Journal of Artificial Intelligence & Cloud Computing
type: channel
description: Journal of Artificial Intelligence & Cloud Computing is an open access peer-reviewed journal from the publishers of Scientific Research and Community (SRC) which publishes articles in the form of written, video and power-point presentations from the arena of Artificial Intelligence & Cloud Computing. The journal focuses to build up accessing the complete content of articles freely from online for reader’s perusal. Articles can be submitted through online from https://srcpublishers.com/ai-cloud-computing/user/register or can send through an e-mail with attachment for artificialintelligence@onlinescientificresearch.net or jaicc@onlinescientificresearch.info 0.5 Impact Factor 84.5% Citations 85% Acceptance
publisher: Scientific Research and Community
language: en
publication name: Journal of Artificial Intelligence & Cloud Computing
issn: 2754-6659
items: Self-Governing Cloud Performance: an MCP-Orchestrated MultiAgent Blueprint for Autonomous SLA Assurance in Multi-Tenant Systems
items: Supporting Partnerships in Participatory, User-centered Co-Design of Artificial Intelligence Mediated Devices for People with Disabilities: Identifying Needs and Opportunities
items: The Anti-Enlightenment Mentality in the Law
items: From Raw Sensor to Business Signal: An Azure Databricks Framework for Diesel Engine Performance Analytics Across Global Fleet Operations
items: Data Quality Engineering Frameworks for Regulatory-Grade AML Transaction Monitoring Systems
items: On the Physical Chemistry of the Greenhouse Effect
items: A Framework for Enterprise Agentic AI: Multi-Agent Architectures and ROI-Driven Adoption
items: The Foundations and Results of Parallel Computing in Terms of Gaisi Takeutis’ Quantum Set Theory
items: Building Partnerships in Participatory, User-centered Design of Artificial Intelligence Mediated Devices for People with Disabilities
items: Network Function Virtualization (NFV) and Software-Defined Networking (SDN) Integration for Network Management
items: AI for Social Good and Responsible Innovation
items: Automatic Detection and Analysis of Extremist Video Content on Social Media Using Text
items: Blockchain-Enhanced AI: Securing Data Pipelines in Hybrid Cloud Environments
items: AI and ML Techniques for Machine Health Condition Monitoring
items: AI-Driven Data Engineering for Intelligent Debt Resolution and Financial Wellness
items: DecepNet: A Hybrid NLP Framework for Robust Phishing Detection Under Adversarial Conditions
items: Energy Supply Infrastructure for the Nechit Monastery: Technical Design and Implementation
items: Challenges for Problems in Mean-Field Setting
items: Professionals’ Perception and Trust in AI Predictions in High-Risk Contexts
items: Agentic AI Revolutionizing Software Engineering
items: Non-Replicable Function-Based Multi-Level Random Dynamic Secret Key Algorithm (Extended Version)
items: The Generative AI Sales Paradox (GASP)-Enhancing Sales Scalability at the Cost of Human-Led Relationship Building in B2B Markets
items: Audit Ready AI Agentic Coding: ISO 27001 Governance for GitHub Copilot in Financial Services
items: Explainable AI in regulated Environments — Making Compliance a Competitive Advantage
items: From Market Noise to Signal: Machine Learning and Quantitative Alpha in Financial Markets
items: Designing AI Agents for Content Automation: From Prompt Engineering to Autonomous Workflows
items: Using Domain Taxonomy to Analyze Tendencies of its Development
items: AI Impact on Professional Identity and Skills Evolution: A Comprehensive Analysis of Workforce Transformation Across Healthcare, Law, Education and Engineering
items: Tripartite Game Evolution and Stabilization Strategies in Artificial Intelligence-Empowered Science & Technology Journals
items: A Review of AMBA Bus Protocols: Focus on AHB, AHB-Lite, and AXI

Self-Governing Cloud Performance: an MCP-Orchestrated MultiAgent Blueprint for Autonomous SLA Assurance in Multi-Tenant Systems
type: item
description: This paper presents a practitioner-oriented architectural blueprint for deploying self-governing performance management in multi-tenant cloud systems. Unlike prior theoretical treatments of AI-driven operations, this work provides DevOps Cloud Solutions Architects with concrete implementation patterns, deployment runbooks, infrastructure-as-code templates, and design decisions for constructing a multi-agent system orchestrated through the Model Context Protocol (MCP). The proposed architecture employs five coordinated agents, each bound by SLA-aware governance policies, that autonomously manage the full performance lifecycle from telemetry ingestion through remediation execution. We detail practical integration patterns for connecting agents to existing observability stacks (Prometheus, Grafana, Datadog), container orchestrators (Kubernetes, ECS), and CI/CD pipelines (ArgoCD, GitHub Actions). Based on published industry benchmarks and analytical modeling, the framework projects a 60-75% reduction in Mean Time to Resolution, SLA compliance exceeding 99.95%, and a 30-40% decrease in infrastructure spend. This paper provides the missing implementation bridge between academic agentic AI research and production-grade cloud operations.
subject: Articles
  topic: section
subject: Multi-Agent Systems
  topic: keywords
subject: Model Context Protocol
  topic: keywords
subject: SLA-Aware Optimization
  topic: keywords
subject: Cloud Performance Management
  topic: keywords
subject: DevOps Architecture
  topic: keywords
subject: Self-Governing Infrastructure
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Observability
  topic: keywords
subject: Infrastructure-As-Code
  topic: keywords
subject: Production Deployment Patterns
  topic: keywords
creator: Manvitha Potluri Potluri
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-06-27
publication date: 2026-06-27
starting page: 1
ending page: 7
doi: 10.47363/0yktdq02

