International Journal of Technology, Management and Humanities
International Journal of Technology, Management and Humanities (IJTMH) The International Journal of Technology, Management and Humanities (IJTMH) is a peer-reviewed, open-access academic journal dedicated to the advancement of interdisciplinary research across the fields of technology, management, and the humanities. The journal, bearing the electronic International Standard Serial Number (e-ISSN) 2454-566X, was established in 2015 with the aim of fostering intellectual dialogue and innovative thinking across diverse disciplines. IJTMH publishes original research articles, case studies, technical notes, and review papers that contribute to the understanding and integration of technological innovations, management practices, and humanistic values. Its scope covers a wide range of topics, including but not limited to information technology, engineering management, business strategies, cultural studies, ethics, and societal impacts of technological change. Over the years, IJTMH has built a strong reputation for promoting research that bridges the gap between technical expertise and human-centered approaches. The journal welcomes contributions from scholars, practitioners, and industry experts worldwide, encouraging a global exchange of ideas and interdisciplinary collaboration. Committed to maintaining high academic standards, IJTMH ensures a rigorous peer-review process and strives for the timely dissemination of knowledge to its diverse readership, including researchers, academicians, and professionals.
Intelligent DevOps and Artificial Intelligence–Driven Continuous Delivery Framework for Secure Enterprise and Logistics Platforms
Modern enterprise applications demand rapid deployment, high availability, and robust security across dynamic cloud-native and on-premise environments. Traditional DevOps pipelines focus on automation and continuous integration/continuous delivery (CI/CD) but often lack intelligent mechanisms for proactive security, anomaly detection, and predictive resource management. This research proposes an intelligent DevOps and artificial intelligence (AI) driven continuous delivery architecture for secure enterprise applications. The framework integrates AI-driven predictive analytics, automated testing, dynamic resource allocation, and security monitoring into CI/CD pipelines to ensure secure, resilient, and efficient delivery. Core components include AI-based anomaly detection, automated code and configuration analysis, intelligent orchestration of deployment workflows, and real-time compliance checks. Experimental evaluation on simulated enterprise applications demonstrates reduced deployment errors, improved system reliability, and enhanced security posture compared to conventional CI/CD pipelines. By integrating AI into DevOps processes, the architecture supports proactive threat mitigation, adaptive pipeline optimization, and continuous security enforcement. This research contributes a comprehensive approach to intelligent, secure, and automated continuous delivery for enterprise applications, bridging the gap between operational efficiency and security compliance in modern DevOps environments.
Intelligent DevOps, Continuous Delivery, CI/CD, Artificial Intelligence, Secure Enterprise Applications, Automated Deployment, Predictive Analytics, Anomaly Detection, Security Monitoring, Pipeline Optimization
AI Driven Zero Trust Security Model for Enterprise Data Protection and Intelligent Infrastructure Management
The increasing complexity of enterprise IT environments, coupled with the rise of cloud adoption, remote work, and sophisticated cyber threats, has exposed traditional perimeter-based security models as insufficient for protecting sensitive enterprise data. Zero Trust Security (ZTS) has emerged as a robust framework emphasizing continuous verification, least-privilege access, and granular security policies to safeguard organizational assets. When combined with Artificial Intelligence (AI), Zero Trust models can become more adaptive, predictive, and proactive in detecting and mitigating security threats. This research proposes an AI-driven Zero Trust Security model designed to enhance enterprise data protection and intelligent infrastructure management. The framework integrates AI-powered analytics, machine learning-based anomaly detection, behavioral monitoring, and automated policy enforcement across enterprise networks, cloud platforms, and critical infrastructure components. By continuously analyzing user behavior, network traffic, and system interactions, the system identifies potential security breaches, enforces dynamic access controls, and reduces the attack surface. The research methodology includes architectural modeling, simulation of enterprise scenarios, and performance evaluation against traditional security approaches. Results demonstrate that AI-enhanced Zero Trust architectures significantly improve threat detection accuracy, automate compliance management, and optimize security policies for dynamic enterprise environments. This model supports intelligent infrastructure management while ensuring robust data protection, regulatory compliance, and resilience against emerging cyber threats.
Zero Trust Security, Artificial Intelligence, Enterprise Data Protection, Intelligent Infrastructure, Behavioral Analytics, Machine Learning Security, Cyber Threat Detection, Access Control, Network Security, Autonomous Security Management
Intelligent Cloud-Native Architecture for AI-Driven Cybersecurity Healthcare Analytics Financial Systems and Autonomous Infrastructure
The increasing complexity of enterprise digital ecosystems has created significant challenges in managing security, scalability, and real-time data analytics across various industries. Modern enterprises, particularly in sectors such as healthcare and finance, rely heavily on cloud infrastructures to process massive volumes of sensitive data. However, traditional enterprise architectures often struggle to address cybersecurity threats, data management complexities, and infrastructure scalability requirements. To overcome these limitations, organizations are increasingly adopting cloud-native architectures integrated with artificial intelligence technologies. This research proposes an intelligent cloud-native enterprise architecture designed to support AI-driven cybersecurity, healthcare data analytics, financial systems, and autonomous infrastructure management. The proposed architecture integrates microservices, containerization, cloud orchestration platforms, and machine learning-based analytics to create a scalable and secure enterprise ecosystem. Artificial intelligence models are used to detect cybersecurity threats, analyze healthcare datasets, and optimize financial operations through predictive analytics. The framework also enables autonomous infrastructure management by incorporating intelligent monitoring systems capable of automatically detecting performance issues and adjusting system resources dynamically. The research presents architectural design principles, system integration strategies, and evaluation methods for implementing intelligent enterprise infrastructures. The proposed architecture aims to improve enterprise security, enhance data-driven decision-making, and support large-scale digital transformation initiatives across modern organizations. Intelligent cloud native architecture, AI driven cybersecurity, healthcare data analytics, financial systems security, autonomous infrastructure management, cloud native enterprise systems, machine learning security analytics, zero trust cloud security, intelligent automation infrastructure, predictive threat detection, secure cloud computing, AI powered digital transformation
Cloud-Native Enterprise Architecture, AI-Driven Cybersecurity, Healthcare Data Analytics.
