Category Archives: Uncategorized

Shravan Kumar Reddy Padur
Affiliation	IT Tech Specialist, Parker Hannifin, Overland Park, Kansas
Email ID:	padurshravan@gmail.com
Publications: Online Patching and Beyond: A Practical Blueprint for Oracle EBS R12.2 Upgrades       IJSRSET, Jun-16 Engineering Resilient Datacenter Migrations: Automation, Governance, and Hybrid Cloud Strategies, IJSRCSEIT, Jan-17  From Centralized Control to Democratized Insights: Migrating Enterprise Reporting from IBM Cognos to Microsoft Power BI, IJSRCSEIT, Feb-20  Intelligent Resource Management: AI Methods for Predictive Workload Forecasting in Cloud Data Centers, URF (UK) Jan-22  AI-Augmented Enterprise ERP Modernization: Zero-Downtime Strategies for Oracle E-Business Suite R12.2 and Beyond  IJSRCSEIT, May-23

Published by: Nishant

Kranthi Kumar Routhu
Affiliation	Oracle HCM Cloud Techno-Functional Lead (Penske), Techwave Consulting Inc, Reading, PA
Email ID:	routhukranthikumar@gmail.com
Publications: With over 19 years of deep expertise in Oracle HCM technologies, I bring a unique blend of functional, technical, and leadership skills that enable successful end-to-end implementation and ongoing support of Oracle Fusion HCM Cloud and Oracle E-Business Suite (11i & R12). I specialize in delivering scalable, secure, and efficient HCM solutions that support diverse industries, including public sector and healthcare organizations. For the past 10+ years, I’ve focused extensively on Oracle Fusion HCM Cloud (currently on version 25B), leading global implementations and support engagements across modules like Global HR, Payroll, Absence Management, Recruiting, Time & Labor, Compensation, Performance Management & Talent Management, and Learning Cloud. My experience spans three full-cycle implementations, and I have played a critical role in every project phase—from requirement gathering and solution design to testing, deployment, and post-go-live support.I’ve spent over 9 years in project leadership roles, working closely with cross-functional teams and stakeholders to drive results and ensure alignment with business objectives. My strong understanding of both business processes and system architecture enables me to identify functionality gaps, design custom solutions, and guide clients through successful digital transformations.

Published by: Nishant

Authors: Hitanshu Bodana, Ronak Singh, Krish Patel

Abstract: The increasing immersion of children in digital ecosystems—mobile applications, virtual environments, and gamified digital spaces—has amplified concerns around excessive screen time, addictive applications, and exposure to harmful content. Traditional parental control systems rely on rigid blocking mechanisms, often creating resistance among children and lacking nuanced, interactive behaviour regulation. This paper presents a Parental Control Time Lock App, a collaborative digital parenting framework integrating real-time monitoring, application-level time budgeting, and OTP-based conditional unlocking. Developed using Kotlin (Android) and a Spring Boot backend, the system enforces usage limits and enables parents to remotely authorize temporary access by providing a secure one-time password. The application incorporates behavioural design elements to promote healthy usage rather than punitive restriction. Testing conducted across 20 families demonstrated a 32% reduction in unregulated screen time and high parental satisfaction. The study contributes to the domains of digital wellbeing, child safety, and human–computer interaction by proposing a hybrid control model that balances autonomy, security, and trust.

Published by: vikaspatanker

Authors: Ayush Kumar, Abhendra Pratap Singh, Uma Gautam, Nandini Sharma

Abstract: For amputees in underdeveloped nations, the high expenses and complexity of commercial upper-limb prosthetics continue to be major obstacle to accessibility. The design, development, and testing of an affordable, 3D printed bionic hand with a dual-sensor interface is presented in this study. This system incorporates Electrocardiography(ECG) as a secondary control modality for improved stability and mode switching, in the contrast to standard myoelectric systems that only use Electromyography (EMG) and are vulnerable to motion artifacts and false triggers. Autodesk Fusion 360 was used to design the mechanical structure, which was then made of polylactic acid (PLA) and has a tendon-driven actuation mechanism controlled by SG90 servo motors. Band-pass filtering and threshold-based algorithms are used by the control logic, which is implemented on an Arduino Uno, to handle biosignals in real time. The ECG signal successfully serves as a safety interlock, and experimental results show a system latency of about 190ms and a strong object grabbing capacity. The combination of multimodal biosignals with additive manufacturing can produce a dependable, accessible, and useful prosthetic solution, as evidence by the fact that the entire fabrication cost was kept under 10000 INR.

