International Journal of Research and Applied Innovations
Welcome to IJEETR The International Journal of Engineering & Extended Technologies Research (IJEETR) is a prestigious, peer-reviewed, open-access international journal committed to fostering innovation, collaboration, and excellence in research across the broad spectrum of engineering and technological sciences. In a rapidly evolving world where technology is reshaping every facet of human life, IJEETR strives to be more than just a journal – it is a knowledge hub where scholars, academicians, researchers, and industry practitioners come together to exchange pioneering ideas, transformative research, and ground breaking solutions to global challenges. We believe in bridging the gap between theory and practice, encouraging interdisciplinary collaboration, and ensuring that innovative discoveries reach a worldwide audience with maximum impact. Why Choose IJEETR? Publishing with IJEETR offers you: Rigorous Peer Review Global Reach Timely Publication Indexing & Abstracting Interdisciplinary Collaboration Impact-Oriented Dissemination IJEETR is currently accepting manuscripts for its upcoming issue. We encourage submissions that present: Novel methodologies and experimental results. Innovative frameworks and technological applications. Critical reviews of contemporary research trends. Submit your manuscript to editor@ijeetr.com and be a part of a global network of innovators shaping the future of engineering and technology. Call for Papers Volume 7, Issue 4, July-August 2025 Deadline: 31st August 2025 Notification: 3 to 4 Days Publication: 24 to 36 Hours E-Certificates: 6 Hours Contact Us For manuscript submission, queries, or collaboration opportunities: Email: editor@ijeetr.com Website: www.ijeetr.com Support: +91-9710583466
Designing Accessibility-First Enterprise Web Platforms at Scale
With the growth of enterprise web platforms to reach a variety of people in industries such as education, health, and financial services, access has been a post-hoc matter instead of a design-level consideration. The framework presented in this paper is a framework of accessibility-first engineering adapted to large-scale enterprises on a web platform, in which the concept of inclusivity is built into system design, software development, and delivery chains. The discussion explores the compatibility of the technologies, such as semantic HTML, ARIA standards, keyboard navigation, and assistive technology, into complex and dynamic web applications, without affecting the performance and scalability. Such essential architectural considerations of implementing accessibility to server-rendered and client-side interfaces are discussed as content updates in real time and component-based design systems. Production scenarios described in case studies indicate the existence of tangible benefits, including enhanced usability, reduced accessibility defects, and increased access by people with disabilities. The article highlights the role played by accessibility-first in creating digital platforms that are not just compliant but also fair, strong, and user-friendly by all users, irrespective of their capabilities. This study is based on the shift in thinking about the web development of an enterprise and the concept of accessibility as a central architectural concern, not the peripheral issue.
Feedback-Driven Runtime Adaptation for Synchronization Primitives
Modern multithreaded applications operate under highly dynamic execution conditions, where contention patterns on shared data structures vary significantly over time due to phase changes, workload skew, and platform-specific scheduling effects. Conventional synchronization primitives—such as pure spin locks, queue-based locks, or blocking mutexes—are static by design and therefore optimized for only a narrow operating regime. As a result, developers are forced to choose a single synchronization strategy that may perform well in one phase but poorly in another. This paper presents Feedback-Driven Runtime Adaptation for Synchronization Primitives, a practical framework that treats synchronization as a closed-loop control problem. Rather than proposing a new lock algorithm, we continuously observe lightweight contention telemetry, estimate runtime pressure, and adapt synchronization behavior using simple, explainable policies augmented with explicit stability constraints. We instantiate the framework for a mutex-like primitive in portable user-space C++ and evaluate it on a modern ARM-based Apple M1 system under non-stationary workloads. Experimental results demonstrate that feedback-driven adaptation induces bounded and meaningful behavioral changes under contention, trading peak throughput for improved predictability and robustness while avoiding oscillatory behavior. The results confirm that controlled runtime adaptation provides a viable alternative to static synchronization strategies in modern systems.
Automating Compliance in Biomedical DevOps: A Policy-as-Code Approach
Governance in regulated biomedical contexts has become more and more reliant on automated mechanisms of governance in order to maintain compliance, security and auditability of swiftly changing software delivery ecosystems. This study shows a Policy-as-Code (PaC) model which includes regulatory criteria into biomedical DevOps pipelines via machine executable, readable policy definitions. The architecture proposed allows compliance controls to be versioned, tested, and enforced with the application code and infrastructure settings to keep them aligned with the regulatory requirements at all times. The framework (which is practiced in the federal biomedical research systems) illustrates the way in which PaC supports real-time compliance verification and automated control enforcement, and the creation of immutable audit artifacts throughout the software lifecycle. It enables to promote conformity in regulatory frameworks, such as NIST SP 800-53, FISMA, and HIPAA, lessening the use of manual reviews and periodic audits. Empirical testing indicates that configuration drift has reduced significantly, the authorization cycles have decreased and compliance overhead is quantifiably reduced without affecting the security or performance of the system. Achieving this by formalizing regulatory requirements by making digital policies a continuously assessed digital policy, the study redefines compliance as an inherent system property and not a post hoc system checking process. The results demonstrate the importance of PaC in promoting secure CI/CD processes, allowing a continuous authorization mechanism, and enhancing governance automation of well-regulated biomedical settings. This publication provides a scaling model of regulatory engineering and provides realistic considerations in the implementation of compliance-aware DevSecOps architectures in public-sector and biomedical cloud systems.