Secure Multi-Cloud DevOps Architecture with AI-Driven Threat Detection and Automated Infrastructure Resilience
The increasing adoption of multi-cloud strategies in enterprises has enhanced scalability, operational flexibility, and service availability. However, it has also introduced complex security challenges, including distributed attack surfaces, inconsistent policy enforcement, and vulnerability management across heterogeneous environments. Traditional security mechanisms are insufficient for dynamic multi-cloud infrastructures, necessitating intelligent and automated approaches to ensure system resilience. This research proposes a secure multi-cloud DevOps architecture integrated with AI-driven threat detection and automated infrastructure resilience mechanisms. The framework leverages machine learning and artificial intelligence models to monitor network traffic, system activity, and application behavior in real time, enabling proactive identification of anomalies and potential threats. Automated resilience modules, including self-healing, fault-tolerant orchestration, and dynamic resource scaling, mitigate the impact of attacks or failures without manual intervention. By integrating AI-based cybersecurity with cloud-native DevOps practices, the architecture ensures secure, scalable, and highly available multi-cloud environments. Continuous monitoring, automated policy enforcement, and predictive threat intelligence provide enterprises with enhanced operational efficiency, regulatory compliance, and infrastructure reliability. The research outlines the architectural principles, implementation strategies, and evaluation metrics for deploying a multi-cloud DevOps platform capable of adapting to evolving security threats and maintaining resilient enterprise operations.
Secure multi cloud DevOps architecture, AI driven threat detection, automated infrastructure resilience, multi cloud security framework, DevOps security automation, machine learning cybersecurity analytics, cloud infrastructure protection, predictive threat intelligence, intelligent incident response, resilient cloud systems, automated security orchestration, adaptive cyber defense
Secure Cloud Native Architecture for Enterprise Banking and Healthcare Systems with AI Support
The increasing demand for digital services in banking and healthcare sectors has accelerated the adoption of cloud-native technologies and artificial intelligence. Organizations are required to manage large volumes of sensitive data while ensuring security, regulatory compliance, scalability, and real-time analytics. Traditional monolithic systems often fail to provide the flexibility and performance needed for modern enterprise environments. This research proposes an AI-enabled secure cloud-native framework designed to support enterprise banking and healthcare systems while enabling intelligent analytics and scalable digital transformation. The proposed framework integrates artificial intelligence, microservices architecture, containerization, and cloud-native infrastructure to provide a secure and scalable data platform. AI algorithms are employed to analyze enterprise data, detect anomalies, and support predictive decision-making processes. In addition, security mechanisms such as encryption, identity and access management, and automated threat detection are integrated into the architecture to protect sensitive financial and healthcare information. The study presents the architectural design, implementation strategy, and evaluation of the proposed framework. The research demonstrates how cloud-native technologies combined with AI-driven analytics can enhance operational efficiency, improve data security, and enable intelligent automation in enterprise environments. The results indicate that AI-enabled cloud-native systems can significantly support digital transformation initiatives in banking and healthcare sectors by providing scalable, secure, and intelligent data infrastructures.
Artificial Intelligence, Cloud-Native Architecture, Enterprise Banking Systems, Healthcare Information Systems, Intelligent Analytics, Digital Transformation, Microservices Architecture, Data Security, Machine Learning, Cloud Computing
Architectural Evolution in Enterprise Data Modeling: From Dimensional Leadership to Hybrid Integration Frameworks
Enterprise data modeling remains a foundational discipline in the design of large-scale information systems, serving as the structural backbone that enables integration, governance, performance optimization, and long-term adaptability across complex enterprise environments. Between 2000 and 2015, organizations experienced a marked shift from tightly coupled, monolithic enterprise data warehouses toward layered, scalable, and audit-ready architectures capable of accommodating exponential data growth, heterogeneous source systems, distributed processing models, and increasingly stringent regulatory requirements such as SOX and HIPAA. This transition was driven not only by technological advancements but also by business demands for faster analytics, historical traceability, and cross-domain interoperability. In this context, modeling strategies evolved from purely performance-centric schemas to integration-aware, metadata-driven frameworks. This paper synthesizes three dominant paradigms Dimensional Modeling, traditional Enterprise Data Warehouse (EDW) approaches, and Data Vault modeling using evidence mapping and comparative analysis grounded in pre-2016 literature and large-scale implementation studies. By examining conceptual, logical, and physical modeling layers, the study evaluates trade-offs related to scalability, schema evolution, auditability, and governance while identifying architectural integration patterns that balance agility with enterprise control. The resulting synthesis provides a structured perspective on how modeling leadership evolved during this period and how hybrid architectures emerged as a pragmatic solution for enterprise-scale deployments.
Enterprise Data Modeling, Dimensional Modeling, Data Vault, Enterprise Data Warehouse, Large-Scale Information Systems, Metadata Governance, EDW Architecture, Information Integration, Data Warehousing, Evidence Mapping
Event Driven Change Data Capture Architectures for High-Volume Enterprise Data
By May 2016, enterprises operating high-volume transactional platforms were under growing pressure to integrate operational data across systems with lower latency and greater reliability than traditional batch-oriented approaches could provide. Core databases supporting orders, payments, customer records, and inventory were increasingly required to feed analytics platforms, search indexes, downstream services, and reporting systems in near real time. Existing extract, transform, and load pipelines, typically executed on hourly or nightly schedules, introduced unacceptable delays and operational risk as data volumes and integration complexity increased. At the same time, direct querying of production databases for integration purposes imposed additional load and created tight coupling between systems, threatening transactional stability. Change Data Capture emerged as a pragmatic technique for addressing these challenges by enabling systems to observe committed data changes directly from transactional sources. Rather than periodically extracting full datasets, CDC focused on incrementally capturing inserts, updates, and deletes as they occurred. This approach significantly reduced data movement overhead and improved freshness while preserving the performance characteristics of source systems. By mid 2016, CDC was increasingly implemented using database transaction logs, allowing changes to be captured after commit with strong ordering guarantees and minimal intrusion into application logic. As enterprises adopted asynchronous messaging and event-driven integration models, CDC evolved beyond a replication mechanism into a foundational architectural pattern. Captured data changes were increasingly represented as immutable events and published to a messaging infrastructure that decoupled producers from consumers. This enabled multiple downstream systems to consume the same change stream independently, each applying its own processing logic without coordinating directly with the source database. Event-driven CDC is aligned with broader distributed systems principles that favor loose coupling, scalability, and resilience through asynchronous communication. High-volume integration scenarios placed particular emphasis on scalability and fault tolerance. Organizations processing large transaction volumes require CDC pipelines capable of sustaining continuous throughput while tolerating consumer slowdowns, infrastructure failures, and transient network issues. Event-driven CDC architectures addressed these requirements through durable message storage, partitioned processing, and replay capabilities. However, these benefits came with trade-offs related to eventual consistency, operational complexity, and the need for disciplined monitoring of replication lag and processing health. This paper examines event-driven Change Data Capture patterns as they were understood and applied by May 2016, focusing on architectural approaches suitable for high-volume data integration. It analyzes capture mechanisms, messaging integration models, consistency semantics, and operational considerations relevant to enterprise environments. Rather than presenting CDC as a universal solution, the paper situates it as a critical integration pattern whose effectiveness depends on careful design, governance, and alignment with transactional system constraints.
