International Journal of Global Innovations and Solutions
type: channel
description: The International Journal of Global Innovations and Solutions (IJGIS) is a premier, peer-reviewed, diamond open-access publication (online ISSN: 2998-2251) dedicated to showcasing groundbreaking research and innovative solutions addressing the world's most pressing challenges. As the academic arm of the New World Foundation, IJGIS serves as a vital platform for scholars, practitioners, policymakers, and activists from diverse disciplines to share and disseminate their findings.
publisher: The New World Foundation
language: en
publication name: International Journal of Global Innovations and Solutions
issn: 2998-2251
items: Semantic Service Level Indicators for AI-Driven Site Reliability Engineering:A Framework for Detecting Infrastructure-Invisible Agent Failures inProduction
items: A Security Framework for Modern Cloud-Based Financial Software
items: Consensus-Driven Metacognition in Multi-Agent Systems: A Logic-Based Byzantine Fault-Tolerant Protocol
items: Designing Modern Secure WAN Connectivity Using Cisco SD‑WAN, Cloud OnRamp, Azure vWAN, ExpressRoute, and Zscaler
items: A Scalable Cloud-Based CRM System Using Serverless Microservices and Containerise AI
items: Cloud-Native AI Security Architecture for the U.S. Electric Grid
items: Learning-Based Semantic Alignment in Cross-Institution Health Data Exchange
items: Agentic AI–Driven Quality Engineering: A Global Innovation Framework for Autonomous Enterprise Decision Validation
items: Privacy-Preserving Multi-Modal Threat Detection Framework for Educational Facilities: A Conceptual Design
items: Advancements in Physics-Informed Neural Networks (PINNs) for Real-Time Prediction of Complex Fluid Dynamics
items: Leveraging AI for Lowering Patient Financial Responsibility in the USA Healthcare
items: Named Ruleset: A Long-Lived Parallel Development Architecture for Enterprise-Scale Pega Implementations
items: Privacy-Preserving MLOps with Differential Privacy and AI-Guided Tuning
items: A Self-Adaptive, AI-Powered Testing Framework for Cross-Platform Systems: An Architectural Design and Industrial Evaluation
items: Agentic RAG for CRM: From Answers to Actions
items: Composable Multi-Tool Pipelines for AI Agents in DevSecOps: Architecture, Orchestration, and Evaluation
items: Integrating Large Language Models with MLOps Observability for Attack Surface Reduction
items: Database-Level End User Authorization (DB-EUA)
items: A Framework for Autonomous, Cross-Cloud Threat Mitigation Using Multi-Agent Reinforcement Learning
items: A Threshold Cryptography Framework for Secure and Resilient Symmetric Key Management in Multi-Cloud Environments
items: A Lightweight, AI-Enhanced Intrusion Detection System for Wireless Body Area Networks Using Unsupervised Anomaly Detection
items: Performance Impact on Databases Using Serverless Architectures: An Empirical Study
items: Reducing Redundant Computation in Ad-Hoc Multiplayer Gaming through SpatialTask Delegation and Synchronization
items: Low-Latency Group Communication in BLE Networks via Whisper-Inspired L2CAP Relaying
items: Towards Quality of Service Guarantees in Bluetooth A Cross-Layer Framework for LE and Classic Coexistence in the 2.4 GHz IoT Ecosystem
items: ASL-MDFD: Adversarial Self-Supervised Learning for Generalizable GAN-Resilient Multimodal Deepfake Detection
items: End-to-End Zero-Trust Database Migration Frameworks
items: Leveraging Surplus Android Phones in India: Scalable Use Cases in Edge Computing, Digital Health, and Smart Infrastructure
items: Unified Data Access in Multi-Model Databases: Querying Relational, Graph, and Document Data Seamlessly
items: A Novel Framework for Text-Based Fraud Detection in Banking Using Spark NLP and Tableau

Semantic Service Level Indicators for AI-Driven Site Reliability Engineering:A Framework for Detecting Infrastructure-Invisible Agent Failures inProduction
type: item
description: Traditional Site Reliability Engineering (SRE) observability architectures monitor infrastructure-centric signals: latency, error rate, throughput, and saturation. While effective for traditional software architectures, autonomous AI agents introduce a distinct failure mode: infrastructure-invisible semantic failure. An agent fleet can return HTTP 200 responses, maintain 99.9% platform uptime, and fulfill infrastructure SLOs while simultaneously delivering contextually invalid or corrupted task execution outputs. Standard observability frameworks produce zero telemetry for this class of degradation. This paper presents a production-validated framework consisting of four novel Semantic Service Level Indicators (SLIs) engineered specifically for agentic workflows: Decision Quality Rate (DQR), Tool Invocation Efficiency (TIE), Human Escalation Rate (HER), and Approval Queue Depth Drift (AQDD). Furthermore, we design an Agent-to-Agent (A2A) semantic boundary validation protocol operating as an execution circuit breaker at the semantic layer, alongside an Agent Sprawl governance architecture designed to maintain platform reliability across multi-model, multi-framework enterprise deployments. Empirical evidence from a high-throughput production fintech environment demonstrates that this framework successfully identifies semantic architectural degradations up to 6 hours before downstream transaction failures surface, providing a robust operational foundation for mission-critical AI systems infrastructure.
subject: Articles
  topic: section
subject: Site Reliability Engineering (SRE)
  topic: keywords
subject: Agentic AI
  topic: keywords
subject: Observability Metrics
  topic: keywords
subject: Semantic Failure
  topic: keywords
subject: Service Level Indicators (SLIs)
  topic: keywords
subject: Fintech Infrastructure
  topic: keywords
subject: Large Language Models (LLMs)
  topic: keywords
subject: Circuit Breakers
  topic: keywords
subject: Production Governance
  topic: keywords
creator: AJAY DEVINENI
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-07-31
publication date: 2026-07-31
doi: 10.63412/qe1npc17

A Security Framework for Modern Cloud-Based Financial Software
type: item
description: Financial firms are moving more of their software, data, and customer-facing services into cloud environments. The move is understandable: cloud platforms make it easier to scale systems, release products quickly, and support digital banking experiences that customers now expect. The same move, however, also changes the security problem. A bank is no longer protecting only a set of internal servers; it is managing identity, configuration, data movement, third-party platforms, regulatory obligations, and operational resilience across a much larger technology ecosystem. This paper proposes the Financial Cloud Cybersecurity Framework (FCCF), a practical model for securing cloud-based financial software. The framework is organized around three connected layers: technical controls, regulatory alignment, and organizational governance. Technical controls include encryption, identity and access management, zero trust, configuration monitoring, segmentation, and continuous detection. Regulatory alignment connects those controls to requirements such as PCI-DSS, GDPR, CPPA, OCC expectations, audit readiness, and data residency. Organizational governance addresses the people and decision-making structures that often determine whether security controls work in practice, including board oversight, CISO accountability, employee training, incident response, and vendor-risk management. Drawing on case examples from Capital One, JPMorgan Chase, Wells Fargo, and European financial institutions, the paper argues that financial cloud security failures rarely come from a single missing tool. More often, they emerge when technical decisions, compliance responsibilities, and leadership accountability are not joined together. FCCF is presented as an integration framework that can help financial institutions evaluate risk, strengthen resilience, and adopt cloud software without losing sight of customer trust.
subject: Articles
  topic: section
subject: Cloud-security
  topic: keywords
subject: Financial-Software
  topic: keywords
subject: Zero-trust
  topic: keywords
subject: compliance
  topic: keywords
creator: Lalit Agarwal
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-07-01
publication date: 2026-07-01
doi: 10.63412/f5pdc647

