International Journal of Global Innovations and Solutions
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.
A Self-Adaptive, AI-Powered Testing Framework for Cross-Platform Systems: An Architectural Design and Industrial Evaluation
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.
Artificial Intelligence in Software Testing, Cross-Platform Testing, Self-Healing Tests, Machine Learning, Predictive Analytics, DevOps, Continuous Testing, Test Automation.
Copyright (c) 2025 International Journal of Global Innovations and Solutions https://creativecommons.org/licenses/by/4.0
Agentic RAG for CRM: From Answers to Actions
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.
Composable Multi-Tool Pipelines for AI Agents in DevSecOps: Architecture, Orchestration, and Evaluation
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.
Integrating Large Language Models with MLOps Observability for Attack Surface Reduction
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.
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
Database-Level End User Authorization (DB-EUA)
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).
Cryptographically Verifiable Authorization
Regulatory Compliance (HIPAA, SOX, GDPR, CCPA)
A Framework for Autonomous, Cross-Cloud Threat Mitigation Using Multi-Agent Reinforcement Learning
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.
A Threshold Cryptography Framework for Secure and Resilient Symmetric Key Management in Multi-Cloud Environments
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.
Cloud Security, Distributed Key Generation (DKG), Key Management, Secret Sharing, Symmetric Key Encryption, Threshold Cryptography.
A Lightweight, AI-Enhanced Intrusion Detection System for Wireless Body Area Networks Using Unsupervised Anomaly Detection
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.
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
Performance Impact on Databases Using Serverless Architectures: An Empirical Study
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
Reducing Redundant Computation in Ad-Hoc Multiplayer Gaming through SpatialTask Delegation and Synchronization
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 context can significantly improve system performance, with up to 30% reduction in redundant CPU load and a corresponding improvement in network throughput. This work advances decentralized gaming by introducing a scalable, cooperative approach to computation in spatially dynamic, real-time gaming environments.
Low-Latency Group Communication in BLE Networks via Whisper-Inspired L2CAP Relaying
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.
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.
Towards Quality of Service Guarantees in Bluetooth A Cross-Layer Framework for LE and Classic Coexistence in the 2.4 GHz IoT Ecosystem
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.
ASL-MDFD: Adversarial Self-Supervised Learning for Generalizable GAN-Resilient Multimodal Deepfake Detection
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.
Deepfake Detection, Generative Adversarial Networks, Self-Supervised Learning, Adversarial Training, Multimodal Fusion, Cross-Dataset Generalization
End-to-End Zero-Trust Database Migration Frameworks
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.
Leveraging Surplus Android Phones in India: Scalable Use Cases in Edge Computing, Digital Health, and Smart Infrastructure
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.
Unified Data Access in Multi-Model Databases: Querying Relational, Graph, and Document Data Seamlessly
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.
A Novel Framework for Text-Based Fraud Detection in Banking Using Spark NLP and Tableau
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.
