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.
Unified Transformation Architecture for Artificial Intelligence SAP Integration Cloud-Native Computing Enterprise Secure Operations
The rapid evolution of digital technologies has transformed enterprise information systems by integrating Artificial Intelligence (AI), SAP-based enterprise resource planning, cloud-native computing, and enterprise secure operations into a unified digital ecosystem. Organizations increasingly require intelligent, scalable, and secure architectures capable of supporting business innovation while ensuring operational resilience and regulatory compliance. A Unified Transformation Architecture provides a strategic framework that combines AI-driven analytics, SAP integration platforms, cloud-native application development, and zero-trust security principles to create agile and intelligent enterprises. This architecture enables seamless interoperability among heterogeneous systems, automates business processes through machine learning, supports real-time decision-making, and enhances cybersecurity through continuous monitoring and adaptive threat detection. Cloud-native technologies such as containers, microservices, orchestration platforms, and DevSecOps practices further improve scalability, deployment flexibility, and service availability. The convergence of these technologies facilitates digital transformation by reducing operational complexity, improving resource utilization, and accelerating innovation across industries. This study examines the conceptual foundations, technological components, integration mechanisms, security considerations, and implementation methodologies associated with Unified Transformation Architecture. The research emphasizes the importance of standardized integration frameworks, intelligent automation, resilient infrastructure, and governance models that collectively enhance enterprise performance. The proposed methodology demonstrates how organizations can successfully implement secure, AI-enabled, cloud-native SAP ecosystems capable of addressing modern business challenges while maintaining operational excellence and long-term sustainability.
Artificial Intelligence, SAP Integration, Cloud-Native Computing, Enterprise Security, Digital Transformation, Microservices, DevSecOps, Zero Trust Architecture, Machine Learning, Kubernetes, Enterprise Architecture, Intelligent Automation, Cloud Computing, Business Process Integration, Cybersecurity
Intelligent Enterprise Technologies for Cloud Native Computing Autonomous Operations Data Analytics and Cybersecurity
Intelligent enterprise technologies have become fundamental to modern organizations seeking to improve operational efficiency, strengthen cybersecurity, and accelerate digital transformation through cloud-native computing, autonomous operations, and advanced data analytics. The convergence of artificial intelligence (AI), machine learning, cloud-native architectures, and intelligent automation enables enterprises to build scalable, resilient, and secure digital ecosystems capable of adapting to dynamic business environments. Cloud-native computing provides flexible infrastructure through microservices, containerization, orchestration platforms, and continuous integration and deployment, while autonomous operations reduce manual intervention by enabling intelligent monitoring, predictive maintenance, and automated decision-making. Advanced data analytics transforms vast volumes of structured and unstructured organizational data into actionable insights that support strategic planning, operational optimization, and personalized customer experiences. Cybersecurity remains a critical component of intelligent enterprise technologies by protecting digital assets, ensuring regulatory compliance, and mitigating increasingly sophisticated cyber threats through AI-powered detection and automated response mechanisms. Despite numerous advantages, organizations face challenges related to legacy system integration, workforce skill development, ethical AI implementation, data governance, and evolving cybersecurity risks. This study investigates the role of intelligent enterprise technologies in supporting cloud-native computing, autonomous operations, data analytics, and cybersecurity using a qualitative research methodology based on secondary data analysis. The findings demonstrate that strategic governance, technological innovation, organizational readiness, and continuous learning are essential for achieving resilient, secure, and sustainable enterprise transformation.
Artificial Intelligence, Intelligent Enterprise Technologies, Cloud-Native Computing, Autonomous Operations, Data Analytics, Cybersecurity, Machine Learning, DevSecOps, Kubernetes, Microservices, Predictive Analytics, Digital Transformation, Cloud Security, Enterprise Architecture, Business Intelligence
Enhancing Organizational Cyber Resilience Through the NIST Cybersecurity Framework
Today’s cyber threats are greater than ever before, posing new threats to operational continuity, data security, and stakeholder trust for organizations. In today’s fast-paced digital transformation, cyber resilience has emerged as a strategic priority and is not just about cyber security, but also about governance, risk management, incident response and business recovery. The study compares and contrasts the NIST Cybersecurity Framework with other frameworks and models that offer a structured and risk-based approach to identifying, protecting, detecting, responding, and recovering from cybersecurity incidents and will explore its benefits to improving the cyber resilience of organizations. The paper summarizes the key pillars of the framework and discusses how they relate to organizational governance, continuity planning, security culture and continuous improvement. It also examines key issues that can affect the effective implementation of these, such as leadership support, employee awareness, third-party risk management, and the use of new technologies like AI to detect and respond to threats. The study identifies best practices that help organizations sustain operations and comply with regulations without compromising their cyber-resilience. It concludes that the NIST Cybersecurity Framework provides an expansive cybersecurity baseline for developing adaptive, resilient, and sustainable cybersecurity programs that can help reduce the impact of disruptions and meet long-term organizational goals. The results offer valuable practical guidance for organizational leaders, cybersecurity personnel, policy-makers and researchers on how to better improve cyber-resilience in the digital era that is growing more complex.
