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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Optimizing Problem Management through Proactive Knowledge Base Development
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.
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.
Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
Comparative Study of Tier I vs Tier II Support Models in ITSM
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.
Tier I support, Tier II support, IT Service Management, comparative analysis, resolution time, customer satisfaction, operational costs, AI integration, remote work, support efficiency.
Enhancing Root Cause Analysis Efficiency in Large-Scale IT Operations
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.
Root Cause Analysis, IT Operations, Automation, Machine Learning, System Integration, Incident Management, Scalability, Efficiency, Case Studies, IT Infrastructure.
Incident Response Optimization in Global Command Centers: Lessons from Financial IT Operations
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.
Enterprise Security, Configuration Management Database, Incident Management, ITIL, Cybersecurity, Proactive Defense, Vulnerability Management, Automation, Risk Assessment, IT Operations
Improving Enterprise Security Posture through Integrated CMDB and Incident Management
Operational Excellence in IT Audit through AI-Augmented Control Testing
The integration of Artificial Intelligence (AI) into IT audit processes has ushered in a transformative era, enhancing operational excellence through AI-augmented control testing. This paper explores the evolution of IT audit methodologies, emphasizing the pivotal role of AI in automating control assessments, improving accuracy, and ensuring compliance. By examining current practices and proposing a simulation-based approach, the study aims to provide a comprehensive understanding of AI's impact on IT audit efficiency and effectiveness.
AI in IT Audit, Control Testing, Operational Excellence, Automation, Compliance, Simulation Research
ServiceNow CMDB Governance as a Pillar for Cybersecurity Resilience
In the contemporary digital landscape, organizations face escalating cyber threats that necessitate robust resilience strategies. The Configuration Management Database (CMDB) within ServiceNow offers a centralized repository of IT assets and their interdependencies, serving as a critical component in enhancing cybersecurity resilience. This manuscript explores the pivotal role of CMDB governance in fortifying an organization's cybersecurity posture. By ensuring data accuracy, establishing clear governance frameworks, and integrating CMDB with other IT service management processes, organizations can proactively identify vulnerabilities, streamline incident response, and ensure compliance with regulatory standards. The research underscores the importance of a well-maintained CMDB as a foundational element in achieving operational resilience and mitigating cyber risks.
ServiceNow CMDB, Cybersecurity Resilience, IT Governance, Configuration Management, Incident Response, Operational Resilience, IT Service Management, Data Accuracy, Compliance, Risk Management
Continuous Compliance in Hybrid IT Environments with Integrated ITSM-GRC Workflows
In the evolving landscape of hybrid IT infrastructures, organizations face the dual challenge of maintaining operational agility while ensuring stringent compliance with regulatory standards. Traditional compliance models, characterized by periodic audits and manual interventions, are increasingly inadequate in addressing the dynamic nature of modern IT environments. This paper explores the integration of IT Service Management (ITSM) and Governance, Risk, and Compliance (GRC) frameworks to establish a continuous compliance model within hybrid IT ecosystems. By embedding compliance controls into ITSM workflows and leveraging automation, organizations can achieve real-time compliance monitoring, proactive risk management, and enhanced operational resilience. The study presents a conceptual framework, outlines key research objectives, and discusses the implications of this integrated approach on organizational compliance strategies.
Hybrid IT, Continuous Compliance, ITSM, GRC, Automation, Policy-as-Code, Risk Management, Cloud Security, Compliance Automation, Regulatory Compliance
2nd International Seminar on Bridging Tradition and Technology: AI Perspectives through the lens of Indian Knowledge System
This special issue of the Scientific Journal of Artificial Intelligence and Blockchain Technologies (SJAIBT) presents the proceedings of the Second International Seminar on “Bridging Tradition and Technology: AI Perspectives through the Lens of Indian Knowledge Systems,” an interdisciplinary forum dedicated to the convergence of artificial intelligence (AI) and Indian Knowledge Systems (IKS). Across 67 peer-reviewed papers, the issue explores how AI intersects with diverse fields—including education, ethics, environmental sustainability, digital literacy, gender studies, public policy, and cultural heritage—to address contemporary challenges of sustainability, equity, and cultural preservation. An overarching theme of the seminar is the interdisciplinary bridging of traditional wisdom and modern technology. The collected studies emphasize that aligning AI innovation with indigenous knowledge and ethical principles is key to developing responsible AI systems and fostering inclusive technological progress. Key focal areas include the transformation of education through AI-enhanced learning aligned with IKS perspectives, as well as strategies for women’s empowerment and digital literacy aimed at fostering an inclusive AI-driven society. In addition, several papers propose innovative teacher training models grounded in ancient pedagogical insights to prepare educators for AI-integrated classrooms. Other contributions examine holistic health frameworks that integrate AI with traditional wellness practices and explore AI-driven approaches to environmental sustainability informed by indigenous ecological wisdom. Together, these works underscore a paradigm in which cutting-edge AI applications are harmonized with cultural heritage, social values, and environmental sustainability objectives. This integrated approach illustrates a roadmap for future research and public policy initiatives that leverage both historical wisdom and modern science for societal benefit.
