Daily Archives: December 15, 2025

Authors: Srinivasa Chakravarthy Seethala

Abstract: This research paper investigates the role of machine learning enhanced query optimization techniques in improving the performance, scalability, and reliability of SQL and NoSQL databases used for high volume big data processing in financial and healthcare applications. Traditional rule based and cost based query optimizers often struggle to adapt to dynamic workloads, heterogeneous data distributions, and rapidly evolving access patterns that characterize modern financial transactions and healthcare data ecosystems. This study addresses the research problem of how adaptive machine learning driven optimization models can overcome these limitations by learning from historical query execution patterns, system telemetry, and workload characteristics. A mixed methodology is adopted, combining conceptual framework design, algorithmic modeling, and comparative performance analysis across representative SQL and NoSQL environments handling large scale transactional and analytical workloads. The findings demonstrate that machine learning enhanced optimizers significantly reduce query latency, improve resource utilization efficiency, and enhance workload predictability under peak data volumes compared to traditional optimization approaches. The paper highlights key innovations such as predictive cost modeling, adaptive index selection, and real time execution plan refinement driven by supervised and reinforcement learning techniques. From an academic perspective, this research contributes to the evolving discourse on intelligent data management systems by extending optimization theory into data driven adaptive architectures. From an industry standpoint, the results provide actionable insights for designing resilient, high performance data platforms capable of supporting mission critical financial and healthcare operations where accuracy, compliance, and responsiveness are paramount.

DOI: http://doi.org/10.5281/zenodo.17938651

Published by: vikaspatanker

Authors: Srinivasa Chakravarthy Seethala

Abstract: This study proposes an AI driven compliance intelligence platform designed to enable continuous monitoring and automated risk assessment within highly regulated CRM and ERP environments. Organizations operating in finance, healthcare, public sector, and other compliance intensive domains increasingly rely on complex enterprise platforms where regulatory obligations evolve faster than traditional audit and control mechanisms can adapt. The research addresses the limitations of static compliance models by introducing an architecture that integrates machine learning, natural language processing, and policy aware analytics to interpret regulatory requirements, monitor transactional and configuration level signals, and dynamically assess compliance risk in real time. A mixed methodological approach is adopted, combining conceptual system design with simulated enterprise data flows and scenario based evaluations across common regulatory regimes such as data protection, financial controls, and access governance. The findings demonstrate that AI driven compliance intelligence can significantly improve early risk detection, reduce manual audit effort, and enhance traceability across CRM and ERP processes by continuously correlating system behavior with regulatory intent. The platform introduces adaptive risk scoring, automated control validation, and explainable compliance insights that support both operational teams and governance stakeholders. From a strategic perspective, the study contributes to a forward looking compliance paradigm that shifts organizations from periodic, reactive audits toward proactive and continuous assurance models. Academically, the research extends existing literature on enterprise governance by formalizing compliance intelligence as a scalable, data driven capability embedded within enterprise software ecosystems.

DOI: http://doi.org/10.5281/zenodo.17938605

Published by: vikaspatanker

Authors: Srinivasa Chakravarthy Seethala

Abstract: Real time event streaming pipelines form the operational backbone of modern digital platforms, supporting continuous data ingestion, processing, and delivery across cloud native and distributed environments. Despite their importance, reliability management in such pipelines remains largely reactive, relying on threshold based monitoring and post failure diagnostics that are insufficient for preventing cascading disruptions. This study addresses the problem of anticipating reliability degradation in real time event streaming systems by proposing a predictive reliability engineering framework grounded in multi modal deep learning. The primary objective is to enable early identification of failure precursors by jointly analyzing heterogeneous telemetry sources, including system metrics, execution logs, distributed traces, and event level metadata. A mixed method research approach is adopted, combining quantitative modeling of historical incident data with qualitative architectural analysis of streaming platforms to inform model design and integration. The proposed framework employs temporal and representation learning techniques to fuse multi modal signals and generate probabilistic reliability risk scores ahead of observable failures. Experimental evaluation across representative streaming workloads demonstrates improved failure prediction accuracy, longer warning lead times, and reduced false alert rates compared to single source monitoring baselines. The findings highlight the innovation of multi modal fusion for reliability prediction and its implications for proactive operational decision making. From an academic perspective, the study advances reliability engineering by introducing predictive, data driven models tailored to real time pipelines. From an industry standpoint, the framework supports more resilient event driven architectures through earlier intervention, reduced downtime, and improved service continuity, reinforcing the strategic value of intelligent reliability management in high availability systems.