Supporting Partnerships in Participatory, User-centered Co-Design of Artificial Intelligence Mediated Devices for People with Disabilities: Identifying Needs and Opportunities
type: item
description: One billion people, fifteen percent of the world’s population, have experienced a disabling condition that impacts their physical or cognitive function according to the World Bank Group. Such disabilities can result in challenges that present major barriers to entering and persisting in the labor force. In the United States, at least 80% of people with disabilities were not part of the 2024 labor force according to the US Bureau of Labor Statistics. This disparity has been particularly pronounced in technology-related employment. Only 3% of the science, technology, engineering, and math (STEM) workforce are people with disabilities and representation in STEM workforces remains relatively unchanged from a decade ago, despite an increase in the number of people with disabilities across professions since 2011. Americans with disabilities earn only 66 cents for every dollar earned by their counterparts without disabilities. While employment opportunities world-wide in technology industries continue to grow, they are largely inaccessible to people with disabilities. This inaccessibility is primarily because standard computing interfaces are nearly impossible to operate for many people with disabilities, especially those with physical challenges. The research presented in this paper explores the challenges that people with physical disabilities have in accessing and using technologies and provides merit for the role that artificial intelligence (AI) and machine learning may play in changing the outcomes for people with disabilities. Such adaptations may provide people with disabilities with full accessibility to technology when it is facilitated by AI powered, machine learning mediated accessibility devices. This research presents a structure and process for obtaining information about the individual and collective needs of people with physical disabilities in designing accessible hardware and associated software wrappers to meet their needs. Furthermore, it highlights pr…
subject: Articles
  topic: section
subject: Use-inspired Research,
  topic: keywords
subject: Human-computing Interaction
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Participatory Research Engagement
  topic: keywords
creator: Gisele Ragusa Ragusa
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-06-04
publication date: 2026-06-04
starting page: 1
ending page: 7
doi: 10.47363/JAICC/2026(5)531

The Anti-Enlightenment Mentality in the Law
type: item
description: If Europe imagined the Enlightenment, it was America that realized it [1]. In one of his many letters to Thomas Jefferson, John Adams wrote that "the 18th century, notwithstanding all its errors and vices, has been, of all that are past, the most honorable to human nature [2]." The period from the last quarter of the 16th Century through the end of the 18th Century has often been characterized as "The Enlightenment." During this period, various philosophers began the intellectual detachment from medieval scholasticism and its mystical base [3]. The result was an emphasis on an empiricism that supported what was viewed as a healthy skepticism. The leaders of this movement included such writers as Copernicus, Galileo, John Locke, David Hume, Voltaire, Montesquieu, and scientists such as Benjamin Franklin. Collectively, particularly in the 18th Century, they were known as philosophes and were acknowledged as the intellectual leaders of the time. Their work began with inquiry into the natural sciences and its extension into politics and economics that were at least as impactful. While it is easy to suggest that they were anti-clerical, indeed to some extent hostile to religion, that sells them short. It is true that they did not concern themselves with extensive religious debates, but they never completely divorced thought from its traditional religious origins. They simply went beyond it in their thought process, driven as it was with an emphasis on the distinction between what is "material" and "mental or ideal". Put another way, the "empirical" view of the philosophes was grounded in observation of factually definable phenomena. Samuel Johnson pointed this out in the act of kicking a stone as a means to refute the arguments of Bishop Berkeley as to what exists and what does not [4].
subject: Articles
  topic: section
subject: Anti-Enlightenment
  topic: keywords
subject: Law
  topic: keywords
creator: Honorable Xiąże John McClellan Marshall John McClellan Marshall
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-06-04
publication date: 2026-06-04
starting page: 1
ending page: 3
doi: 10.47363/JAICC/2026(5)530

From Raw Sensor to Business Signal: An Azure Databricks Framework for Diesel Engine Performance Analytics Across Global Fleet Operations
type: item
description: A framework for the scalable analytics of diesel engine performance across global fleet operations is presented. Key performance metrics from raw sensor data are defined, demonstrating a clear link to business objectives such as fuel consumption and maintenance tackling times. Modelling such metrics requires appropriate sensor data (estimated here at 25 to 35 signals) underpinned by data processing and feature engineering activities, supported by both real-time and batch solutions. Performance from the analytics framework is achieved through a breadth of modelling approaches: time-series modelling of performance drift, classification modelling of engine failure avoidance, and popular machine-learning performance-influence exploration. Supporting infrastructure is within the Azure Databricks ecosystem, alongside a dialogue with fleet operations through an Microsoft Azure application. The solution, wholly designed and implemented by a single data scientist, provides all components required for production-ready analytics. Results are being deployed within a multi-Cloud platform that extends from raw sensor data through to business summary dashboards, encompassing a broad range of analytics beyond those for diesel engines.
subject: Articles
  topic: section
subject: Diesel Engine Analytics
  topic: keywords
subject: Fleet Performance Monitoring
  topic: keywords
subject: Predictive Maintenance Models
  topic: keywords
subject: Sensor Data Engineering
  topic: keywords
subject: Real-Time Fleet Analytics
  topic: keywords
subject: Time-Series Performance Modeling
  topic: keywords
subject: Engine Failure Classification
  topic: keywords
subject: Azure Databricks Analytics
  topic: keywords
subject: Multi-Cloud Analytics Platforms
  topic: keywords
subject: Fleet Operations Intelligence
  topic: keywords
creator: Rajesh Mattaparthi Mattaparthi
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-05-25
publication date: 2026-05-25
doi: 10.47363/JAICC/2022(1)529