Event-driven architecture, change data capture, high volume data integration, log-based replication, distributed messaging systems, transactional databases, asynchronous data pipelines, data consistency, fault-tolerant data integration, enterprise data platforms. These keywords reflect the architectural and operational themes associated with CDC based integration as practiced by May 2016, emphasizing scalable event propagation, minimal source system impact, and reliable downstream data consumption.
Cloud Aligned ETL Framework Architectures for Enterprise Data Modernization at Scale
By January 2016, enterprises across industries were undergoing large-scale data modernization initiatives driven by the rapid growth of data volume, diversity, and analytical demand. Traditional ETL systems, largely designed for centralized data warehouses and predictable batch workloads, increasingly struggled to meet requirements for scalability, flexibility, and operational efficiency. At the same time, the emergence of cloud infrastructure and distributed data processing frameworks created new opportunities to rethink how data integration and transformation pipelines were architected at enterprise scale. This paper examines the concept of cloud native ETL frameworks as it was understood and practiced in early 2016. Cloud native ETL in this context refers to ETL architectures that leverage distributed computing models, elastic infrastructure, and modular pipeline design rather than tightly coupled, monolithic execution engines. The discussion focuses on how enterprises began adapting ETL workloads to run on shared clusters and cloud-based environments while maintaining data correctness, performance predictability, and governance requirements. The analysis explores the role of distributed processing frameworks such as Hadoop MapReduce and Apache Spark in enabling scalable transformation pipelines, as well as early dataflow-oriented systems that introduced higher-level abstractions for pipeline definition and execution. Architectural patterns related to ingestion, transformation orchestration, intermediate storage, and failure handling are examined to highlight how cloud aligned designs addressed the limitations of earlier ETL platforms. Finally, the paper synthesizes architectural principles and design considerations relevant to enterprise-scale ETL modernization efforts as of January 2016. These principles emphasize decoupling of pipeline components, alignment with distributed execution models, and operational resilience in heterogeneous data environments. The intent is to provide a historically grounded view of cloud native ETL foundations that informed the subsequent evolution of enterprise data engineering platforms.
Cloud native ETL frameworks, enterprise data modernization, distributed data processing, extract transform load architectures, Hadoop ecosystem, Apache Spark, data pipeline orchestration, scalable data integration, cloud based infrastructure, batch data processing. These keywords represent the core technical and architectural themes relevant to enterprise scale ETL systems as understood by January 2016, emphasizing the transition from centralized ETL tools toward distributed and cloud aligned data processing frameworks. The focus remains on scalability, reliability, and maintainability of data pipelines operating across heterogeneous data sources and evolving infrastructure environments.
Reinforcement Learning for Autonomous Systems
Reinforcement Learning (RL) is a machine learning paradigm in which agents learn optimal behaviors through interactions with their environment by maximizing cumulative rewards. In the context of autonomous systems, RL provides a powerful framework for decision-making, control, and adaptation in dynamic, uncertain, and complex environments. Applications span autonomous vehicles, drones, robotics, smart traffic management, and industrial automation. This paper explores the principles of RL, including value-based, policy-based, and model-based approaches, and examines their integration into autonomous systems for real-time control and planning. Key challenges such as safety, sample efficiency, reward design, and generalization are discussed. Moreover, the paper highlights methods for combining RL with simulation, imitation learning, and safe exploration strategies to ensure reliability in high-stakes environments. Reinforcement Learning thus emerges as a critical enabler for autonomous systems capable of learning, adapting, and operating efficiently while minimizing human intervention.
Reinforcement Learning, Autonomous Systems, Deep Reinforcement Learning, Policy Optimization, Value-Based Learning, Safe RL, Robotics, Autonomous Vehicles, Simulation, Adaptive Control, High-Stakes Decision Making.
AI-Powered Personalized Education Systems
Artificial Intelligence (AI) is transforming education by enabling personalized learning experiences that adapt to individual student needs, preferences, and learning styles. AI-powered personalized education systems leverage machine learning, natural language processing, recommendation algorithms, and data analytics to tailor content delivery, monitor progress, and provide real-time feedback. These systems aim to enhance learning outcomes, improve engagement, and address diverse educational challenges across K-12, higher education, and professional training contexts. This article explores the design, methodologies, and applications of AI-driven personalized education systems, including intelligent tutoring systems, adaptive assessment platforms, learning analytics, and automated feedback mechanisms. Challenges related to data privacy, algorithmic bias, system interpretability, and equitable access are discussed, along with strategies for integrating AI into existing educational infrastructures. Future directions include multimodal learning platforms, AI-driven curriculum design, lifelong learning support, and collaborative human-AI teaching models. AI-powered personalized education promises to make learning more effective, engaging, and inclusive, paving the way for a new era of adaptive and intelligent educational environments.