API-Driven Account Onboarding Framework with Real-Time Compliance Automation
In this study, the proposed API-based digital account onboarding framework is introduced, and it is implemented in terms of MuleSoft-based integration patterns, which are used to optimise the onboarding process. The framework supports important operations like identity checking, customer data checking, and real-time compliance checks using reusable APIs and event-on-command workflows. Computerised regulatory implementation and centralised record keeping enhance audit preparation and lessen the duration required to finish the onboarding process. This paper illustrates the application of API-led connectivity to develop a scalable, safe and regulatory onboarding system that meets the needs of the regulated enterprise setting. Efficiency is afforded in both the validation requirements in the integration, and it minimises risks of manual errors and loss of time. The framework supports the increasing demand for high-speed and safe digital onboarding with a high degree of regulatory compliance in highly-regulated industries, by focusing on automation and API-based connectivity. The architecture is a tactical method to update the enterprise systems, which allows businesses to improve the onboarding processes without compromising security and compliance. The study makes valuable contributions to the research on the role of API-led integration to enhance the efficiency of operations and customer experience in regulated industries.
Designing Intelligent Enterprise Architectures Integrating Generative AI with Cyber Defense and Data Driven Decision Frameworks
Enterprises are increasingly dependent on digital platforms to support strategic decision-making, operational efficiency, and competitive advantage. At the same time, escalating cyber threats and the growing complexity of data ecosystems challenge the effectiveness of traditional enterprise architectures. This paper proposes an intelligent enterprise architecture that integrates generative artificial intelligence with cyber defense mechanisms and data driven decision frameworks. The proposed architecture unifies advanced analytics, generative AI reasoning capabilities, and security-aware data pipelines to enable proactive, adaptive, and resilient decision intelligence. Generative AI is leveraged not only for predictive insights but also for scenario simulation, automated reasoning, and contextual explanation, while cyber defense components ensure continuous monitoring, threat detection, and risk mitigation. The framework embeds governance, transparency, and human oversight to support trustworthy and compliant AI adoption. By aligning decision intelligence with cybersecurity and architectural governance, the proposed approach enables enterprises to transition from reactive defense and fragmented analytics toward intelligent, integrated, and resilient enterprise systems. This research contributes a conceptual and methodological foundation for next-generation enterprise architectures capable of supporting secure, data driven, and AI-augmented decision-making across complex digital environments.
Predictive Monitoring for Distributed and Relational Database Management Systems (RDBMS): A Comprehensive Analysis
Predictive monitoring represents a paradigm shift in database management that transforms reactive maintenance practices into proactive optimization strategies. Traditional database monitoring systems detect problems only after they manifest, resulting in costly downtime and performance degradation. Predictive monitoring leverages machine learning algorithms, statistical analysis, and time-series forecasting to anticipate system failures, performance bottlenecks, and resource limitations before they impact end users. Both distributed databases and relational database management systems benefit from predictive capabilities that enable early detection of node failures, query performance degradation, and hardware malfunctions. Implementation strategies encompass automated scaling mechanisms, resource optimization algorithms, and capacity planning procedures that respond to predicted workload changes. Real-world applications demonstrate significant improvements in system availability, cost reduction through proactive maintenance, and enhanced customer experiences across e-commerce platforms, financial transaction systems, and healthcare data management environments. Advanced monitoring perspectives integrate with automation tools to create self- healing systems that perform root cause analysis and continually improve models. The operational benefits include substantial cost savings, minimized downtime, and optimized performance metrics. Strategic advantages encompass enhanced decision-making capabilities, competitive positioning through system reliability, and improved customer satisfaction. The evolution toward predictive database monitoring enables organizations to maintain high-performance, reliable systems that support critical business operations while reducing administrative overhead and infrastructure costs.
