Journal of Artificial Intelligence & Research
type: channel
description: The Journal of Artificial Intelligence & Research (JAIR) is a peer-reviewed, open-access journal dedicated to publishing original research articles, reviews, and short communications across all domains of artificial intelligence. JAIR welcomes submissions from researchers globally, embracing diverse perspectives and methodologies. Journal Snapshot Journal Name: Journal of Artificial Intelligence & Research (JAIR) Editor-in-Chief: Dr T S Arulananth (see Editorial Board) Publisher Imprint: The Science Brigade Publishers Publisher: Libertatem Media Private Limited h-index: 15 (By Google Scholar) i10-index: 22 (By Google Scholar) Journal Initials: JAIR Research Scope: Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision, Robotics, Knowledge Representation, Automated Reasoning, AI Applications Publication Mode: Digital (On this Website) Frequency: Semi-Annual (2 Issues a year) Launch Year: 2021 Review Mode: Double Blind Peer Review Plagiarism Allowed: 10% (as per Turnitin) Coverage: Worldwide Language: English Editorial Contact - editor@thesciencebrigade.com
publisher: The Science Brigade Publishers
language: en
publication name: Journal of Artificial Intelligence & Research
issn: 0000-0000
copyright: License Terms Ownership and Licensing: Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. License Permissions: Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers. Additional Distribution Arrangements: Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal. Online Posting: Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers. Responsibility and Liability: Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
items: Real-Time Exposure Aggregation and Tail Risk Estimation: AI-Powered Systems for Dynamic Multi-Factor Financial Risk Assessment
items: Regulatory Text Interpretation and Compliance Obligation Mapping: AI-Driven Frameworks for Financial Regulatory Compliance Management
items: Process Mining and Robotic Process Automation in Retail Banking: AI Applications for Systemic Operational Efficiency Enhancement
items: Natural Language Symptom Triage and Asynchronous Clinical Decision Support: AI-Based Solutions for Enhancing Telemedicine Quality and Access
items: Motivational Dialogue Systems and Adherence Trajectory Modelling: AI-Driven Platforms for Enhancing Patient Engagement and Therapeutic Adherence
items: Stochastic Regime Switching and Long Short-Term Memory Networks: AI-Based Computational Models for Financial Market Volatility Forecasting
items: Explainable Feature Attribution and Temporal Credit Scoring: AI-Enhanced Models for Borrower Credit Risk Assessment
items: Default Probability Estimation Under Data Scarcity: Machine Learning Algorithm Development for Robust Credit Risk Modelling
items: Inclusive Credit Scoring and Barrier-Free Interface Design: AI-Driven Approaches to Financial Services Accessibility Enhancement
items: Knowledge Graph-Augmented Financial Signal Processing: AI-Driven Computational Frameworks for Multi-Source Financial Data Analytics
items: Convolutional Feature Extraction and Multi-Modal Lesion Localisation: AI-Based Systems for Automated Medical Image Analysis and Diagnostic Interpretation
items: Care Transition Intelligence and Multi-Provider Communication Optimisation: Real-Time AI Systems for Enhanced Patient Care Coordination
items: Functional Recovery Trajectory Prediction and Adaptive Therapy Scheduling: Machine Learning Approaches to Enhanced Patient Outcomes in Rehabilitation Medicine
items: Project Co-Creation: Engaging Stakeholders as Active Partners in AI Augmented Workflows
items: Dynamic Trust Score Explanation and Adjustment in Zero Trust Architecture Using Large Language Models
items: Enhancing Disaster Response Systems: Predicting and Mitigating the Impact of Natural Disasters Using AI
items: Predictive Analytics for Autonomous Database Scaling in AI-Powered Smart Cities
items: AI-Driven Optimization of Cloud Resources Allocation for Cost-Effective Scaling
items: The Impact of Artificial Intelligence on Business Data Governance and Ethical Decision-Making Frameworks
items: Retrieval-Augmented Generation (RAG) Workflows Combined with Fine-Tuning for Accelerated Reasoning in Dynamic Knowledge Domains
items: Real-Time Customization and Personalization in Multi-Tenant PaaS Using Generative AI
items: Protecting Privileged Cloud Accounts in Banking Systems Through Advanced PAM Solutions
items: Optimizing Resource Isolation Techniques in Multi-Tenant PaaS Architectures Using Kubernetes and Virtualization
items: Exploring AI-Driven Personalization Frameworks for Enhancing Customer Retention and Engagement in SaaS Platforms
items: Designing AI Models for Dynamic Key Management in Blockchain-Integrated IoT Systems
items: AI-Driven Enhancements for Secure API Gateways in Cross-Platform Data Integration Architectures
items: Integrating Machine Learning Algorithms with Cybersecurity Observability Frameworks for Real-Time Threat Detection and Automated Incident Response
items: Leveraging Reinforcement Learning Algorithms for Dynamic Resource Scaling and Cost Optimization in Multi-Tenant Cloud Environments
items: Frameworks for Embedding Deep Learning Models in Enterprise Applications for Predictive Marketing Analytics
items: Developing Scalable Enterprise Architectures for Artificial Intelligence Integration in Omni-Channel Sales Strategies

