International Journal of AI, BigData, Computational and Management Studies
ML Pipeline Monitor: A Comprehensive OpenTelemetry-Based Observability Platform for Production Machine Learning Systems
As machine learning (ML) systems transition from research prototypes to production deployments, the need for comprehensive monitoring and observability becomes critical. Traditional application monitoring tools fail to address ML-specific challenges such as data drift, model performance degradation, and concept drift. I present ML Pipeline Monitor, a production-ready observability platform built on OpenTelemetry standards that provides end-to-end monitoring for ML pipelines. The system integrates with major cloud ML platforms (AWS SageMaker, Azure ML, Google Cloud Vertex AI) and ML frameworks (PyTorch, TensorFlow, MLflow) to collect multi-dimensional telemetry data including model performance metrics, data quality indicators, and infrastructure health. I implement statistical drift detection algorithms (Kolmogorov–Smirnov test, Population Stability Index, Chi-square test) and anomaly detection methods (Isolation Forest, One-Class SVM) to identify degradation in real-time. Through evaluation on production-style workloads, I demonstrate that the system detects model drift with 94% accuracy, identifies performance anomalies with < 5% false positive rate, and adds < 1ms overhead to prediction latency. The system monitors pipelines processing 10,000+ predictions per second while maintaining < 5% CPU overhead. I provide an open-source implementation for local, cloud, and multi-cloud deployments.
Cervical Muscle Activation and Strain Responses in Helmeted Individuals during Post-Concussion Head Injury Rehabilitation
Post-concussion rehabilitation increasingly emphasizes neuromuscular control of the cervical spine, particularly in athletic and military populations where helmet use often continues during recovery. The cervical musculature is essential for head stabilization, proprioceptive input, and attenuation of mechanical loads following mild traumatic brain injury (mTBI). However, helmets alter head mass distribution and inertial properties, potentially influencing cervical muscle activation and spinal strain during rehabilitation tasks. Despite widespread helmet use, limited research has examined its biomechanical implications in post-concussion rehabilitation settings. This study investigates cervical muscle activation and biomechanical strain responses in helmeted individuals undergoing post-concussion rehabilitation using a mixed-methods approach. Thirty athletes (aged 18–35) with medically diagnosed concussions within the previous six months completed standardized head movements and dynamic stabilization exercises under helmeted and non-helmeted conditions. Quantitative data were collected using surface electromyography (sEMG) to assess muscle activation and strain sensors to measure cervical loading. Additionally, semi-structured interviews with six rehabilitation specialists provided qualitative insights into observed movement strategies and fatigue patterns. Results demonstrated significantly increased sEMG activity in the sternocleidomastoid and upper trapezius muscles during helmeted dynamic stabilization tasks (p < 0.01). Cervical strain analysis revealed elevated shear forces at the C3–C5 spinal levels when helmets were worn. Clinicians consistently reported altered motor control strategies, earlier onset of fatigue, and increased muscular compensation under helmeted conditions. These findings indicate that helmet use meaningfully modifies cervical neuromuscular activation and strain during post-concussion rehabilitation. Incorporating helmet-related biomechanical considerations into rehabilitation planning may improve recovery outcomes and inform safer return-to-play or return-to-duty protocols.
The Role of Machine Learning in Predicting Project Delays
Cost overruns and stakeholder disputes are caused by delays in construction projects. This technique is employed in this research for delay forecasting and remediation of the project's progress through ML and integrated with project scheduling software Primavera P6. ML algorithms can analyze historical project data to find delay patterns before they actually happen. The results presented in this paper demonstrate that the delay prediction using the ML models trained on the Primavera P6 dataset is accurate 83 percent of the time and thus represents a way to enhance project risk management and performance.
AI-Driven Metabolic Engineering for Sustainable Microbial Rubber Production
The increasing global demand for rubber, coupled with environmental concerns associated with conventional plantation-based and petrochemical-derived rubber production, has intensified the search for sustainable and scalable alternatives. The potential of microbial rubber biosynthesis has come up as a viable alternative as it can be controlled, it is less land-dependent, and it may have lesser environmental impact. Nevertheless, the classic methods of microbial engineering are limited in many cases by incomplete knowledge of pathways, low productivity, and the use of time-consuming trial-and-error methods. Recent developments in artificial intelligence (AI), machine learning and computational metabolic engineering have dramatically changed this picture. The predictive modeling of complex metabolic networks, rational strain design and intelligent optimization of genetic and enzymatic pathways involved in the biosynthesis of rubber are made possible by AI-led methodologies. Moreover, the combination of AI and the high-throughput screening platforms and real-time bioprocess control systems has resulted in fast strain selection and enhancing fermentation performance, allowing the process to achieve industrial scalability. The article is a review of AI-based metabolic engineering approach to make microbial rubber production sustainable, encompassing the development of pathway forecasts, multi-omics data-docking, and tailored bioprocess engineering. Such issues as data quality, model interpretability, and scale-up are critically discussed, and new opportunities emerging with explainable AI, autonomous biofoundries, and AI-synthetic biology co-design are discussed. Connecting information technology and biotechnology, AI-based solutions provide a revolutionary solution to create environmentally sustainable, economically feasible, and industrially scalable microbial systems of rubber production.
