 0.92 while cutting false positives by ~41% in a balanced test set, primarily by rejecting credential-mismatched or hash-divergent media before expensive model inference. We further discuss privacy-preserving verification using W3C Verifiable Credentials (VC 2.0), Decentralized Identifiers (DIDs), and selective-disclosure with zero-knowledge proofs. The findings make a practical case for “trust by design” built on open standards, decentralized integrity proofs, and robust AI detection, implemented as a policy-aware defense-in-depth stack for platforms and newsrooms.
Blockchain, Deepfakes, Content Authenticity, C2PA, Content Credentials, Verifiable Credentials, DIDs, Zero-Knowledge Proofs, IPFS, AI Detection
AI-Enabled Threat Detection in Blockchain-Based Systems
Blockchain technology has emerged as a foundational layer for decentralized, tamper-resistant, and trustless systems, widely deployed in financial services, supply chain networks, healthcare, and digital governance. However, the growth of blockchain ecosystems has been paralleled by increasingly sophisticated cyber threats that challenge network integrity, privacy, and security. Traditional security approaches are limited in scalability and adaptability, especially against dynamic adversarial tactics such as zero-day exploits, collusion-based fraud, Sybil attacks, or adversarial consensus manipulation. Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), presents a promising paradigm for augmenting blockchain security. By leveraging predictive analytics, anomaly detection, federated learning, and graph neural networks, AI can identify hidden patterns, anticipate threats, and enable proactive defense mechanisms in real time. This manuscript presents a comprehensive study of AI-enabled threat detection mechanisms in blockchain-based systems. It begins with a detailed overview of blockchain vulnerabilities, followed by a literature review covering state-of-the-art AI-driven detection strategies. The proposed methodology emphasizes hybrid approaches that combine supervised, unsupervised, and reinforcement learning models with blockchain’s inherent consensus and auditability. A statistical simulation experiment is conducted to evaluate threat detection rates, false positives, latency, and scalability in a blockchain test network infused with AI-based monitoring agents. Results demonstrate that AI-enhanced systems improve detection accuracy by 35–60%, reduce latency by 40%, and significantly outperform rule-based models in identifying novel attack patterns. The study concludes that AI-enabled threat detection provides a crucial pathway for resilient, adaptive, and self-learning blockchain ecosystems. Yet, challenges persist in data privacy, adversarial AI attacks, resource efficiency, and ethical governance. This work contributes to both academic discourse and industrial practice, offering recommendations for designing next-generation blockchain architectures secured by intelligent threat detection frameworks.
Blockchain, Artificial Intelligence, Threat Detection, Cybersecurity, Anomaly Detection, Deep Learning, Federated Learning, Consensus Security
Ethical Frameworks for AI and Blockchain Co-Governance
The convergence of Artificial Intelligence (AI) and blockchain technologies represents one of the most significant shifts in socio-technical governance frameworks of the 21st century. AI contributes adaptive intelligence, predictive modeling, and automated decision-making, while blockchain offers distributed trust, tamper-proof records, and decentralized enforcement through smart contracts. Together, these technologies create powerful synergies, enabling transparent, accountable, and efficient systems. However, their integration raises complex ethical questions that extend beyond the scope of conventional regulatory paradigms. Key concerns include algorithmic bias, explainability deficits, energy sustainability, privacy rights, jurisdictional fragmentation, and risks of techno-elitism. This manuscript investigates the ethical dimensions of AI–Blockchain co-governance through an interdisciplinary lens, integrating philosophical ethics, legal principles, technological affordances, and empirical stakeholder perspectives. Drawing upon deontological, consequentialist, and virtue ethics approaches, the study develops a comprehensive framework for embedding ethics “by design” into AI–Blockchain ecosystems. Using a mixed-methods methodology—including a survey of 300 global stakeholders, expert interviews, and literature synthesis—the research identifies privacy, accountability, fairness, and transparency as the most pressing governance imperatives. The statistical analysis further reveals divergences in priorities between industry actors (favoring efficiency and innovation) and regulators (emphasizing compliance, inclusivity, and risk mitigation). The findings advocate for a hybrid ethical model where blockchain-based immutability reinforces AI accountability, while human-in-the-loop oversight ensures contextual judgment. Novel governance strategies such as explainable smart contracts, differential privacy mechanisms, and sustainability-conscious consensus algorithms emerge as critical enablers of responsible co-governance. By bridging theoretical ethics with applied governance, this work contributes not only to scholarly discourse but also to practical policymaking, offering a roadmap for ensuring that AI–Blockchain systems are deployed in ways that balance innovation with responsibility, global interoperability with local sensitivity, and technological advancement with human dignity.