DOI: http://doi.org/10.5281/zenodo.17938555

Published by: vikaspatanker

Authors: Mridulaxika, Gurpreet Singh, Varuna Tyagi

Abstract: Student dropout is a persistent challenge in higher education, leading to academic, financial, and institutional losses. Accurate early prediction of at-risk students can significantly improve retention through timely interventions. This paper presents a comparative analysis of three ensemble-based machine learning models AdaBoost, Gradient Boosting Machine (GBM), and a proposed Advanced Extreme Gradient Boosting (ADVXGBoost) algorithm for predicting student dropout risk. The models were evaluated using a dataset of 5,000 student records containing demographic, academic, and behavioral attributes. Performance was assessed using 10-fold stratified cross-validation in the WEKA Explorer environment. Experimental results demonstrate that ADVXGBoost outperforms AdaBoost and GBM, achieving the highest accuracy of 90.76%, the lowest error rates, and balanced class-wise prediction. The findings confirm the effectiveness of enhanced boosting techniques for reliable student dropout prediction and decision-support systems in educational institutions.

Published by: vikaspatanker

Authors: Dr. Arati Basumatary

Abstract: Social media has become a special part of society. In the present age, social media is considered the best medium for communication across the world. The availability of the internet has made social media popular with the facility of instant exchange. Especially platforms like Facebook, WhatsApp, Instagram, YouTube etc. have facilitated communication, photo-video sharing from anywhere. From the new generation to the older generation, people are now attracted to and experienced with the use of such social media. It can be said that social media is a suitable platform not only for entertainment but also for education, business etc. The widespread use and popularity of social media is also observed in Assamese culture. Assamese songs-dances, food etc, the original cultural elements have been able to be presented on the world stage through social media. However, in this context, it can be assumed that the authenticity and values of Assamese traditional culture are somewhat hindered. This proposed research paper will also discuss the impact of social media on Assamese culture, including both positive and some negative impacts.

Published by: vikaspatanker

Authors: Manuela christy dany S

Abstract: Wastewater stabilization is one of the primary methods of environmental engineering that can protect public health and preserve aquatic ecosystems. In this regard, increasing wastewater generation due to urbanization, industrialization, and population growth has increased the need for cost-effective, sustainable, and efficient treatment technologies. Wastewater stabilization techniques undertake the reduction of organic matter, nutrients, pathogens, and toxic substances through biological, chemical, and physical processes. Among these technologies, WSPs, sludge stabilization methods, and integrated hybrid systems have shown high efficiency, especially in developing countries and rural regions. This review paper covers a critical and comprehensive synthesis of recent studies on the various technologies of wastewater stabilization. Major topics that will be covered in this review include fundamental aspects of stabilization, design and operational issues of WSPs, stabilization techniques of sludge, hybrid and decentralized systems, and more recent studies on modeling optimization, and AI applications. Much emphasis is placed on the environmental, economical, and public health impacts, along with the shortcoming of the existing systems. Also, resource recovery, energy-neutral treatment, and climate-resilient design are some of the emerging trends pointed out in this review. This paper intends to provide an in-depth understanding among students, researchers, and practitioners regarding the different stabilization technologies of wastewaters and their future research directions towards sustainable wastewater management.

DOI: https://doi.org/10.5281/zenodo.17935085

Published by: vikaspatanker

Authors: Salihu Aliyu Lawan, Shuaibu Garba Ngulde, Babagana Ibrahim Bukar

Abstract: In this paper, we study some action of S6 on A6. With Particular cases for n 2, 3, …,. and provide new combinatorial and structural insight into direct product actions of symmetric groups. Groups, Algorithms and Programming software (GAP) have been used to compute the elements of stabilizer S6. Orbit-stabiliser theorem and Cauchy-Frobenius lemma were applied to determine the number of S6(x)-orbits and their corresponding length respectively. We established that the action is transitive, faithful and imprimitive for n ≥ 2. Further results include explicit descriptions of point stabilizers, computation of orbit sizes using the Orbit–Stabilizer Theorem. We also established kernel of the action S6 on A6, and the construction of associated suborbitalni graphs and Upper bounds for the diameter of the resulting graphs are obtained, we the generalized the action (Sn)k for any n ≥ 2 on Cartesian products of k sets.

DOI: https://doi.org/10.5281/zenodo.17937347

Published by: vikaspatanker

Authors: Rutuja Toggi, Akanksha Kawade, Shraddha Deshmukh, Ulka Nikalje, Vaishnavi Hippargi, Professor N.J.Shaikh

Abstract: Digital technologies such as cloud platforms, online video streaming, internet browsing, and IoT devices significantly contribute to global carbon emissions, yet traditional carbon calculators often ignore these digital footprint s. This project introduces an AI-driven Carbon Emission Monitoring Security System that tracks carbon output from digital activities while ensuring cybersecurity. The system leverages Flutter, Spring Boot, MySQL, Python ML, and a React.js dashboard to monitor activities, detect anomalies, score threats, and generate explainable AI reports. Security features include JWT authentication, MFA, refresh tokens, and RBAC. The AI models automatically retrain with new feedback, providing real-time alerts, analytics dashboards, and secure file scanning.

DOI:

Published by: vikaspatanker