Consensus-Driven Metacognition in Multi-Agent Systems: A Logic-Based Byzantine Fault-Tolerant Protocol
type: item
description: Large language model (LLM) agents fail in a distinctive way: they produce confident wrong answers. Recent work has begun adapting Byzantine fault tolerance (BFT) to multi-LLM networks — notably the weighted BFT protocol of Wang et al. [1] and the Aegean consensus engine [2] — typically by treating agent trust as a scalar that flows continuously through the protocol. This paper takes the opposite stance: discretization is a feature, not a bug. MBFT (Metacognitive BFT) commits a swarm decision via a small, finite ladder of confidence tiers, defeasible counterproofs, and a reputation-gated veto. The resulting protocol is auditable, deterministically replay able, and its safety / liveness claims are mechanically checkable — properties that continuous Bayesian aggregators struggle to provide. Bayesian and continuous trust schemes remain the right tool for noisy, well calibrated, high-volume regimes (recommender systems, sensor fusion, market making); we argue that high-stakes reasoning swarms — legal, medical, safety-critical agentic deployments — belong to the discrete, defeasible regime that MBFT formalizes. We accompany the paper with an open-source reference implementation whose property suite encodes each theorem as an executable test.
subject: Articles
  topic: section
subject: multi-agent systems
  topic: keywords
subject: large language models
  topic: keywords
subject: Byzantine fault tolerance
  topic: keywords
subject: metacognition
  topic: keywords
subject: defeasible reasoning
  topic: keywords
subject: epistemic logic
  topic: keywords
subject: consensus protocols
  topic: keywords
subject: AI safety
  topic: keywords
subject: auditability
  topic: keywords
subject: hallucination mitigation
  topic: keywords
subject: weighted voting
  topic: keywords
subject: reputation systems
  topic: keywords
creator: Saurav Bhattacharya
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-04-26
publication date: 2026-04-26
doi: 10.63412/8vgf0b98

Designing Modern Secure WAN Connectivity Using Cisco SD‑WAN, Cloud OnRamp, Azure vWAN, ExpressRoute, and Zscaler
type: item
description: As enterprises accelerate cloud adoption, connecting branch offices, data centers, and remote users to Azure workloads demands architectures that balance performance, security, and operational simplicity. Traditional hub‑and‑spoke WANs with centralized firewall backhaul introduce latency, cost, and policy drift, particularly across geographically distributed sites. This paper presents a policy‑driven reference architecture that integrates Cisco SD‑WAN for application‑aware routing and segmentation, Cisco Cloud OnRamp for Multicloud with Catalyst 8000V NVAs deployed inside Azure Virtual WAN hubs for direct branch‑to‑Azure private access, Azure ExpressRoute for deterministic private transport, and Zscaler for cloud‑delivered security inspection and zero‑trust private application access. We describe the design principles, component roles, reference traffic flows, layered security controls, and best practices that enable intent‑based traffic steering, consistent security enforcement, and zero‑trust access across the hybrid enterprise.
subject: Articles
  topic: section
subject: SD‑WAN
  topic: keywords
subject: Azure Virtual WAN
  topic: keywords
subject: ExpressRoute
  topic: keywords
subject: Zscaler
  topic: keywords
subject: zero trust
  topic: keywords
subject: cloud security
  topic: keywords
subject: hybrid cloud
  topic: keywords
subject: network architecture
  topic: keywords
subject: intent‑based networking
  topic: keywords
subject: Cloud OnRamp
  topic: keywords
creator: Akhil Singhal
creator: Abhishek Sinha
creator: Anoop Kumar Singh
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-05-26
publication date: 2026-05-26
doi: 10.63412/x8yfvv40

A Scalable Cloud-Based CRM System Using Serverless Microservices and Containerise AI
type: item
description: CRM systems now use cloud-native technologies, and there has been a shift in the architecture to modular and highly scalable, with many supporting backends and integrations. The given paper will introduce a scalable cloud-based CRM solution that involves serverless microservices and containerized AI-based recommendation engines. The suggested architecture enables the CRM to be highly available, cost-efficient, real-time, and scalable, which resolves the performance bottlenecks of traditional CRM systems. Our technology enables us to decouple traditional CRM features into fully deployable serverless services, such as customer data ingestors, analytics, and marketing automation. These functions connect with each other through event-based approaches and API gateways, rendering them loosely coupled and simpler to maintain. To provide personalized recommendations, we package the AI models in Docker containers and can easily manage them by using Kubernetes, so that the optimal usage of resources and portability can be ensured. We conducted extensive trials with the top cloud providers, including AWS and GCP, to compare their performance standards in terms of latency, throughput, cost-effectiveness, and AI prediction accuracy. The results led to a 40 percent lower cost of operation and a 50 percent faster response rate of our system than the monolithic ones. The paper also addresses the issues of design trade-offs, implementation problems, future work, along with key Privacy and Security Risks to be considered. As our evidence shows, using serverless microservices and containerized AI engines would provide an attractive paradigm for next-generation CRM platforms.
subject: Articles
  topic: section
subject: Cloud Computing, Serverless Architecture, CRM System, Microservices, Containerization, AI Recommendation Engine, Kubernetes, Scalability, Event-Driven Architecture, Personalization
  topic: keywords
creator: Saad Khan
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-04-01
publication date: 2026-04-01
doi: 10.63412/50axb055

Cloud-Native AI Security Architecture for the U.S. Electric Grid
type: item
description: The U.S. energy sector plays a central role in national resilience because every other critical infrastructure depends on it. When the grid is disrupted, the effects quickly spread into public safety and the economy. As utilities move toward cloud-based operations, expand telemetry, and integrate more distributed energy resources (DERs), the line between operational technology (OT) and IT becomes thinner. This increases both the attack surface and the risk of cyber physical infrastructure failures. Artificial intelligence (AI) can strengthen grid awareness by detecting anomalies, threat, forecasting issues, and supporting predictive maintenance. At the same time, AI brings new risks such as poisoned telemetry, adversarial inputs, model tampering, and supply chain vulnerabilities that can reduce trust in systems that must operate safely. This paper introduces a practical, Azure-aligned reference architecture for securing cloud-native AI systems in the U.S. electric grid. The design includes OT-aware Zero Trust connectivity, layered security controls across data ingestion, storage, training, and inference, and resilient edge-cloud deployment patterns that maintain reliability even when connectivity is limited. Governance is guided by the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF 1.0), using its Govern, Map, Measure, and Manage functions to improve traceability, monitoring, and risk handling throughout the AI lifecycle. A transmission-grid anomaly detection case study demonstrates how these principles apply in real deployments, including secure telemetry ingestion, model registry protections, and fail safe behaviors that align with operational needs. The architecture also includes modern practices like tracking where models come from, AI red teaming, privacy preserving monitoring, and safeguards for foundation models to manage new risks from autonomous DER behavior and changing federal AI safety guidelines.
subject: Articles
  topic: section
subject: Artificial intelligence, critical infrastructure, cybersecurity, electric grid, industrial control systems, NIST AI RMF, operational technology, Zero Trust, Azure.
  topic: keywords
creator: Sandip Patel
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-04-01
publication date: 2026-04-01
doi: 10.63412/26p8n109