Real-Time Exposure Aggregation and Tail Risk Estimation: AI-Powered Systems for Dynamic Multi-Factor Financial Risk Assessment
type: item
description: The increasing importance of the dynamic assessment of financial risk is closely related to market volatility and uncertainty, which cause rapid fluctuations in the economic and financial spheres. The paper studies AI-powered systems in the context of financial risk.
subject: Articles
  topic: section
subject: real-time exposure aggregation
  topic: keywords
subject: tail risk estimation
  topic: keywords
subject: systems
  topic: keywords
subject: dynamic multi-factor financial risk assessment
  topic: keywords
subject: machine learning
  topic: keywords
creator: Dzmitry Tsetseruk
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 21
ending page: 34

Regulatory Text Interpretation and Compliance Obligation Mapping: AI-Driven Frameworks for Financial Regulatory Compliance Management
type: item
description: In today’s environment, it has become essential for companies in the financial sector to comply with rules, regulations, and guidelines that govern their practices. Officially, regulatory compliance is the adherence of an organization to laws, legislation, and conduct regulations, standard industry guidelines, organizational policies, agreements, requirements, representations, and commitments. Compliance goes beyond the legal requirements that a business entity may be subject to.
subject: Articles
  topic: section
subject: regulatory text interpretation
  topic: keywords
subject: compliance obligation mapping
  topic: keywords
subject: frameworks
  topic: keywords
subject: financial regulatory compliance management
  topic: keywords
subject: machine learning
  topic: keywords
creator: Arvind Pandey
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 35
ending page: 42

Process Mining and Robotic Process Automation in Retail Banking: AI Applications for Systemic Operational Efficiency Enhancement
type: item
description: Artificial intelligence (AI) is increasingly being leveraged across the banking sector to enhance operational efficiency. The banking sector is fiercely competitive, and access to advanced technology is vital to maintain efficiency and competitiveness. The use of technological tools, particularly AI, has the potential to significantly reduce operational risk, increase efficiency, and lower costs.
subject: Articles
  topic: section
subject: process mining
  topic: keywords
subject: robotic process automation
  topic: keywords
subject: retail banking
  topic: keywords
subject: ai applications
  topic: keywords
subject: systemic operational efficiency enhancement
  topic: keywords
subject: machine learning
  topic: keywords
creator: Esteban Botaro
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 12
ending page: 20

Natural Language Symptom Triage and Asynchronous Clinical Decision Support: AI-Based Solutions for Enhancing Telemedicine Quality and Access
type: item
description: On AI-based solutions in telemedicine and practical healthcare application Introduction Today, the evolution of technologies can greatly influence the everyday life of people around the globe. The profound effects of these developments are visible in different sectors, including healthcare. A few decades ago, the delivery and availability of proper healthcare were restricted by geographical boundaries and patient access to facilities. However, this situation has changed dramatically.
subject: Articles
  topic: section
subject: natural language symptom triage
  topic: keywords
subject: asynchronous clinical decision support
  topic: keywords
subject: solutions
  topic: keywords
subject: enhancing telemedicine quality
  topic: keywords
subject: machine learning
  topic: keywords
creator: Anna Schmidt
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 11

Motivational Dialogue Systems and Adherence Trajectory Modelling: AI-Driven Platforms for Enhancing Patient Engagement and Therapeutic Adherence
type: item
description: Technological innovations, such as artificial intelligence (AI), have found their place in healthcare, with over one third of global investment being committed to developing AI-driven healthcare platforms. The quest, however, for impactful and scalable AI-driven platforms that benefit the patient or healthcare provider continues. An important part of this is patient engagement and, for long-term conditions, adherence.
subject: Articles
  topic: section
subject: motivational dialogue systems
  topic: keywords
subject: adherence trajectory modelling
  topic: keywords
subject: platforms
  topic: keywords
subject: enhancing patient engagement
  topic: keywords
subject: machine learning
  topic: keywords
creator: Michael Abrahamson
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 40
ending page: 47

Stochastic Regime Switching and Long Short-Term Memory Networks: AI-Based Computational Models for Financial Market Volatility Forecasting
type: item
description: Understanding the causes and consequences of market volatility is of both academic and practical interest. Academic research is devoted to identifying the impact of market volatility on the investment environment, the effectiveness of policy actions, the link between the volatility of asset returns and other macroeconomic variables, the association between volatility and corporate finance, and option pricing.
subject: Articles
  topic: section
subject: stochastic regime switching
  topic: keywords
subject: long short-term memory networks
  topic: keywords
subject: computational models
  topic: keywords
subject: financial market volatility forecasting
  topic: keywords
subject: machine learning
  topic: keywords
creator: Lena Nilsson
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 43
ending page: 52

Explainable Feature Attribution and Temporal Credit Scoring: AI-Enhanced Models for Borrower Credit Risk Assessment
type: item
description: Credit risk evaluation within banking systems has always been and is still a moot issue. Traditional credit risk assessment models quickly become irrelevant because they do not meet modern trends in banking and new financial processes, instruments, and products. Many recent discussions expose the problems of the existing credit risk assessment methodologies and discuss the need for innovation and the use of the possibilities offered by artificial intelligence and machine learning.
subject: Articles
  topic: section
subject: explainable feature attribution
  topic: keywords
subject: temporal credit scoring
  topic: keywords
subject: ai-enhanced models
  topic: keywords
subject: borrower credit risk assessment
  topic: keywords
subject: machine learning
  topic: keywords
creator: Katarzyna Szymkowiak
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 10