Blockchain Technology, Big Data, and Government Policy as Catalysts of Global Economic Growth
This paper examines three key factors that significantly influence the global economy: blockchain technology, big data, and government policy. The first section explores blockchain technology, including its historical development, fundamental working principles, and applications beyond cryptocurrency. It also analyzes how blockchain enhances transparency, efficiency, and trust, thereby contributing to global economic growth. The second section focuses on big data, explaining its definition, core characteristics, and the ways it improves organizational decision-making, efficiency, and innovation. Additionally, it discusses the future potential of big data and its expanding role in driving economic development worldwide. The third section highlights the importance of government and policy in strengthening the global economy by examining the government’s role in economic regulation and stability. It provides examples of existing policies and proposes additional measures that governments can implement to promote sustainable economic growth.
A Study and Exploration of Numerous Impacts in Children’s Literature: Conditioned Discrimination
A group’s diversification can be observed and experienced in terms of its varied ethnicity, class, race, religion, language, colour, gender of the group members. But it is also perceived that discrimination based on many factors is created in society. As a social taboo, gender inequality is considered to be an idea and a situation that, women and men are not equal. It is also a grave thing that this conditioned discrimination is deep-rooted. It may be socially constructed or empirically grounded. Gender difference shows and promotes unequal treatment to or perceptions of individuals just because of their gender. It arises from variances in socially constructed gender roles which unfortunately produces different responsibilities. In India, this gender discrimination refers to health, education, economic and political inequalities between men and women. It influences women's health, their educational achievement, economic conditions and India's sex ratio. In literature, such discrimination is evident since long. Through the theme of gender issues, it discusses inhuman mindset based on gender prevailing in the society and furthers some effective solutions to check such conditioned discrimination.
A Unified AI and Cloud Security Model for Financial Fraud Prevention and Medical Image Intelligence in 5G-Powered Web Applications
The rapid integration of artificial intelligence (AI), cloud computing, and 5G networking is enabling transformative solutions across industries. This study proposes a unified AI and cloud security model that combines financial fraud prevention and medical image intelligence within 5G-powered web applications. The framework leverages cloud-native security features and AI-driven analytics to detect, prevent, and respond to threats in real time. In financial systems, machine learning models analyze transaction patterns, user behavior, and network anomalies to identify fraudulent activities, reducing financial losses and improving trust. In healthcare, deep learning algorithms interpret medical images such as X-rays, CT scans, and MRIs, enabling faster and more accurate diagnoses. The 5G-enabled web application layer ensures high-speed data transfer, low latency, and reliable access for mobile and IoT devices, supporting telemedicine and remote financial services. The unified model emphasizes secure data storage, end-to-end encryption, identity management, and compliance with privacy regulations. By integrating AI, cloud security, and 5G technologies, the proposed model provides a scalable, interoperable, and resilient platform that enhances fraud prevention and medical intelligence. The framework also supports real-time monitoring and automated response mechanisms, contributing to safer and more efficient digital ecosystems.
AI-Enabled Cloud Lakehouse for Large-Scale Data Warehousing: SAP Integration for Secure Analytics and Tableau-Driven Reporting
The rapid growth of enterprise data across transactional systems, IoT platforms, cybersecurity logs, and analytical applications has intensified the need for scalable, secure, and intelligent data warehousing architectures. Traditional data warehouses struggle to accommodate heterogeneous data formats, real-time analytics, and advanced artificial intelligence (AI) workloads. This paper presents an AI-enabled cloud lakehouse framework for large-scale data warehousing that integrates SAP enterprise systems with advanced security analytics and Tableau-driven reporting. The proposed architecture combines the flexibility of data lakes with the governance and performance of data warehouses, enabling unified analytics across structured, semi-structured, and unstructured data. AI and machine learning models are embedded within the lakehouse to support secure analytics, fraud detection, and anomaly identification, while SAP integration ensures consistency with enterprise transactional data. Tableau is employed as a visualization layer to provide dynamic, interactive, and role-based reporting for decision-makers. Experimental analysis and enterprise use-case evaluations demonstrate that the proposed framework improves data accessibility, analytics performance, security visibility, and decision accuracy compared to conventional warehouse-centric approaches. The framework is particularly suitable for data-intensive enterprises seeking secure, scalable, and insight-driven analytics in cloud environments.
Event-Driven BI Pipelines for Operational Intelligence in Industry 4.0
The emergence of Event-Driven Business Intelligence (BI) pipelines is an innovative operational intelligence approach in Industry 4.0 because it allows real-time decision-making by processing all types of dynamic and diversified data. These pipelines are created to compute and analyse the events as they happen and enable real-time insights and responses to businesses that need to function in a highly connected and data-heavy environment. An Event-Driven BI pipeline is often designed based on event producers, an event processing engine, data storage systems and analytics modules connected by an event bus or message broker. The ongoing emissions of events by event producers, IoT devices, sensors and enterprise systems are captured and processed in real time. The event processing engine has the role of filtering, enriching, and aggregating events, and finally stores them in the data warehouses or distributed storage systems. The pieces of information stored are then used by analytics and BI dashboard tools, which analyse the business data to produce operational insights. The key points of the architecture include the processing of event streams, real-time data ingestion, and data storage, wherein the low-latency and high-throughput processing are guaranteed. It can be described that the Industry 4.0 systems have enhanced operational efficiency, predictive maintenance, and better resource management through the implementation of Event-Driven BI pipelines in them. The paradigm shift lets businesses be active to new challenges, and opportunities provide it with a competitive advantage in the digital era.