Data Quality Engineering Frameworks for Regulatory-Grade AML Transaction Monitoring Systems
type: item
description: Anti-Money Laundering (AML) systems function within tightly enforced compliance architecture defined by global regulators. The increasing scale and sophistication of financial fraud and money laundering have made robust AML controls critically important. Breakdowns in AML controls can expose institutions to reputational damage, regulatory penalties, and systemic financial risk. These challenges necessitate the adoption of systematic, data-driven approaches for effective risk detection and mitigation. Contemporary AML platforms integrate rule engines with machine learning models to evaluate transactional risk patterns, various risk scoring models demonstrating data integrity checks across multiple stages of data lifecycle. Despite these advancements, financial crime incidents continue to rise, indicating gaps in current system effectiveness. Still a lot needs to be explored in data engineering to help in data analysis and resolution. This paper proposes data quality engineering (DQE) model for regulatory grade AML environments. DQE framework focusses on 5 data quality dimensions - completeness, accuracy, consistency, timeliness, and lineage integrity. This architecture uses Microsoft Azure, Azure Databricks and data will be stored in Delta Lake. Technical validations performed using above model also aligns with the regulatory compliances and obligations under the Bank Secrecy Act (BSA), Financial Action Task Force (FATF) recommendations, and Office of Foreign Assets Control (OFAC). This study hypothesizes that DQE implementation can reduce false negatives and can improve financial crime detection.
subject: Articles
  topic: section
subject: Data Quality Engineering
  topic: keywords
subject: AML Transaction Monitoring
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Azure Databricks
  topic: keywords
subject: Delta Lake
  topic: keywords
subject: Financial Crime Analytics
  topic: keywords
subject: Data Lineage
  topic: keywords
subject: BSA/AML
  topic: keywords
creator: Mukesh Kumar Mishra Mishra
rights: Copyright (c) 2022 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-05-15
publication date: 2026-05-15
starting page: 1
ending page: 3
doi: 10.47363/JAICC/2026(5)527

On the Physical Chemistry of the Greenhouse Effect
type: item
description: In the present, a looming catastrophe is feared due to climate change. People are looking for ways to prevent the anticipated warming of the climate. The primary approach promoted is the reduction of carbon dioxide, CO2, emissions from human activities. This would require a major transformation of existing ways of life and production. There are legitimate doubts about the reasoning that human-generated CO2 is the main cause of global warming. We examined the physico-chemical properties of CO2 and the so-called greenhouse gases to determine their influence on the heat exchange between the Earth's atmosphere and space. Their role as converters of infrared radiation from the sun into heat is a fact; conversely, they are equally indispensable as transmitters of heat from the Earth's atmosphere into infrared radia-tion to balance the Earth's radiation equilibrium. The balances of CO2 exchange with the plant world and the oceans, in comparison with anthropogenic emissions, are critically assessed. The anthropogenic energy flow is four orders of magnitude smaller than that of daily solar radiation. The planetary regulatory system of ice ages and warm periods, which has been operating for hundreds of thousands of years, is being discussed. It must be taken into account in the climate discussion of the past centuries. The CO2 as the driving force in the CO2-T correlation is being questioned.
subject: Articles
  topic: section
subject: Greenhouse Gases
  topic: keywords
subject: Heat Capacity
  topic: keywords
subject: Energy Transmitters and Emitters,
  topic: keywords
subject: Radiation Balance
  topic: keywords
subject: Exchange of Energy and CO2
  topic: keywords
creator: Martin Büow Büow
creator: Helmut Ullmann Ullmann
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-05-04
publication date: 2026-05-04
starting page: 1
ending page: 6
doi: 10.47363/JAICC/2026(5)526

A Framework for Enterprise Agentic AI: Multi-Agent Architectures and ROI-Driven Adoption
type: item
description: The rapid advancement of agentic artificial intelligence (AI) is transforming how intelligent systems are designed, deployed, and monetized across research and enterprise environments. Unlike traditional generative AI or rule-based automation, AI agents introduce autonomous, goal-driven capabilities that can reason, plan, collaborate, and execute actions across complex workflows. This session presents a focused examination of the latest trends, enabling protocols, and measurable return on investment (ROI) driving the adoption of AI agents. The talk highlights key developments such as multi-agent architectures, tool-augmented reasoning, retrieval-augmented generation (RAG), and scalable agent orchestration frameworks that enable coordinated intelligence. It further explores emerging protocols and standards, including model context exchange, agent communication schemas, and governance mechanisms essential for interoperability, security, and compliance. A central emphasis is placed on the economic and operational impact of AI agents. Drawing from real-world enterprise implementations, the session demonstrates how organizations achieve measurable ROI through productivity gains, cost optimization, cycle-time reduction, and revenue enablement. The presentation concludes with a pragmatic roadmap for responsibly scaling agent-based systems from experimentation to sustainable value creation.
subject: Articles
  topic: section
subject: Architectures
  topic: keywords
creator: Padmanabham Venkiteela
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)20

The Foundations and Results of Parallel Computing in Terms of Gaisi Takeutis’ Quantum Set Theory
type: item
description: The foundations and results of parallel computing in terms of Gaisi Takeutis’ quantum set theory are presented in this talk. This approach is based on • The extension of the von Neumann’s uniqueness theorem for the solutions of the canonical commutations relations in quantum mechanics of finitely many degrees of freedom to quantum local field theories of infinitely many degrees of freedom; • The results of the mathematician Gaisi Takeuti about quantum set theory in extension theory; and • The results of the physicist Lev Davidovics Landau about the behavior of the quantum liquids He3 and He4 resulting in a more general approach than the one based on the notion of the quantum bit.
subject: Articles
  topic: section
subject: Computing
  topic: keywords
creator: Miklós Banai
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)19

Building Partnerships in Participatory, User-centered Design of Artificial Intelligence Mediated Devices for People with Disabilities
type: item
description: One billion people, fifteen percent of the world’s population, have experienced a disabling condition that impacts their physical or cognitive function according to the World Bank Group. Such disabilities can result in challenges that present major barriers to entering and persisting in the labor force. In the United States, at least 80% of persons with disabilities were not part of the 2024 labor force according to the US Bureau of Labor Statistics. This disparity has been particularly pronounced in technology related employment. Only 3% of the science, technology, engineering, and math (STEM) workforce are people with disabilities and representation in STEM workforces remain relatively unchanged from a decade ago despite an increase in the number of people with disabilities across professions since 2011. Americans with disabilities earn only 66 cents for every dollar earned by their counterparts without disabilities. While employment opportunities world-wide in technology industries continue to grow, they are largely inaccessible to people with disabilities. This inaccessibility is primarily because standard computing interfaces are nearly impossible to operate for many people with disabilities. This presentation will discuss the results of research on the role that artificial intelligence (AI) and machine learning plays on changing the outcomes on people with disabilities, by providing them with full accessibility to technology when it is mediated by AI powered, machine learning mediated accessibility devices. It presents a structure and process for obtaining information about the individual and collective needs of people with disabilities in designing accessible hardware and associated software wrappers to meet their needs and then working with them in technology design and use. Results reveal that people with disabilities play a significant role in designing, testing and developing accessibility tools for themselves and others associated with technology use in …
subject: Articles
  topic: section
subject: Building
  topic: keywords
creator: Gisele Ragusa
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)18