Organizational cyber resilience, NIST Cybersecurity Framework, cybersecurity governance, cyber risk management, business continuity, incident response, cybersecurity resilience, digital transformation, threat detection, information security
Artificial Intelligence and SAP for Intelligent Wastewater Treatment Plant Management
Wastewater treatment plants are complex, continuously operating facilities whose performance directly affects public and environmental health, requiring careful coordination of biological treatment processes, mechanical equipment, chemical dosing, energy consumption, and regulatory compliance reporting. Many plants generate substantial volumes of process, sensor, and maintenance data, yet this data frequently remains fragmented across supervisory control and data acquisition (SCADA) systems, laboratory information management systems (LIMS), and enterprise resource planning (ERP) platforms that were not designed to interoperate closely, limiting the extent to which plant operators can act on predictive insight in a timely, coordinated manner. This paper examines how artificial intelligence (AI) and machine learning (ML) can be integrated with SAP enterprise systems, particularly SAP S/4HANA, SAP Enterprise Asset Management (EAM), and SAP Environment, Health, and Safety (EHS) management, to support intelligent wastewater treatment plant operations. We propose an integration architecture in which AI-driven process anomaly detection, equipment failure prediction, and effluent quality forecasting outputs are translated into structured SAP maintenance notifications, work orders, and compliance records, connecting plant-level predictive intelligence to the enterprise systems that manage plant maintenance, procurement, and regulatory reporting. The paper discusses relevant AI/ML techniques for treatment process optimization and equipment health monitoring, SAP-specific integration mechanisms, and the public health and regulatory compliance considerations that make explainability and data governance particularly important in this domain. We conclude with a discussion of implementation challenges and directions for future research on integrated plant management systems.
Wastewater treatment; artificial intelligence; machine learning; SAP; enterprise asset management; SCADA integration; predictive maintenance; process optimization; public health; regulatory compliance.
Multimodal AI for Pilot Skill Assessment Using Physiological Signals
Yet traditional methods of evaluating pilot skill, focused mainly on subjective instructor ratings or post hoc evaluation of aviation performance, only measure the observable products of cognitive functioning of skilled flying, and flight-related cognitive functioning is vitally important to modern aviation. This paper aims at developing four new frameworks: the MultiModal Aviator Performance and State Assessment AI Framework (MAPS-AI), that combines four physiological signals modalities (EEG, fNIRS, ECG and eye-tracking) and deep learning architectures for the purpose of real-time and objective monitoring of cognitive state in-flight while performing cognitive tasks during both flight training and flight operations. This framework brings together multi-sensor fusion and AI classification pipelines, limits of which are explored in the psychophysiological literature on pilot mental workload, the cognitive fatigue literature and EEG monitoring in actual flight conditions, fNIRS-based engagement detection in landing scenarios, multimodal workload assessment, and recent developments in compact EEG deep learning. A six-state cognitive state taxonomy is proposed and evidence is compiled demonstrating that incorporating progressive multimodal fusion yields a classification accuracy of around 83% compared a multimodal (EEG+EEG+EEG+EEG) approach with a accuracy of around 65% (EEG-only). Discussions of practical implications for aviation training, support systems for instructors, and human-machine interface are presented. The framework offers a conceptually sound basis for building an operationally viable assessment tools for pilots based on AI.
multimodal AI; pilot skill assessment; EEG; fNIRS; physiological signals; deep learning; mental workload
Machine Learning in Mental Healthcare: Improving Diagnosis and Treatment of Anxiety and Depression
Mental health disorders, particularly anxiety and depression, constitute a global public health crisis affecting over 700 million individuals worldwide. Traditional diagnostic methodologies rely heavily on subjective self-reporting and clinician observation, leading to significant delays in accurate diagnosis and treatment initiation. This paper provides a comprehensive review of the application of machine learning (ML) techniques in improving the diagnosis and treatment outcomes for anxiety and depression. We examine supervised learning models including Support Vector Machines (SVM), Random Forests, and deep neural networks applied to clinical datasets, electronic health records, and neuroimaging data. Additionally, we explore Natural Language Processing (NLP) approaches for sentiment analysis in social media and therapy transcripts, and the integration of wearable biosensor data with ML pipelines. Our analysis demonstrates that ML models consistently achieve diagnostic accuracy rates of 75–92% across varied datasets, outperforming traditional screening instruments. We also discuss current ethical challenges, data privacy concerns, explainability of AI models, and propose a framework for responsible clinical integration. The findings suggest that ML-driven tools, when deployed ethically, hold substantial promise for augmenting clinical decision-making, personalizing treatment pathways, and extending mental healthcare access to underserved populations.
Deep Learning, Natural Language Processing, Clinical Decision Support, Explainable AI
Intelligent Cloud Framework for Enterprise Reference Data Governance and Machine Learning-Based Transaction Risk Management
Enterprise reference data governance and transaction risk management have become critical components of modern digital platforms due to the increasing volume, velocity, and complexity of organizational data. Traditional governance approaches often struggle to maintain data quality, consistency, and compliance while effectively identifying fraudulent or high-risk transactions in real time. Machine learning (ML) offers innovative solutions by enabling automated data validation, anomaly detection, predictive analytics, and intelligent decision-making. This study examines the application of machine learning techniques in enterprise reference data governance and transaction risk management across digital platforms. The proposed framework integrates supervised and unsupervised learning algorithms to improve data accuracy, eliminate duplication, classify reference entities, and detect suspicious transaction patterns. By leveraging advanced analytics, organizations can enhance regulatory compliance, operational efficiency, and risk mitigation capabilities. The study also explores the role of ML-driven governance models in supporting data stewardship, master data management, and continuous monitoring processes. Furthermore, the research highlights the benefits and challenges associated with implementing machine learning solutions, including data privacy concerns, algorithmic bias, and integration complexity. The findings suggest that machine learning significantly improves governance effectiveness and transaction security while enabling organizations to adapt to evolving digital ecosystems. The proposed framework provides valuable insights for enterprises seeking to strengthen data governance practices and minimize transaction-related risks in a competitive digital environment.