Design of a Secure SAP-Enabled Cloud Framework Using Generative AI and GANs for Healthcare Incrementality Analytics
The increasing adoption of data-driven decision-making in healthcare has intensified the need for secure, scalable, and intelligent analytical frameworks capable of accurately measuring intervention effectiveness. This paper proposes the design of a secure SAP-enabled cloud framework that leverages Generative Artificial Intelligence (AI) and Generative Adversarial Networks (GANs) to support incrementality analytics in healthcare systems. The proposed architecture integrates heterogeneous healthcare data sources within a cloud-native SAP environment, enabling secure data ingestion, governance, and high-performance analytics. Generative AI models are employed to simulate counterfactual scenarios, while GANs enhance data augmentation and bias mitigation for robust incrementality testing. Security is enforced through a zero-trust approach incorporating encryption, identity-aware access control, and continuous compliance monitoring to address regulatory requirements such as HIPAA and GDPR. Experimental analysis demonstrates improved accuracy in treatment impact assessment and campaign effectiveness evaluation compared to traditional statistical approaches. The framework provides a scalable foundation for trustworthy and intelligent healthcare analytics in cloud-based enterprise environments.
An Intelligent Risk-Aware AI and LLM Platform for Secure Banking Operations and Trade Safety Analytics in Cloud-Based Web Applications
The rapid digitization of banking operations and trade systems has increased efficiency, accessibility, and global financial integration, but it has also amplified the risks of fraud, cyberattacks, and operational anomalies. Traditional detection systems are often static, reactive, and unable to handle high-velocity, multi-source data in real time. This paper proposes an intelligent risk-aware AI and Large Language Model (LLM) platform for secure banking operations and trade safety analytics within cloud-based web applications. The platform integrates generative and predictive AI models with LLMs to identify anomalies, forecast potential threats, and generate human-readable explanations and automated reports. Secure Extract, Transform, Load (ETL) pipelines ensure consistent, high-quality data from heterogeneous sources, while a risk-aware module dynamically evaluates threat severity, prioritizes mitigation, and adjusts system parameters. Cloud-native deployment enables scalable, low-latency, and fault-tolerant analytics suitable for high-speed web applications. Evaluation on real and simulated banking and trade datasets demonstrates improved detection accuracy (>95%), a 35–40% reduction in false positives, and enhanced operational resilience. This work provides a blueprint for deploying adaptive, interpretable, and secure AI platforms that integrate risk-awareness, cloud scalability, and 5G-ready web technologies to safeguard modern financial ecosystems.
Enhancing Healthcare Financial Decision-Making using Secure AI-Enabled SAP Cloud Platforms
Healthcare organizations increasingly rely on data-driven financial systems to manage costs, ensure regulatory compliance, and improve operational efficiency. The integration of Secure AI-Enabled SAP Cloud platforms offers a robust solution for enhancing healthcare financial decision-making by combining advanced analytics, automation, and enterprise-grade security. This approach leverages artificial intelligence to analyze large volumes of financial, clinical, and operational data in real time, enabling accurate forecasting, fraud detection, revenue cycle optimization, and strategic planning. Cloud-based SAP architectures provide scalability, reliability, and interoperability while ensuring compliance with healthcare regulations such as HIPAA and data protection standards. Security mechanisms including identity access management, encryption, and continuous monitoring further protect sensitive financial and patient data. By unifying finance, analytics, and security within an intelligent SAP cloud ecosystem, healthcare organizations can achieve improved transparency, faster decision cycles, and sustainable financial performance. The proposed framework highlights how secure AI-driven SAP platforms can transform healthcare financial management into a resilient, efficient, and insight-driven process.