DOI: https://doi.org/10.5281/zenodo.17786605

Published by: vikaspatanker

Authors: Mohamad Siraj

Abstract: This study examines the impact of artificial intelligence (AI) on human resource efficiency among secondary teachers in international schools. While AI is increasingly promoted as a means to reduce teacher workload and enhance productivity, empirical evidence from school settings—particularly international schools—remains limited. The research focuses on how AI is used in teachers’ work, how it affects perceived workload and efficiency, and how organisational conditions shape these effects. A quantitative, cross-sectional survey design was employed. Data were collected from 150 secondary teachers working in 18 international schools, using a structured online questionnaire. The instrument captured AI usage patterns, perceptions of AI (perceived usefulness, perceived ease of use, AI anxiety, autonomy), HR-efficiency outcomes (perceived administrative workload, instructional efficiency, overall efficiency, job satisfaction) and organisational factors (leadership support, training and infrastructure). Descriptive statistics, reliability and factor analyses, correlations and multiple regression models were used to analyse the data. Findings indicate that AI is widely used for lesson planning, resource creation and assessment, but less so for administrative work and rarely for pastoral care or live classroom interaction. Teachers generally perceive AI as useful and moderately easy to use, yet administrative workload remains high. Perceived usefulness and actual AI usage are strong positive predictors of instructional and overall efficiency, and are associated with somewhat lower perceived administrative workload. AI anxiety is linked to higher workload and lower efficiency. Organisational support—through leadership, training and clear policies—consistently amplifies positive outcomes and reduces anxiety. The study concludes that AI currently offers incremental rather than transformative efficiency gains. Its contribution to human resource efficiency and teacher well-being depends on strategic, task-focused implementation and supportive organisational conditions, rather than on technology alone. Recommendations are offered for school leaders, HR practitioners and teachers, alongside directions for future research on AI, workload and sustainability in international education.

DOI: http://doi.org/10.5281/zenodo.17785987

Published by: vikaspatanker

Authors: Ashok Kumar

Abstract: Artificial Intelligence (AI) has revolutionized predictive performance tuning in cloud computing environments, offering significant advancements in resource allocation, fault detection, and autonomic optimization. In an era marked by increasing computational complexity, unpredictable traffic patterns, and heightened demands for availability, integrating AI into cloud operations enables proactive identification and mitigation of latency, bottlenecks, and system inefficiencies. This abstract provides a concise overview of how AI-driven techniques—such as machine learning models, deep neural networks, and reinforcement learning algorithms—have become indispensable for predictive analytics, facilitating dynamic resource scaling, workload balancing, and anomaly detection. AI systems leverage vast datasets generated by cloud infrastructures to uncover hidden patterns, optimize service level agreements (SLAs), and deliver high-performance computing with reduced costs and improved reliability. Challenges remain, especially regarding model interpretability, real-time adaptability, and ethical deployment. Nevertheless, the synergistic evolution of AI and cloud computing stands poised to redefine best practices in predictive performance tuning, fostering new paradigms of automation, resilience, and intelligence in the digital ecosystem.

DOI: http://doi.org/10.5281/zenodo.17777827

Published by: vikaspatanker

Authors: Arjun Prasad

Abstract: Artificial intelligence (AI) has emerged as a transformative force across numerous technological domains, with its impact acutely felt in the design and operation of modern data centers. As the demand for cloud services, big data analytics, and internet-based applications surges, data centers have grown exponentially in size and complexity, concurrently escalating their energy consumption. Addressing energy efficiency within these large-scale computing infrastructures is paramount not only from an operational cost perspective but also for environmental sustainability. AI-based workload schedulers have been increasingly adopted as innovative solutions to optimize resource utilization and curtail energy wastage. These intelligent schedulers leverage machine learning algorithms, predictive analytics, and real-time monitoring to dynamically allocate workloads based on energy profiles, cooling capacities, and computing requirements. The integration of AI fosters adaptive scheduling strategies that can respond to fluctuating workloads, minimize idle hardware, and optimize server usage, thereby enhancing energy efficiency. This article comprehensively explores the multifaceted impact of AI-driven workload scheduling on the operation of energy-efficient data centers. It delves into state-of-the-art AI scheduling techniques, mechanisms for workload prediction, energy consumption modeling, and the synergies between hardware infrastructure and intelligent scheduling systems. Furthermore, the article discusses challenges such as scalability, algorithmic complexity, and integration with existing data center management frameworks. By synthesizing contemporary research findings and industry practices, this work aims to provide a detailed understanding of how AI can revolutionize energy management in data centers, ultimately contributing to reduced carbon footprints and sustainable growth in the digital era.

