Category Archives: Uncategorized

Authors: Daniel Sørensen, Hiroshi Nakamura, Dr. Matteo Rinaldi, Elena Petrova, Ananya Kulkarni

Abstract: Large organizational information systems were historically designed to record and process structured transactions across business functions such as finance, supply chain, and human resources. While these systems ensured operational consistency and data reliability, their architecture primarily focused on transaction processing rather than continuous intelligence generation. Growing data volumes, distributed digital infrastructures, and the need for rapid decision making now require ERP environments to evolve beyond batch reporting and static analytics. This research presents an engineering framework for transforming transaction oriented ERP systems into data centric intelligence ecosystems through the integration of streaming analytics, DevOps automation, and predictive modeling. The proposed architecture enables continuous data ingestion, real time analytics pipelines, automated deployment of analytical services, and embedded predictive intelligence capable of supporting proactive operational decisions. By integrating data engineering principles with scalable analytics infrastructures, the framework demonstrates how operational data streams can be converted into actionable insights that improve forecasting accuracy, operational efficiency, and organizational responsiveness. The study contributes a unified approach for designing ERP ecosystems that support both reliable transaction processing and continuous intelligence generation within complex digital enterprises.

DOI: https://doi.org/10.5281/zenodo.19105020

Published by: vikaspatanker

Authors: Dr. Matteo Rinaldi, Hiroshi Nakamura, Elena Petrova, Daniel Sørensen, Ananya Kulkarni

Abstract: This paper explores how machine learning–driven query optimization can elevate the performance, scalability, and operational resilience of SQL and NoSQL database systems deployed in high-volume financial and healthcare environments. Conventional rule-based and cost-based optimizers frequently encounter limitations when confronted with volatile workloads, uneven data distributions, and rapidly shifting access behaviors that define contemporary transaction processing and clinical data infrastructures. The central inquiry of this study examines whether adaptive, data-aware optimization models—trained on historical execution traces, telemetry signals, and workload metadata—can deliver superior efficiency and stability in such dynamic contexts. The research employs a blended methodological approach that integrates architectural framework design, algorithmic prototyping, and comparative benchmarking across representative relational and non-relational database platforms operating under large-scale transactional and analytical loads. Empirical evaluation indicates that learning-enabled optimizers meaningfully lower query response times, improve compute and memory utilization, and enhance predictability during peak data surges when compared to traditional strategies. Core contributions include the development of predictive cost estimation models, context-aware index adaptation mechanisms, and real-time execution plan adjustments powered by supervised and reinforcement learning paradigms. Collectively, the study advances the theoretical foundations of intelligent data management by embedding adaptive learning into optimization workflows, while offering practical guidance for engineering robust, high-throughput database infrastructures capable of sustaining accuracy, compliance, and responsiveness in mission-critical financial and healthcare systems.

DOI: https://doi.org/10.5281/zenodo.19104981

Published by: vikaspatanker

Authors: Sebastian Moreau, Yuki Matsumoto, Adrian Kovalenko, Matteo Ricci, Ananya Kulkarni

Abstract: Digital transformation in human capital management has created complex, distributed ecosystems in which employee data originates from connected devices, cloud platforms, transactional systems, and external intelligence services. Fragmented architectures limit the ability to sense patterns, contextualize signals, and coordinate timely action across SAP SuccessFactors and heterogeneous cloud HCM landscapes. This study introduces a digital nervous system architecture that integrates Internet of Things telemetry, scalable big data infrastructures, and artificial intelligence driven cognition into a unified sensing and response framework. The proposed model organizes system design into sensing layers for real time signal acquisition, transmission layers for streaming and synchronization, cognitive layers for predictive and prescriptive analytics, and response layers for coordinated orchestration across talent, payroll, performance, and compliance domains. A formal Enterprise Signal Latency Index is developed to quantify responsiveness across distributed platforms, alongside a Neural Stability Metric that measures adaptive coherence within the integrated HCM ecosystem. Through architectural modeling and scenario based evaluation, the research demonstrates reductions in signal propagation delay, improved anomaly detection accuracy, enhanced decision synchronization across platforms, and strengthened systemic resilience. The findings establish a scalable blueprint for constructing intelligent, continuously learning digital infrastructures that unify IoT, big data, and artificial intelligence within multi cloud human capital environments.