Robust Supply Chain Systems in Cloud-Distributed Environments: Design Patterns and Insights
Global supply-chain platforms run at unparalleled scale as geographically dispersed networks are being operated with continuous volatility in their operations. To be able to design systems that can be depended upon in such circumstances, there must be architectural methods that take into consideration failure, inconsistency, and disruption as regular functions deserving of consideration instead of rare accidents. The paper will analyze the architectural designs that are required to construct resilient supply-chain platforms within distributed cloud environments. It conceptualizes the modern supply chains as complex adaptive systems that are vulnerable to partial service failures, asynchronous data propagation, as well as real-time external shocks, e.g. demand fluctuations, logistics delays and infrastructure outages. The paper compares significant design patterns, such as scopes of limited transactions, compensation-style and saga-style workflows, and fault-isolation boundaries which restrict the scope of failures in services. Besides, the paper highlights other concepts of resilience engineering, like graceful degradation, strategic redundancy, and recovery-first, that focus on quick stabilization rather than consistency. Combining these architectural patterns and real-life constraints experienced in large-scale supply-chain operations, the paper will offer a systematic platform for designing platforms that maintain continuity, observability, and reliability. The results can provide concrete recommendations to architects and engineers who want to create cloud-native supply-chain systems that would remain operational even in the face of ongoing uncertainty and unforeseeable disruptions.
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Cyber-Aware AI and Serverless Analytics for Cloud-Native SAP and Apache Iceberg Architectures
The transition toward cloud-native enterprise architectures has accelerated the adoption of SAP systems and modern open table formats such as Apache Iceberg to support scalable analytics and data-driven decision-making. However, this evolution also introduces significant cybersecurity, governance, and operational challenges, particularly in serverless computing environments. This paper presents a cyber-aware AI and serverless analytics framework for cloud-native SAP and Apache Iceberg architectures. The proposed approach integrates machine learning models with serverless analytics pipelines to monitor data access patterns, transactional behavior, and network interactions across SAP workloads. Cyber risk intelligence is embedded into the analytics layer to enable proactive detection of anomalies, security threats, and policy violations. Apache Iceberg is leveraged to ensure reliable data versioning, schema evolution, and time-travel analytics, supporting trustworthy AI model training and auditability. The framework is designed to be cloud-native, elastic, and cost-efficient while maintaining strong security and compliance guarantees. Experimental evaluation demonstrates improved threat detection accuracy, optimized resource utilization, and enhanced resilience compared to traditional monolithic analytics architectures. The results highlight the effectiveness of combining AI, serverless computing, and cyber-aware analytics for secure and scalable enterprise data platforms.
AI-Driven Predictive Threat Detection for Secure Multiparty SAP Business Processes in Broadband Clouds
The increasing adoption of cloud-based SAP systems across broadband networks has amplified the complexity and scale of cyber threats targeting enterprise business processes. Traditional security mechanisms often lack the intelligence and adaptability required to detect sophisticated, multiparty, and process-level attacks in real time. This paper proposes an AI-driven predictive threat detection framework designed to secure multiparty SAP business processes deployed in broadband cloud environments. The proposed architecture integrates advanced machine learning models with large-scale data analytics platforms to analyze transactional logs, network traffic, and process execution traces for early threat identification. By leveraging predictive analytics, the framework anticipates anomalous behavior before it propagates across interconnected enterprise systems. The solution supports secure data sharing among multiple stakeholders while preserving process integrity and regulatory compliance. Experimental analysis demonstrates improved detection accuracy, reduced false positives, and enhanced resilience compared to rule-based and reactive security approaches. The results highlight the effectiveness of AI-powered security intelligence in safeguarding mission-critical SAP workflows in high-bandwidth, cloud-native enterprise ecosystems.
Privacy-Preserving AI and Machine Learning for Enterprise Risk Detection in SAP-Based Cloud Business Processes
Enterprises increasingly rely on SAP-based cloud business processes to manage critical financial, healthcare, and operational data, making them attractive targets for sophisticated cyber and operational risks. While artificial intelligence and machine learning enhance risk detection capabilities, they also raise significant concerns regarding data privacy and regulatory compliance. This paper presents a privacy-preserving AI and machine learning framework for enterprise risk detection in SAP-based cloud business processes. The proposed approach combines advanced analytics, knowledge extraction, and privacy-enhancing techniques to analyze transactional data, system logs, and process execution traces without exposing sensitive information. Machine learning models are employed to identify risk patterns, anomalies, and potential security incidents across interconnected enterprise workflows. The framework supports secure data governance, compliance with data protection regulations, and scalable deployment in cloud environments. Experimental evaluation demonstrates improved risk detection accuracy, reduced false positives, and enhanced system resilience compared to traditional rule-based approaches. The results highlight the effectiveness of privacy-preserving AI in enabling trustworthy and intelligent risk management for SAP-driven enterprise cloud ecosystems.