Default Probability Estimation Under Data Scarcity: Machine Learning Algorithm Development for Robust Credit Risk Modelling
type: item
description: Credit risk modeling involves using statistical models for evaluating the creditworthiness of individuals and companies, enabling lenders to make informed investing decisions. In the financial sector, assessing the risk of loan defaults is critical for credit approval, loan pricing, and credit limit decisions. Inaccurate credit risk assessments and valuations can result in business losses, and thus lending institutions are at risk.
subject: Articles
  topic: section
subject: default probability estimation under data scarcity
  topic: keywords
subject: machine learning algorithm development
  topic: keywords
subject: robust credit risk modelling
  topic: keywords
creator: Ana Castaño
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 68
ending page: 80

Inclusive Credit Scoring and Barrier-Free Interface Design: AI-Driven Approaches to Financial Services Accessibility Enhancement
type: item
description: Introduction Artificial intelligence (AI) technologies are transforming the financial services industry by providing institutional stakeholders with the resources required to fundamentally change the way they serve their clients. AI allows for significant increases in efficiency across many operational aspects within financial services by automating processes that have been traditionally human-dependent, such as analyzing data, optimizing workflows, and interacting with customers.
subject: Articles
  topic: section
subject: inclusive credit scoring
  topic: keywords
subject: barrier-free interface design
  topic: keywords
subject: approaches to financial services accessibility enhancement
  topic: keywords
subject: machine learning
  topic: keywords
creator: Christos Papachristou
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 22
ending page: 29

Knowledge Graph-Augmented Financial Signal Processing: AI-Driven Computational Frameworks for Multi-Source Financial Data Analytics
type: item
description: The rise of technology in the 21st century has had a profound impact on the operations of the financial sector. The increasing use of artificial intelligence is at the forefront of this trend, and it is speculated that it may change the way businesses within minimally diverse sectors like finance will function. Data analytics is now a necessity for entities and corporations when making significant business decisions. Financial transactions are increasing rapidly, necessitating banks to automate.
subject: Articles
  topic: section
subject: knowledge graph-augmented financial signal processing
  topic: keywords
subject: computational frameworks
  topic: keywords
subject: multi-source financial data analytics
  topic: keywords
subject: machine learning
  topic: keywords
creator: Aisha Bashir
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 30
ending page: 39

Convolutional Feature Extraction and Multi-Modal Lesion Localisation: AI-Based Systems for Automated Medical Image Analysis and Diagnostic Interpretation
type: item
description: Introduction Artificial intelligence (AI) revolves around the use of systems that can perform analysis and make decisions based on prior training. In medicine, including radiology and pathology, tremendous volumes of medical images are encountered every day. AI has the potential to interpret these images with ever-increasing accuracy and process them efficiently.
subject: Articles
  topic: section
subject: convolutional feature extraction
  topic: keywords
subject: multi-modal lesion localisation
  topic: keywords
subject: systems
  topic: keywords
subject: automated medical image analysis
  topic: keywords
subject: machine learning
  topic: keywords
creator: Krzysztof Kowalski
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 57
ending page: 67

Care Transition Intelligence and Multi-Provider Communication Optimisation: Real-Time AI Systems for Enhanced Patient Care Coordination
type: item
description: The integration of advanced technologies such as artificial intelligence (AI) is critical in customizing healthcare interventions which, in turn, revolutionizes patient care management. In modern research and healthcare setups, providers and researchers have recognized the importance of healthcare delivery informed by digital health.
subject: Articles
  topic: section
subject: care transition intelligence
  topic: keywords
subject: multi-provider communication optimisation
  topic: keywords
subject: real-time ai systems
  topic: keywords
subject: enhanced patient care coordination
  topic: keywords
subject: machine learning
  topic: keywords
creator: Hiroki Nakahara
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 46
ending page: 56

Functional Recovery Trajectory Prediction and Adaptive Therapy Scheduling: Machine Learning Approaches to Enhanced Patient Outcomes in Rehabilitation Medicine
type: item
description: Improving patient outcomes is a critical aim for any healthcare system. New technological capabilities, particularly in machine learning and artificial intelligence, have led to proof of concepts that can be leveraged in healthcare. Whether for improving workflows, out-of-clinic interventions, or enhancing treatments or pharmaceutical regimens, these emerging methods have vast potential in enhancing population health.
subject: Articles
  topic: section
subject: functional recovery trajectory prediction
  topic: keywords
subject: adaptive therapy scheduling
  topic: keywords
subject: machine learning approaches to enhanced patient outcomes
  topic: keywords
subject: rehabilitation medicine
  topic: keywords
creator: Jérémy Fix
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 11
ending page: 21

Project Co-Creation: Engaging Stakeholders as Active Partners in AI Augmented Workflows
type: item
description: The rise of AI enabled work environments has created a fundamental shift in the role of stakeholders within projects. Rather than being consulted or managed, stakeholders are increasingly positioned as co creators who shape project outputs in partnership with intelligent systems. This article presents a comprehensive exploration of project co-creation, positioning human actors as collaborators in AI augmented workflows. The analysis examines the transformation of project roles, governance, value delivery, decision rights, ethics, and continuous learning mechanisms required to sustain Co-Creation. A structured model for stakeholder participation is proposed, followed by strategic recommendations for practice. The findings offer a forward focused perspective on how project organizations can unlock sustainable innovation through shared accountability, hybrid intelligence, and collaborative governance.
subject: Articles
  topic: section
creator: Lakshmi Triveni Kavuru
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 30
ending page: 45