Learning-Based Semantic Alignment in Cross-Institution Health Data Exchange
type: item
description: Healthcare ecosystems today are built upon the exchange of health data between multiple types of organizations, including provider organizations, payers, laboratories, public health agencies, and government programs. While syntactical standards, such as HL7 v2, FHIR, X12, and CDA have been widely adopted to enable the exchange of data electronically, the lack of consistent semantics across institutions continues to create barriers to the correct interpretation of data exchanged electronically, automated workflows and analytics. The variability in schema design, terminology use, contextual meaning, and workflow specific customization creates ambiguity in data exchanged electronically which cannot be eliminated by using rule based mapping techniques or static ontologies. In this paper we present a novel, learning-based semantic alignment framework for cross-institutional health data exchange which utilizes representation learning, contextual embeddings and probabilistic alignment scoring to align schematically disparate healthcare datasets. Our proposed approach consists of three main components; schema-level feature learning to extract common features from the source and target schemas, a context aware similarity model that measures the similarity between the two schemas, and an explainability driven validation process to ensure that the alignment decisions made are trusted and suitable for regulated environments. We also present a multilayer architecture to facilitate both semantic discovery, alignment confidence estimation, human-in-the-loop review and regulatory auditability. We evaluate our proposed approach on synthetic but realistic healthcare schemas developed from electronic health record (EHR) data, claims data and enrollment system data, and compare our approach with two baseline approaches; one that uses rules to align the two schematics and another that uses an ontology to align the two schematics. Finally, our proposed approach includes governance contro…
subject: Articles
  topic: section
subject: Semantic Interoperability
  topic: keywords
subject: Health Data Exchange
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Schema Alignment
  topic: keywords
subject: Healthcare Interoperability
  topic: keywords
subject: FHIR,
  topic: keywords
subject: Claims Data
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: HIPAA
  topic: keywords
subject: NIST AI RMF
  topic: keywords
creator: Gayathri Surianarayanan
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-02-28
publication date: 2026-02-28
doi: 10.63412/688p6356

Agentic AI–Driven Quality Engineering: A Global Innovation Framework for Autonomous Enterprise Decision Validation
type: item
description: The rapid evolution of enterprise software systems characterized by continuous delivery pipelines, large-scale data integration, and AI-enabled decision processes has exposed fundamental limitations in traditional quality assurance (QA) and validation practices. Manual and rule-based automation approaches struggle to scale across complex, distributed environments where system behavior, data patterns, and operational risks evolve continuously. This paper presents an Agentic AI--Driven Quality Engineering framework that redefines quality engineering as an autonomous, intelligent capability for enterprise decision validation. The proposed framework leverages agentic artificial intelligence, in which multiple autonomous agents collaboratively execute and govern core quality engineering functions, including test orchestration, enterprise data integrity validation, anomaly detection, defect triage, and release-readiness assessment. Unlike conventional QA frameworks that rely on static test assets and predefined logic, the agentic approach incorporates adaptive reasoning and learning mechanisms that evolve based on historical defects, system telemetry, and operational feedback. Designed for seamless integration with CI/CD pipelines and enterprise data platforms, the framework enables continuous quality monitoring and near real-time decision support. A decision confidence scoring mechanism is introduced to quantify release readiness and operational risk, providing transparent and governance-aligned insights for engineering and leadership stakeholders. The framework is evaluated through enterprise-scale validation scenarios involving complex system integrations and high-volume datasets, demonstrating measurable improvements in defect detection efficiency, reduction in manual validation effort, and accelerated decision cycles compared to traditional QA models. By positioning quality engineering as an AI-driven, autonomous function, this research contributes a scalable global …
subject: Articles
  topic: section
subject: Agentic AI
  topic: keywords
subject: Quality Engineering
  topic: keywords
subject: Autonomous Validation
  topic: keywords
subject: Enterprise Systems
  topic: keywords
subject: AI-Augmented QA
  topic: keywords
subject: Decision Intelligence
  topic: keywords
subject: Global Innovation
  topic: keywords
creator: Srikanth Reddy Singireddy
creator: Abinaya Mettuapatti Sivagnanam
creator: Gayathri Surianarayanan
creator: Tapathi Madireddi
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-02-28
publication date: 2026-02-28
doi: 10.63412/98h64d16

Privacy-Preserving Multi-Modal Threat Detection Framework for Educational Facilities: A Conceptual Design
type: item
description: School safety systems must detect threats rapidly while protecting student privacy and minimizing false alarms. This paper presents a conceptual framework combining edge-based open-vocabulary object detection with millimeter-wave radar sensing. Our three-tier confidence system matches response intensity to detection certainty, incorporating teacher verification at intermediate levels. The framework processes all data locally and includes automatic face anonymization, minimal retention policies, and no identity tracking. We provide practical camera placement methodology using pixels-per-meter calculations and detailed implementation guidelines. While this work presents a design proposal rather than experimental validation, it offers a comprehensive blueprint balancing security effectiveness with ethical requirements. The framework is designed to be reproducible and adaptable to diverse educational environments.
subject: Articles
  topic: section
subject: edge computing
  topic: keywords
subject: open vocabulary detection
  topic: keywords
subject: mmWave radar
  topic: keywords
subject: privacy-preserving systems
  topic: keywords
subject: human-in-the-loop
  topic: keywords
subject: school security
  topic: keywords
creator: Abinaya Mettupatti Sivagnanam
creator: Gayathri Surianarayanan
creator: Srikanth Singireddy
creator: Vinoth Asoor Palanivel
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-05-26
publication date: 2026-05-26
doi: 10.63412/n4nyve97