Machine Learning, Enterprise Data Governance, Reference Data Management, Transaction Risk Management, Digital Platforms, Artificial Intelligence, Data Quality, Fraud Detection, Predictive Analytics, Data Compliance, Anomaly Detection, Risk Assessment, Data Stewardship, Master Data Management, Digital Transformation
Intelligent Enterprise Systems Using Agentic AI Cloud Computing SAP Cybersecurity and Explainable AI
The rapid evolution of Artificial Intelligence (AI) has given rise to agentic AI systems that act autonomously, adaptively, and intelligently within enterprise environments. Intelligent enterprise systems, particularly those powered by SAP, are increasingly integrating agentic AI with cloud computing to enhance cybersecurity and operational resilience. Explainable AI (XAI) further strengthens these systems by ensuring transparency, accountability, and trust in automated decision-making. This paper explores the convergence of agentic AI, secure cloud computing, SAP cybersecurity, and explainable AI within intelligent enterprise platforms. It examines how agentic AI can autonomously detect threats, optimize workflows, and provide predictive insights, while XAI ensures that stakeholders understand and trust AI-driven outcomes. A literature review highlights existing research on AI-enabled SAP systems, cloud security frameworks, and explainable AI models. A mixed-method research methodology is proposed, combining case studies with quantitative analysis to evaluate the effectiveness of agentic AI-driven SAP platforms. Advantages such as automation, proactive risk management, and transparency are discussed alongside disadvantages including complexity, cost, and ethical challenges. The findings suggest that agentic AI and XAI represent a paradigm shift in enterprise technology, enabling secure, intelligent, and trustworthy digital ecosystems.
Agentic AI, intelligent enterprise systems, SAP cybersecurity, explainable AI, cloud computing, predictive analytics, enterprise resilience, digital transformation, autonomous systems, AI transparency
Role of Metadata Management and Data Governance in Achieving Regulatory Compliance in Enterprise Data Ecosystems
As enterprise data ecosystems grow, so do the complexities of meeting regulatory mandates, data integrity, and transparency in the organization’s operations. Data management is getting more complex as organizations adopt distributed architectures, such as cloud-based and hybrid, which means it is more difficult to manage data on more than a single platform. This has led to good data governance and metadata management becoming a key part of enterprise data strategies. This study looks at 2025 regulatory compliance in an enterprise data ecosystem and the role of metadata management and data governance in the process. Data visibility, traceability, and control requires the proper foundation of metadata, and data governance provides the right policies, standards and accountability structures to ensure compliance. Combined, they help organizations manage regulatory needs like data lineage tracking, auditability, and data quality assurance. The paper suggests an integrated approach to align metadata management to governance principles and streamline compliance readiness and mitigate data risks. It also provides an example of how to make data more transparent, increase control and facilitate informed decisions with structured data governance practices. The results illustrate the importance of metadata and governance to establishing scalable, compliant and resilient enterprise data ecosystems.
Metadata Management, Data Governance, Regulatory Compliance, Enterprise Data Ecosystems, Data Lineage, Data Quality, Compliance Frameworks, Data Transparency
Enhancing Enterprise Data Governance Maturity Through Cloud Based Data Integration Frameworks in Modern Organizations
As digital transformation takes center stage, data has become a critical resource for modern businesses to inform their decisions, drive innovation, and gain a competitive edge. But with many heterogeneous data sources and legacy system restrictions, effective data governance is challenging. Traditional data governance models are not always scalable, real-time, or flexible enough to integrate, making it difficult to achieve higher levels of data governance maturity. This research paper investigates the use of cloud-based data integration frameworks for improving the maturity of enterprise data governance in today’s business landscape. The research topics include the history of data governance models, and the challenges of traditional integration architectures. It offers a vision of a scalable ingestion pipeline, a centralized metadata management system, automated policy enforcement and real-time analytics within a cloud-based data integration architecture. The framework adopts cloud computing principles like elasticity, distributed processing, and service-oriented architecture to enhance data quality, consistency, accessibility, and compliance. It covers topics related to security, privacy issues, vendor dependency, and provides strategic recommendations for organizations looking to evolve to more advanced, cloud-based data governance ecosystems.
Data Governance, Cloud Computing, Data Integration, Governance Maturity, Enterprise Data Management, Data Quality, Metadata Management, ETL/ELT
Biochar-Based Treatment Technologies for PFAS Removal from Industrial Storamwater and Wastewater: Mechanisms, Field Applications, and Future Regulatory Implications
Per- and polyfluoroalkyl substances (PFAS) represent a critical emerging contaminant class due to their extreme persistence, bioaccumulation potential, and documented adverse health effects in humans and wildlife. Biochar, a pyrolytic carbonaceous material derived from renewable biomass, has emerged as a promising sustainable and cost-effective adsorbent for PFAS removal from industrial stormwater and wastewater. This review synthesizes current knowledge on biochar-based treatment technologies for PFAS remediation, encompassing adsorption mechanisms, surface modification strategies, field-scale applications, and regulatory frameworks. Biochar's high microporous volumes (0.1–1.0 cm³/g), aromaticity, and surface oxygen-containing functional groups make it effective for PFAS adsorption. (Chavan et al., 2024) The adsorption mechanism is composed mainly of electrostatic interaction between positively charged biochar and deprotonated PFAS, hydrogen bonding between anionic species of PFAS and functional groups, and hydrophobic interaction between long-chain hydrophobic PFAS and biochar, with electrostatic interaction playing a major role in acidic media. (Zhang et al., 2023) Despite biochar's advantages, challenges remain in achieving complete defluorination, managing short-chain PFAS, optimizing regeneration, and scaling field applications. This review identifies key research gaps, technological barriers, and opportunities for integrating biochar technologies into comprehensive treatment trains to address the growing regulatory pressures and environmental risks posed by PFAS contamination globally.
Real-Time Claims Processing Using Event-Driven Architectures
In today's fast-paced insurance and financial landscape, where speed, accuracy, and automation are paramount, real-time claims processing is a feature that can significantly impact customer satisfaction and operational efficiency. However, traditional batch-type processing systems can face challenges such as latency, scalability, and slow decision-making, especially in high-volume settings. Event-Driven Architectures (EDA) offer a game-changing solution, allowing the system to respond immediately to new events like claims submissions, verification updates, and fraud alerts. Asynchronous communication and decoupling of system components enables EDA to facilitate continuous data flow, real-time analytics and automated decision-making across distributed services. This architectural model offers greater responsiveness, better system scalability and greater fault-tolerance in dynamic operational environments. In addition, the stream processing and event correlation functionality helps speed fraud detection and streamlines claims validation. Overall, EDA provides a solid basis for developing new, adaptive, efficient and resilient real-time claims processing systems.