Dynamic Trust Score Explanation and Adjustment in Zero Trust Architecture Using Large Language Models
type: item
description: Dynamic trust scoring in ZTA enables frequent risk assessment using constant inputs from various security sources. Multiple data sources are combined to compute a continuously updated score reflecting the level of trust in a given access or transaction. The study utilizes explainable Large Language Models (LLMs) to generate comprehensible explanations for why trust levels are altered. The model leverages a RAM pipeline to consolidate diverse security signals, enhance them with contextual data, and generate human-readable justifications explaining trust score updates. The system associates the explanations with corresponding ZTA policies, allowing it to perform security measures like two-factor authentication prompts, elimination of access requests, and segregation of devices. Practical applications have demonstrated that the approach successfully handles suspicious login attempts and identifies misuse of critical assets. Adding LLM-generated explanations to ZTA has shown to improve the timeliness and accuracy of security decisions and makes the system better prepared for emerging cyber risks and threats.
subject: Articles
  topic: section
subject: Dynamic trust
  topic: keywords
subject: Zero Trust
  topic: keywords
subject: Large Language
  topic: keywords
subject: Signal aggregation
  topic: keywords
subject: Adaptive enforcement
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Digvijay Parmar
rights: Copyright (c) 2025 Digvijay Parmar https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-27
publication date: 2025-04-27
starting page: 1
ending page: 30

Enhancing Disaster Response Systems: Predicting and Mitigating the Impact of Natural Disasters Using AI
type: item
description: Natural disasters, including hurricanes, wildfires, and earthquakes, cause catastrophic human and economic losses due to their unpredictable nature and the limitations of traditional response mechanisms. This study explores the application of artificial intelligence (AI) in enhancing disaster response systems by leveraging machine learning, data analytics, and real-time environmental monitoring. The proposed AI-driven framework integrates historical disaster data, meteorological trends, and sensor networks to predict disaster occurrences and optimize resource allocation for emergency management. Machine learning models, particularly deep learning architectures, enable precise forecasting of disaster patterns, thereby reducing response times and improving preparedness. The framework’s implementation in disaster-prone regions demonstrates its effectiveness in mitigating casualties and economic losses. The study also highlights computational challenges, data integration complexities, and the need for robust AI governance in disaster scenarios. Future advancements in AI-driven disaster response will further refine predictive capabilities and enhance adaptive mitigation strategies.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: machine learning
  topic: keywords
subject: data analytics
  topic: keywords
creator: Hassan Rehan
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-04-14
publication date: 2022-04-14
starting page: 501
ending page: 540

Predictive Analytics for Autonomous Database Scaling in AI-Powered Smart Cities
type: item
description: Predictive analysis plays a crucial role in optimising autonomous database scaling in AI-powered smart cities, ensuring efficient resource allocation, real-time adaptability, and seamless data processing. The rapid growth of urban data makes it compulsory to use advance machine learning algorithms in statistical models to predict workload fluctuation and prevent computational resources. The objective of this paper is to explore the integration of predictive models which includes time-series forecasting, reinforcement learning, and deep learning-based anomaly detection, which is used to enhance database elasticity, minimize latency, and optimize storage utilization.
subject: Articles
  topic: section
creator: Raghu Murthy Shankeshi
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-09-11
publication date: 2024-09-11
starting page: 130
ending page: 170

AI-Driven Optimization of Cloud Resources Allocation for Cost-Effective Scaling
type: item
description: The fast progress of cloud computing transformed IT resource management through its combination of exceptional flexibility and scalability characteristics. The management of cloud resources requires complex solutions because performance needs to align with financial goals. The research investigates artificial intelligence-based optimization approaches to cloud resource management with a specific analysis of machine learning technology for process decision support. Evaluating several methods and their practical applications shows how AI technology can significantly transform cloud resource administration and create more efficient and economical scalability. Organizations use artificial intelligence to process large datasets, which allows them to make immediate decisions for their cloud resource allocation. Implementing static rules and heuristic-based methods in traditional methods leads to unsatisfactory resource usage and creates operational expenses. Computer systems employing AI capabilities adjust resource distribution using ongoing demand information, ownership data, and external elements for enhanced real-time operations. Organizations achieve better system performance with enhanced user satisfaction by using adaptable resource management, leading to improved efficiency. Businesses adopting cloud migration create a critical necessity for developing efficient resource management systems. The study examines how reinforcement learning perfectly matches predictive analytics and optimization algorithms for optimizing cloud resource distribution systems. Organizations succeed in saving costs through these advanced methodologies, which support high performance standards. Organizations must implement AI optimization programs because they enable maximum return on cloud spending investments to drive sustainable digital growth.
subject: Articles
  topic: section
subject: cloud computing
  topic: keywords
subject: resource allocation
  topic: keywords
subject: AI optimization
  topic: keywords
creator: Dhruvitkumar V. Talati
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-08-16
publication date: 2024-08-16
starting page: 113
ending page: 130