Advancements in Physics-Informed Neural Networks (PINNs) for Real-Time Prediction of Complex Fluid Dynamics
type: item
description: The integration of physical laws into deep learning architectures has emerged as a transformative paradigm for solving complex partial differential equations (PDEs) in fluid mechanics. Traditional Computational Fluid Dynamics (CFD) methods, while accurate, are computationally expensive and often unsuitable for real-time applications. This paper proposes an enhanced Physics-Informed Neural Network (PINN) framework that utilizes a multi-objective loss function and adaptive weight refinement to solve the Navier-Stokes equations under turbulent conditions. We introduce a novel residual-based attention mechanism that prioritizes steep gradient regions, significantly improving convergence speed by 40%. Our results demonstrate that the proposed model achieves a Mean Squared Error (MSE) of $1.2 \times 10^{-5}$ compared to high-fidelity DNS data, while reducing inference time by three orders of magnitude. This research paves the way for the deployment of deep learning models in safety-critical autonomous systems and real-time industrial monitoring.
subject: Articles
  topic: section
subject: Deep Learning, Physics-Informed Neural Networks (PINNs), Fluid Dynamics, Navier-Stokes Equations, Turbulence Modeling, Machine Learning in Physics.
  topic: keywords
creator: RaviKumar Bhuvanagiri
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-05-26
publication date: 2026-05-26
doi: 10.63412/5beg8z46

Leveraging AI for Lowering Patient Financial Responsibility in the USA Healthcare
type: item
description: In the United States, patient out-of-pocket (OOP) costs, including deductibles, copays, coinsurance, and outstanding balances, have increased sharply over the past few decades. This surge is driven mainly by administrative waste, billing coding errors, inefficient care pathways, and rising premiums. Despite huge investments at the national level in digital health infrastructure, patients continue to face unnecessary financial burdens due to scattered information, complex eligibility rules, and billing/coding errors. This paper examines how Artificial Intelligence (AI) and Machine Learning (ML) can help reduce patient costs by focusing on two main drivers of high costs: administrative waste and clinical inefficiency. Through a detailed review of AI applications in increasing administrative efficiency, enhancing cost transparency, optimizing Revenue Cycle Management (RCM), and predictive analytics, this research shows that AI-driven process optimization can significantly reduce healthcare expenses. The research collects evidence from federal agencies, studies, and industry deployments to support how AI can significantly lower patient costs while improving clinical and operational efficiency. This paper also discusses the challenges and complications of AI deployment in the digital healthcare system.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Artificial Intelligence, Patient Financial Responsibility, Healthcare Costs, Revenue Cycle Management, Predictive Analytics, US Healthcare, Medical Debt, Out-of-Pocket Cost, Financial Transparency, Healthcare Affordability, Medical Billing Optimization, Administrative Waste
creator: MOHINI JINDAL
creator: Sandeep Singh Saini
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-01-24
publication date: 2026-01-24
doi: 10.63412/g6jme257

Named Ruleset: A Long-Lived Parallel Development Architecture for Enterprise-Scale Pega Implementations
type: item
description: Pega Platform™ is a market leader in BPM/CRM, low-code, buy-and-build, and build-for-change solutions, enabling organizations to develop digital solutions more rapidly compared to traditional programming. Pega Platform™ provides a robust application development environment, with separate portals for citizen developers (“App Studio”), system architects (“Designer Studio”), and system administrators (“Admin Studio”) to rapidly design, build, and manage applications. Pega Application code created using these studios is saved to rulesets, with versions, in Pega’s own code repository. When development teams grow large and multiple functionalities must be developed and delivered in parallel, managing code within the same application ruleset version becomes challenging. The Pega Platform uses Pega branches to support parallel development. These Pega branches allow each team to create and update rules without impacting other teams. Pega branching is designed for frequent code merges to trunk versions and continuous release to production. In many scenarios, frequent merging and releasing capabilities to production are not feasible for all parallel development teams; they may need to remain in development mode longer before moving to production. Providing a future version for release is a single solution that most customers adopt; it is efficient when customer priorities and release dates remain stable, but it becomes inefficient when those priorities and dates change. In this research paper, we propose an innovative solution to address this problem: a custom branch that maintains development tracks without merging into the trunk, along with its advantages and disadvantages. This proposed solution, called “Named Ruleset,” enabled parallel development teams to remain in a custom branch for years while still incorporating trunk changes. We envision that this custom branching solution can address the limitations of Pega branching and advance the Pega DevOps practice for larger e…
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Continuous Delivery, Feature Toggles, Healthcare IT Systems, Low-Code Platforms, Long-Lived Development Tracks, Named Ruleset, Pega DevOps, Pega Branching and Merging, Pega Ruleset Versioning, Release Governance
creator: Harikrishnan Muthukrishnan
creator: Chandrashekar Konasirasagi
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-01-24
publication date: 2026-01-24
doi: 10.63412/cw7fjg09

Privacy-Preserving MLOps with Differential Privacy and AI-Guided Tuning
type: item
description: When machine learning systems transition from being deployed within research environments to enterprise-scale deployment pipelines, protecting data privacy poses an increasing challenge while the model is being trained and/or used. Privacy-preserving techniques will predominantly rely on some form of static differential privacy (DP) constraint, with the challenge often being to balance privacy requirements with model performance, particularly with dynamic workloads. In this paper, we propose a new Privacy-Preserving MLOps (PP-MLOps) framework that combines AI-aided adaptive tuning of differential privacy in the automated MLOps lifecycle. These proposed agent approaches allow for a flexible way to continuously assess privacy risks, regulatory obligations to privacy and confidentiality, and the value of model utility metrics while adapting DP scale of noise, depth of clipping, and privacy budgets (ε, δ) in real-time to achieve optimal model utility. The continuous optimization of DP in CI/CD pipeline operations is actualized through also using reinforcement-learning based controllers to adjust for a range of privacy and performance tradeoff situations in real-time. Evaluation simulations show a 20% improvement in model accuracy retention in regulation compliant DP tuning and operational measures against traditional fixed DP configurations of varying distributions and operational risks. This study can lay the foundation for fully autonomous risk sensing and regulatory compliant MLOps while translating theoretical claims of privacy, application and assurance in a pragmatic framework machine learning deployment at scale.
subject: Articles
  topic: section
subject: Reinforcement Learning, Autonomous incident response, Threat detection.
  topic: keywords
creator: Chhaya
creator: Jay Nankani
creator: Rohit Gupta
creator: Atul Khanna
rights: Copyright (c) 2026 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2026-01-24
publication date: 2026-01-24
doi: 10.63412/ezdfsg07