Network Function Virtualization (NFV) and Software-Defined Networking (SDN) Integration for Network Management
type: item
description: Modern network infrastructures are evolving toward increased automation, scalability, and flexibility. Two key technologies: Network Function Virtualization (NFV) and Software-Defined Networking (SDN) have emerged as critical enablers for this transformation. NFV decouples network functions from proprietary hardware by virtualizing them on general-purpose servers, while SDN introduces programmability by separating the control plane from the data plane. However, when deployed independently, these technologies cannot fully realize the vision of dynamic and intelligent network management. This research explores the integration of NFV and SDN as a unified architecture for automated network management and orchestration. The study investigates architectural frameworks, interoperability challenges, and performance implications through analytical modeling and simulation using open-source platforms such as OpenStack, ONOS, and OPNFV. Results demonstrate that NFV–SDN integration improves service agility, resource utilization, and fault recovery efficiency, thereby advancing the capabilities of next-generation networks. The paper concludes by outlining future research directions, including AI-driven orchestration, security automation, and edge-cloud convergence for intelligent network control.
subject: Articles
  topic: section
subject: Network Function Virtualization (NFV),
  topic: keywords
subject: Software Defined Networking (SDN),
  topic: keywords
subject: Network Management and Orchestration (MANO)
  topic: keywords
subject: Virtual Network Functions (VNFs),
  topic: keywords
subject: SDN Controller Integration
  topic: keywords
subject: Network Automation
  topic: keywords
subject: Cloud and Edge Networking
  topic: keywords
subject: 5G Network Infrastructure
  topic: keywords
creator: Prasanth Kosaraju Kosaraju
creator: Venu Madhav Nadella Nadella
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-05-13
publication date: 2026-05-13
doi: 10.47363/JAICC/2022(1)506

AI for Social Good and Responsible Innovation
type: item
description: As artificial intelligence becomes increasingly embedded in the tools we use every day, the way we work, learn, and lead is evolving rapidly. This talk explores how AI can be leveraged as a force for social good—enhancing productivity and decision-making not by replacing people, but by augmenting human potential and promoting inclusion. Drawing on real-world examples, the session highlights strategies for designing responsible, human-centered AI systems that address societal challenges, support equitable access, and create positive social impact. Emphasis is placed on ethical implementation, transparency, and inclusive innovation, demonstrating how AI can drive meaningful results while contributing to sustainable growth and the well-being of diverse communities.
subject: Articles
  topic: section
subject: Innovation
  topic: keywords
creator: Noha Elfiky
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)17

Automatic Detection and Analysis of Extremist Video Content on Social Media Using Text
type: item
description: Our prior work on radicalization focuses on Increasing our understanding of online radicalization efforts from analyzing right and left-leaning group videos in social media including groups from outside the USA. We studied what viewers found the videos appealing, what techniques were employed in producing radical content, and how we could computationally identify these techniques. More recently we have worked to identify potentially effective de-radicalizing techniques and worked to build a unified multimodal processing pipeline toolkit to help us create conversational systems on social media that are useful in de-radicalizing viewers.
subject: Articles
  topic: section
subject: Automatic Detection
  topic: keywords
creator: Julia Hirschberg
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)16

Blockchain-Enhanced AI: Securing Data Pipelines in Hybrid Cloud Environments
type: item
description: Blockchain-Enhanced AI: Securing Data Pipelines in Hybrid Cloud Environments: The data is vulnerable to tampering and breaches, which would completely undermine the accuracy of AI-driven insights, when running data pipelines in hybrid cloud setups.Blockchain Enhanced AI is a framework that combines the unchangeable records of blockchain technology with AI to check the accuracy of real-time data in multi-cloud systems. This can be done with the help of smart contracts that automatically flag anomalies, using sophisticated machine learning models such as LSTM networks. We can virtually eliminate the possibility of man-in-the-middle attacks by hashing the data blocks and validating them via consensus algorithms. In software engineering, the process streamlines the DevOps pipeline and reduces the risk of breaches by 30% on simulations in AWS and Azure. At the heart of the framework lies a robust core architecture and also includes privacy-preserving zero-knowledge proofs, and we’ve put this system to the test with empirical results in prototypes processing enormous petabyte-scale datasets. Attendees will be able to learn about the real-world implementation of this technology, challenges to scaling, and the ethical concerns around decentralized AI governance, and in doing so will help in developing more secure cloud-native applications.
subject: Articles
  topic: section
subject: Hybrid Cloud Environments
  topic: keywords
creator: Ankur Mahida
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)15

AI and ML Techniques for Machine Health Condition Monitoring
type: item
description: Reliable machine condition monitoring systems are critically needed in industries to recognize equipment defects at their earliest stage so as to improve production quality, operation efficiency and safety. An AI and machine-learning (ML)-based monitoring system consists of modules such as data acquisition, signal processing, fault diagnosis and prognosis. Smart sensor-based data acquisition systems are used to collect signals wirelessly. Signal processing is a procedure to extract representative features from measurement for system analysis and fault detection in machinery systems. Diagnosis is a procedure to classify features/patterns into different categories corresponding to different equipment health states. Prognosis is a process to predict the remaining useful life of the damaged equipment to schedule predictive maintenance operations. New AI tools such as adaptive neurofuzzy and evolving fuzzy techniques are used in automatic diagnostic and prognostic operations. Appropriate ML algorithms can be used to improve decision-making convergence and adaptive capability to accommodate different machinery conditions.
subject: Articles
  topic: section
subject: Machine Health
  topic: keywords
creator: Wilson Wang
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)14