Event-Driven Architecture, Real-Time Processing, Claims Management, Distributed Systems, Stream Processing, Fraud Detection, Microservices, Scalability
Cloud-Based Healthcare CRM Systems for Improved Patient Engagement
Cloud-based healthcare Customer Relationship Management (CRM) systems have emerged as a transformative approach to enhancing patient engagement, improving care coordination, and enabling personalized healthcare delivery. By leveraging cloud computing infrastructures, healthcare organizations can integrate patient data, communication channels, and analytics tools into a unified platform that supports real-time interaction between spatients and providers. These systems facilitate efficient management of electronic health records, enable remote monitoring, and support patient-centered services such as appointment scheduling, automated reminders, and telehealth consultations. The adoption of cloud-based CRM in healthcare also enhances scalability, reduces operational costs, and improves accessibility to healthcare services across diverse populations. Despite these advantages, the implementation of cloud-based healthcare CRM systems presents challenges related to data security, interoperability with legacy systems, and user adoption, particularly in resource-constrained environments. However, ongoing advancements in cloud technologies and healthcare informatics continue to address these limitations, making such systems increasingly viable and effective. Overall, cloud-based healthcare CRM systems represent a significant advancement in digital health innovation, offering substantial potential to improve patient satisfaction, engagement, and overall healthcare outcomes.
Cloud Computing, Healthcare CRM, Patient Engagement, Electronic Health Records, Telehealth, Digital Health Systems, Patient-Centered Care, Health Informatics, Healthcare Analytics, Remote Monitoring
Federated Learning Architecture for Privacy-Preserving Rural Telehealth Intelligence Across Multi-State U.S. Healthcare Networks
Rural healthcare systems in the United States continue to face persistent challenges related to limited specialist access, fragmented electronic health records, provider shortages, delayed diagnosis, and uneven telehealth infrastructure. Although telehealth has expanded clinical reach across underserved communities, the effective use of distributed patient data for artificial intelligence remains restricted by privacy regulations, institutional data silos, cybersecurity risks, and cross-state governance barriers. This paper proposes a federated learning architecture for privacy-preserving rural telehealth intelligence across multi-state U.S. healthcare networks. The proposed framework enables rural clinics, telehealth providers, hospitals, and state health networks to collaboratively train predictive models without transferring raw patient data to a central server. It integrates local model training, secure aggregation, differential privacy, encrypted communication, audit controls, and clinical decision-support feedback to support privacy-aware intelligence generation. The architecture is designed to improve chronic disease risk prediction, remote patient monitoring, early deterioration detection, patient stratification, and telehealth triage while reducing exposure of sensitive health information. By combining federated learning with privacy-preserving security layers and rural health governance principles, the paper contributes a scalable model for trustworthy, equitable, and interoperable telehealth intelligence in geographically dispersed healthcare environments.
Federated learning; rural telehealth; privacy-preserving AI; differential privacy; secure aggregation; healthcare networks; clinical decision support.
Municipal Solid Waste Management in Zambian Cities, 2018 to 2023: Pilots, Impacts and Lessons Through the TownLoop Framework
Between 2018 and 2023 Zambia put in place its first dedicated law for solid waste, the Solid Waste Regulation and Management Act No. 20 of 2018, and began turning it into working systems in its cities. This paper reviews that experience in Lusaka, in the Copperbelt cities of Ndola and Kitwe, and in a group of smaller provincial towns, using only evidence published before 2024. It reads the Zambian record through the TownLoop framework, a locally financed, closed loop resource recovery model built for small and secondary African cities. The fit is close, partly because Lusaka's Manja Pamodzi scheme is one of the cases from which TownLoop was drawn. A separate chapter compares how Ghana and Zambia put zoned private and community collection into practice, drawing on the documented experience of Accra and Tamale alongside Lusaka and Ndola. The evidence shows clear institutional progress in Zambia: a purpose built law, a dedicated Lusaka utility, collection zones served by community enterprises and franchise contractors, and a visible recycling chain. It also shows the weaknesses the framework predicts. Collection reached only about a third of the capital's waste, payment and verification were thin, the incentive still rewarded tonnage over coverage, the work was heavily donor funded, the organic half of the stream went to landfill rather than to compost, and the model spread slowly beyond Lusaka. The paper closes by turning TownLoop's mechanisms into recommendations for the next phase of reform.
Enterprise Workflow Automation and Workforce Productivity: Evaluating the Economic Benefits of Service Now Adoption Across Industries
The concept of Enterprise Service Management (ESM) has come to the fore as a means for organisations to implement digital technology in a way that enables them to become more cost-efficient, service-smooth and operationally effective. With continuous investments in automating workflows and building integrated service platforms, it's imperative to measure Return on Investment (ROI) to determine the effectiveness and business value of these efforts. This study aims to discuss about 'ServiceNow' as the major Enterprise Service Management (ESM) platform that supports business functions to reduce costs and improve organization's efficiency. The benefits of ServiceNow's features, such as workflow automation, self-service portals, knowledge management, analytics, and the low-code/no-code development tools, for enhancing service performance and optimizing resources are examined. In addition, it explores important ROI metrics, including employee satisfaction, process efficiency, service quality enhancements, operational cost reductions, and productivity improve-ments. Implementations costs are one challenge to ROI measurement along with problems faced with organizational change management, integration complexity and governance concerns are also discussed in the study. Results show that by using ServiceNow, organisations can experience significant service delivery acceleration, transparency of operations and productivity of the workforce, and less of manual processes and administrative costs. The paper wraps up by explaining that a systematic ROI evaluation process is essential for delivering the greatest ROI for an ESM investment, and that ServiceNow offers an excellent groundwork for organizations set on achieving operational excellence and long-term digital transformation triumph.