A Self-Adaptive, AI-Powered Testing Framework for Cross-Platform Systems: An Architectural Design and Industrial Evaluation
type: item
description: The escalating complexity of cross-platform applications, which must operate seamlessly across a heterogeneous ecosystem of devices and operating systems, has rendered conventional script-based test automation unsustainable. These legacy approaches are plagued by inherent brittleness, inadequate test coverage, and exorbitant maintenance overhead, creating a critical bottleneck in modern DevOps pipelines. This paper proposes a novel, integrated framework for an intelligent testing ecosystem that leverages machine learning to engender self-adaptation, predictive analytics, and autonomous operation. We delineate a modular architecture incorporating three core intelligent capabilities: cognitive test generation using reinforcement learning and natural language processing, self-healing test execution via multi-modal locator strategies and computer vision, and predictive defect localization through ensemble-based risk modeling. The framework's efficacy is empirically validated through two longitudinal industrial case studies in the FinTech and E-commerce domains. Quantitative results demonstrate a 55-70% reduction in test maintenance effort, a 40% improvement in test coverage, and a 60-62.5% acceleration in regression testing cycles. Furthermore, we critically discuss implementation challenges—including data dependency, computational overhead, and model explainability—and propose a research trajectory toward causal inference and end-to-end autonomous testing systems. Our findings substantiate that the integration of AI is not merely an incremental enhancement but a paradigmatic shift essential for achieving robust, continuous quality assurance in cross-platform development.
subject: Articles
  topic: section
subject: Artificial Intelligence in Software Testing, Cross-Platform Testing, Self-Healing Tests, Machine Learning, Predictive Analytics, DevOps, Continuous Testing, Test Automation.
  topic: keywords
creator: Prathap Raghavan
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-12-21
publication date: 2025-12-21
doi: 10.63412/dhfapm89

Agentic RAG for CRM: From Answers to Actions
type: item
description: Many CRM teams need more than a text answer. They also need the agent to take the next step. We offer a hands on blueprint for an agent that writes a grounded reply and performs a safe action in the CRM. The design is small on purpose: a compact action record with a signature, a policy guard for roles, limits, and required fields, and a simple split between edge and cloud for speed and privacy. An illustrator style diagram (illatrater) follows one case end to end, from a customer request, to retrieved policy, to an approved update with an audit receipt. We close with light weight metrics and a staged roll out plan that a small team can try, measure, and refine.
subject: Articles
  topic: section
subject: Retrieval Augmented Generation (RAG)
  topic: keywords
subject: Agentic AI
  topic: keywords
subject: CRM Automation
  topic: keywords
subject: Policy Guard
  topic: keywords
subject: Auditable Actions
  topic: keywords
subject: Edge and Cloud Computing
  topic: keywords
creator: Abinaya Mettupatti Sivagnanam
creator: Srikanth Singireddy
creator: Vinoth Asoor Palanivel
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
doi: 10.63412/30syg211

Composable Multi-Tool Pipelines for AI Agents in DevSecOps: Architecture, Orchestration, and Evaluation
type: item
description: The integration of AI agents into DevSecOps workflows represents a paradigm shift from static au- tomation to autonomous, goal-driven systems capable of complex reasoning and adaptation. This paper investigates the design and implementation of composable multi-tool pipelines for AI agents in DevSecOps environments, focus- ing on architectural patterns, orchestration strategies, and evaluation methodologies. We propose a novel hierarchical orchestration model that balances centralized control with specialized execution, enabling dynamic workflow recon- figuration based on real-time context. Our analysis of orchestration patterns reveals that hierarchical supervision achieves optimal scalability and fault tolerance for complex DevSecOps workflows. Through case studies in automated security remediation and intelligent CI/CD optimization, we demonstrate the practical benefits of agent-driven automation. We introduce a multi-dimensional evaluation framework incorporating performance, security, and hu- man trust metrics, addressing the critical gap between technical capability and real-world adoption. The proposed architecture and evaluation methodology provide a foundation for deploying trustworthy, efficient, and scalable AI-driven DevSecOps systems in enterprise environments.
subject: Articles
  topic: section
subject: AI AGENTs
  topic: keywords
subject: DevSecOps
  topic: keywords
subject: Multi-agent
  topic: keywords
subject: Security
  topic: keywords
creator: Akshay Mittal
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-10-31
publication date: 2025-10-31
doi: 10.63412/jjwed788

Integrating Large Language Models with MLOps Observability for Attack Surface Reduction
type: item
description: Machine learning systems are increasingly being deployed in mission-critical environments, yet their attack surface is expanding due to complex CI/CD pipelines, distributed deployment, and lack of proactive observability. This paper proposes a novel integration of Large Language Models (LLMs) with MLOps observability frameworks to enhance security posture. By leveraging LLMs for real-time anomaly detection, incident reasoning, and adaptive response within MLOps pipelines, the framework aims to reduce exploitable vulnerabilities while maintaining system performance and compliance. We present an architecture where LLMs act as intelligent security co-pilots, continuously correlating logs, telemetry, and model metrics to detect adversarial activities and misconfigurations. Experimental evaluation demonstrates improved detection of adversarial injection, misconfiguration drift, and pipeline-based exploits, while significantly lowering response latency. This research highlights how LLM-augmented observability can evolve MLOps pipelines into self-defensive systems with reduced attack surface.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: AI-based Cloud Cost Management, Cloud Computing, Cost Optimization, Automation, Real-time Monitoring, Anomaly Detection, Optimization Recommendations, Machine Learning Algorithms, Cost Reporting Automation, Predictive Analysis, Cross-cloud Optimization, Security Compliance Monitoring, Cost-cutting Tactics, Financial Projections, Operational Effectiveness, Budget Overruns, Hybrid Cloud, Multi-cloud Environments, Intelligent Cost Allocation, Workload Optimization
creator: Chhaya Gunawat
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
doi: 10.63412/a4t8kg68

Database-Level End User Authorization (DB-EUA)
type: item
description: Application servers are traditionally the policy enforcement point for databases. In that model, the database cannot verify the end user’s identity or intent for each operation; it can only trust whatever context the application supplies. This creates systemic exposure to server compromise, confused-deputy problems, and weak provenance. DB-EUA moves verifiable authorization into the data path: every create/read/update/delete (CRUD) is accompanied by a user-authenticated, cryptographically verifiable token that the database (or a hardened database proxy) validates and binds to the session executing the SQL. The result is a tamper-evident, user-attributable audit trail and strong least-privilege enforcement at the DB layer—aligned with Zero Trust principles and regulatory accountability requirements. We present: A precise threat model and trust assumptions. A two-token architecture (server channel token + per-user operation token). A reference implementation blueprint for PostgreSQL Row-Level Security (RLS) with a wire-protocol proxy. Hardening guidance (key management, mTLS, channel binding, log hygiene). Compliance mappings (HIPAA, SOX, GDPR, CCPA) and a summary matrix. A practical vendor roadmap (DB engines, cloud DBs, gateway/proxy vendors, backend platforms).
subject: Articles
  topic: section
subject: Zero Trust
  topic: keywords
subject: Row-Level Security (RLS)
  topic: keywords
subject: Cryptographically Verifiable Authorization
  topic: keywords
subject: Verifiable Credentials (VC)
  topic: keywords
subject: Selective Disclosure JWT (SD-JWT)
  topic: keywords
subject: Regulatory Compliance (HIPAA, SOX, GDPR, CCPA)
  topic: keywords
creator: Saurav Bhattacharya
creator: Durga Krishnamoorthy
creator: Manan Wason
creator: Madhavi Najana
creator: Ankush Rastogi
creator: Uttam Kumar
creator: Gaurav Deshmukh
creator: Dewank Pant
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-12-21
publication date: 2025-12-21
doi: 10.63412/j4rckr15