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AI-Enabled Cloud Framework for SAP Financial and Healthcare Data Governance with Risk and Testing Mechanisms
The integration of artificial intelligence (AI) with cloud-based enterprise systems has transformed the way healthcare and financial organizations manage, govern, and analyze sensitive data. This paper proposes an AI-enabled cloud framework for SAP financial and healthcare data governance with integrated risk assessment and testing mechanisms. The framework leverages cloud-native services to support scalable data processing while enforcing governance policies related to data quality, access control, privacy, and regulatory compliance. AI-driven techniques are employed to automate data classification, anomaly detection, and risk evaluation across heterogeneous SAP financial and healthcare datasets. The proposed framework incorporates continuous testing and validation mechanisms to ensure data integrity, system reliability, and compliance with evolving standards. Secure data pipelines and API-based interoperability enable controlled data exchange between healthcare and financial systems without compromising confidentiality. The framework demonstrates how AI-enabled cloud architectures can enhance governance effectiveness, reduce operational risk, and improve trust in enterprise healthcare and financial data ecosystems.
Agile Project Management Frameworks for Software-Intensive Organizations
Agile Project Management (APM) has emerged as a transformative framework for software-intensive organizations seeking flexibility, responsiveness, and continuous value delivery in highly volatile and complex environments. Traditional project management approaches, often linear and rigid, struggle to accommodate the dynamic nature of software development cycles and rapidly shifting customer expectations. In contrast, Agile frameworks such as Scrum, Kanban, Lean, and SAFe provide iterative, adaptive, and collaborative methodologies that emphasize customer collaboration, rapid prototyping, and continuous feedback. This paper explores the implementation, adaptation, and outcomes of Agile Project Management frameworks in software-intensive organizations, highlighting their effectiveness in enhancing project performance, team collaboration, and stakeholder satisfaction. The study begins by examining the core principles of Agile as defined in the Agile Manifesto, with a focus on their alignment with the challenges of software engineering, including scope volatility, technical complexity, and evolving user requirements. It also addresses how Agile frameworks support cross-functional teamwork, promote transparency, and reduce time-to-market. Using a mixed-methods approach that includes a review of recent empirical studies, case analyses from industry, and interviews with project managers and developers, the research identifies critical success factors for Agile adoption, such as leadership support, organizational culture, team maturity, and the availability of Agile coaching. Findings suggest that Agile frameworks lead to improved project visibility, risk management, and customer-centricity. However, challenges persist in scaling Agile across large organizations, integrating Agile with legacy systems, and ensuring consistent metrics for performance evaluation. Additionally, organizations transitioning from traditional methods often encounter resistance to change, a lack of standardized practices, and difficulties in aligning Agile with business-level strategic planning. The paper presents mitigation strategies including hybrid models, Agile maturity assessments, and phased implementation techniques to support sustainable transformation. The conclusion advocates for a contextualized Agile adoption strategy, where frameworks are tailored to the specific organizational size, structure, and project complexity. The paper contributes to the body of knowledge by offering a comprehensive synthesis of best practices, lessons learned, and future research directions in Agile project management within software-intensive environments. It serves as a valuable reference for IT leaders, project managers, and change agents aiming to foster agility, innovation, and operational excellence in their software development initiatives.
Cloud-Enabled ERP Systems: Challenges, Opportunities, and Performance Evaluation
Cloud-enabled ERP systems leverage cloud computing technologies to enhance scalability, flexibility, and cost-efficiency in enterprise resource planning, enabling organizations to integrate business processes in real time while addressing challenges related to data security, system customization, interoperability, and performance optimization; this study examines the key challenges and opportunities of cloud-based ERP adoption and evaluates system performance in terms of operational efficiency, responsiveness, reliability, and business value creation
Technology Adoption Models for Industry 4.0 and Smart Enterprises
The rapid emergence of Industry 4.0, characterized by the convergence of cyber-physical systems, the Internet of Things (IoT), big data, artificial intelligence (AI), and cloud computing, is reshaping the operational and strategic landscape of modern enterprises. As industries undergo digital transformation, understanding and facilitating the adoption of these advanced technologies becomes paramount. Technology adoption models provide structured frameworks to analyze the factors influencing the assimilation of Industry 4.0 technologies in smart enterprises. This paper explores key technology adoption models, including the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovations (DOI), and the Technology–Organization–Environment (TOE) framework, to evaluate their relevance and adaptability in the context of Industry 4.0. Through a comparative analysis, the study investigates how these models accommodate the unique attributes of Industry 4.0 technologies—such as interoperability, real-time data analytics, automation, and machine learning. It highlights how traditional adoption models may fall short in addressing the complexities of organizational readiness, workforce competency, digital infrastructure, and the strategic alignment required for smart enterprise implementation. Furthermore, the research integrates insights from empirical studies and industry practices to propose an enhanced hybrid framework that blends the strengths of existing models while incorporating critical success factors for Industry 4.0 readiness. The paper emphasizes that successful adoption is not solely driven by technological factors but also hinges on organizational culture, leadership support, stakeholder involvement, and external environmental pressures such as regulatory compliance and competitive dynamics. By leveraging case studies from manufacturing, logistics, and service industries, the study showcases practical applications of adoption frameworks and identifies key enablers and barriers to technology integration. Ultimately, this research contributes to both theoretical advancement and practical guidance for policymakers, industry leaders, and technology strategists aiming to accelerate digital transformation. It advocates for a flexible, dynamic, and context-aware approach to technology adoption that aligns with the evolving demands of smart enterprises. The proposed integrated model serves as a decision-making tool for assessing adoption readiness, designing implementation strategies, and measuring transformation outcomes in the Industry 4.0 era.