A Self-Healing Generative AI Framework for Regulated Decision Workflows: A Healthcare Claims Case Study
Regulated enterprise decision workflows, particularly in healthcare claims adjudication, operate under strict requirements for auditability, policy compliance, and operational reliability. Despite increasing adoption of automation and artificial intelligence, most decision pipelines remain vulnerable to data quality issues, policy interpretation errors, and manual exception handling, leading to costly downstream effects such as claim rework and appeal backlogs. Traditional rule-based systems and isolated machine learning models lack the adaptability and contextual reasoning needed to address these challenges in real time. This paper introduces a self-healing generative AI framework designed for regulated decision workflows, combining large language models with workflow state monitoring, policy constraints, and governance controls. The proposed architecture continuously observes decision execution, detects semantic and procedural anomalies, and generates corrective recommendations while preserving full audit trails and human oversight. Rather than replacing existing adjudication logic, the framework augments enterprise workflows with explainable, policy-aware reasoning that enables safe and controlled remediation. A healthcare claims adjudication use case is presented to demonstrate how the framework identifies common processing failures—such as missing documentation, rule misapplication, and classification inconsistencies—and supports faster resolution without compromising regulatory requirements. The paper discusses operational outcomes including improved error containment, reduced manual intervention, and enhanced audit readiness. While evaluated in a healthcare context, the framework is intentionally designed to generalize across other regulated domains, including insurance underwriting, financial decision pipelines, and public-sector benefit administration, highlighting its broader applicability and industry relevance.
Preventing Discriminatory Risk Assessment: A Bias Detection Framework for LLM-Powered Insurance Decision Support
The increasing adoption of large language models (LLMs) in insurance underwriting and risk assessment has introduced new forms of algorithmic bias that are not adequately addressed by traditional fairness evaluation techniques. Unlike conventional predictive models, LLM-powered decision support systems reason over unstructured documentation, policy language, and contextual narratives, creating additional pathways for both direct and proxy-based discrimination. In regulated insurance environments, such bias poses significant ethical, legal, and regulatory risks, particularly when AI systems influence high-impact financial decisions. This paper proposes a bias detection framework for LLM-powered insurance decision support systems designed to prevent discriminatory risk assessment while preserving human oversight and auditability. The framework continuously monitors model interactions and decision context to identify bias signals arising from protected attributes, proxy indicators, documentation asymmetry, and inconsistent reasoning patterns. Bias detection is achieved through a combination of prompt instrumentation, contextual feature analysis, counterfactual evaluation, and policy-aligned constraints that operate alongside existing underwriting workflows. Rather than enabling autonomous decision-making, the framework treats LLMs as assistive reasoning components whose outputs are evaluated for fairness risk before informing human judgment. Representative underwriting use cases demonstrate how the framework surfaces biased reasoning, supports corrective intervention, and reduces downstream risk of unfair outcomes. The results indicate improved transparency, bias containment, and regulatory readiness without compromising operational efficiency. While evaluated in an insurance underwriting context, the proposed framework generalizes to other regulated decision domains where generative AI systems influence consequential human decisions.
The Future of Serverless Architectures in Data Engineering
With every increase in the complexity and volume of data engineering workloads, organizations face highly important architectural choices to make in terms of compute execution environments. The serverless computing platforms and containerized orchestration systems presented by two dominant paradigms provide different favourable measurements in terms of performance, cost, scalability, and operating overheads. Although the concept of a serverless platform features automatic scaling, simplified operations, and a pay-as-you-use business model, the containerized offers predictable performance, control of resources in fine-grained way and it is useful with long-running workloads. This paper compares empirically and data-driven two architectures (serverless and containerized) in terms of data engineering pipelines, namely, ETL workflows, event-driven processing, streaming analytics, and batch jobs. Based on the public cloud benchmark datasets, academic measurement literature, Open Telemetry traces, and published pricing models, the research conducts measurement of latency, throughput, economic efficiency, scalability under burst workload, and the reliability of this operation. The results reveal that serverless systems perform better in bursty and event-driven workloads that have unpredictable demand, but containerized systems have better performance in sustained and high-throughput pipes and resource-intensive workloads. The conclusion of the paper ended with practical recommendations to offer the builders on the best model of execution without violating workload characteristics.