A Framework for Autonomous, Cross-Cloud Threat Mitigation Using Multi-Agent Reinforcement Learning
type: item
description: The rapid enterprise adoption of multi-cloud, microservice architectures introduces unprecedented complexity and security challenges. Traditional, reactive security models are proving inadequate, as code changes can propagate to global production systems within minutes, leaving minimal time for after-the-fact audits. Existing security solutions often operate in silos, failing to provide a coordinated and autonomous defense posture capable of addressing threats that span heterogeneous cloud environments. This paper introduces a novel framework for autonomous, cross-cloud threat mitigation that utilizes Multi-Agent Reinforcement Learning (MARL). In our proposed system, lightweight, self-defending artificial intelligence agents are deployed within each cloud environment to act as intelligent sentinels inside the software-delivery pipeline. These agents learn collaboratively to identify and remediate security risks in real-time, functioning as self-healing remediation agents. Through simulated multi-cloud failure scenarios, we demonstrate that this approach can significantly reduce mean-time-to-resolution for security incidents, projecting improvements comparable to the 60\% reduction in vulnerability patch time observed in related empirical studies.
subject: Articles
  topic: section
subject: Autonomous Systems
  topic: keywords
subject: Cloud-Native Security
  topic: keywords
subject: MARL
  topic: keywords
subject: Multi-Cloud Security
  topic: keywords
creator: Akshay Mittal
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
doi: 10.63412/kb44xf51

A Threshold Cryptography Framework for Secure and Resilient Symmetric Key Management in Multi-Cloud Environments
type: item
description: This paper addresses the critical security and availability risks inherent in centralized key management systems (KMS) for cloud data protection. The compromise or failure of a single KMS can lead to catastrophic data breaches or loss of access. We propose a decentralized framework to mitigate this single point of failure. Technology or Method: We introduce the Threshold Key Management System (TKMS), a novel framework that leverages threshold cryptography to manage symmetric encryption keys. The framework employs a (k, n)-threshold secret sharing scheme to shard a master symmetric key across n independent Key Management Nodes (KMNs), which are distributed across multiple cloud provider infrastructures. Key generation is performed collaboratively using a Distributed Key Generation (DKG) protocol, eliminating the need for a trusted dealer. Results: The security analysis demonstrates that TKMS guarantees key confidentiality as long as fewer than k KMNs are compromised. The system provides high availability, tolerating the failure of up to n − k nodes. The projected performance evaluation indicates that the cryptographic overhead is manageable and offers a favorable trade-off for the significantly enhanced security and resilience. Conclusions: TKMS presents a robust and fault-tolerant alternative to traditional cloud KMS. By distributing trust across multiple administrative domains, it significantly raises the bar for attackers and protects against provider-level failures. Impact: This work provides a practical blueprint for building highly secure, resilient, and trust-minimized data protection services in the cloud, with direct applications in securing sensitive corporate data, personal information, and critical infrastructure backups.
subject: Articles
  topic: section
subject: Cloud Security, Distributed Key Generation (DKG), Key Management, Secret Sharing, Symmetric Key Encryption, Threshold Cryptography.
  topic: keywords
subject: cryptography
  topic: keywords
subject: key generation
  topic: keywords
subject: secret sharing
  topic: keywords
subject: threshold cryptography
  topic: keywords
creator: Dewank Pant
creator: Avijit Kumar
creator: Shruti Lohani
creator: Manan Wason
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-08-30
publication date: 2025-08-30
doi: 10.63412/65na4629

A Lightweight, AI-Enhanced Intrusion Detection System for Wireless Body Area Networks Using Unsupervised Anomaly Detection
type: item
description: The proliferation of Wireless Body Area Networks (WBANs) in remote healthcare monitoring has introduced unprecedented capabilities for patient care but also significant security vulnerabilities with life-threatening implications. Traditional intrusion detection systems (IDS), often relying on static signatures or simplistic rule-based anomaly detection, are ill-equipped to handle the dynamic and sophisticated nature of modern cyber threats, particularly zero-day attacks. This paper introduces a novel, hybrid, AI-enhanced IDS framework designed for the resource-constrained WBAN environment. The proposed system integrates a lightweight, on-node filtering mechanism with a powerful, centralized, unsupervised convolutional autoencoder (CAE) deployed on the network coordinator. This architecture leverages a rich feature set, including physiological time-series data and a sophisticated energy consumption profile, to achieve robust detection. Unlike conventional models that depend on arbitrary thresholds, our approach employs a data-driven anomaly detection mechanism based on the CAE’s reconstruction error, enabling it to identify both known attack patterns and novel anomalies with high fidelity. The framework is validated using a benchmark physiological dataset and simulated attacks. The results demonstrate a significant advancement over baseline models, achieving a superior F1-Score of 0.96 and an Area Under the Curve (AUC) of 0.98, showcasing its efficacy and potential for securing next-generation medical WBANs.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Wireless Body Area Networks (WBANs), Intrusion Detection System (IDS), Anomaly Detection, Convolutional Autoencoder (CAE), Unsupervised Learning, Cybersecurity in Healthcare, Medical IoT Security, Denial-of-Sleep Attack, Energy Consumption Profile (ECP), AI-Enhanced Security, Deep Learning for IDS, TinyML Security, Real-Time Threat Detection, Zero-Day Attack Detection, Physiological Signal Monitoring
creator: Dewank Pant
creator: Shruti Lohani
creator: Manan Wason
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-08-30
publication date: 2025-08-30
doi: 10.63412/mqxqss19

Performance Impact on Databases Using Serverless Architectures: An Empirical Study
type: item
description: Serverless computing is rapidly gaining traction as a viable alternative for deploying and managing various applications. Serverless databases, in particular, promise scalability, cost-effectiveness, and reduced operational overhead. However, the performance characteristics of serverless databases, especially in comparison to traditional database deployments, are not yet fully understood. This paper presents an empirical study investigating the performance impact of utilizing serverless database architectures. We conduct experiments using a benchmark workload against both a serverless database (AWS Aurora Serverless v2) and a provisioned database (AWS Aurora PostgreSQL). Our results reveal that while serverless databases offer significant benefits in terms of scalability and cost, they can introduce latency overhead due to cold starts and autoscaling mechanisms. We analyze these performance nuances, providing insights into the trade-offs between serverless and provisioned database deployments for various application scenarios
subject: Articles
  topic: section
subject: Serverless Computing
  topic: keywords
subject: Database Performance
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: AWS Aurora Server- less v2
  topic: keywords
subject: Autoscaling
  topic: keywords
subject: Cold Start
  topic: keywords
subject: Autonomous Databases
  topic: keywords
subject: Serverless Databases
  topic: keywords
subject: Serverless Database Performance
  topic: keywords
creator: Arvind Reddy Toorpu
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-08-30
publication date: 2025-08-30
doi: 10.63412/xyhfy869