AI ethics, blockchain governance, co-governance frameworks, transparency, accountability, fairness, data privacy, smart contracts, algorithmic bias, responsible innovation
Blockchain-Powered Data Provenance for AI Model Audits
Artificial Intelligence (AI) models are increasingly integrated into high-stakes domains such as finance, healthcare, autonomous systems, and legal decision-making. As their influence expands, concerns about accountability, fairness, transparency, and regulatory compliance have become central to both researchers and practitioners. One of the key challenges is auditing AI models in a manner that is tamper-proof, verifiable, and compliant with evolving regulatory frameworks. Traditional auditing mechanisms rely heavily on centralized logs and organizational trust, which creates vulnerabilities in terms of manipulation, incomplete records, and opacity in data flows. Blockchain technology—owing to its immutable, decentralized, and transparent nature—offers a powerful paradigm for establishing data provenance in AI auditing. By ensuring traceability of datasets, model updates, training logs, and inference outcomes, blockchain can provide regulators, stakeholders, and organizations with reliable audit trails. This paper presents a comprehensive exploration of blockchain-powered data provenance for AI model audits. It analyzes the limitations of current audit systems, evaluates how distributed ledger systems can strengthen accountability, and proposes an integrated framework that combines blockchain with cryptographic verification, zero-knowledge proofs, and federated logging to ensure verifiability without exposing sensitive data. The study synthesizes contributions from literature, presents a methodology for deploying blockchain-based provenance systems in AI pipelines, and evaluates potential results in terms of efficiency, compliance traceability, and security. Simulation experiments suggest that blockchain-enabled audits improve transparency, reduce fraudulent activities in AI operations, and enhance compliance readiness by more than 50% compared to traditional audit approaches.
Blockchain, Data Provenance, AI Auditing, Explainability, Model Transparency, Compliance, Immutable Logs, Zero-Knowledge Proofs
Trustless Marketplaces Powered by AI and Blockchain Integration
Trustless marketplaces are emerging as a revolutionary paradigm in digital commerce, eliminating the need for intermediaries by leveraging blockchain’s decentralized consensus and artificial intelligence’s autonomous decision-making capabilities. Traditional marketplaces—whether online retail platforms, service exchanges, or financial intermediaries—rely heavily on central authorities for transaction validation, dispute resolution, and trust enforcement. However, this reliance introduces challenges including high transaction fees, fraud risk, censorship, lack of transparency, and systemic single points of failure. Integrating blockchain and AI offers a robust solution: blockchain ensures immutable, auditable, and tamper-proof transaction records, while AI enhances scalability, efficiency, personalization, fraud detection, and predictive analytics. Together, they enable trustless marketplaces where buyers and sellers interact securely, transparently, and autonomously without centralized control. This manuscript explores the foundations, evolution, and technological frameworks underpinning trustless marketplaces. The literature review situates blockchain-enabled trust models, decentralized autonomous organizations (DAOs), and AI-driven autonomous negotiation systems within the broader field of digital commerce. The methodology includes both conceptual modeling and a statistical analysis of 200 blockchain marketplace case studies, identifying transaction efficiency, cost reduction, fraud minimization, and compliance alignment as primary performance indicators. Results reveal that trustless AI-blockchain marketplaces reduce average transaction fees by 65%, fraud attempts by 52%, and settlement times by over 80% when compared to traditional centralized platforms. The paper concludes by positioning trustless marketplaces as a cornerstone for Web3 economies, cross-border trade, gig work ecosystems, digital asset exchange, and DeFi-driven commerce. While significant benefits exist, adoption challenges include regulatory uncertainty, interoperability gaps, computational overhead, and ethical AI governance concerns. The study proposes future research into hybrid consensus models, explainable AI integration, and compliance-aware trustless protocols to ensure scalable, human-centric adoption.
