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

Authors: Sachin Kumar

Abstract: The Dual Role of Artificial Intelligence in Cyber Security: From Automated Defense to Adversarial Exploitation Abstract The rapid integration of Artificial Intelligence (AI) into the digital landscape has fundamentally transformed the field of cyber security. This paper examines the bidirectional impact of AI: its role as a powerful defensive mechanism capable of real-time threat detection and response, and its emergence as a sophisticated tool for adversarial exploitation. By analyzing Machine Learning (ML) models in intrusion detection, the rise of "Agentic" autonomous security systems, and the threats posed by adversarial ML and deepfakes, this study proposes a framework for AI-resilient security operations. The research concludes that while AI significantly enhances defensive capabilities, it also necessitates a new era of proactive, adaptive security strategies to counter AI-driven threats.

DOI: https://doi.org/10.5281/zenodo.19091390

 

Published by: vikaspatanker

Authors: Sachin Kumar

Abstract: Hardening the Core: Strategic Defense-in-Depth for Windows-Based Domain Controllers Abstract In the modern enterprise landscape, the Active Directory (AD) infrastructure and its constituent Domain Controllers (DCs) represent the "crown jewels" of organizational identity and access management. As the central repository for user credentials, group policies, and authorization data, a compromised Domain Controller grants an adversary virtually unlimited "keys to the kingdom." This paper provides a comprehensive analysis of the threat landscape targeting Windows-based Domain Controllers and proposes a robust, multi-layered defense-in-depth framework. By integrating administrative isolation, host-level hardening, network segmentation, and advanced monitoring, organizations can significantly reduce the attack surface. The study concludes with a strategic roadmap for implementing these defenses without compromising the high availability required for critical identity services.

DOI: https://doi.org/10.5281/zenodo.19091242

 

Published by: vikaspatanker

Authors: Ragul. M, Amna Saliha P I K, Dr. K. Brindha

Abstract: Digital health data grows fast. From patient files to scans, genes, fitness trackers, and billing logs – each piece adds up quick. Not just more information – but faster flows, messier formats. Yet within that chaos sit chances to do things differently. Hidden patterns start showing when tools can keep pace. Big data analytics steps into that role. Instead of static reports, it offers insights that shift as new facts arrive. Systems built on platforms like Hadoop or Spark handle loads regular software cannot. Cloud storage keeps the doors open for constant updates. Machine learning digs through noise to spot trends. Deep learning maps complex relationships in images or signals. Language parsers decode doctor notes once locked in freeform text. Five areas see clear change. One: guessing illness before symptoms show. Two: guiding long-term conditions day by day. Three: smoothing how hospitals run – from beds to staff shifts. Four: tracking drug effects after release. Five: treatments shaped around individual biology. Evidence comes from sifting 112 studies published between 2015 and 2024. Patterns emerge only when scale meets smart design. Raw power alone does nothing. It takes thoughtful layers – a stack where speed, structure, and smarts connect. Tests on standard collections like MIMIC-III, NIH Chest X-Ray, and eICU show accuracy between 87.6% and 94.1% for core predictions. Yet problems remain – privacy concerns linger just as much as biased models do. Different systems still struggle to work together while rules keep shifting. On top of that, new paths are forming: shared learning setups pop up alongside tools making AI clearer and analysis at the device level grows more common. For those working in health data, science, or hospital operations, this piece lays out how to grasp, judge, fit in big data methods where things never stay simple.

DOI: https://doi.org/10.5281/zenodo.19091089

 

Published by: vikaspatanker

Authors: Vikasini E, Daniya U, Mr.P. Jayasheelan, Guide Dr.P.Jayasheelan

Abstract: Paper registers and card systems for taking student and employee attendance are slow and full of mistakes. People can fake entries. Proxy marking is easy. Schools and workplaces need something more reliable and automatic to track who shows up. So we built an AI-powered smart attendance management system that uses facial recognition to record attendance in real time. The system is written in Python and uses OpenCV and the face recognition library. A SQLite database stores the structured data. A camera-enabled desktop app captures facial images. It matches people against a pre-registered face database and logs attendance with timestamps. No manual data entry. No easy way to mark attendance for someone else. The graphical interface uses Tkinter. Admins can manage records and run reports. They can also view attendance history. Tests show the system reaches high recognition accuracy under controlled lighting. It also cuts down administrative work a lot. This research shows how artificial intelligence and computer vision can be applied to institutional management systems to improve efficiency, reliability and accountability.

DOI: https://doi.org/10.5281/zenodo.19090670

 

Published by: vikaspatanker

Authors: Pratikbhai Patel

Abstract: The globalization of power systems with smart grid infrastructure has increased the demand of using high efficiency power conversion technologies that can incorporate into the national grids renewable energy sources, including solar and wind. This study focuses on the significance of a developed Pulse Width Modulation (PWM) methodology in streamlining the inverter efficiency, minimizing the harmonic distortion, and the grid stability. The research uses a systematic analysis methodology to assess converter topologies, modulation schemes, energy storage integration, electric vehicle mechanism, demand response scheme, and computational intelligence scheme under smart grid conditions. The results show that the optimised PWM methods have a significant drop in switching and conduction losses, thermal performance, and quality of voltage waveforms. These enhancements lead to the growth in the level of renewable penetration, system reliability, and low cost of operation. In addition, the study emphasizes the need to coordinate inverter efficiency standards, adaptive control, and digital grid coordination to facilitate sustainable development goals in the world. These findings validate that power electronic conversion systems that operate on high efficiency are critical enablers of resilience, scale and environmentally sustainable smart grid infrastructures globally.

DOI: https://doi.org/10.5281/zenodo.19088267

Published by: vikaspatanker

Authors: Dudekula Imam Khasim Vali, V.E.S.Mahendra Kumar

Abstract: The economy of all structures is being impacted by the current cost of building materials. It has a significant impact on the global environmental housing system. Conventional aggregates, such as gravel, and fine aggregate, such as sand in concrete, will be utilized for control. Robo sand (stone dust) will be used as fine aggregate to replace the sand in concrete, while natural material such as coconut shell will be researched as a coarse aggregate. In this study, sample specimens are prepared and tested using M25 grade concrete that has a combination of natural material coconut shell content as coarse aggregate in the proportions of 0%, 5%, 10%, 15%, 20%, and 25%, and Robo sand (stone dust) as fine aggregate with a complete 100% replacement of natural sand. for workability, compressive strength, split tensile strength and flexural strength for 7,14 and 28 days respectively and also showing the comparative results with Conventional M25 grade concrete. By this project investigation, concrete may be less dense, light weight concrete by coconut shells and good quality of concrete by Robo sand.

 

 

Published by: vikaspatanker