Digital Twins as a Platform: A Reference Architecture for Global R&D
Modern HVAC, water heating, and complex electromechanical systems are increasingly software-defined products operating inside distributed, multi-device ecosystems. However, global R&D workflows remain constrained by the limited availability of physical prototypes, fragmented telemetry models, region-specific SKUs, and long supply-chain cycles. These constraints delay firmware development, algorithm design, system optimization, compliance testing, and predictive analytics. This paper introduces Digital Twins as a Platform (DTaaP), a unified architecture that enables R&D teams across geographies to model, simulate, test, and optimize connected products long before hardware exists. By integrating canonical identity, metadata-driven equipment trees, component/system-level simulation engines, synthetic telemetry generators, and cloud-native pipeline orchestration, we demonstrate how manufacturers can shift from hardware-first to twin-first engineering. The proposed architecture accelerates R&D by 6–12 months, reduces late-stage defects by 30–40%, and standardizes intelligence across regional product lines. It positions digital twins not as project artifacts but as an enterprise-wide capability that harmonizes R&D, factory operations, predictive analytics, and global product strategy.
Agentforce + Data Cloud: The New Era of Real-Time, Adaptive, and Predictive Lightning Applications
The convergence of Salesforce Agent force and Data Cloud represents a paradigm shift in enterprise application architecture, fundamentally redefining how organizations build, deploy, and operationalize intelligent Lightning applications. This paper examines the technical foundations, architectural patterns, and business implications of combining Salesforce's unified data fabric with autonomous AI agent orchestration. Through detailed analysis of zero-copy data architecture, metadata-driven agent frameworks, and real-time inference capabilities, we demonstrate how this integration enables a new generation of adaptive applications that respond dynamically to contextual signals, predict user intent, and autonomously execute complex workflows. Drawing on enterprise use cases across service automation, predictive sales operations, and compliance-driven industries, this research quantifies the measurable impact on operational efficiency, customer experience velocity, and total cost of ownership. We conclude with a forward-looking assessment of AI-native application architectures and their implications for the Salesforce ecosystem through 2030, positioning Agentforce and Data Cloud as foundational elements of autonomous enterprise computing.
5G/6G Integration with Artificial Intelligence for Smart Cities and Autonomous Systems: AI-Enabled Spectrum Management and Sensor Fusion for Urban Mobility and V2X Communications
The high urbanization rate of the contemporary societies has amplified the need of smart, efficient and robust communication structures that could facilitate smart cities and autonomous systems. The 5G and the new 6G wireless networks are envisaged as the primary enablers of such environments, which provide ultra-low latency, massive connectivity and high data rates. Parallel to this, artificial intelligence (AI) has been developed as a disruptive technology of maximizing network functions, spectrum usage, and multi-modal data fusion. In this article, the 5G/6G integration with AI in smart cities and autonomous system is thoroughly reviewed and analyzed with references to the AI-enabled spectrum management and sensor fusion in urban mobility, intelligent traffic system, and vehicle-to-everything (V2X) communications. The most important architectural frameworks, learning paradigms, optimization methods and communication models have been elaborated. In addition, this article raises issues of concern regarding scalability, privacy, reliability, and energy efficiency and forms future research directions of the AI-native 6G smart city ecosystems. The results show that the application of AI to the 5G/6G networks is a fundamental aspect of sustainable, secure, and smart cities.
Beyond Telematics: Leveraging Generative AI for Synthetic Accident Reconstruction and Liability Attribution in Autonomous Vehicle Claims
Autonomous vehicles are changing how we think about car accidents and insurance claims. Traditional telematics systems capture basic data about crashes, but they can't always tell us what really happened or who was at fault. This paper looks at how generative AI models can create detailed accident reconstructions from limited sensor data. We explore methods for building synthetic scenarios that help insurance companies and courts figure out liability when self-driving cars are involved. Our approach combines machine learning with physics-based modeling to generate multiple possible accident sequences. We tested this framework using real-world data from 47 autonomous vehicle incidents reported between January and September 2023. Results show that generative models can produce accurate reconstructions in 82% of cases where traditional methods fail. The system also helps identify gaps in sensor coverage and suggests improvements for future vehicle designs. This matters because someone needs to figure out who pays when a robot crashes your car.