Enterprise Service Management (ESM), Return on Investment (ROI), ServiceNow, Organizational Efficiency, Cost Reduction, Workflow Automation, Digital Transformation, Low-Code Development, No-Code Platforms, Service Delivery Optimization.
Adaptive Enterprise Intelligence Architecture for AI-Powered Cloud Operations Threat Mitigation and Business Process Automation
The rapid adoption of cloud computing, artificial intelligence (AI), and digital transformation initiatives has significantly reshaped enterprise operations across industries. Organizations increasingly depend on cloud environments to support scalable infrastructure, data-driven decision-making, and automated business processes. However, this transformation introduces challenges related to cybersecurity threats, operational complexity, resource optimization, and governance. Adaptive Enterprise Intelligence Architecture (AEIA) emerges as an integrated framework that combines artificial intelligence, machine learning, cloud orchestration, threat intelligence, and business process automation to enhance organizational resilience and operational efficiency. This study explores the design and implementation of an adaptive enterprise intelligence architecture capable of supporting AI-powered cloud operations, proactive threat mitigation, and intelligent automation. The architecture leverages real-time analytics, predictive modeling, autonomous decision-making mechanisms, and continuous monitoring to improve performance while reducing risks. Furthermore, the framework enables enterprises to dynamically adapt to changing business requirements, evolving cyber threats, and fluctuating workloads within cloud ecosystems. The research examines existing technological developments, identifies critical architectural components, and proposes a methodology for implementing adaptive intelligence systems across enterprise environments. The findings suggest that integrating AI-driven operational intelligence with cloud-native security and automation capabilities can significantly improve organizational agility, cybersecurity posture, resource utilization, compliance management, and overall business productivity in increasingly complex digital ecosystems.
AI-Enhanced Agile Management for Intelligent Enterprise Decision Making and Business Optimization
Artificial Intelligence (AI) and Agile Management have emerged as transformative forces that enable organizations to improve decision-making capabilities and optimize business performance in highly dynamic environments. The integration of AI technologies with agile management practices creates intelligent enterprises capable of responding rapidly to market changes, customer demands, and operational challenges. This study examines the role of AI-enhanced agile management in facilitating intelligent enterprise decision-making and business optimization. The research explores how AI technologies such as machine learning, predictive analytics, natural language processing, and automation support agile frameworks by providing real-time insights, data-driven recommendations, and adaptive operational capabilities. Through agile methodologies, organizations can continuously evaluate business processes, implement improvements, and respond proactively to emerging opportunities. The study highlights the benefits of combining AI with agile principles, including increased efficiency, enhanced customer satisfaction, improved forecasting accuracy, and accelerated innovation. However, challenges such as data privacy concerns, implementation complexity, organizational resistance, and skill shortages remain significant barriers. The findings suggest that organizations adopting AI-enhanced agile management achieve superior strategic flexibility and operational resilience. The research concludes that AI-driven agility represents a critical capability for modern enterprises seeking sustainable growth, competitive advantage, and intelligent business optimization in an increasingly digital and data-intensive business environment.
Artificial Intelligence, Agile Management, Intelligent Decision Making, Business Optimization, Machine Learning, Predictive Analytics, Digital Transformation, Enterprise Agility, Data-Driven Decision Making, Organizational Innovation, Automation, Strategic Flexibility
Measuring Impact: Metrics for Evaluating Girl-Focused Media Interventions in Low-Resource Settings
Media interventions specifically targeting adolescent girls have proven to be valuable strategies for education, empowerment, health promotion and social inclusion, especially in low-resource areas where alternative support and safety mechanisms might be less available. Although radio programs, digital campaigns, educational programs, social media campaigns and community-based communication programs are all gaining ground, the measurement of their effectiveness is still disjointed and unstandardized. Current assessment tools tend to focus on reach and participation measures without considering more fundamental outcomes of agency, aspirations, psychosocial health, behaviour change and empowerment. This paper explores building a set of comprehensive metrics for assessing the effects of the comprehensive media interventions targeting girls in low-resource contexts. It suggests a framework of evaluation in five dimensions, a multidimensional approach combining quantitative and qualitative measures in each dimension, including reach and engagement, knowledge acquisition, psychosocial development, behavioral outcomes, and empowerment outcomes. The study also seeks to discuss methodological issues related to collection of data, validity check and contextualisation in conditions where resources are limited. Highlighting the importance of comparing output, outcome and impact indicators, the paper pinpoints indicators that offer substantial evidence of programme effectiveness and are feasible to implement in situations of infrastructural and socioeconomic constraints. The results underscore the need to shift from exposure measures to assessment systems which reflect shifts in confidence, decision-making ability, educational goals, social engagement, and long term social outcomes. The proposed framework provides practical guidance for researchers, policymakers, development organizations, and media practitioners to create evidence-based interventions and develop a standardized method to assess impact. In conclusion, the study makes a valuable contribution to the issue of accountability, program design, and the effectiveness of media-based programs to improve the wellbeing and empowerment of girls in low-resources contexts.
Girl-focused media interventions, impact evaluation, empowerment metrics, adolescent girls, low-resource settings, media effectiveness, behavioral change, psychosocial outcomes, monitoring and evaluation, gender empowerment.