Achieving Seamless Integration of Financial and Supply Chain Systems using modern SAP tools
Financial and supply chain system integration is required to improve operational efficiency and decision-making processes in contemporary businesses. Nevertheless, most organizations continue to experience difficulties in achieving seamless integration due to heterogeneous systems, poor real-time data exchange, and inefficient process workflows. Conventional approaches tend to fail to offer real-time visibility, leading to the creation of data silos, which make it difficult for financial and supply chain departments to collaborate effectively. This research investigates the potential presented by existing SAP tools to bridge the gaps and provide a common platform for financial and supply chain function integration. SAP S/4HANA, in combination with integrated modules such as SAP Ariba, SAP Integrated Business Planning (IBP), and SAP Fiori, provides sophisticated capabilities to improve operational efficiency, improve data accuracy, and facilitate seamless communication between finance and supply chain departments. The research investigates the processes through which the tools facilitate data synchronization, automate processes, and improve cross-functional collaboration. By concentrating on real-time analytics, cloud-based applications, and AI-based insights, this research seeks to illustrate how contemporary SAP tools can overcome conventional integration challenges, allowing businesses to optimize their supply chain and financial functions. The research findings will identify best practices, outline common issues, and offer actionable recommendations for organizations seeking to implement a strong, future-proof integrated system. This research extends the knowledge of how businesses can leverage the wide capabilities of SAP tools to achieve operational excellence and facilitate long-term growth.
Financial integration, supply chain systems, SAP S/4HANA, SAP Ariba, SAP IBP, real-time data synchronization, workflow automation, cross-functional collaboration, AI-driven insights, cloud-based solutions, operational efficiency, data accuracy.
Copyright (c) 2025 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
Blockchain Governance Models for Open Source AI Communities
Open source AI communities increasingly steward high-impact artifacts—foundation model weights, safety evaluations, curated datasets, and inference tooling—whose governance must balance openness, safety, sustainability, and inclusivity. Blockchain technologies promise tamper-evident records, programmable institutions, and novel incentive designs for these communities, yet the governance models available—from straightforward token-weighted voting to quadratic and reputation-weighted systems—differ markedly in participation, resilience, and fairness
AI-Assisted Sharding Techniques for Blockchain Scalability
Sharding—partitioning a blockchain’s state and workload across multiple committees—remains one of the most promising approaches to scale permissionless ledgers without sacrificing decentralization. However, static shard layouts and coarse, rule-based reconfiguration often underperform when faced with bursty demand, non-stationary access patterns, or cross-shard dependencies. This manuscript proposes an AI-assisted sharding framework that integrates (i) graph-neural forecasters for short-horizon workload prediction on account/contract interaction graphs, (ii) a reinforcement-learning (RL) policy for online decisions about shard sizing, committee rotation, and cross-shard routing, and (iii) safety wrappers that enforce cryptographic and protocol constraints (e.g., minimum committee sizes, randomness beacons, and rotation limits) to preserve security.
AI-Augmented Blockchain Fork Detection and Recovery Protocols
Forks—temporary or permanent divergences in a blockchain’s ledger—arise from benign network asynchrony, configuration heterogeneity, or adversarial behavior such as selfish mining and eclipse/routing attacks. Although modern fork-choice rules (e.g., longest-chain/most-work, GHOST variants) and finality gadgets reduce the practical impact of short reorgs, detection remains largely reactive and recovery is often ad hoc at the protocol or operational layer. This paper proposes an AI-augmented framework for (i) early fork risk prediction from peer-to-peer (P2P) telemetry and block-header streams, (ii) real-time fork detection using multi-view anomaly models, and (iii) policy-driven recovery that coordinates mempool hygiene, relay routing, and safe rollback/finality choices.