Adjusting Propensity Model Scores During Economic Shifts: A Framework for Short-Term and Long-Term Adaptation
The automotive industry experienced significant disruptions during the COVID-19 pandemic, with consumer behavior undergoing dramatic shifts. Vehicle purchases declined due to economic uncertainties, while service intervals were extended as customers postponed maintenance visits. These changes rendered pre-pandemic propensity models less effective in predicting customer behavior, as they relied on outdated patterns. Rebuilding models with updated data was not feasible in the short term due to limited post-pandemic data. This paper presents a framework to adjust propensity model scores using post-recession priors, ensuring relevance during dynamic periods. The methodology leverages observed response rates to recalibrate predictions, maintaining business utility. Applications in vehicle purchase and service propensity modeling are explored, with short-term strategies for immediate adjustments and long-term strategies to enhance model resilience.
NLP-Driven Sentiment Analysis of Earnings Calls and Its Impact on Stock Volatility
Among the most significant mediums where companies report to investors, analysts and regulating bodies are the earnings calls where companies report their performance in the financial realm, their strategy and future prospects. Unlike regulatory filings which are usually stagnant and very formal, earnings calls are two-way communications, and they contain undertones, language choices, and feelings that might cause serious impacts to the market perception. Recent advances in the field of natural language processing (NLP) have assisted researchers to quantitatively measure such sentiments, offering predictive data as to how the markets would react. The paper will analyze the extent to which the sentiment as it is determined by the earnings call transcripts can predict and explain short-term stock volatility. We train both lexicon-based and transformer-based deep learning models, including FinBERT, to learn sentiment dimensions, including positivity, negativity, uncertainty, and litigious tone. Volatility is measured by realized volatility realized through intraday prices per event of earnings calls. Regression based models and machine learning classifiers are then employed to find predictive relationships. The findings point to the fact that the more sophisticated NLP models are more effective than the methods, which rely on dictionaries, and that uncertainty and negative tones are very closely connected with the volatility. The work has resulted in the area of financial text analytics as it has served to address the gaps in studying the interaction between the analysis of narrative disclosure and the model of market risk and its practical and theoretical implications on the investors, analysts, and policymakers.
Serverless AI Architectures for Scalable and Cost-Efficient E-Commerce Platforms
The contemporary e-commerce landscape necessitates real-time adaptability and massive scalability, driving the migration from restrictive monolithic systems to modern, composable architectures. Serverless computing, particularly Function-as-a-Service (FaaS), provides the foundational paradigm for this transformation, enabling horizontal scaling and consumption-based billing for dynamic workloads. This paper rigorously explores the efficacy of serverless architectures in deploying resource-intensive Artificial Intelligence (AI) and Machine Learning (ML) inference tasks, which are crucial for enhanced customer satisfaction and market competitiveness in e-commerce. Architectural analysis reveals that the core benefit of serverless AI is conditional upon functional decomposition, demonstrating that transforming monolithic ML processes into parallel functions can reduce execution time by over 95% at comparable costs. However, this architectural approach introduces persistent challenges, including critical trade-offs between cost and performance and performance degradation due to cold start latency, particularly for large models (LLMs). Advanced mitigation frameworks, such as TIDAL, address these challenges by reducing cold start latency by 1.79x to 2.11x for GPU-based LLMs. Finally, the report discusses the necessity of specialized security protocols to guard against financial risks unique to serverless systems, such as Denial-of-Wallet (DoW) threats. The synthesized findings establish that serverless AI is an inherently superior model for scalable e-commerce, provided organizations strategically overcome these architectural and operational complexities.
Privacy-Preserving Smart and Secure Contract Solutions for Digital Supply Chain Payments
Digital supply chain payments increasingly rely on automated and distributed platforms, yet existing solutions struggle to balance transparency with the confidentiality required by commercial and financial stakeholders. While blockchain-based smart contracts enable tamper-evident settlement and traceability, they often expose sensitive transaction metadata, contractual terms, and risk indicators, limiting adoption in multi-party supply chain environments. This paper presents a privacy-preserving smart and secure contract framework for digital supply chain payments that separates correctness verification from information disclosure. The proposed model combines a permissioned or consortium blockchain with off-chain encrypted data storage, cryptographic commitment schemes, and zero-knowledge proofs to ensure that payment obligations, milestone fulfillment, and financing conditions can be verified without revealing proprietary business details. Tokenized payment obligations represent invoices and receivables on the ledger, while milestone-based smart contracts coordinate delivery confirmation, early financing, dispute resolution, and settlement. Sensitive financial data and documents remain off-chain, anchored to the ledger only through hashes, commitments, and succinct proofs. Optional confidential computing components further enable secure evaluation of dynamic pricing or credit logic. A comprehensive security analysis demonstrates resistance to unauthorized state modification, double financing, insider misuse, and inference attacks under both honest-but-curious and malicious adversary models. Performance evaluation shows that the computational and communication overhead introduced by privacy-preserving mechanisms remains practical for real-world supply chain payment workflows, with low latency, efficient storage growth, and scalable operation across multi-tier ecosystems. The results indicate that the proposed framework provides a viable foundation for secure, privacy-aware, and auditable digital supply chain finance.
