Category Archives: Uncategorized

Authors: Komal Jain, Ajeet Kumar, Shravanthi R, Ritu Chauhan

Abstract: Oracle Solaris Logical Domains (LDOMs) offer flexible, high-performance virtualization at the hardware layer, enabling fine-grained resource allocation across critical workloads. However, as enterprise infrastructures grow in complexity and scale particularly in edge and hybrid environments the need for dynamic and intelligent load balancing becomes paramount. Traditional static and reactive policies fall short in addressing modern demands marked by workload volatility, bursty usage patterns, and constrained physical resources. In this context, Edge AI models present a transformative approach to adaptive load management. This review explores how AI particularly Edge-deployed supervised, unsupervised, time-series, and reinforcement learning models can be leveraged to predict resource saturation, detect faults, and proactively manage LDOM reallocation and live migrations. Emphasis is placed on integrating AI pipelines with Solaris-native telemetry tools (kstat, vmstat, prstat) and automating control actions using the ldm command suite. Real-world case studies across telecom, financial, and healthcare sectors are analyzed to demonstrate improvements in SLA compliance, resource efficiency, and fault avoidance through AI-assisted decisions. We further address system-level integration with Oracle Ops Center, highlight governance concerns such as model explainability and override control, and explore lightweight inference frameworks suitable for constrained control domains. Challenges in data quality, model trust, and automation safety are also discussed. The review concludes by outlining future directions including federated learning, policy-aware AI agents, cross-domain telemetry fusion, and convergence with AI-Ops ecosystems. By embedding intelligence directly into the LDOM infrastructure, organizations can evolve from static resource provisioning to a self-optimizing virtualization platform—capable of continuous learning, rapid adaptation, and resilience at the edge. This shift is vital to meet the performance and operational demands of modern digital infrastructure.

DOI: https://doi.org/10.5281/zenodo.15846618

Published by: vikaspatanker

Authors: Lakshmi Menon, Aravind Krishnan, Ramya K, Vineeth Das

Abstract: Modern IT environments, characterized by hybrid infrastructure, rapid virtualization, and regulatory constraints, demand sophisticated systems management platforms that go beyond manual operations. Oracle Enterprise Manager Ops Center (OEMOC) has long served as a unified platform for provisioning, patching, asset discovery, and monitoring in Oracle Solaris and Linux-based data centers. However, as operational complexity scales, traditional rules-based workflows face limitations in managing configuration drift, correlating events, and predicting performance degradation. This has prompted a shift toward integrating artificial intelligence into Ops Center’s telemetry and operational lifecycle. This review explores the application of AI and machine learning techniques to optimize various facets of OEMOC. From predictive asset discovery and patch prioritization to real-time anomaly detection and resource planning, AI offers the potential to transform the platform into a proactive, self-optimizing system. The review evaluates supervised, unsupervised, and reinforcement learning models that can be trained on logs, asset data, and historical events collected across Enterprise Controllers and Agent Controllers. Specific emphasis is placed on using time series forecasting for utilization prediction, clustering techniques for configuration drift detection, and NLP algorithms for intelligent alert triage. Additionally, the review delves into the architectural integration of AI pipelines with OEMOC components, the use of SNMP, syslog, and ITSM APIs for external telemetry fusion, and case studies from financial, government, and telecom deployments. The article also addresses challenges related to model explainability, data governance, and integration within legacy environments. In doing so, it outlines a roadmap for enhancing Ops Center with intelligent automation, turning it from a monitoring tool into a closed-loop operations platform capable of dynamic remediation and resource optimization.