AI in Education, Personalized Learning, Adaptive Learning Systems, Intelligent Tutoring Systems, Learning Analytics, AI Feedback Systems, Educational Data Mining, Student-Centered Learning, Adaptive Assessment, AI Curriculum Design, Multimodal Learning, Human-AI Collaboration in Education, Educational Technology, Machine Learning in Education, Digital Learning Platforms.
Explainable AI (XAI) for High-Stakes Decision Systems
High-stakes decision systems powered by Artificial Intelligence (AI) are increasingly deployed in domains such as healthcare, criminal justice, finance, and autonomous transportation, where errors can result in severe ethical, legal, and societal consequences. Despite the high predictive performance of complex models—particularly deep learning architectures—their opaque “black-box” nature limits trust, accountability, and regulatory compliance. Explainable AI (XAI) seeks to address this challenge by developing methods and frameworks that make AI system decisions transparent, interpretable, and understandable to human stakeholders. This paper explores the role of XAI in high-stakes environments, emphasizing the need for transparency, fairness, robustness, and human oversight. It examines key explanation techniques, including model-intrinsic interpretability, post-hoc explanation methods (such as feature attribution and surrogate models), counterfactual reasoning, and visualization-based approaches. The discussion highlights practical applications in clinical diagnosis, credit risk assessment, judicial risk scoring, and autonomous systems, where explainability directly influences safety, user trust, and legal accountability. Furthermore, the paper analyzes technical and ethical challenges, including the trade-off between accuracy and interpretability, explanation fidelity, bias detection, adversarial manipulation, and compliance with regulatory frameworks such as the General Data Protection Regulation (GDPR). It argues that effective XAI must be context-aware, stakeholder-centered, and aligned with domain-specific requirements. The study concludes that Explainable AI is not merely a supplementary feature but a foundational requirement for deploying AI responsibly in high-stakes decision systems. Future research directions include standardized evaluation metrics for explanations, human-centered design methodologies, and the integration of causal reasoning into interpretable AI models.
Explainable AI (XAI), High-Stakes Decision Systems, Interpretability, Transparency, Trustworthy AI, Model Explainability, Ethical AI, Accountability, Bias Mitigation, Human-AI Collaboration, Regulatory Compliance, Fairness, Robustness.
A Cloud-Native AI Framework for Real-Time Detection of Quantum Computing Attacks on Classical Cryptography
The fast pace of quantum computing is a big threat to the classical cryptographic schemes, making more prevalent encryption patterns like RSA and ECC susceptible to attacks heretofore computationally infeasible. With the shortening of the timeline to inspect quantum supremacy, organizations must consider proactive actions to counteract quantum-based intrusions, especially cloud-native setups that store sensitive information and vital infrastructures. In this paper, an innovative Cloud-Native Artificial Intelligence (AI) Framework, QCAI-Guard, that is focused on real-time detection of quantum computing attacks on classical cryptographic protocols, is introduced. The suggested architecture can use the scalability and flexibility of the cloud native platforms, namely container-based microservices that are dynamically deployed through Kubernetes and proposed threat detection models that are driven by advanced AI but trained using synthetic quantum intrusion datasets. It uses the strategy of layers of security, where it uses zero trust principles, AI to detect anomalies, entails quantum signature responses, and post-quantum secure communication protocols. Moreover, we suggest an event streaming pipeline with a high level of security for real-time alerting and threat attribution. The simulated cloud environment provided experimental evaluations over simulated attack vectors by quantum-caused attacks on TLS and VPN protocols. These outcomes indicate the high accuracy of the framework during the initial attack detection with low latency, proving its practicality in real-world applications. Based on its efficacy in various metrics, performance benchmarks indicate its effectiveness in terms of detection accuracy, response time and scalability of the system. Also, the modular architecture allows full integration with already existing cloud security infrastructures and future expansion to new post-quantum algorithms. QCAI-Guard provides enterprises and government agencies with a quantum-safe solution in the future by integrating the concepts of cloud-native computing, artificial intelligence, and post-quantum security.
Quantum Computing Attacks, Cloud-Native Security, Artificial Intelligence, Post-Quantum Cryptography, Real-Time Threat Detection, Zero Trust Architecture, Microservices
Strengthening U.S. Food Safety Through Evidence-Based Nutrition Policy Integration
The policy of food safety, as well as nutrition, has always been viewed as a separate area of regulation in the United States, even though the two policies share the aim of ensuring and enhancing the well-being of the population. The paper discusses the possibility of integrating the evidence-based nutrition research systematically into the national food safety system to control both food-borne diseases and the prevalence of the chronic disease caused by the diet. It discusses regulatory functions of the U.S. Food and Drug Administration in nutrition-based food safety regulation, and interagency cooperation with the United States Department of Agriculture on issues like food inspection, food labeling, and dietary advice. The analysis outlines the policy gaps which are critical as there are no compliance oriented food safety standards taking into consideration the nutritional quality and long-term health outcomes. Special focus is on the fact that the vulnerable population (children, low-income households and communities with low access to nutritious foods) are disproportionately affected by these gaps. Through the co-ordination of food safety regulation with the current nutrition science, the study identifies opportunities to simultaneously enhance the federal food safety systems and the national priorities in nutrition and health of the population. The results highlight the significance of integrative governance, evidence-based policymaking and equity-focused solutions in creating a health-sustaining and more resilient food system.
Food safety policy; Nutrition integration; Evidence-based regulation; Public health nutrition; FDA–USDA collaboration; Vulnerable populations
Cloud Driven Enterprise Network Architecture for Oracle HR Systems Government Services and Biomedical Artificial Intelligence Platforms
Cloud-driven enterprise network architecture plays a critical role in supporting integrated human resource systems, digital government services, and biomedical artificial intelligence (AI) platforms. Modern organizations increasingly rely on scalable cloud infrastructures to manage workforce operations, public service delivery, and data-intensive biomedical research. Enterprise solutions such as Oracle Corporation Human Resource (HR) systems are widely deployed across government institutions for workforce management, payroll automation, and compliance monitoring. Simultaneously, cloud ecosystems provided by Amazon Web Services and Microsoft Azure enable high-performance computing, secure data exchange, and AI-driven biomedical analytics. This research examines architectural frameworks integrating cloud-native infrastructure, software-defined networking (SDN), zero-trust security, and AI orchestration layers within enterprise networks. It evaluates interoperability among Oracle HR platforms, digital government service portals, and biomedical AI research systems that process large-scale genomic and clinical datasets. The study explores performance optimization, cybersecurity integration, regulatory compliance, and governance models necessary for sustainable digital transformation. By analyzing technical components and operational methodologies, this research proposes a comprehensive architecture model designed to enhance scalability, resilience, security, and innovation across public sector and biomedical AI ecosystems.