Synthetic Data Generation for Validation of Clinical Research Software
Validation of clinical research software is limited by the lack of realistic, privacy-safe datasets capable of exercising complex protocol and workflow logic. This study proposes a hybrid synthetic-data generation framework that combines deterministic clinical simulation, deep generative modeling, and differential privacy to create statistically faithful and audit-traceable datasets tailored for DCT and eCOA validation. In a Phase-II–scale evaluation, the approach achieved 38% higher defect detection, JS divergence = 0.054, and membership-inference AUC ≈ 0.52, demonstrating that synthetic data can support scalable, privacy-preserving, and empirically rigorous validation of regulated clinical-research platforms.
Electronic Health Records (EHR) Simulation
Comparative Study of Three Artificial Intelligence Algorithms for the Prediction and Classification of Cardiovascular Diseases from ECG Signals: Application to Chadian Patients
In this paper, a comparative study of three artificial intelligence algorithms: Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), and Logistic Regression (LR) is conducted for the prediction and classification of cardiovascular diseases (CVD) based on electrocardiogram (ECG) data from Chadian patients.The experimental analysis relies on a dataset of 1,025 patients, comprising 14 clinical and electrocardiographic attributes, collected from local hospital databases. Model performance was evaluated using standard metrics such as accuracy, precision, recall (sensitivity), and F1-score. The results demonstrate that the ANN model outperforms the other approaches, achieving an accuracy of 99.6%, a precision of 100%, a recall of 99.4%, and an F1-score of 99.6%, compared to slightly lower performance obtained with the KNN and LR models. These findings confirm the strong potential of artificial neural networks for ECG signal analysis and decision support in the diagnosis of cardiovascular diseases. In the Chadian context, characterized by limited medical and technological resources, this approach represents a promising solution for early detection of CVD and improved patient management. However, further studies are required to validate the robustness of the proposed model on larger datasets and to integrate additional clinical risk factors.
AI-Driven Reusable Unified Extract for Multi-State Medicaid and Federal Reporting- a Product that saves Millions of Taxpayer Money through process efficiency and reusability
The state and federal government policy developers of the United States are continuously exploring ways to enhance the strength of administering Medicaid facilities. Artificial intelligence (AI) is under research for implementation to offer these facilities to stakeholders, based on insights into security and the benefits of integrated data management. These processes involve different patient data dimensions, including insurance, medical history, diagnostic records, and other electronic healthcare records. The services. This technology enhances capabilities to support healthcare processes through AI-driven content utilization frameworks. These allow optimization of MEDICAID content extraction at the national level, with integrity, to create esteem extracts/ reports. Increasing the automation of complex content flows and enabling effective use of the content across various functions. This paper presents a literature review and reports on an AI-powered, reusable data processing framework for generating accurate reports on Medicaid services at the state and federal levels. This follows recommendations to increase processing esteem through these advancements
Agentforce Unleashed: How AI Agents Will Automate 70% of Salesforce Development Tasks
The Salesforce ecosystem stands at the precipice of a transformational shift as Agentforce Salesforce's AI-native agent platform emerges to fundamentally reshape how enterprises design, build, and maintain CRM systems. This comprehensive research article examines how autonomous AI agents will automate an estimated 70% of traditional Salesforce development tasks by 2027, representing a paradigm shift from manual, code-heavy implementations to intelligent, self-optimizing systems. Drawing on architectural analysis, market projections, and enterprise use cases, this paper explores the technical foundations of Agentforce, its integration with Data Cloud and Einstein AI, and the implications for developer productivity, enterprise agility, and total cost of ownership. We analyze how requirement gathering, user story creation, Flow building, Apex code generation, Lightning Web Component development, testing, deployment, and continuous optimization will transition from human-driven processes to AI-orchestrated workflows. The research further examines architectural patterns, governance frameworks, risk mitigation strategies, and the trajectory toward a fully AI-native Salesforce platform by 2030. For CTOs, architects, and technology leaders, this analysis provides strategic insights into preparing organizations for an era where AI agents become the primary workforce in platform development and maintenance
Bridging Data Science and Compliance Intelligent AML Systems by Design: Domain-Aware Machine Learning