Reducing Redundant Computation in Ad-Hoc Multiplayer Gaming through SpatialTask Delegation and Synchronization
type: item
description: Multiplayer gaming in ad-hoc networks introduces significant computational challenges due to the absence of centralized servers and the reliance on resource-constrained devices. Each device must independently handle rendering, physics, and game logic computations, often leading to redundant processing when players are in close spatial proximity and share overlapping gameplay perspectives. This redundancy not only wastes energy and computing resources but also contributes to increased latency and reduced responsiveness in fast-paced interactive environments. This paper proposes a decentralized framework that leverages spatial awareness, context-based task delegation, and event-driven synchronization to eliminate redundant computation in multiplayer ad-hoc gaming environments. The approach enables dynamically assigning specific computational tasks—such as physics simulations or shared environment rendering—to a single device, which then broadcasts the result to nearby peers. Inspired by edge computing and fog-based processing models [5], [6], the system reduces resource contention while maintaining a consistent game state across devices using selective synchronization guided by vector-field consistency principles [10]. An efficient peer-to-peer protocol governs the selective dissemination of data, ensuring that only relevant and significant updates—such as changes in player location, interactions, or object states—are transmitted, minimizing network overhead. By adapting techniques from event-driven multiplayer synchronization [7] and integrating them with real-time proximity analysis, this framework achieves lower latency and improved energy efficiency without compromising gameplay fidelity. The proposed model is particularly beneficial for infrastructure-less environments, including mobile ad-hoc networks (MANETs), edge-deployed AR/VR multiplayer systems, and local-area gaming scenarios. Our results demonstrate that task delegation based on player position and conte…
subject: Articles
  topic: section
subject: Adhoc Networks
  topic: keywords
subject: Multiplayer Gaming
  topic: keywords
subject: Redundant Computing
  topic: keywords
subject: Decentralized Processing
  topic: keywords
subject: Spatial Synchronization
  topic: keywords
subject: Peer to Peer Communcation
  topic: keywords
subject: Task Oflloading
  topic: keywords
subject: Vector Field Consistency
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Fog Computing
  topic: keywords
creator: Nikheel Vishwas Savant
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-08-02
publication date: 2025-08-02
doi: 10.63412/qfapc349

Low-Latency Group Communication in BLE Networks via Whisper-Inspired L2CAP Relaying
type: item
description: Reliable group communication in Bluetooth Low Energy (BLE) networks is constrained by limited broadcast support, high advertising congestion, and lack of structured flow control. Existing mechanisms such as GATT notifications and BLE Mesh either do not scale well or introduce latency and energy inefficiencies under dynamic or mobile conditions. This paper presents a theoretical yet implementation-aligned broadcast architecture that adapts the Whisper Protocol—a low-redundancy flooding strategy from wireless sensor networks—for BLE using L2CAP Connection-Oriented Channels (CoC). The proposed system enables decentralized, peer-assisted data dissemination across bonded devices by integrating randomized backoff, relay suppression, flow control awareness, and QoS tagging. Unlike BLE Mesh, this approach leverages secure ACL links and credit-based gating to prevent packet loss and ensure low-latency message propagation in trusted peer clusters. Though no prototype is implemented, the paper contributes a deployment blueprint with detailed evaluation metrics—delivery ratio, latency, energy consumption, and memory overhead—suitable for simulation or future testbed validation. The architecture is designed for real-time use cases such as wearable coordination, smart home alerting, and industrial safety messaging, where deterministic behavior and energy-aware group communication are essential.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Bluetooth Low Energy (BLE), L2CAP Connection-Oriented Channels (CoC), Whisper Protocol, peer-to-peer communication, wireless broadcasting, flow control, low-latency networking, embedded systems, group messaging, smart wearables, industrial IoT, energy-efficient networking.
creator: Nikheel Vishwas Savant
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-06-22
publication date: 2025-06-22
doi: 10.63412/rm60fn61

Towards Quality of Service Guarantees in Bluetooth A Cross-Layer Framework for LE and Classic Coexistence in the 2.4 GHz IoT Ecosystem
type: item
description: Bluetooth technology has become foundational for short-range communication in a wide range of Internet of Things (IoT) applications, encompassing both Bluetooth Low Energy (LE) and Classic protocols. With the 2.4 GHz ISM band increasingly congested by coexisting technologies such as Wi-Fi, Zigbee, and proprietary wireless protocols, Bluetooth now faces critical challenges in delivering consistent Quality of Service (QoS) for time-sensitive and bandwidth-intensive use cases. In contrast to Wi-Fi, which benefits from robust QoS frameworks like IEEE 802.11e and Wi-Fi Multimedia (WMM), Bluetooth's current design lacks built-in mechanisms for traffic prioritization, deterministic latency, or adaptive scheduling. This paper identifies core limitations of Bluetooth’s transport architecture under interference-prone conditions and proposes a novel cross-layer QoS framework. Our design introduces traffic classification at the application layer, connection event prioritization at the link layer, coexistence-aware adaptive frequency hopping, and enhanced controller-host coordination through vendor-specific HCI extensions. We validate the proposal using simulations and empirical tests involving mixed BLE/Classic traffic and controlled Wi-Fi interference. The results demonstrate improvements in latency, jitter, and reliability, especially for critical IoT use cases such as voice, telemetry, and health monitoring. The framework provides a scalable path forward for integrating Bluetooth QoS into future specifications and enabling its use in high-density, real-time applications.
subject: Articles
  topic: section
subject: Bluetooth Low Energy
  topic: keywords
subject: Bluetooth Classic
  topic: keywords
subject: Quality of Service
  topic: keywords
subject: IOT
  topic: keywords
subject: LE Audio
  topic: keywords
subject: wireless coexistence
  topic: keywords
subject: adaptive frequency hopping
  topic: keywords
subject: multi-radio systems
  topic: keywords
subject: Wi-Fi interference
  topic: keywords
subject: latency-sensitive applications
  topic: keywords
creator: Nikheel Vishwas Savant
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-08-02
publication date: 2025-08-02
doi: 10.63412/jkt8nk33

ASL-MDFD: Adversarial Self-Supervised Learning for Generalizable GAN-Resilient Multimodal Deepfake Detection
type: item
description: The rise of hyper-realistic synthetic media generated by Generative Adversarial Networks (GANs) and diffusion models poses significant challenges to deepfake detection systems, particularly in cross-dataset and cross-GAN generalization. In this work, we propose ASL-MDFD: a novel framework that unifies Adversarial training, Self-Supervised Learning (SSL), and Multimodal Fusion to detect deepfakes across diverse sources. Our approach leverages rotation prediction, patch shuffling recovery, and contrastive audio-visual alignment as pretext tasks to learn intrinsic representations without heavy reliance on labels. Simultaneously, adversarially perturbed examples generated using PGD simulate artifacts from unseen GANs, improving model robustness. The multimodal architecture integrates visual, audio, and temporal streams using cross-modal attention to detect inconsistencies in facial textures, voice artifacts, and motion dynamics. Evaluated across FaceForensics++, DFDC, Celeb-DF, StyleGAN3, and StarGANv2 datasets, ASL-MDFD achieves state-of-the-art performance, including 92.3% AUC on Celeb-DF and 88.7% accuracy on StyleGAN3 fakes, significantly outperforming existing baselines. Our results demonstrate the effectiveness of combining SSL, adversarial resilience, and multimodal cues in building robust, generalizable deepfake detectors.
subject: Articles
  topic: section
subject: Deepfake Detection, Generative Adversarial Networks, Self-Supervised Learning, Adversarial Training, Multimodal Fusion, Cross-Dataset Generalization
  topic: keywords
creator: suryarprakash Nalluri
creator: Aiman Shariff
creator: Chethan T.P
creator: Aisirii V Hegde
creator: Indu Mahesh
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-11-01
publication date: 2025-11-01
doi: 10.63412/btg5gj03