Cloud Computing, Enterprise Architecture, Oracle HR Systems, Government Digital Services, Biomedical Artificial Intelligence, Cloud-Native Platforms, Human Capital Management (HCM), Digital Transformation, AI-Driven Analytics, Interoperability Frameworks, Secure Cloud Infrastructure, Data Governance, Regulatory Compliance, Microservices Architecture, Intelligent Automation
Automating Software Builds with Jenkins: Design Patterns and Failure Handling
Automated software build systems now form an essential part of the current software engineering practice, which allows quicker feedback, more stable integration and better software quality. Jenkins has been a popular automation server since it is extensible, flexible in pipeline model, and well-supported ecosystem. This paper looks at the software build automation provided by Jenkins with a special focus on how software design patterns and systematic failure handling mechanisms are applied. It discusses the degree to which established design patterns may be used to organize and construct pipelines to achieve enhanced modularity, reusability, and maintainability as well as dealing with the complexity of large-scale automation environments per se. The paper also examines typical types of build and pipeline failures and fault-tolerant strategies that help increase system robustness, minimize downtime, and minimize the spread of failures. With the implementation of design oriented automation combined with the use of structured failure management, build systems implemented using Jenkins can attain greater reliability, scalability, and efficiency. Whether through architectural discipline applied to building automation or through proactive failure management applied to making the continuous integration processes more resilient, the findings provided serve to underscore the significance of designing to avoid certain failures in the present case.
Jenkins, build automation, continuous integration, software design patterns, failure handling, fault tolerance
An Analysis of B2C and B2B Dynamics in the U.S. Consumer Goods Market
The U.S. consumer goods market is characterized by the coexistence of business-to-consumer (B2C) and business-tobusiness (B2B) exchange systems that differ substantially in structure, behavior, and strategic orientation. Despite their interdependence, these market types are often examined separately, resulting in limited understanding of their comparative dynamics within a single industry context. This study presents a comprehensive analysis of B2C and B2B dynamics in the U.S. consumer goods market by examining buying behavior, value creation mechanisms, distribution channel structures, relationship management practices, and the role of digital transformation. The analysis demonstrates that B2C markets are primarily driven by consumer perceptions of value, brand equity, service quality, and emotional engagement, whereas B2B markets emphasize organizational buying processes, efficiency, risk mitigation, and long-term relational exchanges. Differences in channel governance and supply chain coordination further distinguish the two market structures, influencing performance outcomes and competitive positioning. The study also highlights the increasing importance of e-commerce and integrated supply chain systems in reshaping both B2C and B2B interactions. The findings suggest that firms operating across both market domains must adopt integrated strategic approaches that align marketing, channel management, and relationship development to sustain competitive advantage. By clarifying the structural and strategic distinctions between consumer and organizational markets, this research contributes to marketing theory and provides actionable insights for managers navigating hybrid market environments within the U.S. consumer goods sector.
B2C marketing, B2B marketing, consumer goods market, relationship marketing, distribution channels, supply chain management.
Leveraging Social Media to Enable Emerging Consumer Brands to Compete with Established Brands
The expansion of social media has fundamentally altered the competitive landscape of consumer markets, enabling emerging brands to challenge the dominance of established firms. Historically, established brands benefited from superior financial resources, strong brand recognition, and control over traditional advertising channels. In contrast, emerging brands faced significant barriers to market entry and visibility. Social media platforms have reduced these barriers by offering accessible, interactive, and cost-efficient channels for brand communication, consumer engagement, and value creation. This study examines how emerging consumer brands leverage social media to enhance competitiveness through strategic branding, consumer engagement, analytics-driven decision making, and innovation support. By synthesizing existing theoretical and empirical research, the study demonstrates that social media enables direct brand–consumer interaction, facilitates electronic word-of-mouth, and supports the development of online brand communities. These mechanisms allow emerging brands to strengthen brand equity, improve customer relationships, and accelerate brand awareness despite limited resources. The findings further suggest that the strategic use of social media analytics and influencer collaboration enhances content effectiveness and performance measurement, while social media participation in product development fosters innovation and organizational learning. Overall, the study positions social media as a competitive equalizer that enables emerging consumer brands to adopt agile, data-informed, and engagement-driven strategies to compete with established brands. The research contributes to marketing literature by offering an integrated perspective on how social media reshapes brand competition and strategic brand management in contemporary consumer markets.
Social media marketing, Emerging consumer brands, Brand competitiveness, Consumer engagement, Digital branding, Online brand communities.
How Blockchain Technology Could Improve Transparency and Reduce Corruption in the Banking System
Corruption and transparency: Corruption and absence of transparency have been thorny issues in the banking systems especially in the developing and fragile economies where the inadequacy of the institutions and the absence of supervision erode the public confidence and sustainability of the financial systems. A new digital technology called blockchain has come up and has the potential to solve such problems by radically transforming the way records of financial transactions are stored, checked, and tracked. This paper will discuss how blockchain technology can enhance transparency and curb corruption in the banking system through its centralized properties of decentralization, immutability and distributed consensus. The paper based on the applicable theoretical frameworks, such as Principal–Agency Theory and institutional governance theories, examines why and how blockchain can help curb discretionary authority, increase audit trail, improve regulatory supervision, and curtail fraud and embezzlement, insider abuse opportunities. There is a special focus on the situation in the developing and conflict-prone banking systems, and it is illustrated by the Democratic Republic of the Congo, where money dependency, poor supervision, and low confidence of the population contribute to the risks of corruption. The paper goes further to describe how blockchains are used in practice, including distributed ledgers, smart contracts, and blockchain-enabled KYC and AML systems, and as well as discusses challenges in implementing blockchains, in terms of infrastructure, regulation, and capacity building. On the whole, the research can add to the scholarly and policy discussion about financial governance by showing how blockchain innovation can yield transparency, accountability, and trust restoration in the banking sector and stating the way to introduce it in a responsible and context-aware way.