The rapid increase in global financial transactions has made the old, rules based, Anti-Money laundering (AML) systems ineffective and efficient. Such systems produce too many false positives, are expensive to operate, and fail to keep pace with more advanced and progressive typologies of crime. The paper suggests an innovative architecture of the next generation AML systems by integrating the powerful machine learning (ML) with deep compliance knowledge domain. We support a domain-sensitive strategy in which the development of the ML model, which includes feature engineering and natural language processing (NLP), pattern recognition and anomaly detection, is domain-directed, meaning that it is driven by the knowledge in the financial crime field, regulatory reporting, and operational specificity of investment banking. The paper examines the technical architectures that combine unsupervised learning to detect anomalies, supervised models to detect typology, and NLP to generate alert narrative automatically and analysis of unstructured data. More importantly, it deals with the extremely important issues of model explainability and adversarial robustness and the combination of both with the current compliance processes in order to make them regulatory-acceptable and effective. This paradigm will increase the precision of detection, minimize the workload on investigators, and bring a more intelligent and lively approach to financial crime defense through the combination of the technical profundity of data science with the fine-tuning of compliance logic
A Scalable Enterprise Framework for AI-Driven Invoice Processing Using Document Intelligence
Invoice processing remains a critical yet labor-intensive back-office function in many organizations, even those that have adopted modern ERP platforms. Manual validation of heterogeneous invoices introduces delays, errors, and scalability constraints, particularly during volume spikes such as quarter-end or year-end. This paper presents a scalable, enterprise-grade framework for automating invoice processing using Document AI (DocAI). The proposed architecture combines optical character recognition (OCR), natural language processing (NLP), and machine learning (ML) with a confidence-driven, human-in-the-loop workflow and robust ERP integration. Unlike many prior works that focus primarily on algorithms or small, static datasets, this work reports on the design, deployment, and evaluation of a production system implemented in a live finance environment. The framework supports multi-format ingestion, modular AI components, and elastic scaling to handle monthly invoices without service degradation. In the evaluated deployment, the system reduced end-to-end processing time by improving field-level extraction accuracy and decreasing the human review rate after iterative retraining. The paper details the system architecture, design decisions, implementation methodology, and operational results, and concludes with lessons learned and future directions for extending document intelligence across related financial processes
Vision Transformers (ViT) for Small-Scale Image Classification with Token Reduction
Convolutional Neural Networks (CNNs) are no longer considered a superior choice in image classification over Vision Transformers (ViTs), which have shown to be highly effective on a large scale because they can exploit self-attention aspects in capturing long-range dependencies. Nevertheless, in small datasets of images like CIFAR-10, CIFAR-100 and SVHN, standard ViTs are usually inefficient in data usage, expensive to run, and redundant in token representation. This weakness is due to the fact that ViT requires large training corpora and its self-attention has a quadratic complexity with regard to the number of tokens. In order to overcome these issues, the initial studies suggested token-cutting techniques such as pruning, pooling, and a combination of CNN and ViT networks to reduce tokens on the input side and maintain the needed visual information. These ways seek to reduce overfitting and increase computation efficiency and generalization at low-data regimes. This review gives a thorough analysis of Vision Transformers in small-scale image classification with regard to developments. The paper provides an overview of ViTs development, their drawbacks when working with small datasets, an overview of initial token reduction methods, and their performance on a variety of benchmark tasks. Its discussion reflects both advantages and disadvantages of token reduction and provides the future research prospects. The article is a reference material to scholars conducting an investigation on effective transformer-based models specialized to small-scale image categorization tasks
AI-Powered ETL Automation for Compliant Data Migration
Data governance poses an increasingly complex challenge for organizations that operate on multiple heterogeneous systems, including various clouds, to ensure compliance with numerous regulations, like GDPR or HIPAA. Whereas traditional approaches to ETL (Extract–Transform–Load) have problems with manual schema mapping, weak transformation logic, and traceability, AutoETL-C provides a solution to these problems with a suggested depreciation of ETL, as it incorporates AI to deliver an automated and policy-compliant ETL. The AI to automate ETL creates a more seamless experience for users, combining (i IJAIBDCMS-V6I4P116 neural schema matching via domain-adapted language models, (ii) hybrid transformation through rules and learning, automated constraint discovery, and invasive trace law, (iii) data quality monitoring with continuous anomaly detection, (iv) and full-coverage lineage capture, including all governance and compliance limited controls. AutoETL-C was tested on three migration scenarios—core-to-lakehouse for banking, EHR modernization for healthcare, and ERP-to-warehouse for retail—public and synthetic datasets as well as anonymized enterprise data were used. AutoETL-C outperformed its manually configured ETL, template-based ETL and ML-assisted mapping, setting a new industry standard while proving compliance with Shelby Control Model documentation, HIPAA Security Rule controls, and data maps. Overall, results suggest that migration risk and time-to-value can be substantially improved with augmented, privacy-aware data engineering