AI-Driven Data Engineering for Intelligent Debt Resolution and Financial Wellness
type: item
description: Rising global debt presents a significant challenge that affects not only financial stability but also mental well-being and access to opportunities, while traditional debt recovery methods remain reactive and lack personalization. This work proposes an AI-driven data engineering framework that leverages behavioral data, public datasets, and social signals, combined with real-time processing technologies such as Kafka, Spark, and BigQuery, to enable early detection of financial distress. Advanced AI models, including risk classification, sentiment analysis, and recommendation systems, identify at-risk individuals, assess their readiness for support, and deliver personalized interventions. The approach outlines a comprehensive financial empowerment journey that moves from detection and engagement to guidance and long-term support, helping individuals achieve sustainable financial health. Emphasizing privacy, transparency, and ethical AI practices aligned with global standards, this framework aims to improve early intervention outcomes, reduce debt burdens, and shift the focus from reactive recovery to proactive financial empowerment.
subject: Articles
  topic: section
subject: Data Engineering
  topic: keywords
creator: Tenny Enoch Devadas
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)13

DecepNet: A Hybrid NLP Framework for Robust Phishing Detection Under Adversarial Conditions
type: item
description: Phishing attacks continue to evolve with increasing sophistication, particularly with the emergence of AI-generated content that closely mimics legitimate communication. Traditional phishing detection systems, which rely on static text classification models, often struggle to generalize across diverse datasets and fail under subtle adversarial modifications. In this work, we propose DecepNet, a hybrid phishing detection framework that integrates natural language processing with structural and behavioral feature analysis to enhance detection performance and robustness. The proposed system combines TF-IDF-based representations and transformer-based semantic modeling with a learned deception function designed to capture persuasive intent through linguistic, contextual, and behavioral signals such as urgency, authority cues, and threat-reward framing. The model is trained on a multi-source dataset constructed from publicly available corpora, including Apache Spam Assassin and Enron Spam Dataset, along with additional phishing samples derived from URL-based sources and synthetically generated adversarial data. To evaluate robustness, we introduce an adversarial testing setup involving paraphrasing, tone modification, and obfuscation techniques. Experimental results demonstrate that conventional machine learning models experience noticeable performance degradation under such conditions, while the proposed framework maintains improved stability and generalization across datasets. Comparative analysis with baseline models, including Logistic Regression, Random Forest, and transformer-based classifiers, highlights the effectiveness of integrating deception-aware features. This study emphasizes the importance of combining semantic understanding with behavioral modeling in phishing detection and provides a practical, adaptable framework for addressing evolving social engineering threats in real-world environments.
subject: Articles
  topic: section
subject: Phishing Detection
  topic: keywords
subject: Natural Language Processing (NLP)
  topic: keywords
subject: Adversarial Machine Learning
  topic: keywords
subject: Deception Modeling
  topic: keywords
subject: Transformer Models
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Email Security
  topic: keywords
subject: Hybrid Machine Learning
  topic: keywords
creator: Gargi Choudhury
creator: Shivam Choudhury
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
doi: 10.47363/JAICC/ICADCCS2026/2026(5)12

Energy Supply Infrastructure for the Nechit Monastery: Technical Design and Implementation
type: item
description: This technical documentation outlines the comprehensive engineering design for the modernization and implementation of a reliable electricity supply system for the Monastic House and cell building at the Nechit Monastery, located in Borlești, Neamț County, Romania. The project addresses the necessity for upgraded energy characteristics to support the facility’s 24/7 operating mode, transitioning it from domestic to non-domestic consumer status with a maximum projected power absorption of 210 kW (247.06kVA). The proposed technical solution involves the construction of a new 1960-meter mixed medium-voltage connection. This infrastructure includes a 60-meter Overhead Power Line (OHL/LEA) and a 1900-meter Underground Power Line (LES) utilizing NA2xS(F) 3x(1x150) mm² cabling. Central to the system is the installation of a new pole-mounted transformer substation (PTA 4 Nechit) rated at 20/0.4 kV and 250 kVA. The design meticulously details the placement of concrete pillars (type SC 15014), modular separators, and zinc oxide arresters for surge protection. Furthermore, the documentation specifies strict adherence to Romanian technical norms (NTE, PE) and environmental regulations (GEO 195/2005). It includes detailed safety protocols, quality control measures for earthing installations with dispersion resistance below 1 ohm, and semi-direct electricity metering integrated into the AMR remote reading system. The execution plan, estimated to span five months, ensures that all materials and construction technologies comply with SREN-ISO9001/2001 standards, providing a sustainable and safe energy framework for the monastery’s expansion.
subject: Articles
  topic: section
subject: Electrical Infrastructure
  topic: keywords
subject: Transformer Substation (PTA)
  topic: keywords
subject: Medium-Voltage Connection
  topic: keywords
subject: Energy Modernization
  topic: keywords
subject: Nechit Monastery
  topic: keywords
creator: Dr. arch. eng. Bârdan Marius Vasile
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)11

Challenges for Problems in Mean-Field Setting
type: item
description: In this presentation: I will show my previous work about how to deal with the challenges for problems in mean-field setting. One of the biggest challenges lies in the bad properties of the Wasserstein space. For example, it is infinite dimension and it is only a Polish metric space, without any computation properties as Hilbert space. I will present how the challenges can be overcome in some specific problems. One is about solving mean-field control problem in continuous time via reinforcement learning method. Another is about numerically solving mean-field McKean-Vlasov SDE via Euler-Maruyama Scheme. I will show the theoretical results we obtained. At the end, I show the future work.
subject: Articles
  topic: section
subject: Wasserstein Space
  topic: keywords
creator: Song Xuanye
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)10