Blockchain technology; Banking system; Transparency; Corruption reduction; Financial governance; Distributed ledger technology.
Real Time AI and Machine Learning Systems for Privacy Preserving Digital Advertising in Healthcare
Real-time artificial intelligence and machine learning systems are increasingly shaping the future of digital advertising in healthcare, where personalization, regulatory compliance, and data privacy must be carefully balanced. This paper presents a privacy-preserving architectural framework for healthcare-focused digital advertising platforms that leverage real-time AI and machine learning while safeguarding sensitive patient and consumer data. The proposed system integrates distributed data processing, secure model training, and privacy-enhancing technologies to enable intelligent ad targeting, performance optimization, and contextual relevance without exposing personally identifiable or protected health information. Machine learning models are deployed in real time to analyze behavioral signals, contextual metadata, and anonymized engagement patterns across healthcare digital channels. Privacy-preserving techniques such as data anonymization, tokenization, differential privacy, and federated learning are incorporated to ensure compliance with healthcare data protection regulations while maintaining model accuracy. The framework supports streaming analytics and low-latency inference to enable adaptive advertising strategies based on real-time user interactions, clinical content engagement, and platform performance metrics. The architecture is designed for scalability and resilience, leveraging cloud-native principles, distributed machine learning pipelines, and automated orchestration to handle high-volume advertising workloads across healthcare ecosystems. Security-by-design principles are embedded throughout the system to protect data at rest, in transit, and during model execution. Continuous monitoring and AI-driven anomaly detection enhance trust by identifying misuse, bias, or policy violations in advertising workflows. By unifying real-time AI intelligence with privacy-preserving machine learning and secure data processing, the proposed approach enables ethical, compliant, and effective digital advertising in healthcare environments. This framework supports responsible innovation by aligning personalized advertising outcomes with patient trust, regulatory mandates, and the broader objectives of digital health transformation.
real-time AI, machine learning systems, privacy-preserving analytics, digital advertising, healthcare data protection, federated learning, differential privacy, secure data pipelines, intelligent targeting, ethical AI, cloud-native analytics, regulatory compliance
Data-Driven Urban Resilience for Sustainable Cities: Leveraging Smart Infrastructure and Predictive Analytics
Urban areas are increasingly challenged by climate-related hazards, infrastructure stress, and rapid urbanization, making urban resilience a critical component of sustainable city development. In response, cities are adopting data-driven approaches that combine smart infrastructure and predictive analytics to enhance their capacity to anticipate, absorb, and recover from disruptions. This research examines how smart infrastructure systems and data-driven predictive analytics contribute to urban resilience and long-term sustainability. The study adopts a conceptual and analytical approach, synthesizing established theories of urban resilience, smart city development, and urban computing to develop an integrated framework for data-driven resilience planning. Smart infrastructure, supported by Internet of Things technologies and real-time data platforms, enables continuous monitoring of urban systems, improving infrastructure reliability, service efficiency, and adaptive capacity. Predictive analytics further enhances resilience by enabling forecasting, early warning, and proactive decision-making across critical urban sectors such as transportation, environmental management, and infrastructure maintenance. The proposed framework demonstrates how the interaction between digital infrastructure, analytics capabilities, and governance coordination can strengthen urban adaptability and sustainability performance. The findings highlight that data-driven urban resilience is not solely a technological outcome but also depends on effective institutional coordination and strategic integration of analytics into urban planning processes. This study contributes to the growing body of smart city and resilience research by clarifying the mechanisms through which data-driven systems support sustainable and resilient urban transitions.
Urban resilience, Smart infrastructure, Predictive analytics, Sustainable cities, Urban computing, Internet of Things, Smart city governance.
Governance, Institutions, and Urban Resilience: Evaluating Policy Coherence in Sustainable City Transitions
Rapid urbanization, climate variability, and infrastructure pressures are increasing the importance of resilience in contemporary urban planning and governance. Cities are required not only to manage environmental and social risks but also to coordinate institutional responses across multiple policy domains to ensure sustainable development. Governance systems and institutional arrangements therefore play a central role in shaping how urban areas respond to shocks, adapt to change, and transition toward sustainability. Despite growing policy attention to resilience, fragmented institutional structures and inconsistent policy implementation often limit the effectiveness of resilience planning in cities. This study examines the relationship between governance institutions, policy coherence, and urban resilience in the context of sustainable city transitions. Using a qualitative institutional and policy evaluation approach, the research analyzes how coordination among planning institutions, environmental governance systems, and infrastructure policy frameworks influences resilience outcomes. The study emphasizes the importance of integrated governance mechanisms and cross-sector policy alignment in strengthening adaptive capacity and improving sustainability performance in urban systems. The findings suggest that policy coherence and institutional coordination significantly enhance resilience planning and support sustainable urban transitions, while governance fragmentation weakens long-term resilience outcomes. Strengthening institutional collaboration and aligning urban policies across sectors are therefore essential for building resilient and sustainable cities.
urban resilience, governance institutions, policy coherence, sustainable cities, institutional coordination, urban planning
Transforming Database Leadership in the Era of Cloud-Native Automation and Resilient Operations
The rapid expansion of cloud platform adoption and the growing shift toward automated infrastructure created conditions in whichtraditional database administration models were no longer adequate for ensuring reliable and scalable data operations. The present studyexamines how database leadership must evolve within cloud native environments shaped by virtualization, managed database services,configuration automation, resilient architectures, and early DevOps influenced collaboration models. A mixed method approach was used,combining qualitative analysis of operational patterns across organizations adopting cloud hosted data platforms with quantitativeassessment of changes in workload distribution, automation coverage, and reliability indicators following platform oriented transformations. Findings indicate that organizations that reposition database professionals as strategic leaders of automated workflows,resilience planning, and cross functional coordination achieve measurable gains in operational stability and delivery velocity. Evidencealso shows that automation of provisioning, configuration, and schema changes reduces manual interventions and lowers error rates, whileresilience patterns such as replication and controlled failover improve service continuity. The research highlights how databaseresponsibilities expand beyond maintenance toward platform stewardship, architectural decision making, and proactive reliabilitymanagement. These shifts contribute to a more integrated data ecosystem in which database teams collaborate directly with application andoperations groups, improving communication pathways and enabling more predictable release cycles. The study’s contributions lie inidentifying the structural, technical, and organizational adjustments needed to align database leadership with cloud native automationpractices, offering a reference framework for institutions seeking to modernize data operations and strengthen the resilience of theirinformation environments.