End-to-End Visibility in Cloud Deployments: Building Real-Time Program Health Systems
Cloud-native applications now run across distributed services, containers, and serverless functions, each emitting its own logs, metrics, traces, and events. While modern observability tools collect these signals effectively, they tend to process them in isolation, leaving engineers to manually correlate symptoms during incidents. This fragmentation slows detection, clouds root-cause analysis, and weakens real-time understanding of program health. This paper introduces the Real-Time Program Health (RTPH) Framework, a multi-layer model that unifies telemetry ingestion, real-time stream processing, machine-learning-based anomaly detection, and health scoring into a single, interpretable view of system behavior. RTPH is evaluated in a hybrid cloud environment running microservice workloads on Kubernetes, with synthetic faults injected under controlled conditions. Its performance is compared against established observability stacks that include metrics, logging, and tracing tools. Experimental results show that RTPH reduces anomaly detection latency by 32–45%, lowers false-positive alerts by 28–40%, and correctly correlates 87–93% of cross-service anomalies, while keeping CPU and memory overhead below 8% and 6% per node, respectively. These findings indicate that unified, real-time health modeling can provide more accurate, actionable visibility into cloud deployments than traditional, signal-specific monitoring approaches
Priority Queues in Large-Scale Distributed Systems under Heavy Workloads
Large-scale distributed systems often face periods of heavy workloads where a surge in tasks can overwhelm processing capacity. In such scenarios, critical operations risk being delayed as they compete with a backlog of routine tasks. Priority queues offer a solution by ensuring that high-priority work is serviced ahead of less critical tasks, thereby preserving system responsiveness and meeting service-level objectives. This paper examines the importance of priority queuing in distributed systems handling massive workloads, highlighting how the Priority Queue pattern can prevent important tasks (such as monitoring queries or high-value data updates) from being starved of resources. We discuss real-world scenarios—including an overloaded database where monitoring queries get stuck, and a catalog processing pipeline where urgent updates languish behind bulk jobs—and illustrate design patterns that mitigate these issues. We explore implementations ranging from cloud infrastructure (e.g., AWS SQS and workload management) to industry-scale solutions like Facebook’s FOQS distributed priority queue. Through diagrams and case studies, we show how priority-based task scheduling improves observability, data freshness, and overall system reliability under heavy load. The paper provides an academic discussion in a formal tone, supported by references to established patterns and recent industry and research insights
Unified Event-Driven Architecture across AWS and Azure: Leveraging Kafka + Confluent for Real-Time Enterprise Intelligence
Modern enterprise intelligence demands a real-time, unified data plane that abstracts cloud-specific messaging and security complexities. This paper details the implementation of a Unified Event-Driven Architecture (UEDA) using Apache Kafka and Confluent Cluster Linking to bridge AWS and Azure environments. We analyze how this architecture overcomes multi-cloud fragmentation by enforcing centralized data contracts via the Schema Registry, aligning security policies through centralized role-based authorization across components using RBAC (environment-scoped), and achieving high availability through offset-preserving cluster replication. Furthermore, we provide prescriptive guidance on cost optimization, focusing on strategies to minimize cloud egress charges, and analyze the critical tuning levers required to meet stringent Recovery Time Objective (RTO) and Recovery Point Objective (RPO) targets. The UEDA establishes a resilient, scalable foundation for complex cross-cloud streaming applications and future integration with Edge computing and AI/ML initiatives
Enterprise-Scale PII De-Identification with Microsoft Presidio Anonymizer: Architecture, Use Cases, and Best Practices
Stricter privacy regulations and the rapid adoption of AI and analytics have increased the need for robust, repeatable mechanisms to detect and de-identify personally identifiable information (PII) across heterogeneous data sources. Microsoft Presidio is an open-source framework that provides context-aware PII detection and anonymization for text, images, and other modalities. This paper presents a practical architecture and implementation blueprint for enterprise-scale PII de-identification using the Presidio Anonymizer. We describe patterns for anonymizing production logs and telemetry, constructing privacy-preserving datasets for machine learning and large language models (LLMs), enabling safe data sharing with vendors, supporting non-production environments, meeting regulatory requirements (GDPR, HIPAA, PCI, and others), protecting data sent to LLMs and SaaS tools, and redacting PII in documents and images. For each use case, we outline threat models, design decisions, operator choices, and integration patterns with modern data and AI stacks. We also discuss operational considerations such as performance, extensibility, reversibility, and governance, making this a reusable reference for large organizations and a concrete demonstration of technical leadership in privacy-by-design systems