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.
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
Bridging IT Audit and ITSM: A Process-Centric Approach Using ServiceNow
This research explores the integration of IT Audit and IT Service Management (ITSM) through a process-centric approach utilizing ServiceNow. The study examines how aligning ITSM processes with auditing frameworks enhances organizational efficiency, compliance, and service delivery. By analyzing existing literature and conducting simulations, the paper identifies key areas where ServiceNow can facilitate this integration, offering a comprehensive model for organizations aiming to streamline their IT operations.
IT Audit, IT Service Management, ServiceNow, Process Integration, Compliance, Simulation, Organizational Efficiency, Service Delivery, Framework Alignment, IT Operations.
Automating Compliance Monitoring in ITSM Platforms Using Machine Learning
This manuscript explores the integration of Machine Learning (ML) into IT Service Management (ITSM) platforms to automate compliance monitoring. As organizations face increasing regulatory demands and complex IT environments, traditional manual compliance processes become inadequate. ML offers a transformative approach by enabling proactive, real-time compliance oversight. The paper discusses the application of ML in automating compliance tasks, enhancing accuracy, and reducing operational overhead. Furthermore, it presents a case study demonstrating the effectiveness of ML-driven compliance automation in an enterprise ITSM context.
Machine Learning, IT Service Management, Compliance Monitoring, Automation, Predictive Analytics, Anomaly Detection, Service Level Agreements, Regulatory Compliance, IT Operations, Enterprise ITSM
ServiceNow Performance Analytics for Real-Time IT Audit Dashboards
This study examines the integration of ServiceNow Performance Analytics into IT audit dashboards, focusing on its real-time data visualization capabilities. The research aims to assess how these dashboards enhance audit efficiency, accuracy, and decision-making processes. Through a simulation-based methodology, key performance indicators (KPIs) were analyzed to determine the impact of real-time analytics on audit outcomes. Findings indicate significant improvements in audit response times and issue resolution, demonstrating the effectiveness of ServiceNow Performance Analytics in modernizing IT audit practices.
ServiceNow, Performance Analytics, IT Audit, Real-Time Dashboards, Key Performance Indicators, Simulation Study, Data Visualization, Audit Efficiency, Decision-Making, IT Governance
Reducing Audit Lifecycle Costs through CMDB Automation and Standardization
This study explores the impact of Configuration Management Database (CMDB) automation and standardization on reducing audit lifecycle costs in IT environments. By integrating automated discovery tools and standardized processes, organizations can achieve significant improvements in data accuracy, compliance, and operational efficiency. The research highlights best practices, presents a statistical analysis of audit preparation time reduction, and identifies key areas for further enhancement.
CMDB, automation, standardization, audit lifecycle, IT asset management
Enhancing IT Audit Readiness through Automated Evidence Collection in ServiceNow
In today’s fast-changing regulatory and technology landscape, organizations face mounting pressure to maintain continuous audit readiness. Manual evidence collection remains a major bottleneck, characterized by errors, delays, and resource drain. This study proposes a framework for enhancing IT audit readiness by leveraging automated evidence collection within ServiceNow, integrating governance, risk, and compliance (GRC) modules, workflow automation, and continuous monitoring. We conduct a literature review, design a methodology, collect data from practitioners in organizations using ServiceNow, and perform statistical analysis to validate hypotheses about efficiency gains, risk reduction, and audit outcome improvements. Our findings show a statistically significant reduction in audit preparation time and error rates when automation is adopted, while also revealing organizational, process, and technical barriers. We identify research gaps and offer recommendations for practitioners. The framework and results provide a roadmap for firms seeking to transition from reactive, manual audits toward proactive, always-audit-ready operations.