Cloud native database platforms, database leadership, infrastructure automation, DevOps collaboration, managed relational services, configuration automation, operational resilience, database provisioning, schema change management, failover strategies, replication patterns, monitoring practices, incident response models, data platform governance, reliability engineering, distributed data environments
The Future of It Program Management: Ai-enhanced Cybersecurity for Next-gen Infrastructure Systems
The rapid evolution of digital technologies and the increasing complexity of information systems have significantly transformed the landscape of IT program management. As organizations migrate toward cloud-based, distributed, and data-intensive infrastructures, cybersecurity risks have grown in scale, sophistication, and frequency. Traditional security mechanisms, which rely heavily on rule-based detection and manual intervention, are increasingly inadequate for protecting next-generation infrastructure systems. In this context, artificial intelligence (AI) has emerged as a critical enabler for strengthening cybersecurity and redefining the future of IT program management. This article explores how AI-enhanced cybersecurity solutions are reshaping IT program management practices in next-generation infrastructure environments. It examines the integration of AI technologies—such as machine learning, deep learning, natural language processing, and predictive analytics—into cybersecurity frameworks to enable proactive threat detection, automated incident response, and continuous risk assessment. By leveraging large volumes of operational and security data, AI-driven systems can identify anomalies, predict potential attack vectors, and respond to threats in real time, thereby improving the resilience and reliability of critical IT infrastructures. The study also highlights the strategic implications of AI-enabled cybersecurity for IT program managers. As cybersecurity becomes deeply embedded within program governance, planning, and execution, program managers must adapt their approaches to risk management, resource allocation, and stakeholder coordination. AI-driven insights support more informed decision-making, enhance situational awareness, and enable alignment between cybersecurity objectives and broader organizational goals. However, the adoption of AI in cybersecurity introduces new challenges, including data privacy concerns, algorithmic transparency, model robustness, and regulatory compliance. By synthesizing existing academic literature and industry perspectives, this article provides a comprehensive view of how AI-enhanced cybersecurity is transforming IT program management in next-generation infrastructure systems. The findings suggest that organizations that strategically integrate AI into their cybersecurity and program management frameworks are better positioned to manage complex risks, ensure operational continuity, and sustain long-term digital transformation. Ultimately, the study underscores the importance of adopting AI-driven cybersecurity not only as a technical solution but as a core component of modern IT program management practice.
Artificial Intelligence; IT Program Management; AI-Enhanced Cybersecurity; Next-Generation Infrastructure Systems; Cyber Risk Management; Machine Learning; Predictive Analytics; Threat Intelligence; Network Security; Cloud and Edge Computing Security; Digital Transformation; Cyber Resilience; Automated Incident Response; Governance, Risk, and Compliance (GRC); Infrastructure Stability
Comparative analysis of NLP feature extraction methods for SMS spam classification
Short Message Service (SMS) spam classification is a critical application of natural language processing (NLP) aimed at mitigating unsolicited and malicious communications. This study presents a comparative analysis of widely used NLP feature extraction methods for SMS spam detection, evaluating their effectiveness, efficiency, and robustness. Traditional approaches such as Bag-of-Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF) are compared with distributed representations including Word2Vec, GloVe, and contextual embeddings derived from transformer-based models. Using standard benchmark SMS datasets, these feature extraction techniques are assessed in conjunction with common machine learning classifiers. Performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and computational cost. The results highlight the strengths and limitations of each method, showing that while traditional features offer simplicity and efficiency, advanced embeddings provide superior contextual understanding and classification performance. The study offers practical insights to guide the selection of feature extraction methods for effective and scalable SMS spam classification systems.
Enterprise-Scale AI Architecture for Secure Mobile Platforms with Governance-Driven Automation Large-Scale Data Warehousing and Machine Learning
Enterprise-scale adoption of artificial intelligence requires architectures that are secure, scalable, and governed across heterogeneous digital ecosystems. This paper presents an enterprise-scale AI architecture designed for secure mobile platforms that integrates governance-driven automation, large-scale data warehousing, and machine learning capabilities. The proposed architecture enables cross-domain intelligence by unifying data ingestion, storage, processing, and analytics while enforcing security, privacy, and compliance policies throughout the AI lifecycle. Large-scale data warehousing serves as the foundation for managing structured and unstructured data, supporting real-time and batch analytics for machine learning model training and inference. Governance-driven automation ensures transparency, auditability, and ethical AI operations through policy enforcement, access control, and continuous monitoring. The architecture supports scalable deployment across enterprise environments, enhances decision intelligence, and enables secure, data-driven automation for modern mobile platforms. This work demonstrates how integrated AI, data, and governance frameworks can address operational complexity and regulatory requirements in enterprise-scale systems.
Trustworthy and Adaptive AI Systems for Enterprise Analytics Cybersecurity and Decision Optimization Using API-First and Cloud-Native Architectures
The increasing reliance on data-driven decision-making in modern enterprises has accelerated the adoption of artificial intelligence, cloud-native platforms, and API-first architectures. However, challenges related to trust, security, adaptability, and governance continue to hinder the large-scale operationalization of AI systems. This paper proposes a trustworthy and adaptive AI framework designed for enterprise analytics, cybersecurity, and decision optimization using API-first and cloud-native architectural principles. The framework integrates modular AI services, real-time analytics pipelines, and adaptive learning mechanisms to support scalability, resilience, and rapid innovation. Trustworthiness is ensured through explainable AI, policy-driven governance, continuous monitoring, and secure data exchange via standardized APIs. Cybersecurity capabilities are embedded across the architecture using zero-trust principles, AI-driven threat detection, and automated risk assessment. Experimental and conceptual evaluation demonstrates that the proposed approach improves decision accuracy, system interoperability, and security posture while maintaining low latency and operational flexibility. The findings highlight how enterprises can balance performance, trust, and security in complex digital ecosystems by aligning AI design with cloud-native and API-centric strategies.