DOI: https://doi.org/10.5281/zenodo.19104930

Published by: vikaspatanker

Authors: Camila Duarte, Romain Delacroix, Arjun Mehta, Tobias Lindgren, Ananya Kulkarni

Abstract: Workforce systems are increasingly expected to operate as adaptive intelligence infrastructures rather than static repositories of employee transactions. Conventional ERP based human capital platforms were engineered to ensure data accuracy, compliance integrity, and standardized administrative workflows; however, their batch oriented processing logic and retrospective analytics limit organizational capacity to detect emerging workforce risks and coordinate timely interventions. This study proposes an event-driven, machine learning enabled ERP architecture that transforms human capital systems into continuously responsive ecosystems capable of real time sensing, predictive modeling, and automated orchestration. The framework integrates streaming event pipelines, dynamic feature engineering, embedded predictive and prescriptive models, and governance aligned control mechanisms within a unified enterprise environment. A novel Continuous Workforce Optimization Index is introduced to quantify adaptive stability across engagement momentum, performance variability, capacity distribution, and compliance consistency. Through architectural modeling and scenario based simulation, the research demonstrates measurable reductions in decision latency, improvements in prediction accuracy, enhanced intervention precision, and strengthened systemic resilience when compared with traditional batch driven ERP configurations. The findings establish a scalable blueprint for designing next generation human capital architectures that enable sustained, continuous workforce optimization in complex and rapidly evolving enterprise contexts.

DOI: https://doi.org/10.5281/zenodo.19104880

Published by: vikaspatanker

Authors: Ramani Teegala

Abstract: This research examines the growing need for structured, reliable, and productivity focused engineering environments during a period when organizations were transitioning from traditional development models to more collaborative and tool integrated ecosystems. It investigates the fragmentation of workflows, uneven tool adoption, and the absence of standardized development practices that often constrained Java and Spring engineers, resulting in delivery delays, quality inconsistencies, and extended onboarding cycles. The research aims to establish a systematic approach for designing and implementing developer experience platforms that unify development workflows, automate repetitive tasks, and promote consistency across large engineering teams. Using a mixed methods approach that integrates qualitative interviews with engineering leaders and quantitative analysis of productivity indicators, the study identifies platform capabilities that most effectively enhance developer outcomes, including integrated build automation, standardized project templates, curated toolchains, and centralized knowledge resources. The findings show that well structured developer experience platforms reduce cognitive load, improve code quality, and accelerate delivery cycles while strengthening collaboration and engineering confidence. Academically, the work contributes a structured model for evaluating developer experience within Java and Spring ecosystems and provides a foundational basis for future research in platform centric software engineering. Strategically, it demonstrates how organizations can leverage such platforms to modernize engineering culture, reinforce architectural alignment, and achieve sustainable delivery performance. The study concludes that formalizing developer experience as an operational discipline holds long term significance for both industrial practice and academic advancement.