Next-Generation Enterprise Platforms through Explainable AI and Zero Trust Security for Digital Resilience
The rapid evolution of digital transformation has significantly increased the complexity and importance of mission-critical enterprise platforms across industries such as healthcare, finance, manufacturing, telecommunications, and government services. These platforms require high levels of reliability, security, scalability, and transparency to support essential organizational operations. Traditional approaches to enterprise system management are often insufficient in addressing emerging challenges associated with cyber threats, increasing data volumes, regulatory compliance, and dynamic business requirements. This study explores the integration of Explainable Artificial Intelligence (XAI), Zero Trust Security, and Adaptive Infrastructure Engineering as a comprehensive framework for enhancing mission-critical enterprise platforms. Explainable AI improves transparency and trust by enabling stakeholders to understand and validate AI-driven decisions. Zero Trust Security strengthens cybersecurity by continuously verifying users, devices, and applications regardless of network location. Adaptive Infrastructure Engineering provides flexible and resilient technological foundations capable of responding dynamically to changing operational demands and environmental conditions. The research examines how the convergence of these technologies contributes to improved decision-making, enhanced cybersecurity resilience, optimized resource utilization, and operational continuity. Furthermore, the study investigates implementation strategies, challenges, and opportunities associated with deploying intelligent and secure enterprise environments. The findings suggest that combining explainable intelligence, continuous security validation, and adaptive infrastructure mechanisms creates a robust ecosystem capable of supporting sustainable innovation, organizational agility, and long-term business success in increasingly complex digital environments.
Explainable Artificial Intelligence, XAI, Zero Trust Security, Adaptive Infrastructure Engineering, Mission-Critical Systems, Enterprise Platforms, Cybersecurity, Digital Transformation, Artificial Intelligence, Cloud Computing, Infrastructure Resilience, Enterprise Security, Risk Management, Intelligent Systems, Automation, Data Governance, Predictive Analytics, Organizational Agility, Infrastructure Optimization, Secure Digital Ecosystems
Transforming Enterprise Office Networks with EVPN-VXLAN: A BGP-Based Approach to Layer 2 Elimination
As new demands for scalability, flexibility, cloud integration, and operational efficiency arise in enterprise office networks, there is a need for significant transformation. While using traditional Layer 2 spanning tree protocols may give some enterprises lower traffic utilization, slower convergence, higher broadcast overheads, and limited scalability, it can also add complexity when it comes to managing them.Traditional Layer 2 spanning tree protocols can also limit an enterprise's traffic utilization, convergence rate, broadcast overheads, and scalability in a large-scale network, while also increasing management complexity. In this study, the authors present an enterprise network modernization approach that removes legacy L2 dependencies by implementing Ethernet VPN (EVPN) and Virtual Extensible LAN (VXLAN) technologies in combination with a Border Gateway Protocol (BGP)-based control plane. The proposed architecture will bring EVPN-VXLAN over-lay technology to the enterprise office infrastructures to meet the needs of scalable segmentation, optimized traffic forwarding, enhanced redundancy, and rapid converging. The model enhances network resiliency, operational management, and seamlessly integrates into SDN and cloud networking environments, all while replacing legacy Layer 2 mechanisms with a BGP-based approach. The study also considers the architectural benefits of EVPN-VXLAN in the areas of throughput, fault tolerance, scalability, and broadcast traffic reduction when compared to traditional enterprise networking solutions. Furthermore, the study sheds light on the importance of programmable networking, automation, and intelligent monitoring for enhancing enterprise network performance and adaptability. The results prove that EVPN-VXLAN is a powerful, future-proof architecture that can enable enterprise digital transformation projects while solving the problems of traditional Layer 2 architectures.
EVPN-VXLAN, BGP, Enterprise Networks, Layer 2 Elimination, Network Transformation, VXLAN Overlay, Digital Transformation, Software-Defined Networking, Enterprise Architecture, Network Scalability.
Autonomous AI Driven Monitoring and Performance Scaling for Cloud Native SAP Enterprise Platforms
Cloud-native enterprise platforms, particularly SAP-based ecosystems, have become the backbone of modern digital business operations, supporting mission-critical workloads across finance, supply chain, human capital management, and analytics. As organizations increasingly migrate SAP workloads to cloud-native infrastructures, the complexity of managing performance, availability, security, and scalability has grown exponentially. Traditional monitoring tools and manual scaling approaches are no longer sufficient to handle dynamic workloads, microservices-based architectures, and distributed computing environments. Autonomous AI-driven monitoring and performance scaling has emerged as a transformative paradigm that leverages machine learning, predictive analytics, anomaly detection, and self-healing mechanisms to optimize cloud-native SAP enterprise systems. This essay explores how autonomous AI systems enhance observability, predict system failures, optimize resource allocation, and enable real-time performance scaling in SAP cloud environments. It further examines the integration of AI-driven telemetry analysis, intelligent alerting systems, and adaptive infrastructure orchestration in improving system resilience and operational efficiency. The study also highlights challenges such as model drift, data heterogeneity, governance complexity, and explainability in autonomous decision-making systems. A qualitative conceptual methodology based on secondary literature synthesis is used to analyze existing frameworks and emerging trends. Findings indicate that autonomous AI-driven monitoring significantly improves system uptime, reduces operational costs, enhances scalability, and strengthens reliability in cloud-native SAP enterprise platforms, enabling organizations to achieve self-optimizing and intelligent enterprise infrastructures.
Autonomous AI, SAP enterprise systems, cloud-native architecture, performance scaling, intelligent monitoring, machine learning, AIOps, predictive analytics, microservices, observability, DevOps automation, cloud orchestration, system resilience, anomaly detection, self-healing systems
Intelligent Enterprise Technologies for Cloud Security Data Analytics and AI-Based Distributed Systems Frameworks
The rapid advancement of enterprise computing technologies has accelerated the integration of cloud security, data analytics, and artificial intelligence (AI)-based distributed systems across modern organizations. Enterprises increasingly rely on intelligent technological infrastructures to improve operational efficiency, cybersecurity resilience, scalability, and real-time decision-making capabilities. This study explores the role of intelligent enterprise technologies in securing cloud environments, managing large-scale data analytics, and supporting distributed systems frameworks driven by AI and machine learning algorithms. The research examines how intelligent systems enhance threat detection, predictive analytics, automated resource allocation, and distributed processing within enterprise ecosystems. It also investigates the integration of edge computing, blockchain technologies, and real-time analytics for secure and adaptive enterprise operations. The study adopts a qualitative and conceptual research methodology using secondary data from scholarly journals, industry reports, and enterprise case studies. The findings indicate that AI-powered distributed systems significantly strengthen cybersecurity performance, optimize data management, and improve enterprise scalability. However, challenges such as interoperability, governance complexity, implementation costs, and privacy concerns remain significant barriers. The proposed intelligent enterprise framework contributes to sustainable digital transformation by enabling secure, intelligent, scalable, and data-driven enterprise environments.