Professionals’ Perception and Trust in AI Predictions in High-Risk Contexts
type: item
description: This presentation examines how professionals perceive, calibrate, and operationalize trust in AI-driven predictions when decisions carry high stakes and low tolerance for error. We explore the psychological and organizational drivers of trust—such as perceived competence, transparency, accountability, and prior experience—as well as factors that undermine it, including model opacity, uncertainty miscommunication, and automation bias. Building on real-world high-risk scenarios (e.g., healthcare, critical infrastructure, and safety-relevant operations), we discuss how explanation quality, confidence/uncertainty reporting, and humanin- the-loop workflows affect decision quality and responsibility allocation. The talk proposes practical design and governance recommendations to improve appropriate reliance, including uncertainty-aware interfaces, auditability, training for judgment calibration, and socio-technical safeguards that align AI outputs with professional standards and regulatory constraints. The goal is to move beyond “trust vs. distrust” toward measurable, context-sensitive trust calibration that improves outcomes without eroding accountability.
subject: Articles
  topic: section
subject: AI Trust Calibration
  topic: keywords
subject: High-Stakes Decision-Making
  topic: keywords
subject: Uncertainty Communication
  topic: keywords
subject: Human-AI Collaboration
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Helder Rodrigo Pinto
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)9

Agentic AI Revolutionizing Software Engineering
type: item
description: Agentic AI marks a paradigm shift in software engineering, empowering autonomous agents to independently pursue goals, reason through complex decisions, and execute multi-step workflows with minimal human intervention. Unlike reactive AI assistants, these systems—powered by advanced large language models—proactively handle code generation, debugging, testing, and deployment, dramatically compressing development cycles while elevating code quality. Agentic AI agents excel in complex tasks like code generation, debugging, and testing. They interpret requirements, generate structured code scaffolds, and self-validate through feedback loops, reducing development cycles significantly. For instance, multiagent systems collaborate—one agent designs architecture while another handles refactoring and compliance checks. In practice, these agents integrate into CI/CD pipelines, managing builds, deployments, and optimizations autonomously. They analyze real-time data from code quality metrics, user feedback, and performance telemetry to prioritize features or detect technical debt preemptively.? However, challenges persist in terms of ensuring ethical decision-making, handling edge cases in open-ended problems, and integrating with legacy systems – necessitating human-AI models.
subject: Articles
  topic: section
subject: Software Engineering
  topic: keywords
creator: Akhila Harinarayana
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)8

Non-Replicable Function-Based Multi-Level Random Dynamic Secret Key Algorithm (Extended Version)
type: item
description: Based on the proof that, as an encryption function, the residual function y=(ax) mod m is a non-replicable function, this paper constructs multi-level encryption functions y1=(a1x)mod m1,…, yn=(anyn-1)mod mn. And based on this the paper constructs the multilevel random dynamic secret key algorithm XM-1. XM-1 has three-fold safeguards. First, the encryption function is not replicable. Secondly, the secret keys and the number of secret keys are randomized. Thirdly, when the system does not run, there is no secret key. After slight changes, the four examples for constructing XM-1 given in this paper can be put into practical use.
subject: Articles
  topic: section
subject: Residual Function
  topic: keywords
subject: Not Replicable Function
  topic: keywords
subject: Multi-Level Encryption Function
  topic: keywords
subject: XM-1
  topic: keywords
creator: Xunwei Zhou
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)7

The Generative AI Sales Paradox (GASP)-Enhancing Sales Scalability at the Cost of Human-Led Relationship Building in B2B Markets
type: item
description: This study examines a growing concern in B2B sales, which it refers to as the Generative AI Sales Paradox (GASP). Generative AI tools help sales teams move faster and reach more clients. Still, they can unintentionally weaken the kind of personal relationships that matter most in high-value, trust-based transactions. This research focuses on industries such as manufacturing, professional services, and enterprise technology, where long-term client trust is essential. Both qualitative and quantitative methods are used in this research. On the qualitative side, six B2B firms using Generative AI in their sales operations were studied. While sales leaders praised the improvements in lead follow-up and speed, they also shared customer concerns about the loss of personal interaction. Clients often felt that AI-generated responses lacked empathy or genuine understanding. Survey data from 250 companies backed this up. While most firms achieved efficiency gains of 25-40%, many also reported a decline in customer satisfaction, particularly those that relied heavily on AI. In these cases, growth in long-term revenue either stalled or declined. The problem appeared to stem from weaker client connections. To help explain these findings, this study used the Scalability vs. Authenticity Trade-Off Theory (SATOT). This framework suggests that while AI is useful for scaling tasks, it struggles with the emotional and social complexity involved in building lasting relationships. In response, this study proposes a blended approach. Let AI handle repetitive tasks, but keep humans in charge of complex, trust-building conversations. This hybrid model can help firms gain efficiency without sacrificing client loyalty. In short, firms that manage AI use carefully, without letting it replace human interaction, are more likely to thrive in the long run.
subject: Articles
  topic: section
subject: Generative AI Sales Paradox (GASP)
  topic: keywords
creator: Ryosuke Nakajima
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)6

Audit Ready AI Agentic Coding: ISO 27001 Governance for GitHub Copilot in Financial Services
type: item
description: Fintechs increasingly rely on fast software delivery while facing bank grade expectations for auditability, change control, and data protection. Agentic AI coding is evolving the Software development cycle and in the same time is introducing new governance & compliance challenges. This session presents a practical, ISO/IEC 27001 aligned governance model for deploying GitHub Copilot in GitHub.com (chat and platform features) with “audit ready by design” evidence. We translate ISO 27001 risk treatment and the Statement of Applicability (SoA) into enforceable controls across three layers: (1) Copilot policy governance (enterprise/org policy enforcement for feature availability and model access), (2) agentic visibility and audit trails using GitHub’s AI Controls, including agent session visibility and agentic audit log events to answer “what happened, when, and on whose behalf,” and (3) secure operating practices for regulated SDLC—segregation of duties, mandatory reviews, and evidence collection for audits. Attendees will get a repeatable blueprint: a Copilot-specific SoA pattern, a control to evidence map (policies → logs → audit artifacts), and a minimal governance checklist that preserves developer velocity while strengthening compliance posture. We also discuss how vendor assurance artifacts support third party risk conversations, noting GitHub’s published statement that Copilot Business and Copilot Enterprise are included in the scope of GitHub’s ISMS.
subject: Articles
  topic: section
subject: Fintechs
  topic: keywords
creator: Ibrahim Diab
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-22
publication date: 2026-04-22
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)5