The Impact of Artificial Intelligence on Business Data Governance and Ethical Decision-Making Frameworks
type: item
description: Artificial Intelligence (AI) has emerged as a transformative force in the realm of business data governance and ethical decision-making, reshaping the methods organizations employ to manage, utilize, and regulate data within increasingly complex digital ecosystems. As businesses rely more heavily on AI-driven systems to analyze and derive actionable insights from vast amounts of structured and unstructured data, the role of data governance policies becomes both critical and nuanced. This research investigates the intersection of AI technologies and business data governance, emphasizing the implications for ethical decision-making frameworks. By analyzing contemporary AI applications in business, the study highlights the dual potential of AI to enhance data governance practices through improved accuracy, scalability, and predictive capabilities, while simultaneously introducing challenges related to data privacy, transparency, and accountability. Central to this exploration is the understanding that AI systems inherently rely on massive datasets for training and deployment, necessitating rigorous adherence to principles of data stewardship, integrity, and regulatory compliance. The study examines how AI influences the development and enforcement of governance policies, particularly in addressing issues of bias, fairness, and the ethical use of data. Moreover, the research delves into the legal and regulatory landscape, illustrating how the rapid advancement of AI technologies often outpaces existing frameworks, leading to ethical dilemmas and operational risks. Key concerns such as algorithmic opacity, data ownership, and consent management are evaluated, offering insights into how businesses can align their data governance practices with evolving ethical standards and regulatory requirements. The interplay between AI and ethical decision-making frameworks is scrutinized, with particular attention to the implications of automated decision-making systems. AI-driven de…
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: business data governance
  topic: keywords
subject: ethical decision-making
  topic: keywords
subject: data privacy
  topic: keywords
creator: Visweswara Rao Mopur
rights: Copyright (c) 2020 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2020-10-14
publication date: 2020-10-14
starting page: 221
ending page: 262

Retrieval-Augmented Generation (RAG) Workflows Combined with Fine-Tuning for Accelerated Reasoning in Dynamic Knowledge Domains
type: item
description: The advent of Retrieval-Augmented Generation (RAG) has transformed the paradigm of leveraging large language models (LLMs) for tasks requiring dynamic reasoning and real-time information synthesis. By incorporating retrieval mechanisms into generative workflows, RAG enables LLMs to access and integrate up-to-date external knowledge into their responses, mitigating the challenges posed by static training datasets and knowledge obsolescence. This research paper explores the synergistic integration of RAG workflows with supervised fine-tuning to develop advanced LLM-based systems optimized for domains characterized by rapidly evolving information landscapes, such as medical diagnostics and legal research. We propose a novel framework that merges RAG with iterative fine-tuning to enhance both reasoning accuracy and inference speed. The methodology involves incorporating retrieval modules within the fine-tuning pipeline, allowing LLMs to dynamically query external knowledge bases during training. By using domain-specific curated datasets and retrievers, this approach not only supplements static model parameters but also promotes the alignment of generated outputs with real-time domain expertise. In this context, we emphasize the importance of fine-tuning in optimizing model parameters to adapt retrieval-informed generations, ensuring coherence, factuality, and context sensitivity. The paper further discusses critical components of the proposed workflows, including retrieval infrastructure, indexing techniques, fine-tuning strategies, and evaluation metrics. Key technical advancements, such as the use of dense vector representations for improved retrieval precision and the implementation of adaptive retriever fine-tuning, are highlighted. Additionally, we explore the integration of reinforcement learning paradigms to refine retrieval and generation pipelines, thereby fostering self-correcting behaviors in LLMs. Applications in medical diagnostics demonstrate the efficacy …
subject: Articles
  topic: section
subject: Retrieval-Augmented Generation
  topic: keywords
subject: fine-tuning
  topic: keywords
subject: large language models
  topic: keywords
creator: Sayantan Bhattacharyya
creator: Muthuraman Saminathan
creator: Debabrata Das
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-06-11
publication date: 2024-06-11
starting page: 526
ending page: 566

Real-Time Customization and Personalization in Multi-Tenant PaaS Using Generative AI
type: item
description: The advent of generative AI has introduced transformative possibilities for multi-tenant Platform-as-a-Service (PaaS) ecosystems, enabling unprecedented real-time customization and personalization capabilities. This research investigates the application of generative AI in tailoring tenant-specific user interfaces (UIs), workflows, and content within multi-tenant architectures. By leveraging advanced AI models such as transformer-based neural networks and diffusion models, the study demonstrates how these technologies facilitate dynamic adaptations of PaaS environments, ensuring seamless alignment with tenant-specific requirements. Key challenges in multi-tenancy—such as maintaining performance efficiency, ensuring tenant data isolation, and balancing customization with system integrity—are systematically analyzed. This paper also delves into the architecture of AI-enhanced multi-tenant systems, emphasizing the integration of generative AI components into traditional PaaS layers. Techniques for generating tenant-specific dashboards, workflows, and role-specific content are explored through detailed technical case studies. For instance, the study presents a proof-of-concept implementation wherein generative AI models dynamically construct dashboards tailored to diverse user roles within a healthcare management system, showcasing role-based data visualizations and insights contextualized to operational needs. The research highlights the pivotal role of AI in achieving dynamic tenant segmentation and real-time contextual adaptation. By employing reinforcement learning techniques and fine-tuning generative models on tenant-specific datasets, PaaS providers can achieve granular personalization without compromising scalability. Furthermore, the integration of AI-driven analytics facilitates continuous feedback loops, enabling adaptive learning to refine tenant-specific customization over time. A critical focus of the paper is the evaluation of performance trade-offs and r…
subject: Articles
  topic: section
subject: generative AI
  topic: keywords
subject: multi-tenant PaaS
  topic: keywords
subject: real-time customization
  topic: keywords
creator: Akhil Reddy Bairi
creator: Abdul Samad Mohammed
creator: Aarthi Anbalagan
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-05-02
publication date: 2023-05-02
starting page: 439
ending page: 479