Cloud Security, Artificial Intelligence, Distributed Systems, Enterprise Technologies, Data Analytics, Machine Learning, Cybersecurity, Big Data, Edge Computing, Blockchain, Intelligent Systems, Cloud Computing, Predictive Analytics, Digital Transformation, Enterprise Frameworks
Cloud-Native AI and Data Governance Architectures for Real-Time Fraud Detection and Financial Risk Prediction
Cloud-native AI and data governance architectures are becoming essential components in modern financial systems, particularly for real-time fraud detection and financial risk prediction. As digital financial transactions increase in volume and complexity, traditional rule-based systems are no longer sufficient to detect sophisticated fraud patterns or assess dynamic financial risks. Cloud-native architectures enable scalable, elastic, and distributed processing of large-scale financial data streams, while AI-driven models provide predictive intelligence for identifying anomalies and forecasting risk exposure. However, the effectiveness of these systems heavily depends on strong data governance frameworks that ensure data quality, privacy, regulatory compliance, and secure data access. This study explores the integration of cloud-native computing, artificial intelligence, and governance mechanisms to build robust fraud detection and risk prediction systems. It highlights the use of microservices, container orchestration, and real-time streaming analytics for efficient processing of financial data. Additionally, the research examines how machine learning models, including deep learning and ensemble methods, enhance fraud detection accuracy and risk scoring. The study also emphasizes governance strategies such as data lineage tracking, policy enforcement, and compliance automation. Overall, the convergence of these technologies enables financial institutions to build intelligent, secure, and adaptive systems capable of responding to evolving cyber threats and financial uncertainties in real time.
Cloud-native AI, data governance, fraud detection, financial risk prediction, real-time analytics, microservices, Kubernetes, machine learning, compliance, cybersecurity
Intelligent Enterprise Cloud Platforms for Financial Risk Prediction Cybersecurity and Data Governance
Intelligent enterprise cloud platforms are transforming modern financial systems by integrating advanced technologies such as Artificial Intelligence (AI), cloud computing, cybersecurity frameworks, and data governance mechanisms. Financial institutions increasingly rely on cloud-based enterprise infrastructures to manage transactions, customer information, regulatory compliance, and risk analysis processes. However, the rapid growth of digital financial services has also increased exposure to cyber threats, data breaches, fraud, and operational risks. Traditional financial risk management approaches often lack the scalability, speed, and intelligence required to process large volumes of real-time financial data. Intelligent cloud platforms address these limitations by utilizing machine learning, predictive analytics, and automated monitoring systems to improve financial risk prediction and cybersecurity management. These platforms support secure data storage, real-time fraud detection, governance compliance, and intelligent decision-making across enterprise operations. Furthermore, robust data governance frameworks ensure data integrity, privacy, accessibility, and regulatory adherence within cloud environments. This study explores the role of intelligent enterprise cloud platforms in financial risk prediction, cybersecurity enhancement, and data governance management. The research also examines existing technologies, implementation methodologies, operational benefits, challenges, and future implications associated with cloud-based intelligent financial systems in modern enterprise environments.
Intelligent Enterprise Systems, Cloud Computing, Financial Risk Prediction, Cybersecurity, Data Governance, Artificial Intelligence, Machine Learning, Financial Technology, Cloud Security, Predictive Analytics, Enterprise Platforms, Risk Management
Cloud-Native DevOps with Federated Learning and Scalable Enterprise Infrastructure Modernization
Cloud-native DevOps has become a foundational approach for organizations seeking scalable, resilient, and automated enterprise infrastructure solutions. Simultaneously, federated learning has emerged as an advanced decentralized machine learning technique that enables collaborative model training without transferring sensitive data to centralized repositories. This research explores the integration of cloud-native DevOps practices with federated learning to modernize enterprise infrastructure in distributed computing environments. The study investigates how technologies such as containerization, Kubernetes orchestration, microservices, Infrastructure as Code (IaC), and Continuous Integration/Continuous Deployment (CI/CD) pipelines can support secure and scalable federated learning ecosystems. The research further examines the role of automation, observability, edge computing, DevSecOps, and hybrid cloud deployment in enhancing operational efficiency and intelligent decision-making. A detailed literature review identifies critical challenges including scalability limitations, interoperability complexity, network latency, governance issues, and cybersecurity threats in modern enterprise systems. The proposed methodology introduces a cloud-native federated architecture capable of automating machine learning operations while preserving privacy and compliance requirements. The study concludes that integrating federated learning with cloud-native DevOps significantly improves infrastructure scalability, deployment flexibility, security resilience, and enterprise modernization capabilities, thereby supporting next-generation digital transformation initiatives across industries.
Cloud-native DevOps, Federated Learning, Enterprise Infrastructure Modernization, Kubernetes, CI/CD, Microservices, Infrastructure as Code, Hybrid Cloud, DevSecOps, Distributed Machine Learning, Automation, Cloud Computing, Edge Computing, MLOps, Scalable Systems.