Explainable AI in regulated Environments — Making Compliance a Competitive Advantage
type: item
description: The EU AI Act is coming — and for many medium-sized companies, it feels like a threat rather than an opportunity. But the real challenge isn’t the regulation itself: it’s closing the gap between what AI systems do and what decision-makers can actually explain, audit, and defend. Stephan shows how Explainable AI (XAI) methods can be embedded directly into automation workflows, turning regulatory requirements into a practical framework for trustworthy process automation. The result: AI that mid-market companies can deploy, control, and stand behind — today, not just when the auditor knocks.
subject: Articles
  topic: section
subject: AI
  topic: keywords
creator: Stephan Hausberg
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-22
publication date: 2026-04-22
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)4

From Market Noise to Signal: Machine Learning and Quantitative Alpha in Financial Markets
type: item
description: Theme The convergence of Artificial Intelligence (AI), Quantitative Finance, and Blockchain technologies is reshaping how capital is analyzed, deployed, and optimized across both traditional and decentralized markets. Core Focus • Evolution of Trading: From structured Wall Street quant models to decentralized, data-driven Web3 ecosystems. • AI/ML in Action: Transforming market “noise” into predictive signals through reinforcement learning, deep neural networks, and regime-detection models. • Systematic Risk Control: Application of institutional-grade portfolio management—drawdown control, position sizing, and multi-asset allocation—to highly volatile crypto markets. Methodology & Frameworks • Integration of AI/ML models with advanced backtesting and optimization platforms such as Strategy Quant and LuxAlgo, validated through out-of-sample and Monte Carlo testing. • Use of on-chain analytics, order-flow intelligence, and sentiment data to enhance predictive accuracy. • Emphasis on robustness and transparency—bridging quantitative rigor and explainable AI for practical deployment. Applications • Democratization of institutional-grade tools for retail traders and family offices. • Quantitative wealth-management frameworks for decentralized funds, DAOs, and algorithmic asset allocators. • Emergent AI-agent and blockchain-based ventures building the next generation of autonomous trading, custody, and analytics infrastructure. Key Insight By uniting quantitative discipline, AI innovation, and venture execution, this research proposes a scalable framework for transforming market complexity into actionable clarity—and for capturing sustainable alpha in the next era of intelligent, decentralized finance.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
creator: Mikhail Urinson
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-22
publication date: 2026-04-22
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)3

Designing AI Agents for Content Automation: From Prompt Engineering to Autonomous Workflows
type: item
description: The evolution of large language models (LLMs) has significantly transformed how humans interact with artificial intelligence systems. Moving beyond simple prompt-based interactions, modern AI systems are increasingly capable of operating as autonomous agents that can execute complex, multi-step workflows. This session explores how prompt engineering, structured workflows, and orchestration frameworks such as LangChain and LangGraph can be combined to create reliable and scalable AI systems. The presentation includes a practical case study of an AI agent built using these frameworks, demonstrating end-to-end workflow orchestration and real-world applicability. Objectives • Explain the transition from prompt-based systems to agent-based architectures • Demonstrate reliable AI workflow design using modern frameworks • Highlight real-world challenges such as hallucination, consistency, and control • Provide actionable insights for implementing AI agents in practical scenarios Methodology The presentation introduces a structured framework for building AI agents, focusing on practical implementation. Key elements include: 1. Prompt orchestration and chaining 2. Context management and memory handling 3. Decision-making loops and agent workflows 4. Output validation and error handling These concepts are illustrated through a real-world content automation agent. System Architecture & Implementation Approach The proposed framework is demonstrated through a real-world AI agent designed for content automation. The system leverages orchestration frameworks such as LangChain and LangGraph to structure multi-step workflows and enable controlled execution. The architecture consists of the following components: Input Understanding & Context Setup User inputs (e.g., topic, audience, tone) are transformed into structured prompts. Context is enriched using templates and optional memory components to ensure consistency. Prompt Orchestration & Chaining The system breaks down tasks into s…
subject: Articles
  topic: section
subject: Autonomous Workflows
  topic: keywords
creator: Nadia Ashraf
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-05-12
publication date: 2026-05-12
doi: 10.47363/JAICC/ICADCCS2026/2026(5)2

Using Domain Taxonomy to Analyze Tendencies of its Development
type: item
description: There are several approaches to the computerized analysis of tendencies in research within a domain. One should mention most popular approaches such as intercitation analysis, recognition and analysis of most influential studies, and content analysis. This study follows a different approach involving domain taxonomy (hierarchical classification) as the main source of categories and subjects for further analysis. We illustrate working of our approach over the domain of Data Science. We take relevant parts of the ACM Computing Classification System, in a slightly modified and updated form, to serve as the domain taxonomy, so that its obtained 381 leaf concepts are categories under consideration. We downloaded abstracts of 67,757 research papers published in 59 relevant Springer journals from 2003-2024 as the text sample for our analysis. We developed and validated three mathematical methods forming a backbone of our approach. These are: (i) Method for assessment of extent of relevance between categories and texts involves the currently popular idea of using the conditional probability of the last symbol in a string with respect to its previous substring (ii) Method for computing square topic-to-topic co-relevance matrix with an adequate measure involving the so-called Kulczynski index and (iii) A special additive-spectral fuzzy clustering method, FADDIS. These are implemented in our GOT software. We divided the period 2003-2024 into two subperiods and computed a 381,67757 topic-to-text relevance matrix for each of the subperiods. We found seven fuzzy clusters, pertaining to main areas of current interest in data science, at each of the two subperiods. Our clusters show both stability in the research interests across the 22-year period and changes within them. These results demonstrate effectiveness of our approach. The main issue in extending that to other domains is lack of taxonomies. Joint work with Luis Caneco, Susana Nascimento (both from the New Lisbon Universit…
subject: Articles
  topic: section
subject: Computerized Analysis
  topic: keywords
creator: Boris Mirkin
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-22
publication date: 2026-04-22
starting page: 1
ending page: 1
doi: 10.47363/JAICC/ICADCCS2026/2026(5)1