Protecting Privileged Cloud Accounts in Banking Systems Through Advanced PAM Solutions
type: item
description: The proliferation of cloud computing in the banking sector has introduced both unprecedented opportunities and significant security challenges, particularly concerning the management and protection of privileged cloud accounts. These accounts often hold elevated permissions, rendering them high-value targets for malicious actors. The complexity of cloud environments and the dynamic nature of modern banking systems necessitate robust Privileged Access Management (PAM) solutions tailored to cloud-specific requirements. This paper examines advanced strategies for protecting privileged cloud accounts in banking systems, with a focus on secure credential storage, just-in-time (JIT) access mechanisms, and monitoring administrative actions. Employing technical tools such as CyberArk and AWS Secrets Manager, the study evaluates their efficacy in mitigating risks associated with unauthorized access, insider threats, and privilege escalation attacks. The research first delves into secure credential storage techniques, emphasizing encryption, role-based access controls, and integration with hardware security modules (HSMs). By leveraging CyberArk's Vault technology and AWS Secrets Manager, organizations can centralize sensitive information, enforce strict access policies, and ensure compliance with regulatory frameworks such as GDPR and PCI DSS. Furthermore, the implementation of JIT access mechanisms is explored as a critical measure to minimize the attack surface. This involves granting ephemeral, task-specific permissions to users and applications, thereby reducing the risk of lateral movement within the network. Solutions like CyberArk's Alero and AWS Identity and Access Management (IAM) policies are analyzed for their effectiveness in achieving this objective. The paper also highlights the importance of comprehensive monitoring of administrative actions within cloud environments. Real-time auditing, behavioral analytics, and anomaly detection are essential for identifying…
subject: Articles
  topic: section
subject: privileged access management
  topic: keywords
subject: secure credential storage
  topic: keywords
creator: Sayantan Bhattacharyya
creator: Debabrata Das
creator: Abdul Samad Mohammed
rights: Copyright (c) 2022 Sayantan Bhattacharyya, Debabrata Das, Abdul Samad Mohammed https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-03-07
publication date: 2022-03-07
starting page: 458
ending page: 500

Optimizing Resource Isolation Techniques in Multi-Tenant PaaS Architectures Using Kubernetes and Virtualization
type: item
description: In the evolving landscape of cloud computing, Platform as a Service (PaaS) environments have become increasingly vital in enabling rapid application development, deployment, and scalability. Multi-tenant PaaS architectures, where multiple independent tenants share a common infrastructure, necessitate robust techniques for resource isolation to ensure security, performance, and fairness. The challenge of efficiently isolating resources while maintaining high utilization rates has driven the exploration of advanced isolation methods, with containerization and virtualization being at the core of modern solutions. This research paper delves into optimizing resource isolation techniques within multi-tenant PaaS architectures, focusing on the interplay between containerization, virtualization, and the Kubernetes orchestration framework. By leveraging Kubernetes namespaces, pod security policies, and network policies, the study highlights how these technologies can be utilized to enhance isolation, minimize resource contention, and ensure a secure and efficient multi-tenant environment. Containerization has become the predominant approach for managing workloads in multi-tenant environments due to its lightweight nature and ability to isolate applications effectively. Kubernetes, an open-source container orchestration platform, has become the de facto standard for automating deployment, scaling, and management of containerized applications. While Kubernetes provides fundamental isolation mechanisms, including namespaces and resource quotas, optimizing these features for multi-tenant resource isolation requires careful attention to ensure fair allocation of compute, memory, and storage resources. Kubernetes namespaces enable logical partitioning of resources, allowing tenants to operate in separate virtual environments. However, namespace isolation alone does not guarantee complete resource separation. This limitation can lead to potential security risks, performance degrada…
subject: Articles
  topic: section
subject: multi-tenant
  topic: keywords
subject: PaaS
  topic: keywords
subject: resource isolation
  topic: keywords
creator: Sayantan Bhattacharyya
creator: Vincent Kanka
creator: Abdul Samad Mohammed
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-01-12
publication date: 2021-01-12
starting page: 197
ending page: 239

Exploring AI-Driven Personalization Frameworks for Enhancing Customer Retention and Engagement in SaaS Platforms
type: item
description: The utilization of Artificial Intelligence (AI) to enhance user experience in Software as a Service (SaaS) platforms has emerged as a transformative force in the realm of customer retention and engagement. This research paper investigates AI-driven personalization frameworks that leverage machine learning algorithms, predictive analytics, and advanced data processing to deliver adaptive, tailored experiences to users. In particular, it focuses on how these personalization strategies impact user satisfaction, retention, and long-term engagement. The analysis begins with a comprehensive review of the foundational concepts and methodologies that form the backbone of AI personalization in SaaS. This includes a detailed exploration of adaptive content delivery mechanisms, recommendation engines, and sophisticated behavioral analytics, each integral to fostering a user-centric approach. The paper delves into the algorithms and technologies underpinning these strategies, such as collaborative filtering, content-based filtering, and hybrid models that serve as the bedrock of recommendation systems. Adaptive content delivery, driven by AI, has shown considerable promise in enhancing user engagement by presenting relevant and context-aware content that aligns with individual preferences and behaviors. Machine learning models, including deep learning architectures, facilitate real-time processing and dynamic content adaptation based on continuous feedback loops and data-driven insights. The paper further discusses the implementation of these systems, emphasizing data integration from multiple sources, including user interactions, browsing patterns, and historical data, to inform decision-making processes that personalize user experiences. This integration requires sophisticated data pipelines and robust data preprocessing techniques to ensure the accuracy, quality, and relevance of input data used by AI models. The examination extends to the challenges faced by organizations i…
subject: Articles
  topic: section
subject: AI-driven personalization
  topic: keywords
subject: SaaS platforms
  topic: keywords
subject: adaptive content delivery
  topic: keywords
creator: Vicrumnaug Vuppalapaty
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-03-10
publication date: 2022-03-10
starting page: 417
ending page: 457