DOI: http://doi.org/10.5281/zenodo.17777795

Published by: vikaspatanker

Authors: Meera Reddy

Abstract: The adoption and integration of automated compliance tools have revolutionized cloud governance, transforming how organizations ensure regulatory adherence, security, and operational efficiency. These tools leverage automation, artificial intelligence, and real-time monitoring to streamline compliance processes that were traditionally manual, time-consuming, and error-prone. Automated compliance tools enable continuous compliance monitoring, instant remediation of violations, and comprehensive evidence gathering, which collectively enhance governance frameworks' effectiveness and responsiveness. With multi-cloud environments becoming standard, the complexity of governance increases, making automation indispensable to maintaining oversight, cost management, and regulatory compliance across diverse platforms. AI-driven predictive analytics empower these tools to detect anomalies and risks proactively, facilitating smarter policy enforcement and reducing human intervention. Furthermore, automated compliance reduces operational expenses by minimizing manual audit preparation and accelerating reporting and decision-making cycles. This article explores how automated compliance tools improve cloud governance efficiency through policy automation, integration into DevOps pipelines, and real-time compliance dashboards. It also examines best practices for adopting these tools, challenges faced during implementation, and their impact on security, cost optimization, and regulatory alignment in cloud ecosystems. The discussion is framed within the context of evolving compliance frameworks and the pressing need for scalable, adaptive governance strategies essential to modern cloud operations.

DOI: http://doi.org/10.5281/zenodo.17777729

Published by: vikaspatanker

Authors: Rohit Desai

Abstract: Quantum-safe cryptography, also known as post-quantum cryptography, is rapidly emerging as a cornerstone for future-proofing cloud security architectures. As quantum computing accelerates toward practical realization, current cryptographic schemes—especially those foundational to cloud trust models—face an existential threat due to quantum algorithms’ potential to break widely utilized methods such as RSA and ECC. This article offers a comprehensive exploration of the transformative shift toward quantum-safe cryptographic primitives, detailing the strategies cloud service providers, enterprises, and governments are deploying to preempt the quantum risk. It delves into the integration challenges, operational complexity, regulatory mandates, performance considerations, and ecosystem readiness associated with post-quantum security. By examining the evolving landscape of cryptographic standards, the interplay between hardware and software solutions, and the required architectural adaptations, the article provides a nuanced forecast for the next decade of cloud security. Across eight thematic sections, it synthesizes insights from leading research initiatives, governmental policy frameworks, and industry trial deployments, presenting forward-thinking recommendations for stakeholders navigating the quantum leap. The necessity for comprehensive cryptographic agility, layered security frameworks, and global collaboration is emphasized, ensuring that data sovereignty, confidentiality, and integrity are preserved across distributed cloud environments. The analysis concludes with a critical assessment of future-proofing strategies, advocating a multidisciplinary approach to achieving quantum resilience in cloud platforms. The article is tailored for professionals, researchers, and policymakers involved in cloud security, cryptography, and digital trust ecosystems, equipping them with actionable intelligence and a strategic roadmap for quantum-safe transformation.

DOI: http://doi.org/10.5281/zenodo.17777695

Published by: vikaspatanker

Authors: Priya Narayanan

Abstract: Neural network optimization has become a critical driver in advancing real-time cloud decision systems, fundamentally transforming how cloud resources and workloads are managed dynamically and efficiently. As cloud computing infrastructures grow in complexity and scale, neural networks—especially deep learning models—offer powerful capabilities to process vast amounts of data, detect intricate patterns, and predict future states of cloud environments with high accuracy. These capabilities enable cloud platforms to allocate resources, balance loads, and automate decision-making in real-time, thus improving performance, reducing latency, enhancing cost-effectiveness, and boosting energy efficiency. This article explores the multifaceted impact of neural network optimization on cloud decision systems, examining key techniques such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), Bayesian neural networks (BNNs), and graph neural networks (GNNs). It discusses the integration of these models in workload forecasting, resource allocation, and system adaptability, highlighting their role in enabling cloud environments to respond proactively to changing demands. Furthermore, the analysis covers challenges such as model interpretability, real-time processing constraints, and scalability. The article concludes with insights on emerging trends and future directions, emphasizing how neural network optimization will continue to shape the agility and intelligence of cloud decision-making frameworks.

DOI: http://doi.org/10.5281/zenodo.17777606

Published by: vikaspatanker