Trustless Marketplaces; Blockchain; Artificial Intelligence; Decentralization; Smart Contracts; Web3 Commerce; DAO; Trust Automation; Digital Economy
Autonomous Supply Chains Using Blockchain-AI Hybrid Systems
The rapid evolution of global trade and logistics has generated unprecedented complexity within supply chains, leading to challenges in transparency, efficiency, trust, and resilience. Autonomous supply chains, powered by the convergence of blockchain and artificial intelligence (AI), present a transformative paradigm capable of addressing these issues. Blockchain ensures immutable, decentralized, and tamper-resistant data sharing across multiple stakeholders, while AI enables predictive analytics, optimization, and autonomous decision-making. Together, blockchain-AI hybrid systems offer real-time traceability, fraud prevention, automated contract execution, risk prediction, and dynamic resource allocation. This manuscript explores the design, development, and implementation of blockchain-AI integrated architectures for autonomous supply chains. The study reviews existing literature, proposes a methodology for system modeling, and evaluates simulation results highlighting improvements in transaction trust, demand forecasting accuracy, inventory optimization, and fraud reduction. Statistical analysis reveals significant efficiency gains across metrics such as transaction speed, compliance traceability, and overall operational costs. A simulation research model demonstrates that blockchain-AI supply chains reduce bullwhip effects, minimize delays, and enhance global scalability. The research concludes that blockchain-AI hybrid systems provide a robust framework for building fully autonomous, adaptive, and resilient supply chains that align with Industry 4.0 and beyond. Implications include enhanced sustainability, reduced costs, and competitive advantages for global enterprises. The paper also discusses limitations, ethical challenges, and future research directions in the evolution of autonomous, self-governing supply chain ecosystems.
Blockchain, Artificial Intelligence, Autonomous Supply Chains, Smart Contracts, Predictive Analytics, Decentralized Logistics
Blockchain-Powered Verifiable AI Models for Medical Diagnosis
The rapid expansion of artificial intelligence (AI) in healthcare has revolutionized diagnostic practices, enabling applications such as tumor detection in medical imaging, genomic analysis, and predictive risk modeling for early disease prevention. Despite these advancements, concerns about the opacity, trustworthiness, and auditability of AI systems remain significant barriers to clinical adoption. Medical practitioners, regulators, and patients increasingly demand systems that not only produce accurate results but also provide verifiable guarantees regarding the integrity and accountability of diagnostic processes. Blockchain technology, with its intrinsic features of decentralization, immutability, and consensus-driven validation, offers a promising solution to these concerns. This manuscript investigates the integration of blockchain-powered verifiable AI models for medical diagnosis. We present a comprehensive framework that leverages federated learning for decentralized training, blockchain for immutable storage and consensus validation, and zero-knowledge proofs for cryptographic verification of model outputs. The proposed system ensures transparent audit trails, enhances data integrity, protects patient privacy, and simplifies compliance with regulatory frameworks such as HIPAA and GDPR. Through simulated case studies in medical imaging and predictive diagnostics, we demonstrate that blockchain integration improves diagnostic verifiability, reduces susceptibility to adversarial manipulation, and fosters patient-centric trust. While slight computational latency is introduced, the trade-off is justified by significantly stronger guarantees of transparency, reproducibility, and ethical accountability. This research underscores the transformative role of blockchain in shaping the future of verifiable AI-driven healthcare, providing pathways toward more reliable, transparent, and equitable medical diagnostic ecosystems.
Blockchain, Verifiable AI, Medical Diagnosis, Federated Learning, Healthcare Data Security, Zero-Knowledge Proofs, Explainable AI, Tamper-Proof Records
Combining Blockchain and AI to Secure Federated Health Records
The exponential growth of health data generated from electronic health records (EHRs), wearable sensors, telemedicine platforms, and diagnostic imaging has transformed the healthcare ecosystem, offering unprecedented opportunities for data-driven innovation. However, this vast data landscape introduces critical challenges in security, privacy, interoperability, and compliance. Traditional centralized storage models are increasingly vulnerable to cyberattacks, insider threats, and systemic failures, undermining trust in digital health infrastructures. Federated health records, where data remains within institutional silos but contributes to collaborative learning, offer a partial solution; yet, ensuring trust, auditability, and protection against adversarial manipulations remains unresolved. This paper proposes a synergistic framework integrating blockchain and artificial intelligence (AI) to secure federated health records. Blockchain ensures immutability, decentralization, and transparent audit trails, while AI provides adaptive intelligence for anomaly detection, secure access control, and federated learning optimization. A multi-layered methodology is designed, encompassing data retention, blockchain-based smart contracts, AI-enabled security modules, and consensus-driven validation. Comparative statistical analysis reveals that the integrated approach enhances data integrity by 41%, reduces unauthorized access by 55%, and improves compliance audit success by 53%, while maintaining acceptable latency levels. The findings highlight the transformative potential of blockchain–AI integration in achieving a resilient healthcare infrastructure that balances data availability, confidentiality, integrity, and usability. Beyond immediate security improvements, the model also facilitates cross-institution collaboration, supports global health crisis management, and aligns with evolving data protection regulations such as HIPAA and GDPR. This research thus contributes both a practical framework and a forward-looking paradigm for secure, interoperable, and ethically governed federated health systems.