DOI: https://doi.org/10.5281/zenodo.19100556

Published by: vikaspatanker

Authors: Ramani Teegala

Abstract: By April 2021, software quality engineering was under increasing pressure from the combined effects of accelerated release cadences, widespread adoption of microservices, and the operational realities of cloud-native deployments. Banking and other regulated industries faced a particularly acute version of this tension: delivery speed had become a competitive requirement, yet failures carried outsized consequences in customer harm, financial loss, and regulatory exposure. In this context, conventional quality practices such as manual test authoring, rule-based static analysis, and human-driven code review remained necessary but frequently insufficient to scale with system complexity. The problem was not that these practices were ineffective in principle, but that they depended heavily on human attention and stable system boundaries, both of which were increasingly scarce in modern delivery pipelines. AI-augmented software quality refers to the application of machine learning and statistical techniques to improve the effectiveness, coverage, and timeliness of quality controls across the software lifecycle. Unlike general automation, which executes predefined checks, AI-augmentation aims to learn from historical artifacts such as defects, test outcomes, telemetry, and code change patterns in order to anticipate risk and prioritize interventions. By 2021, the software engineering community had accumulated substantial research and industry experience in areas such as defect prediction, anomaly detection in operational metrics, automated test prioritization, and mining software repositories. These approaches did not eliminate the need for engineering judgment or rigorous testing, but they offered a way to focus limited quality effort on the changes, components, and execution paths most likely to fail.Within regulated domains, AI-augmentation for quality must be evaluated through constraints that are distinct from those of consumer software. Quality signals and decisions often need to be explainable, auditable, and reproducible, especially when they influence production readiness, control effectiveness, or incident management. Data used for training and inference can include sensitive operational and development artifacts, requiring governance controls comparable to those used for security and compliance data. Moreover, quality failures in financial systems tend to cluster around concurrency, distributed consistency, configuration drift, and integration boundaries, meaning that a quality system must reason not only about code-level correctness but also about system-level behavior under partial failure. AI methods applied without regard to these constraints risk producing brittle signals that cannot be operationalized, trusted, or defended during audit and post-incident review. This paper examines AI-augmented software quality as understood and practiced by April 2021, with particular attention to how such techniques integrate into modern delivery pipelines and operational feedback loops. It synthesizes research in software analytics, defect prediction, test optimization, and anomaly detection, and it situates these methods within the architectural trends that shaped software systems from 2000 through 2021. The paper proposes a conceptual model in which AI-driven signals complement, rather than replace, established quality controls, and it describes a layered architecture that connects development artifacts, CI/CD execution data, and production telemetry into a cohesive quality intelligence capability. Special attention is given to the interactions between AI-generated quality signals and governance requirements common in regulated environments, including traceability, change control, and evidence preservation. The analysis further explores the practical trade-offs associated with AI-augmented quality, including data quality and labeling challenges, feedback delays, model drift under frequent system change, and the risk of embedding organizational biases into automated decision-making. It evaluates these challenges alongside potential benefits such as earlier risk detection, more efficient test allocation, and improved incident prevention. By framing AI-augmentation as an engineering discipline grounded in measurable outcomes and controlled deployment practices, the paper aims to provide a historically accurate and technically rigorous account of how machine learning techniques can strengthen software quality programs as of April 2021, without relying on later generative AI developments or post-2021 tooling assumptions.

DOI: https://doi.org/10.5281/zenodo.19100296

Published by: vikaspatanker

Authors: Ramani Teegala

Abstract: By December 2021, financial institutions were operating transaction platforms whose end to end behavior increasingly resembled distributed journeys rather than single system events. A single customer initiated action, such as a card purchase, an account to account transfer, or a cross border remittance, could traverse channels, risk engines, limits services, payment rails, settlement systems, dispute workflows, and compliance controls across both internal and external counterparties. This fragmentation created persistent challenges in observability, auditability, and root cause analysis because the underlying data was split across event logs, relational ledgers, message queues, fraud features, and case management systems, each with different identifiers and retention policies. Knowledge graphs matured as a practical representation for integrating heterogeneous entities and relationships, enabling banks to model accounts, customers, devices, merchants, authorizations, postings, reversals, chargebacks, and compliance decisions as a coherent linked structure. In parallel, vector similarity search and embedding based retrieval became increasingly accessible due to open source libraries and emerging vector store implementations, providing a complementary mechanism for approximate matching over high dimensional representations of transactions, sequences, and behavioral signatures. This paper examines how knowledge graphs and vector stores can be combined to represent and analyze financial transaction journeys as understood and practicable by December 2021. The analysis frames the problem through regulated banking constraints, including PCI DSS requirements for cardholder data protection, GLBA expectations for safeguarding customer information, SOX oriented control evidence, Basel Committee guidance on operational risk, and FFIEC style expectations for resilient operations and audit readiness. The paper proposes a conceptual model in which a graph centric system of record captures identity resolution and explicit relationships, while a vector retrieval layer supports similarity based enrichment, anomaly surfacing, and candidate linking for incomplete or ambiguous journey traces. It evaluates architectural trade offs related to consistency, latency, governance, and explainability, emphasizing that approximate methods must be bounded by deterministic controls when outcomes influence fraud actions, customer impact, or regulatory reporting.