DOI: https://doi.org/10.5281/zenodo.15846496

Published by: vikaspatanker

Authors: Ritika Ghosh, Abhishek Dey, Sonali Mondal, Arjun Sen

Abstract: In today's data-intensive environments, the Zettabyte File System (ZFS) plays a central role in ensuring reliable and high-performance storage for applications ranging from databases to high-performance computing and cloud workloads. However, predicting future storage consumption, ARC/L2ARC cache pressure, and snapshot bloat has become increasingly complex due to the dynamic and non-linear nature of modern workload behaviors. Traditional statistical approaches often fall short in capturing these complexities, necessitating the adoption of hybrid AI models that blend statistical, machine learning (ML), and deep learning techniques. These hybrid systems can more accurately model usage trends, recognize anomalous patterns, and respond to previously unseen behaviors, especially when trained on detailed ZFS telemetry. This review article explores the use of hybrid AI techniques for ZFS usage forecasting, focusing on time series modeling, anomaly detection, snapshot growth prediction, and proactive capacity management. It begins with a foundational overview of ZFS architecture, highlighting the importance of ARC, L2ARC, ZIL, and snapshot layers in the overall usage landscape. It then discusses the specific forecasting challenges that arise in ZFS due to caching hierarchies, concurrent access patterns, and latency-sensitive applications. We examine a taxonomy of AI models used in the domain and analyze how hybrid designs can improve accuracy and adaptability. The review further details the construction of end-to-end pipelines for training, evaluating, and deploying predictive models based on ZFS metrics. Case studies from healthcare, research clusters, and enterprise NAS environments are presented to demonstrate the operational impact of intelligent forecasting. Finally, the article outlines future directions including federated learning, online retraining, and integration with AIOps platforms to support self-optimizing storage infrastructures.

DOI: https://doi.org/10.5281/zenodo.15845749

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Authors: Pratibha Kumari, Rajiv Tripathi, Snehal Ramesh, Tanvi Kapoor

Abstract: In modern enterprise IT, data protection is not merely a compliance requirement but a critical operational pillar. Backup systems such as Commvault are expected to perform with high reliability to meet recovery point objectives (RPO) and recovery time objectives (RTO). However, unpredictable backup failures continue to challenge IT operations, causing delays in restoration, potential data loss, and non-compliance with service level agreements (SLAs). Traditional monitoring and alerting are often reactive, which leaves little time for administrators to respond before a backup job fails. Predictive analytics offers a paradigm shift by enabling preemptive identification of failure patterns based on historical and real-time data. In Commvault environments, telemetry from CommServe, MediaAgents, and job logs offers rich sources for modeling and failure prediction. This review investigates how predictive analytics—particularly machine learning (ML) can be applied within Commvault environments to anticipate and mitigate backup failures. We discuss key failure types, log analysis strategies, anomaly detection, model training pipelines, and real-time visualization techniques. The integration of supervised and unsupervised ML algorithms, including regression models, clustering, and sequence prediction (e.g., LSTM), is explored for their applicability in Commvault's operational workflows. In addition, we evaluate the advantages of integrating predictive outputs into SLA-aware orchestration and automated remediation workflows. The article further contrasts Commvault’s predictive capabilities with other backup platforms and explores future research avenues in deep learning, AIOps, and federated modeling for distributed environments. By aligning predictive insights with operational pipelines, enterprises can achieve proactive data protection, reduce downtime, and improve compliance readiness in a cost-effective manner.

DOI: https://doi.org/10.5281/zenodo.15845638

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Authors: Vaishnavi Kane, Assistant Professor Dr. Suhas Mache, Assistant Professor Dr. Arshiya Khan

Abstract: In the modern business environment, accurate sales forecasting is essential for effective decision-making. This paper explores the integration of time series forecasting techniques with Business Intelligence (BI) tools, specifically using Microsoft Power BI, to build an interactive dashboard for sales prediction. We present a model that combines statistical forecasting methods with data visualization to enhance decision-making in sales management. The system is designed to provide real-time insights, support strategic planning, and identify sales trends through dynamic dashboards. Our case study demonstrates that integrating forecasting models within BI platforms significantly improves sales predictability and operational efficiency. This study explores the integration of time series forecasting techniques with Microsoft Power BI to build a dashboard that predicts future sales. By combining forecasting models like ARIMA and Prophet with Power BI’s interactive features, the system enables better business decision-making. The dashboard allows users to visualize historical trends and forecasted sales data in real time.