AI Impact on Professional Identity and Skills Evolution: A Comprehensive Analysis of Workforce Transformation Across Healthcare, Law, Education and Engineering
type: item
description: The integration of artificial intelligence (AI) into professional domains represents a fundamental transformation in how professionals conceptualize their identities, develop competencies, and engage in collaborative work practices. This comprehensive review synthesizes empirical and theoretical literature examining AI's impact on professional identity and skills evolution across healthcare, law, education, and engineering. Drawing from 60 peer-reviewed studies published between 2017 and 2025, this article analyzes how AI technologies reshape professional self-concepts, redefine core competencies, and establish new paradigms of human-AI collaboration. Key findings reveal that AI integration triggers identity tensions characterized by concerns about autonomy, expertise devaluation, and role displacement, while simultaneously creating opportunities for augmented practice and hybrid professional roles. The evolution from specialized technical skills toward human-centric competencies—including critical thinking, ethical judgment, creativity, and emotional intelligence—emerges as a central theme across all professions examined. Healthcare professionals experience AI as both diagnostic augmentation and identity threat; legal practitioners navigate tensions between efficiency gains and professional jurisdiction; educators redefine pedagogical roles from knowledge transmission to mentorship and ethical guidance; and engineers embrace AI-augmented design while maintaining human oversight. This article contributes to professional identity theory by proposing a framework of "AI-extended professional self" that conceptualizes identity transformation as dynamic negotiation between technological augmentation and human expertise preservation. Implications for professional education, organizational culture, and workforce development strategies are discussed, emphasizing the necessity of continuous learning ecosystems, ethical AI literacy, and institutional support for identity adap…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Professional Identity
  topic: keywords
subject: Skills Evolution
  topic: keywords
subject: Workforce Transformation
  topic: keywords
subject: Human-AI Collaboration
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Legal Practice
  topic: keywords
subject: Education
  topic: keywords
subject: Engineering
  topic: keywords
creator: Shankar Subramanian Iyer Iyer
creator: Aaaryan Kapur Kapur
creator: Sangeeta Malhotra Malhotra
creator: Brinitha Raji Raji
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 1
ending page: 16
doi: 10.47363/JAICC/2026(5)525

Tripartite Game Evolution and Stabilization Strategies in Artificial Intelligence-Empowered Science & Technology Journals
type: item
description: Currently, the cognitive synergy between artificial intelligence and human editors will become an important direction to promote the development of scientific and technical journals. The purpose of this paper is to illustrate the evolutionary game dynamics between multiple stakeholders in the intelligent transformation of the publishing industry, reveal the behavioural rationality and strategic decisions of each participant, and explore the optimal coping strategies under the interaction between AI technology and human resources. In order to deeply study the dynamic interaction between technology and human resources under the framework of co-construction of AI and science and technology journals, we divided the relationship between each stakeholder. We established a game relationship model, taking the government and ethical regulators, the editorial boards of scientific and technical journals, and research groups as game participants. Then, we addressed the stabilisation strategy problem and examined the strategic choice dilemmas faced by these three parties. We identified four stabilisation points and studied the evolutionary game through four stages of technology, early stage, development phase, surge phase and maturity phase respectively. Based on the results of the game analysis, the coping strategies of gradient adaptation of technology embedding and business process, capacity cultivation of human capital and organisational development, value reconstruction of academic ecology and scientific research culture, precise insight of user needs and cognitive behaviours, and globalisation and regional differences are proposed from the perspectives of governmental and ethical regulators, editorial boards of science and technology journals, and scientific research groups, respectively. The human-machine collaborative editing model, by integrating human creativity and the efficient processing capability of AI, will achieve a double rise in quality and efficiency in the f…
subject: Articles
  topic: section
subject: Science and Technology Journal,
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Game Theory
  topic: keywords
subject: Human-Machine Collaboration
  topic: keywords
creator: AI Lisha Lisha
creator: ,LI Zhiyuan Zhiyuan
creator: LIU Jun Jun
creator: YANG Pengda Pengda
rights: Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 1
ending page: 10
doi: 10.47363/JAICC/2026(5)512

A Review of AMBA Bus Protocols: Focus on AHB, AHB-Lite, and AXI
type: item
description: A common interface for connecting parts of a system-on-chip (SoC) design is provided by the Advanced Microcontroller Bus Architecture (AMBA). Focusing on the AXI, AHB-Lite, and AHB protocols, this page provides a summary of the AMBA bus protocols. When it comes to communicating between the CPU and high-bandwidth peripherals, the AHB is the way to go, but AHB-Lite is more user-friendly and uses less power. The AXI protocol is an extension of the AHB and includes parallel communication and is also part of the modern SoC architecture. This review discusses the characteristics, functions, and uses of these protocols, their contribution in enhancing system performance, energy efficiency and throughput. Another aspect that has been highlighted in the paper is the development of AMBA protocols in line with the growing complexity of mobile and multi-core processors, with a focus on the capability of supporting high-performance computing systems.
subject: Articles
  topic: section
subject: AMBA Architecture
  topic: keywords
subject: Advanced High-Performance Bus (AHB)
  topic: keywords
subject: AHB-Lite
  topic: keywords
subject: Advanced Extensible Interface (AXI)
  topic: keywords
subject: Soc Interconnect
  topic: keywords
subject: On-Chip Communication
  topic: keywords
subject: Bus Protocols
  topic: keywords
creator: Usha Mohani kavirayani kavirayani
creator: Krishna Bhardwaj Mylavarapu Mylavarapu
creator: Jenitha Pilli Pilli
creator: Prathik Kumar Jannu Jannu
creator: Javed Ali Mohammad Mohammad
creator: Sri Harsha Panchali Panchali
rights: Copyright (c) 2025 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
date: 2025-12-10
publication date: 2025-12-10
starting page: 1
ending page: 8
doi: 10.47363/JAICC/2025(4)523