Secure and Scalable Provisioning for Embedded Systems: A Comparative Study of Techniques, Trust Models, and Future Trends
Provisioning is a foundational step in embedded system deployment, enabling secure, authenticated, and efficient onboarding of devices into trusted networks. This paper surveys prevalent provisioning techniques tailored to embedded environments, categorizing them by mechanism—manual, QR-code-based, out-of-band, and zero-touch. It examines associated cryptographic algorithms, including ECDH, PSK, and certificate-based schemes, and introduces a scalable trust-chaining model for delegated provisioning. A comparative analysis is presented based on security, scalability, user effort, and hardware dependency. The paper concludes with key challenges and future directions such as post-quantum resilience, blockchain-based trust revocation, and standardized onboarding frameworks like FDO and IETF SUIT.<span class="EOP SCXW267165647 BCX4" data-ccp-props="{"335551550":6,"335551620":6,"335559731":202,"335559738":20,"335572071":0,"335572072":0,"335572073":0,"335572075":0,"335572076":0,"335572077":0,"335572079":0,"335572080":0,"335572081":0,"335572083":0,"335572084":0,"335572085":0,"335572087":0,"335572088":0,"335572089":0,"469789798":"nil","469789802":"nil","469789806":"nil","469789810":"nil","469789814":"nil"}">
Trends and Challenges in Data Cleaning for Large-Scale Systems: A Survey
Data cleaning is a critical process to maintain the integrity and usability of large-scale systems that process massive, diverse, and dynamic datasets. As the scale and complexity of data ecosystems grow, traditional cleaning techniques face limitations in addressing challenges such as data heterogeneity, real-time processing demands, and resource constraints. This paper presents a comprehensive survey of the latest trends and persistent challenges in data cleaning for large-scale systems. It examines advancements in automated and AI-driven methods, distributed and cloud-based cleaning frameworks, and real-time error detection techniques for streaming data. Additionally, the survey highlights domain-specific cleaning approaches in sectors like healthcare and finance, where data quality significantly impacts decision-making and operational efficiency. Key challenges, including scalability bottlenecks, the lack of standardized benchmarks, and ethical considerations, are discussed in detail. Finally, the paper identifies open research directions, such as explainable AI in data cleaning, universal metrics development, and sustainable algorithms for resource-efficient processing. By synthesizing recent developments and emphasizing their role in improving decision-making, system performance, and user experience, this survey aims to guide researchers and practitioners toward innovative solutions for enhancing data quality in large-scale systems.
Automation in Data Cleaning, Cloud-Based Data Cleaning, Data Heterogeneity, Data Provenance, Explainable AI, Federated Data Cleaning, IoT Data Cleaning, Machine Learning in Data Cleaning, Metrics for Data Quality, Privacy-Preserving Data Cleaning, Real-Time Data Cleaning, Resource-Efficient Algorithms, Scalability, Standardized Benchmarks, Trends in Data Cleaning, Universal Metrics.
AI-Driven Forecasting for Strategic Inventory Planning in Volatile Markets
This article analyzes the transformation potential offered by Artificial Intelligence (AI) in strategic inventory planning in the event of turbulent markets. Traditional inventory systems are typically inept in counter-reacting towards rapidly evolving changes and lead towards inefficiency, increased cost, and operational disruption. Systematically reviewing the potential offered by AI-based forecasting methods—ranging from predictive analytics and cognitive supply chain platforms to resilience-based forecasting methods—over these weaknesses, this research depicts the immense potential offered by AI in the form of accuracy, responsiveness, cost saving, and resilience. Key challenges in the implementation of AI—ranging from data amalgamation, explanation-friendliness of the algorithm, data quality, and scalability—are critically analyzed and complemented by pragmatic recommendations on the mitigation of these barriers. Detailed tables and graphical representations further validate the potential offered by the use of AI-based forecasting methods in the form of increased forecast accuracy, cost saving, and better tolerance towards supply chain disruption. Strategic implications and research directions and the potential offered by the use of hybrid AI-human forecasting models and explainable AI models in the improvement of forecasting effectiveness and stakeholder acceptability are discussed in the subsequent portion. Practical researchers and researchers have been offered by this research study useful information about the effective management of turbulence in the marketplace using the potential offered by the use of AI.
Artificial Intelligence (AI), Inventory Forecasting, Predictive Analytics, Cognitive Supply Chain, Supply Chain Resilience, Machine Learning (ML), Strategic Inventory Management, Volatile Markets, Demand Forecasting, Supply Chain Disruptions, Data Integration, Algorithm Transparency, Explainable AI (XAI), Real-time Analytics, Risk Management, Inventory Optimization.
The submission covers key subject areas including Artificial Intelligence in Supply Chains, Strategic Inventory Planning, Predictive Analytics, Machine Learning Applications, Supply Chain Management, Inventory Optimization, Volatility Management, Supply Chain Resilience, Cognitive Computing, Risk and Disruption Management, Data-driven Decision Making, and Emerging Technologies in Logistics.
The disciplines relevant to this research include Supply Chain Management, Operations Management, Artificial Intelligence and Machine Learning, Industrial Engineering, Business Analytics, Information Systems, Logistics, and Decision Sciences.