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Authors: Harsha Sammangi, Aditya Jagatha, Hari Gopal Maddireddy

Abstract: This study presents a sentiment-driven Decision Support System (DSS) that leverages advanced word embedding techniques—Word2Vec, GloVe, and BERT—to analyze CEO earnings call transcripts and predict stock market reactions. Tra- ditional lexicon-based sentiment models fail to capture the nuanced, contextual language used by executives. By employing pre-trained embeddings and machine learning classifiers, the study enhances the accuracy of sentiment classification. The proposed system integrates quantitative sentiment scores with event study method- ology to assess the impact of CEO tone on stock performance. Thematic analysis further enriches interpretability by identifying recurring patterns in executive com- munication. Results demonstrate that positive CEO sentiment generally correlates with stock appreciation, while negative sentiment aligns with declines. Among models tested, BERT outperformed others in classification accuracy. This research contributes to real-time financial analytics by embedding sentiment intelligence into DSS frameworks, supporting investors, analysts, and automated trading sys- tems with improved decision-making capabilities grounded in contextual linguistic analysis.

DOI: https://doi.org/10.5281/zenodo.15845150

 

Published by: vikaspatanker

Authors: Jinta Johnson, Assistant Professor Athira B, Professor Dr. Shine Raj G

Abstract: This paper presents a lightweight and intelligent system for the automatic extraction of structured information from academic research papers in PDF format. The proposed system leverages Natural Language Processing (NLP) techniques, TF-IDF-based summarization, and Sentence-BERT semantic similarity to extract and analyze metadata such as title, authors, organizations, keywords, and references. Built using Python and Streamlit, the tool allows users to upload PDF documents, parse academic content, and interactively review summarized metadata, references, and semantic relevance—all in real-time. This paper details the system architecture, implementation pipeline, challenges, and experimental results, demonstrating its effectiveness and scope for future enhancements

 

 

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Authors: Anant Kumar, Professor Gulfam B. Shaikh, Professor Dilip L. Jade

Abstract: With the rapid urbanization of rural regions in India, the demand for healthcare infrastructure has surged dramatically. This research paper explores the architectural planning and design of a Multi-Speciality Hospital in Sikandarpur, Bihta, Patna (Bihar)—a region currently underserved in medical facilities. Emphasizing healing environments, sustainable strategies, patient-centered design, and technological integration, the paper outlines the necessity, process, and architectural responses to contemporary hospital design. A case study of Tata Medical Centre, Kolkata informs the practical and structural feasibility of the proposed design.

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Lakshmi Kalyani Chinthala
Affilation:	Strategic Planning Program Manager San Francisco California
Email-Id:	chinthalakalyani01@gmail.com
Publication: Chinthala, L. K. (2021). Future of supply chains: Trends in automation, globalization, and sustainability. International Journal of Scientific Research & Engineering Trends, 7(6), 1-10. Chinthala, L. K. (2021). Diversity and inclusion: The business case for building more equitable organizations. Journal of Management and Science, 11(4), 85-87. Chinthala, L. K. (2021). Business in the Metaverse: Exploring the future of virtual reality and digital interaction. International Journal of Science, Engineering and Technology, 9(6). ISSN (Online): 2348-4098. Chinthala, L. K. (2025). Consumer experience 2025: The role of personalization and AI in shaping business strategies. International Journal of Modern Science and Research Technology, 3(5), 21–27. Chinthala, L. K. (2025, February). Artificial intelligence in business strategy: Enhancing competitive advantage through machine learning. International Journal of Modern Science and Research Technology, 3(2), 83–90.

 

Published by: vikaspatanker

Authors: Megha Birthare, Neelesh Jain

Abstract: The global rise in social media usage has led to a surge in unwanted bulk SMS, necessitating the development of an effective system to filter out these messages. The most prevalent issue on the internet is spam text messages. Sending a spam-filled SMS is a straightforward task for spammers. Spammers are able to take valuable data, including contacts and files, from our devices. In recent years, several word embedding techniques leveraging deep learning have been developed. These advancements in word representation could offer a reliable remedy for these problems. This study will look at a technique that employs natural language processing to distinguish among spam and ham texts utilizing the SMS Spam Collection Dataset from the UCI Machine Learning Repository. We compared the accuracy and outcomes of using the Bi-LSTM and LSTM. The effectiveness of the dataset is assessed using measures like F1-score, recall, and accuracy. The study demonstrates that the dataset's overall accuracy increases when Bi-LSTM classification is used. Python is used for all work, and a Jupyter notebook is used for implementation.

DOI: https://doi.org/10.61137/ijsret.vol.11.issue4.114

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