IT audit readiness; automated evidence collection; ServiceNow; governance, risk, compliance; continuous monitoring
Integrating CMDB Governance with Identity & Access Management for Enterprise Security
In modern enterprises, Configuration Management Databases (CMDBs) and Identity & Access Management (IAM) are often managed separately, causing gaps in governance, visibility, and security. This study proposes a framework to integrate CMDB governance with IAM systems to enhance enterprise security, reduce risk, and improve compliance. We define governance principles for CMDB, align them with IAM lifecycle events, and simulate integration scenarios to evaluate effects on access anomalies, orphan accounts, and incident response time. Using statistical analysis on simulated data and scenario-based simulation, we show that integrated governance reduces access risk metrics by up to 35 % and improves mean time to detect unauthorized access by 28 %. The findings support adoption of such integration in real enterprises, subject to implementation challenges and tooling alignment.
CMDB governance; IAM integration; enterprise security; access risk; configuration management
Implementing COBIT-Aligned ServiceNow Workflows for Financial Institutions
In an era of tightening regulatory oversight and rising cyber risks, financial institutions must ensure that their IT service workflows are not only efficient, but also deeply aligned with governance, risk, and compliance frameworks. This paper explores the design, simulation, and evaluation of ServiceNow workflows aligned to COBIT (latest version) in a banking context. We propose a methodology to map COBIT control objectives to workflow processes, simulate their performance under realistic incident/change scenarios, and statistically analyze key performance indicators (KPIs). The simulation and analysis demonstrate that COBIT-aligned workflows reduce governance violations, improve mean time to resolution (MTTR), and enhance audit readiness. We conclude with recommendations and limitations.
COBIT, ServiceNow workflows, financial institutions, IT governance, simulation
AI-Powered Audit Analytics: Improving Risk Detection in IT Service Management
IT Service Management (ITSM) remains a critical domain in organizations, yet auditing and risk detection in ITSM processes often lag due to the volume, velocity, and complexity of IT operational data. This paper proposes an AI-powered audit analytics framework tailored for ITSM to enhance risk detection capabilities. We review relevant literature, specify a methodology utilizing machine learning models, and present a statistical analysis on simulated ITSM audit data. The results suggest that anomaly detection models outperform traditional rule-based triggers in identifying risk incidents. We identify key gaps—such as explainability, integration with legacy systems, and domain adaptation—and conclude with recommendations for practice and future research.
AI audit analytics; IT Service Management; risk detection; anomaly detection; audit automation
Zero Trust Principles in ITSM: Linking Governance with Secure Operations
This paper explores the integration of Zero Trust principles within IT Service Management (ITSM) frameworks, emphasizing the enhancement of governance and secure operations. As organizations increasingly adopt digital transformation strategies, traditional security models prove inadequate. Zero Trust, encapsulated by the mantra "never trust, always verify," offers a robust alternative. This study examines the alignment of Zero Trust with ITSM processes, identifies implementation challenges, and proposes strategies for effective integration. Through a systematic literature review and case studies, the research highlights the benefits of adopting Zero Trust in ITSM, including improved risk management, compliance, and operational resilience.
Zero Trust, IT Service Management, Governance, Secure Operations, Digital Transformation
Digital Shadows: How Technology Is Reshaping the Reporting of Domestic Violence
Domestic violence continues to be one of the most pervasive human rights concerns worldwide, yet it remains significantly under-reported due to fear, stigma, and limited access to formal services. The rapid digitisation of everyday life has introduced new dynamics into how survivors disclose and report abuse. This study examines the evolving role of technology in domestic violence reporting, focusing on the growing tension between technology as a tool of empowerment and as a mechanism of control. Drawing on recent empirical studies, policy frameworks, and evaluations of digital interventions, the review highlights three major shifts. First, new reporting pathways—such as online police portals, crisis chats, mobile safety-planning applications, and social-media disclosures—are lowering barriers to help-seeking and enabling survivors to document abuse on their own terms. Second, the same technologies are increasingly weaponised by perpetrators through surveillance, cyberstalking, harassment, and control of digital identities, creating heightened safety risks for survivors who attempt to seek help online. Third, unequal access to secure devices, digital literacy, and supportive environments shapes who can benefit from these emerging tools. The findings emphasise the urgent need for survivor-centred design in digital reporting systems, stronger privacy and data-governance standards, and inclusive implementation strategies that address the digital divide. By mapping both opportunities and risks within these “digital shadows,” the study provides a foundation for safer, more accessible and rights-based responses to domestic violence in the technology-driven era.
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.