Blockchain-Enabled Supply Chain Management for Transparency and Trust
: Blockchain-enabled supply chain management (SCM) has emerged as a transformative approach to addressing long-standing challenges related to transparency, trust, traceability, and efficiency in global supply networks. Traditional supply chain systems are often fragmented, centralized, and opaque, making them vulnerable to data manipulation, fraud, counterfeiting, inefficiencies, and limited stakeholder trust. The increasing complexity of multi-tier supply chains, coupled with growing regulatory and consumer demands for ethical sourcing and sustainability, necessitates innovative technological solutions. Blockchain technology, with its decentralized, immutable, and transparent ledger capabilities, offers a promising foundation for reengineering supply chain processes. This study explores the role of blockchain technology in enhancing transparency and trust across supply chain ecosystems. By enabling real-time, tamper-resistant recording of transactions and asset movements, blockchain facilitates end-to-end visibility among all authorized stakeholders, including manufacturers, suppliers, logistics providers, regulators, and consumers. Smart contracts further automate and enforce predefined business rules, reducing reliance on intermediaries, minimizing disputes, and improving operational efficiency. The integration of blockchain with complementary technologies such as the Internet of Things (IoT), artificial intelligence, and big data analytics strengthens data accuracy and supports predictive and prescriptive decision-making within supply chains. The abstract highlights key benefits of blockchain-enabled SCM, including improved traceability of products, enhanced accountability, reduced fraud and counterfeiting, faster dispute resolution, and strengthened stakeholder trust. Additionally, blockchain supports compliance with regulatory requirements and sustainability initiatives by providing verifiable records of sourcing, production, and distribution activities. Despite these advantages, the adoption of blockchain in supply chains faces several challenges, such as scalability limitations, interoperability issues, high implementation costs, data privacy concerns, and the need for industry-wide collaboration and standardization. This study underscores the strategic importance of blockchain as an enabler of transparent and trustworthy supply chains while acknowledging the technical, organizational, and regulatory barriers that must be addressed for successful implementation. The findings contribute to the growing body of knowledge on digital supply chain transformation and provide insights for practitioners, policymakers, and researchers seeking to leverage blockchain technology for building resilient, secure, and sustainable supply chain networks. Ultimately, blockchain-enabled supply chain management represents a critical step toward fostering trust, accountability, and long-term value creation in modern supply chain ecosystems.
Advanced Data Visualization Techniques for Executive-Level Decision Support
Advanced data visualization techniques have become a critical enabler of executive-level decision support in an era characterized by exponential data growth, increased business complexity, and accelerated strategic cycles. Traditional reporting and static dashboards often fail to convey the multidimensional relationships, temporal patterns, and uncertainty inherent in large-scale organizational data, leading to delayed insights and increased cognitive burden for decision-makers. This paper examines how advanced data visualization approaches transform complex, high-volume, and heterogeneous data into intuitive visual representations that enhance strategic understanding, improve situational awareness, and support evidence-based executive decisions. The study explores modern visualization paradigms, including interactive dashboards, real-time visual analytics, multidimensional and hierarchical visualizations, geospatial intelligence, and narrative-driven visual storytelling. These techniques enable executives to rapidly identify trends, anomalies, correlations, and risk indicators across operational, financial, and strategic domains. By leveraging human perceptual strengths, advanced visualizations reduce cognitive overload, shorten decision cycles, and facilitate more effective communication among executive stakeholders. Furthermore, the paper discusses the integration of advanced visualization techniques with emerging technologies such as big data platforms, artificial intelligence, and predictive analytics. The convergence of visualization and analytics enables executives not only to interpret historical and real-time data but also to explore future scenarios through simulation and what-if analysis. Practical considerations related to usability, scalability, data governance, and executive trust are also addressed. Through conceptual analysis and illustrative use cases, this research demonstrates that advanced data visualization is not merely a presentation tool but a strategic decision-support capability that enhances organizational agility, governance, and competitive advantage in data-driven enterprises.