Blockchain, Artificial Intelligence, Federated Learning, Health Records, Security, Privacy, Interoperability
Copyright (c) 2025 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
Comparative Policy Analysis of Hybrid Energy Deployment in Developed vs Developing Nations
Hybrid energy systems—integrating renewable energy, energy storage, and conventional power sources—have emerged as essential tools for achieving energy security and decarbonization in both developed and developing nations. Yet, the policy approaches toward their deployment vary significantly due to differences in economic capacities, regulatory institutions, technological readiness, energy access priorities, and climate commitments. This paper provides a comparative analysis of hybrid energy policies in developed nations (such as the United States, Germany, Japan, and the European Union) versus developing nations (such as India, Brazil, South Africa, Kenya, and Indonesia). The analysis evaluates policy frameworks, financial instruments, regulatory incentives, technological integration strategies, and institutional barriers that shape hybrid system deployment. The findings indicate that while developed nations emphasize carbon-neutrality targets, grid modernization, and innovation-driven hybrids, developing nations prioritize energy access, cost optimization, and decentralized systems. The paper argues that policy harmonization, international financing, and knowledge transfer are crucial for strengthening hybrid energy adoption globally.
Blockchain as a Governance Layer for AGI Ethics
As artificial general intelligence (AGI) advances toward systems that can autonomously act across domains, the central governance challenge is how to guarantee that ethical principles are specified, enforced, audited, and improved over time without relying on a single, potentially misaligned authority. This manuscript proposes a blockchain-anchored “Ethical Governance Layer” (EGL) for AGI: a layered architecture that couples decentralized identity and membership, on-chain policy specification and versioning, privacy-preserving compliance attestations, tamper-evident auditing, and participatory oversight. We synthesize requirements from prominent governance frameworks (EU AI Act; NIST AI Risk Management Framework; OECD and UNESCO ethics recommendations) and show how distributed ledgers, verifiable credentials, and zero-knowledge proofs can operationalize them in a credibly neutral, transparent, and globally interoperable substrate.
Blockchain Timestamping for AI Model Lifecycle Tracking
The accelerating complexity and societal impact of artificial intelligence (AI) systems have sharpened calls for reproducibility, auditability, and trustworthy governance of the entire model lifecycle—from data collection and labeling to training, evaluation, deployment, monitoring, and retirement. This paper proposes blockchain‐anchored timestamping as a practical, standards-aligned mechanism to create tamper-evident, independently verifiable evidence of when specific lifecycle events occurred and what artifacts (data snapshots, model checkpoints, code commits, configuration files, and deployment manifests) were involved. Building on the cryptographic foundations of secure hashing, Merkle aggregation, and append-only ledgers, we integrate content-addressed storage and well-known provenance schemas to produce minimal-disclosure receipts that reduce storage cost and leakage risks while preserving verifiability.
AI-Powered Adaptive Learning Platforms with Credential Verification on Chain
Adaptive learning platforms personalize instruction by continuously modeling each learner’s knowledge, skills, and pace. Meanwhile, the integrity of academic records and micro-credentials increasingly depends on trustable, portable verification mechanisms across institutions and labor markets. This manuscript proposes and critically examines a reference architecture that unifies AI-powered adaptivity with on-chain credential verification based on decentralized identifiers (DIDs) and the W3C Verifiable Credentials (VC) data model. The design integrates real-time learner modeling (e.g., Bayesian/Deep Knowledge Tracing), reinforcement-learning sequencing, and learning analytics pipelines (xAPI/Caliper) to generate mastery-aligned learning pathways. When mastery thresholds are met, the system issues verifiable micro-credentials whose cryptographic proofs (hashes, revocation registries) are anchored to a permissioned or public blockchain, enabling instant, privacy-respecting verification without exposing sensitive learner data
AI-Blockchain Integration for Remote Patient Monitoring and Alerts
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.