DOI: https://doi.org/10.5281/zenodo.19100103

Published by: vikaspatanker

Authors: Swathi.G, Ch. Keerthana, A.Tarun Teja Charry, B.Lokesh Nagavenkata Sai

Abstract: The fast development of integrated circuits, Synchronous and Asynchronous first input first output, or FIFO, is widely used to solve the problem of data transmission across the clock domain. An important problem with asynchronous FIFO architecture is the generation of empty-full signals, which is the subject of this paper. Achieving signal synchronization across clock domains and converting binary code into Gray code are crucial in reducing the probability of a metastable state. Due to the greatest performance, thrills, and medium end for a large market, as well as the versatility of applications. as a basic foundation for memory. In FPGA-based projects, the FIFO is frequently utilized. However, the issue of inadequate memory despite the aggregate capacity is frequently sufficient occurs in the implementation of multi-channel FIFO due to chip resources and flaws in development tools. This paper implemented the Synchronous and Asynchronous FIFO applications and proposes the use of FIFO in System-on chip memory. These simulations are typically verified using Verilog HDL test benches that generate random data, varying write/read speeds, and asserting boundary conditions, confirming the FIFO's ability to maintain data integrity.

Published by: vikaspatanker

Authors: Pratikbhai Patel

Abstract: In this research paper, the proposed link budget framework is an elaborate link budget analysis framework of a next-generation Low Earth Orbit (LEO) satellite constellations to facilitate seamless transcontinental communications. The paper examines the technical needs required to support high-availability broadband coverage on Earth in dynamic orbital and atmospheric conditions. It combines the free-space path loss models, rain fade models, atmospheric attenuation models, orbital mechanics, adaptive modulation models, optical inter-satellite links integration, and interference resilience in an International Telecommunication Union (ITU)-compatible framework. The results indicate that dynamic environmental modeling, dynamic transmission methods as well as propulsion-enhanced orbital stability are important in ensuring that link margins are consistent in geographically dispersed areas. The study also gives prominence to the need to incorporate climate sensitive attenuation forecasting, spectrum agility, and security-oriented interference mitigation in order to make the system more robust. The paper concludes with the discovery that the next-generation LEO constellations have the potential to scale to low-latency and resilient transcontinental connectivity in case it is backed by an integrated and dynamic link budget design methodology. This framework offers a technically rigorous basis to satellite communication systems in the future in the whole world.

DOI: https://doi.org/10.5281/zenodo.19093210

Published by: vikaspatanker

Authors: Sachin Kumar

Abstract: Programming languages are essential tools for developing software and applications. They are generally classified based on their level of abstraction and programming paradigm. Procedural and high-level programming languages represent two important categories in computer science education and practice. This research paper presents an analysis of C, a procedural programming language, and Python, a high-level programming language. The paper explains their basic concepts, features, execution models, memory management techniques, advantages, limitations, and application areas. The objective of this study is to help students and beginners understand the fundamental differences between procedural and high-level languages through the comparison of C and Python, enabling them to select an appropriate language based on learning and application requirements.

DOI: https://doi.org/10.5281/zenodo.19091567

 

Published by: vikaspatanker