What is AI Consciousness and Identity? A Cross-Disciplinary Inquiry
As artificial intelligence systems grow in complexity, questions surrounding their potential for consciousness and identity have moved from speculative to essential. This paper investigates the theoretical and practical contours of AI consciousness and selfhood by integrating insights from neuroscience, philosophy of mind, cognitive science, and AI engineering. We examine the conceptual parallels and distinctions between human and machine consciousness, explore the evolving architectures of agentic AI systems, and assess how identity can emerge in artificial agents through mechanisms like memory, self-reflection, and narrative continuity. Building on this foundation, we propose a novel Consciousness–Linearity–Identity (CLI) framework that provides a structured method for evaluating emergent properties in AI. We address the ethical ramifications of AI systems that mimic or exhibit traits associated with consciousness and argue for a new socio-technical contract to govern human-AI interactions. This work concludes by calling for an interdisciplinary effort to shape the trajectory of AI development responsibly, with an emphasis on alignment, empathy, and shared understanding between natural and synthetic minds.
Artificial Intelligence, Consciousness, Machine Selfhood, Digital Identity, Agentic Systems, Cognitive Architecture, Moral Consideration, Human-AI Interaction, Narrative Self, Neurophilosophy, Autobiographical Memory, Ethics of AI, Artificial Consciousness, AI Morality, Embodied Cognition, Integrated Information Theory, Large Language Models, Reflective AI, Computational Identity, Interdisciplinary AI Research
Geopolitical Trade Tensions and Their Strategic Impact on Global Supply Chains
Geopolitical trade tensions heavily redrawing supply chain strategies on the global level have an impact on international trade as well as resilience in operations. In this paper, we explore in-depth recent and historical geopolitical events, and examine their immediate and indirect influences on supply chain strategies. Grounded on detailed literature reviews, this research decides effective mitigation tactics like supplier diversification, technological innovation, cognitive supply chain platforms, and public-private partnerships. These are debated based on practical applications of exemplar case studies, highlighting effective and ineffective strategic responses. The paper also provides future trends and recommendations to help policymakers and organizations prepare proactively to manage geopolitical risks. Our research highlights the imperatives of agility, technological innovation, and collaborative systems to improve global supply chains in the face of escalating geopolitical uncertainties.
Keywords: Geopolitical tensions, Global supply chains, Supply chain resilience, US-China trade war, Brexit, Ukraine-Russia conflict, Supplier diversification, Public-private partnerships, Artificial intelligence, Blockchain.
Geopolitical Trade Tensions, Global Supply Chain Management, Supply Chain Resilience, Risk Mitigation Strategies, International Trade Disruptions, Strategic Supply Chain Planning, Digital Transformation in Supply Chains, Artificial Intelligence and Blockchain in Logistics, Public-Private Partnerships, Geoeconomics and Trade Policy
Supply Chain Management, International Business, Operations Management, Logistics, Economics, Political Science, Strategic Management, Information Systems, Industrial Engineering, Public Policy
AI-Enabled Cloud Cost Management Platforms: Automating Cost Reports, Alerts, and Optimization Recommendation
Cloud computing today is an essential component of modern business, yet its escalating price tag is a significant issue facing organizations. AI-based cloud cost management tools are transforming the way businesses track, optimize, and control their cloud expenses. By leveraging automation, real-time monitoring, anomaly detection, and optimization recommendations, AI-based solutions provide cost-cutting tactics, improve financial projections, and optimize operational effectiveness. These tools utilize machine learning algorithms to track expenses, flag anomalies, and make actionable suggestions to prevent budget overruns. This article explores the various aspects of AI-driven cloud cost management, including cost reporting automation, predictive analysis, cross-cloud optimization, and security compliance monitoring.
AI-Enabled Cloud Cost Management Platforms: Automating Cost Reports, Alerts, and Optimization Recommendations
Cloud Computing, Artificial Intelligence, Cost Management, Automation, Operations Management, Financial Management, Cloud Security, Predictive Analytics, Multi-cloud Environments