Designing AI Models for Dynamic Key Management in Blockchain-Integrated IoT Systems
type: item
description: The integration of Internet of Things (IoT) devices with blockchain technology has emerged as a promising solution to enhance the security, scalability, and transparency of IoT networks. One of the critical challenges in this integration is key management, particularly the dynamic management of cryptographic keys for secure communication between devices. Traditional static key management methods are insufficient in the dynamic and decentralized nature of IoT environments. This paper explores the design and application of artificial intelligence (AI) models for dynamic key management in blockchain-integrated IoT systems. The paper discusses the role of AI in adapting to changing network conditions, optimizing key generation and distribution processes, and ensuring the secure and efficient handling of cryptographic keys. It also addresses the challenges of scalability, real-time adaptability, and resource constraints in IoT environments. Through the implementation of machine learning and deep learning models, dynamic key management systems can better protect IoT devices against evolving security threats while maintaining the benefits of blockchain’s decentralized nature. The paper concludes by outlining future research directions, including the integration of AI with advanced cryptographic protocols and the potential for cross-layer security mechanisms.
subject: Articles
  topic: section
subject: AI models
  topic: keywords
subject: dynamic key management
  topic: keywords
subject: blockchain
  topic: keywords
creator: Rajesh Kumar
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-02-16
publication date: 2024-02-16
starting page: 519
ending page: 525

AI-Driven Enhancements for Secure API Gateways in Cross-Platform Data Integration Architectures
type: item
description: The proliferation of cross-platform data integration architectures has necessitated the enhancement of security mechanisms for Application Programming Interfaces (APIs). As businesses increasingly rely on APIs to facilitate communication between disparate systems, ensuring the security of these APIs becomes critical. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving the security of API gateways by identifying vulnerabilities, detecting malicious activities, and automating the enforcement of security policies. This paper explores AI-driven solutions for enhancing the security of API gateways in cross-platform data integration architectures. The paper discusses how AI techniques, such as machine learning (ML) and anomaly detection, can be integrated into API gateway frameworks to provide proactive security measures. Furthermore, it examines the role of AI in managing access control, monitoring API traffic, and preventing common security threats such as API abuse, DDoS attacks, and data breaches. The challenges of implementing AI-driven security measures in API gateways are also addressed, including the need for high-quality data, model training, and integration with existing security infrastructures. Finally, the paper highlights real-world applications and case studies, demonstrating the effectiveness of AI-driven API security solutions in practice.
subject: Articles
  topic: section
subject: AI-driven security
  topic: keywords
subject: API gateways
  topic: keywords
subject: cross-platform data integration
  topic: keywords
creator: Claire Johnson
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-04-13
publication date: 2023-04-13
starting page: 432
ending page: 438

Integrating Machine Learning Algorithms with Cybersecurity Observability Frameworks for Real-Time Threat Detection and Automated Incident Response
type: item
description: This paper explores the integration of machine learning algorithms within cybersecurity observability frameworks to enhance real-time threat detection and automated incident response. As cyber threats become increasingly sophisticated, traditional security measures are no longer sufficient to guarantee robust defense mechanisms. By leveraging the power of machine learning, specifically anomaly detection models, supervised and unsupervised learning techniques, and predictive analytics, the observability of network traffic and system logs can be significantly improved. This integration allows for the identification of previously unknown or evolving threats that might otherwise go undetected by conventional rule-based systems. The research delves into how machine learning models, when applied to large-scale security data, can facilitate the automatic detection of anomalies and the prediction of potential vulnerabilities before they escalate into critical security breaches. Additionally, the paper examines deployment strategies within hybrid cloud environments, where the fusion of machine learning and observability tools can provide proactive security measures, ensuring continuous monitoring and quick response to incidents. The challenges of implementing these models at scale, ensuring minimal false positives, and addressing privacy concerns are also discussed. This paper ultimately aims to demonstrate that integrating machine learning with observability frameworks is a vital step toward achieving a more dynamic, responsive, and secure cybersecurity landscape.
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: observability
  topic: keywords
subject: anomaly detection
  topic: keywords
creator: Sainag Nethala
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-12-05
publication date: 2023-12-05
starting page: 335
ending page: 347