End-to-End Zero-Trust Database Migration Frameworks
type: item
description: The increasing adoption of hybrid and multi-cloud database environments introduce profound security challenges, particularly during database migration. Traditional perimeter-based security models are insufficient in mitigating threats in dynamic and distributed environments. This research investigates the development of end-to-end zero-trust security frameworks specifically tailored for database migration processes. By discarding inherent trust in any user, process, or network component, the zero-trust paradigm ensures a robust security posture through continuous authentication, strict access controls, and micro-segmentation. The paper presents a detailed literature review on recent advances in edge computing databases, performance optimization techniques, and real-time analytics, followed by a proposed methodology for evaluating zero-trust implementation in database migration. Experimental simulations and theoretical frameworks are employed to assess system performance, security, and resilience. The findings reveal significant security and operational benefits, though challenges persist in scalability and policy enforcement. Future research directions are proposed to address these challenges and refine zero-trust designs for next-generation edge and cloud-native databases.
subject: Articles
  topic: section
subject: Zero-trust architecture
  topic: keywords
subject: database migration
  topic: keywords
subject: edge computing
  topic: keywords
subject: micro-segmentation
  topic: keywords
subject: continuous authentication
  topic: keywords
subject: distributed databases
  topic: keywords
subject: security frameworks
  topic: keywords
creator: Arvind Reddy Toorpu
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-06-22
publication date: 2025-06-22
doi: 10.63412/00tzhm26

Leveraging Surplus Android Phones in India: Scalable Use Cases in Edge Computing, Digital Health, and Smart Infrastructure
type: item
description: India’s rapid smartphone adoption and short device replacement cycles have led to a significant accumulation of surplus Android phones, many of which are discarded due to superficial hardware issues or software obsolescence [1]. These discarded devices, despite having adequate processing, sensing, and communication capabilities, often contribute to the country’s growing e-waste problem [2]. This paper presents a theoretical framework to model the repurposing of surplus Android smartphones as a cost-effective and scalable layer of decentralized digital infrastructure. We argue that these devices can be re-integrated into public systems to enable edge computing capabilities for a variety of social impact domains—including healthcare, education, environmental monitoring, and smart agriculture. To support this proposition, we introduce three core constructs: techno-social innovation, describing the co-evolution of community practices and technology; frugal digital infrastructure, which emphasizes low-cost, locally adaptable digital systems; and decentralized edge ecology, a conceptual model for loosely coupled edge devices operating collaboratively in resource-constrained environments. We propose a taxonomy of reuse applications and detail a prototype deployment focused on air quality monitoring using refurbished Android devices. Our findings illustrate how surplus smartphones can contribute to sustainable ICT infrastructure, digital inclusion, and e-waste mitigation efforts. This research extends the discourse on circular technology economies and offers actionable insights for low-resource settings in the Global South.
subject: Articles
  topic: section
subject: Android reuse
  topic: keywords
subject: edge computing
  topic: keywords
subject: Frugal Innovation
  topic: keywords
subject: E-Waste Mitigation
  topic: keywords
subject: Mobile Health
  topic: keywords
subject: Education Technology
  topic: keywords
subject: Environmental Sensing
  topic: keywords
subject: Digital Inclusion
  topic: keywords
subject: Sustainable ICT
  topic: keywords
creator: Nikheel Vishwas Savant
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-06-22
publication date: 2025-06-22
doi: 10.63412/8bkyv215

Unified Data Access in Multi-Model Databases: Querying Relational, Graph, and Document Data Seamlessly
type: item
description: The proliferation of diverse data types in modern applications has catalyzed the development of multi-model databases—systems capable of managing and querying disparate models such as relational, graph, and document data within a unified framework. This paper systematically examines state-of-the-art architectures, query languages, and optimization strategies that enable seamless unified data access across these models. We address recent advances concerning distributed and edge computing environments, real-time streaming analytics, and tackle security, consistency, and reliability risks associated with cross-model operations. Leveraging a comparative approach, we synthesize findings from empirical and theoretical investigations, including GPU-accelerated querying and hybrid execution models. The study concludes by identifying persisting challenges and proposing directions for research on efficient, consistent, and secure multi-model query processing with growing data velocity and scale.
subject: Articles
  topic: section
subject: Multi-model databases
  topic: keywords
subject: unified query processing
  topic: keywords
subject: relational data
  topic: keywords
subject: graph data
  topic: keywords
subject: document databases
  topic: keywords
subject: edge computing
  topic: keywords
subject: distributed systems
  topic: keywords
subject: real-time analytics
  topic: keywords
subject: data consistency
  topic: keywords
subject: query optimization
  topic: keywords
subject: database security
  topic: keywords
creator: Arvind Reddy Toorpu
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-05-31
publication date: 2025-05-31
doi: 10.63412/4qcgbv22

A Novel Framework for Text-Based Fraud Detection in Banking Using Spark NLP and Tableau
type: item
description: Fraud detection in banking is evolving beyond numerical analysis to include unstructured text data, such as transaction notes and customer communications. This paper proposes a novel framework integrating Spark NLP for natural language processing, Great Expectations for data quality, AWS for scalable infrastructure, and Tableau for interactive visualizations to detect fraudulent patterns in text. Emphasizing a contextual fraud scoring approach, the framework combines entity recognition and sentiment analysis to identify suspicious activities with high precision. Designed for compliance with GDPR and PCI-DSS, it offers a scalable, modular solution adaptable to various banking applications. Using example datasets, we illustrate its potential to transform fraud detection. Mermaid diagrams and accessible language make this framework approachable for researchers, practitioners, and non-experts alike.
subject: Articles
  topic: section
subject: Fraud Detection
  topic: keywords
subject: Pyspark
  topic: keywords
subject: NLP
  topic: keywords
subject: Tableau
  topic: keywords
subject: Great Expectations
  topic: keywords
subject: AWS
  topic: keywords
subject: Text Analytics
  topic: keywords
subject: Banking
  topic: keywords
subject: Contextual Fraud Scoring
  topic: keywords
creator: Ram Ghadiyaram
creator: Laxmi Vanam
creator: Durga Krishnamoorthy
creator: Jaya Eripilla
rights: Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
doi: 10.63412/jed09w96