Trustworthy Artificial Intelligence, Enterprise Analytics, Adaptive AI Systems, Cybersecurity, Decision Optimization, API-First Architecture, Cloud-Native Systems, Explainable AI, Zero Trust Security, Intelligent Enterprise Platforms.
A Multi-Domain AI Framework for Enterprise Agility Integrating Retail Analytics with SAP Modernization and Secure Financial Intelligence
In the era of digital transformation, enterprises face mounting pressure to enhance agility, optimize operational efficiency, and strengthen cybersecurity resilience. Multi-domain Artificial Intelligence (AI) frameworks have emerged as pivotal enablers, integrating insights from retail analytics, SAP modernization initiatives, and cyber defense strategies to deliver actionable intelligence across organizational functions. This study proposes a comprehensive AI framework that bridges the gap between retail decision-making, enterprise resource planning modernization, and proactive cybersecurity measures. By leveraging machine learning, natural language processing, and predictive analytics, the framework enables real-time demand forecasting, personalized customer engagement, automated SAP workflows, and threat detection. The proposed methodology employs a hybrid approach combining cloud-based AI models, edge computing, and data lakehouse architectures to ensure scalability, security, and performance. Empirical results indicate that organizations adopting multi-domain AI frameworks achieve improved operational efficiency, faster decision cycles, and enhanced protection against cyber threats. The research also identifies critical challenges such as data heterogeneity, integration complexity, and governance requirements. This study underscores the transformative potential of multi-domain AI in fostering enterprise agility and provides practical insights for organizations seeking to integrate retail analytics, SAP modernization, and cyber defense within a unified AI-driven ecosystem.
Multi-domain AI, enterprise agility, retail analytics, SAP modernization, cyber defense, predictive analytics, AI frameworks, digital transformation, cloud AI, data integration.
Digital Strategy Alignment and Business Performance in SMEs
This study examines the relationship between digital strategy alignment and business performance in small and medium-sized enterprises (SMEs). The purpose of the study is to assess how aligning digital initiatives with overall business strategies influences organizational performance. A quantitative research approach was adopted, using survey data collected from selected SMEs. The data were analyzed using descriptive and inferential statistical techniques to evaluate the impact of digital strategy alignment on financial, operational, and market performance. The findings reveal that SMEs with a high level of digital strategy alignment experience improved efficiency, enhanced customer engagement, and stronger overall business performance compared to those with weak alignment. The study concludes that digital strategy alignment plays a significant role in driving sustainable performance in SMEs and recommends that SME owners and managers integrate digital planning into their core business strategies to maximize competitive advantage.
Change Management Challenges in SME Digital Transformation Initiatives
This study examines the unique change management challenges faced by small and medium-sized enterprises (SMEs) during digital transformation initiatives. While digital transformation is often approached from a technological perspective, this research emphasizes the human and organizational factors that determine success or failure in resource-constrained SME environments. The primary purpose is to identify, categorize, and analyze the most critical change management barriers specific to SMEs undergoing digital transformation. The study aims to provide actionable insights for SME leaders, consultants, and policymakers to better navigate the people-centric challenges of technological adoption. A qualitative multi-case study approach was adopted, involving semi-structured interviews with owners, managers, and employees across 12 SMEs in various sectors currently undergoing or recently completing digital transformation projects. Supplementary data was collected through document analysis and participant observation. Thematic analysis was used to identify recurring patterns and challenges. Successful digital transformation in SMEs is less dependent on technological sophistication and more on effective, empathetic change leadership. SME-specific advantages—such as organizational agility and closer interpersonal relationships—can be leveraged to overcome challenges. Practical mitigation requires visible leadership, continuous communication framed around employee benefits ("WIIFM"), incremental implementation, and investment in ongoing peer-supported learning. Future research should explore sector-specific frameworks and the role of digital coaching in SME contexts.
Ethical and Regulatory Challenges in Managing AI-Centric Cybersecurity Programs for Critical Infrastructure
Artificial intelligence (AI) has become a prevalent feature of modern cybersecurity programs for critical infrastructure systems such as those used in energy, transportation, healthcare, and industrial control systems. AI-based security solutions can provide more accurate detection, adaptive response, and scalability than traditional rule-based systems. However, the use of these systems in safety-critical and highly regulated sectors of the economy raises significant ethical and regulatory issues which present additional challenges to their effective management. These include risks of adversarial machine learning, explainability limitations of complex models, accountability gaps for autonomous decisions, and the challenge of meeting regulatory requirements for cybersecurity practices and data privacy in AI-driven operations. This paper presents a thorough, literature-based exploration of the ethical and regulatory challenges of program management for AI-focused cybersecurity programs for critical infrastructure. Building on existing research in areas such as adversarial machine learning, ethical AI, and cybersecurity for critical infrastructure, this work takes a program-level perspective to understand how technical, ethical, and regulatory risks manifest and intersect at various stages of the AI lifecycle. By reviewing existing frameworks, standards, and principles in these areas, this study identifies the structural limitations of traditional cybersecurity program governance frameworks when applied to AI-based solutions. By conceptually organizing these issues along several dimensions of responsible AI - namely robustness, transparency, accountability, and regulatory compliance - this study aims to provide an analytically coherent foundation to understand responsible AI in the context of critical infrastructure cybersecurity. The insights presented highlight opportunities for holistic governance approaches that balance innovation, ethics, and legal considerations, while supporting sustainable, long-term trust in AI-enabled cybersecurity solutions.
AI-centric cybersecurity, critical infrastructure protection, ethical AI governance, regulatory compliance, adversarial machine learning, cybersecurity program management.