Leveraging Reinforcement Learning Algorithms for Dynamic Resource Scaling and Cost Optimization in Multi-Tenant Cloud Environments
type: item
description: The proliferation of cloud computing has led to the emergence of multi-tenant environments, where resources are shared among several tenants. While multi-tenancy offers significant advantages in terms of cost-efficiency and resource utilization, it introduces several complexities in resource management, especially when addressing the dynamic nature of cloud workloads. In this context, dynamic resource scaling and cost optimization have become critical challenges, particularly when managing fluctuating workloads, ensuring quality of service (QoS), and reducing operational expenses. Traditional approaches to resource management often fail to adapt effectively to these dynamic changes, leading to either resource over-provisioning, which wastes resources, or under-provisioning, which affects performance and user satisfaction. To overcome these challenges, this paper explores the use of reinforcement learning (RL) algorithms, such as Proximal Policy Optimization (PPO) and Deep Q-Learning (DQN), as viable solutions for dynamic resource scaling and cost optimization in multi-tenant cloud environments. Reinforcement learning, as an area of machine learning, offers significant potential for intelligent decision-making in complex, dynamic, and uncertain environments. By interacting with the environment and learning from the consequences of actions, RL algorithms can autonomously adapt resource allocation policies to meet varying workload demands while optimizing costs. The paper focuses on two popular RL techniques, PPO and DQN, both of which are well-suited for cloud resource management tasks. PPO, a model-free RL algorithm, is known for its stability and efficiency in continuous action spaces, making it highly effective for managing resource scaling in virtualized infrastructure. DQN, on the other hand, leverages deep learning to approximate the Q-value function, enabling it to handle large, high-dimensional state spaces commonly encountered in cloud environments. The resea…
subject: Articles
  topic: section
subject: Reinforcement learning
  topic: keywords
subject: Proximal Policy Optimization
  topic: keywords
subject: Deep Q-Learning
  topic: keywords
subject: dynamic resource scaling
  topic: keywords
subject: cost optimization
  topic: keywords
creator: Sandeep Kampa
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-07-18
publication date: 2024-07-18
starting page: 191
ending page: 235

Frameworks for Embedding Deep Learning Models in Enterprise Applications for Predictive Marketing Analytics
type: item
description: The integration of deep learning models into enterprise applications has become a pivotal advancement in predictive marketing analytics, offering unprecedented opportunities for optimizing customer engagement, enhancing campaign effectiveness, and forecasting customer behavior. This paper delves into the architectural frameworks that support the embedding of deep learning models within enterprise-level systems, focusing particularly on their application in predictive marketing analytics. These frameworks serve as the foundational structure that enables organizations to incorporate sophisticated analytical capabilities seamlessly into their existing IT ecosystems. By examining these frameworks, this study elucidates how organizations can effectively deploy deep learning models for crucial marketing functions such as customer segmentation, campaign success rate analysis, and churn prediction. Customer segmentation, an essential aspect of targeted marketing strategies, can be significantly refined through the use of deep learning algorithms, which analyze vast amounts of customer data to identify patterns that traditional statistical methods often miss. Effective customer segmentation frameworks require the efficient integration of data processing pipelines that facilitate feature extraction, model training, and real-time inference. This paper discusses the technical prerequisites for embedding these models within enterprise architectures, including the use of containerization technologies such as Docker and orchestration tools like Kubernetes to ensure scalability and maintainability. Moreover, attention is given to how microservices-based architecture can isolate individual model functionalities, allowing them to be updated and scaled independently without disrupting the broader system. The evaluation of campaign success rates also benefits from deep learning, which can provide nuanced insights into consumer preferences and behavior across diverse segments. Deep lear…
subject: Articles
  topic: section
subject: deep learning
  topic: keywords
subject: predictive marketing
  topic: keywords
subject: enterprise architecture
  topic: keywords
subject: customer segmentation
  topic: keywords
creator: Saumya Dash
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-17
publication date: 2024-12-17
starting page: 149
ending page: 190

Developing Scalable Enterprise Architectures for Artificial Intelligence Integration in Omni-Channel Sales Strategies
type: item
description: The integration of Artificial Intelligence (AI) into omni-channel sales strategies has emerged as a pivotal strategy for modern enterprises aiming to enhance customer engagement, operational efficiency, and adaptive responses to dynamic market conditions. This research paper delves into the complexities of developing scalable enterprise architectures that facilitate the seamless incorporation of AI-driven tools across multiple customer touchpoints, ensuring consistency in user experience and adaptability in response to evolving consumer behavior. The paper explores various design principles for scalable architectures, emphasizing the architectural frameworks that enable AI integration while maintaining data integrity, real-time decision-making capabilities, and flexibility to support an array of AI technologies, including machine learning algorithms, predictive analytics, and natural language processing. A core focus of this study is the exploration of modular, service-oriented architecture (SOA) and microservices-based models as viable frameworks for scalable enterprise solutions that support AI integration. These architectural paradigms are evaluated based on their ability to enhance the deployment of distributed AI systems, enabling robust cross-platform data synchronization and seamless integration with legacy systems. The integration of AI within omni-channel sales channels, such as mobile applications, websites, physical stores, and customer service platforms, demands a comprehensive strategy that ensures a uniform and personalized customer journey. In this context, the paper evaluates strategies that harmonize data flow, standardize communication protocols, and align system performance across diverse channels, thereby maintaining service consistency and reliability. The research further investigates the challenges associated with the architectural scalability required for AI integration, including the management of heterogeneous data sources, scalability of r…
subject: Articles
  topic: section
subject: scalable enterprise architecture
  topic: keywords
subject: AI integration
  topic: keywords
subject: omni-channel sales
  topic: keywords
subject: modular architecture
  topic: keywords
creator: Saumya Dash
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-16
publication date: 2024-12-16
starting page: 112
ending page: 148