AI/ML Powered Intelligent Root Cause Analysis and Automated Remediation for Multi System Data Integrity Issues
Enterprises increasingly rely on complex data ecosystems that span operational databases, event streams, microservices, batch ETL workflows, data warehouses and analytics products. In this environment data integrity incidents rarely originate from a single component. They emerge from interacting failure modes such as schema drift, late or duplicated events, inconsistent reference data, partial writes, replay defects and misaligned transformation logic. These incidents propagate across systems and can silently corrupt business reporting, customer experiences and compliance artifacts. While observability has improved visibility, diagnosis and recovery still depend heavily on human expertise and manual correlation across logs, traces, metrics and change histories. This paper proposes a unified architecture for AI and ML powered intelligent root cause analysis and automated remediation that is tailored for multi system data integrity issues. The approach combines contract based data quality checks grounded in established data quality dimensions [1], pipeline quality taxonomies and root causes mined from developer evidence [5], provenance and lineage reasoning for debugging and trust [2][4], dependency aware localization adapted from microservice RCA [9][10] and incident knowledge retrieval from real cloud incident investigations [11]. A remediation framework applies graduated autonomy and policy gating inspired by agentic AIOps architectures [13] while using large language models only for evidence synthesis and plan drafting under strict guardrails [12]. We describe incident taxonomy, graph based RCA method, remediation catalog design and evaluation metrics for detection, localization and recovery. The proposed system reduces mean time to detection and mean time to recovery while improving auditability and safety of corrective actions in enterprise data platforms
AI Report - Federated AIOps for Multi-Cluster OpenShift
Multi-cluster OpenShift systems produce large volumes of data, containing as many as 400-000 metrics and 200GB of logs each day per cluster, overwhelming operations groups. This study shows that Mean Time to Detect (MTTD) decreases by 40-60%, Mean Time to Resolve (MTTR) reduces by 25-55%, and the number of alerts drops by 70% when Federated AIOps is integrated. With the support of a federated AIOps model on OpenShift RHACM, Prometheus, OpenTelemetry, and a cross-cluster machine learning control plane, this research proposes an inferential approach of automation when passing anomaly detection and root cause analysis (RCA) across geographically separated clusters. The study employed data on six clusters, including AWS, Azure, and on-prem systems, 600 + nodes, 12,000 + pods, and 350 microservices. The findings indicate that Federated AIOps had a 94.3% accuracy in data correlations, false-positive alerts to less than 6 occurrences, and less than 2.5% of CPU overhead per node. Federated AIOps can reduce alert duplication and optimize incident management, which provides an open-source alternative to official AIOps platforms that makes it a low-cost, scalable product in the large-scale setting. This study identifies the practical effect of Federated AIOps in improving operational efficiency, resource utilization, and adherence to data privacy standards and generates significant cost reduction in the multi-cluster OpenShift administration
Intelligent Continuous Integration and Delivery for Banking Systems using Machine Learning Driven Risk Detection with Real World Deployment Evaluation
The pressures to keep this system reliable while increasing the software release cycles have never been greater in the banking sector. This study explores the incorporation of risk detection mechanisms utilizing machine learning into CI/CD pipelines for banking systems. It focuses on how deployment safety for financial institutions can be improved and operational risks reduced via balance using various artificial intelligence techniques as it addresses the question that concerns authorities and the general public: due to the fact that financial institutions are subject to highly regulated and risk-averse environments, how can regulatory compliance be enforced and achieved with the use of artificial intelligence? We used a mixed-method approach integrating a quantitative analysis of deployment metrics and qualitative analysis of risk patterns across multiple banks. This hypothesis stated that machine learning methods should be able to use little to no deployment failures and should be less vulnerable to security issues than traditional methods. In results, there was a 67% reduction in production incidents, 82% increase in risk prediction accuracy, and 45% improvement in deployment cycles. The ML-driven risk scores were statistically associated with the actual deployment outcomes. These results demonstrate that smart CI/CD systems can significantly improve banking workflows without compromising on code security. This research provides valuable frameworks for deploying AI-driven deployment strategies at scale at what framework(AI) in regulated financial environments as well as providing insights for digital transformation initiatives