Transforming Fragmented Enterprise Data into Actionable Insights Using Artificial Intelligence
The increasing volume of fragmented enterprise data across cloud platforms, legacy infrastructures, Internet of Things (IoT) systems, and distributed databases has created major challenges for organizations seeking efficient decision-making and operational intelligence. This study examines how artificial intelligence (AI) can transform fragmented enterprise data into actionable insights capable of improving organizational performance, scalability, and strategic innovation. The paper explores the integration of machine learning, cloud computing, predictive analytics, and intelligent automation in modern enterprise environments. It further evaluates how AI-driven architectures enhance data interoperability, real-time analytics, and enterprise-wide collaboration across sectors such as healthcare, biotechnology, manufacturing, and digital services. The study also discusses the role of scalable cloud infrastructures and advanced data engineering techniques in supporting intelligent business ecosystems. In addition, critical challenges relating to cybersecurity, data governance, organizational agility, and ethical AI implementation are examined. The findings indicate that AI-enabled enterprise intelligence systems significantly improve operational efficiency, support predictive decision-making, and strengthen digital transformation initiatives. The paper concludes that organizations adopting scalable AI frameworks and integrated enterprise architectures are better positioned to achieve sustainable competitiveness and long-term innovation in the evolving digital economy.
Artificial Intelligence, Enterprise Data Integration, Big Data Analytics, Cloud Computing, Digital Transformation, Machine Learning, Intelligent Enterprise Systems.
AI-Driven Enterprise System Integration: Improving Data Interoperability Across Complex Organizations
Artificial Intelligence (AI) has emerged as a transformative force in enterprise system integration, enabling organizations to improve data interoperability across increasingly complex digital environments. Modern enterprises often operate heterogeneous systems that generate fragmented and incompatible datasets, creating challenges in communication, coordination, and decision-making processes. This study examines the role of AI-driven integration technologies in enhancing interoperability among enterprise systems through intelligent automation, machine learning, cloud-based architectures, and real-time data synchronization. The paper explores how AI improves enterprise application integration, optimizes data governance, and facilitates secure information exchange across organizational units. It further analyzes the strategic implications of AI-enabled interoperability for operational efficiency, scalability, and digital transformation. Despite the significant benefits, challenges such as data quality issues, cybersecurity risks, ethical concerns, and legacy system incompatibility remain critical barriers to implementation. The study concludes that AI-driven enterprise integration provides a sustainable pathway for organizations seeking resilient, adaptive, and intelligent information infrastructures in rapidly evolving business ecosystems.
Artificial Intelligence; Enterprise System Integration; Data Interoperability; Digital Transformation; Cloud Computing; Intelligent Automation.
Explainable AI Models for Cloud-Based Fraud Detection Risk Assessment and Secure Financial Decision Intelligence
The rapid adoption of cloud computing, digital banking, online financial services, and intelligent automation has significantly transformed modern financial ecosystems. However, the increasing complexity of cyber fraud, financial crimes, insider threats, and transaction anomalies has created major security and operational challenges for financial institutions. Traditional fraud detection systems often struggle to identify sophisticated attack patterns due to limited scalability, static rule-based mechanisms, and lack of adaptive intelligence. Explainable Artificial Intelligence (XAI) has emerged as a powerful solution capable of combining predictive intelligence with transparent and interpretable decision-making processes. This research presents an advanced framework for Explainable AI Models in cloud-based fraud detection, financial risk assessment, and secure financial decision intelligence. The proposed architecture integrates machine learning algorithms, deep learning models, explainability techniques, cloud-native infrastructure, anomaly detection systems, and cybersecurity frameworks to improve fraud detection accuracy, financial forecasting, and operational transparency. Experimental analysis demonstrates that explainable AI significantly enhances fraud detection precision, reduces false positive alerts, strengthens regulatory compliance, and improves trust in automated financial systems. The study further emphasizes the importance of secure cloud governance, ethical AI operations, and interpretable analytics in financial environments. The findings confirm that explainable AI-driven financial intelligence systems provide scalable, adaptive, secure, and transparent solutions for modern cloud-based financial ecosystems.
Explainable Artificial Intelligence, Cloud Computing,
Fraud Detection, Financial Risk Assessment
Machine Learning, Deep Learning, Cybersecurity, Predictive Analytics, Financial Data Security, Intelligent Automation, Cloud Security, Regulatory Compliance, Behavioral Analytics, Anomaly Detection
Resilient Enterprise Governance and AI-Driven Cloud Computing Architectures for Energy Utilities and Data-Driven Operations
The rapid digital transformation of energy utilities has significantly increased the adoption of cloud computing, artificial intelligence, and data-driven operational frameworks to improve efficiency, reliability, and sustainability. Modern energy enterprises rely on interconnected digital infrastructures to manage power generation, distribution, consumption analytics, and smart grid technologies. However, the growing dependence on cloud ecosystems and intelligent automation introduces challenges related to governance, cybersecurity, data privacy, operational resilience, and regulatory compliance. This study explores the role of resilient enterprise governance and AI-driven cloud computing architectures in supporting secure, scalable, and adaptive energy utility operations. The research examines how cloud-native platforms, distributed computing frameworks, and machine learning algorithms enhance operational decision-making, predictive maintenance, and real-time monitoring within energy systems. Furthermore, the study investigates governance mechanisms that ensure accountability, transparency, risk management, and business continuity across distributed infrastructures. The findings indicate that integrating adaptive governance models with AI-enabled cloud architectures improves system reliability, optimizes resource utilization, and strengthens cybersecurity resilience against evolving threats. The study concludes that resilient enterprise governance combined with intelligent cloud-based technologies forms the foundation for sustainable and future-ready energy utility ecosystems capable of supporting large-scale digital transformation and data-driven innovation.
Resilient enterprise governance, AI-driven cloud computing, energy utilities, smart grids, data-driven operations, cloud security, distributed systems, artificial intelligence, predictive analytics, cybersecurity resilience, digital transformation, enterprise architecture, machine learning, operational efficiency, data governance