Data-Driven Performance Management using Machine Learning and KPI Analytics
This study presents a data-driven performance management framework that integrates machine learning techniques with Key Performance Indicator (KPI) analytics to enhance organizational decision-making and operational efficiency. The proposed approach leverages historical and real-time performance data to identify patterns, predict outcomes, and uncover hidden drivers of employee, process, and organizational performance. Machine learning models such as regression, classification, clustering, and ensemble methods are employed to forecast KPI trends, detect performance anomalies, and support proactive managerial interventions. By aligning predictive insights with strategic objectives, the framework enables continuous monitoring, objective evaluation, and evidence-based performance optimization. The results demonstrate that combining machine learning with KPI analytics improves accuracy in performance assessment, supports timely corrective actions, and fosters a culture of continuous improvement in dynamic business environments.
Multi-Cloud Strategy and Governance Models for Enterprise IT Management
This paper examines multi-cloud strategy and governance models for enterprise IT management, focusing on how organizations can leverage multiple cloud service providers to achieve flexibility, resilience, cost efficiency, and regulatory compliance; it proposes an integrated governance framework that aligns cloud strategy with business objectives through standardized policies for security, data management, vendor control, performance monitoring, and cost optimization, enabling enterprises to manage complexity, mitigate risks, and maximize value from heterogeneous cloud environments.
Cloud-Based Intelligent Management Systems for Scalable Business Operations
Cloud-Based Intelligent Management Systems (CBIMS) integrate cloud computing with artificial intelligence and data analytics to enable scalable, adaptive, and cost-efficient business operations by supporting real-time decision-making, automation, and organizational agility
Secure and Governed Cloud Data Platforms for SAP-Enabled Healthcare Systems with Incrementality-Driven Business Insights
Healthcare organizations are increasingly adopting cloud-based data platforms to support complex business processes, analytics, and digital transformation initiatives. When combined with SAP-enabled enterprise systems, these platforms must ensure strong security, governance, and regulatory compliance while enabling advanced analytical capabilities. This paper presents a secure and governed cloud data platform designed for SAP-enabled healthcare systems with a focus on incrementality-driven business insights. The proposed framework integrates cloud-native data architectures, network-aware security controls, and governance mechanisms to support end-to-end data ingestion, processing, and analytics. Incrementality testing techniques are employed to measure the true impact of business and operational changes, enabling healthcare organizations to make data-driven decisions with reduced risk. Secure APIs and policy-based access control ensure controlled data sharing across clinical, operational, and analytics domains. The framework supports scalability, interoperability, and compliance with healthcare regulations while improving analytical accuracy and business process optimization. Experimental evaluation and architectural analysis demonstrate enhanced data reliability, improved security posture, and actionable insights across SAP-driven healthcare workflows.
Designing Secure Digital Payment and Revenue Attribution Systems using AI and Cloud Security Frameworks
The rapid proliferation of digital payment platforms has revolutionized global commerce, fostering convenience, inclusivity, and economic efficiency. Simultaneously, this growth has escalated security threats and revenue attribution challenges — fraud, identity theft, unauthorized access, and ambiguous transaction verification — threatening financial stability and consumer trust. This research explores the integration of Artificial Intelligence (AI) and cloud security frameworks to design secure digital payment infrastructures and robust revenue attribution systems. A hybrid model leveraging machine learning (ML), predictive analytics, anomaly detection, and cloud-native defenses is proposed to ensure real-time authentication, fraud prevention, data protection, and precise revenue tracking. The framework incorporates Zero Trust security, multi-factor authentication, blockchain verification, secure APIs, and secure cloud storage mechanisms, ensuring compliance with international regulations and optimizing system performance. Leveraging advancements in AI-driven fraud detection and cloud security architectures, the proposed system enhances threat detection accuracy, reduces false positives, and strengthens trust in financial ecosystems. Empirical evaluation highlights significant improvements in transaction integrity, scalability, and threat resilience. Key implementation challenges — such as ethical AI deployment, data privacy concerns, and service latency — are also discussed. This study advances secure transaction frameworks and provides a scalable roadmap for future digital financial systems. IJSRCSEIT
A Comparative Study of Privacy Policies in E-Commerce Platforms
This is the digital era. Every device can generate and process information in the age of Internet of Things (IoT). Security of the new technologies and platforms is debated for various reasons. There are technological platforms that collect data from the users directly while some platforms use the processed data. The focus of this research study is on the privacy of the user data in e-commerce platforms. The privacy policies of e-commerce websites are compared and analyzed to check the different ways user data is collected and processed by careful manipulation of the privacy policies. Privacy policies are not just generic documents with standard terms but there are specific terms that are written using unique style, choice of words, readability scores, etc. so that the data can still be collected but not known to the users. This study aims at analyzing this specific aspect of privacy policies of the e-commerce websites.