International Journal of Humanities and Information Technology
The International Journal of Humanities and Information Technology (IJHIT-ISSN no. 2456- 1142) is a multidisciplinary, peer-reviewed, open-access journal that brings together innovative research from the intersecting domains of humanities and information technology. IJHIT aims to explore how technology influences human culture, society, and communication—and vice versa—by publishing critical and applied scholarship that bridges the digital and the humanistic. IJHIT serves as a platform for academics, researchers, educators, and practitioners to engage with the challenges and opportunities arising from the digital transformation of traditional humanities disciplines, as well as the human-centered design of information technologies.
Federated AI Framework for Intelligent Cyber Defense and Autonomous Enterprise Cloud Data Ecosystems
The rapid expansion of cloud-native enterprise infrastructures, distributed applications, and interconnected digital ecosystems has significantly increased the complexity of cybersecurity management while creating unprecedented opportunities for intelligent data analytics. Traditional centralized Artificial Intelligence (AI) approaches require aggregating sensitive enterprise data into central repositories, increasing privacy risks, regulatory challenges, and potential attack surfaces. This research proposes a Federated Artificial Intelligence (Federated AI) framework that enables intelligent cyber defense and autonomous enterprise cloud data ecosystems through decentralized collaborative learning without exposing confidential organizational information. The proposed framework integrates federated learning, hybrid cloud computing, Zero Trust Security, autonomous AI agents, predictive analytics, and cloud-native orchestration to provide secure, privacy-preserving, and adaptive cyber defense. Federated AI models are trained locally across distributed enterprise nodes while encrypted model updates are securely aggregated to improve global intelligence. Autonomous AI continuously performs anomaly detection, threat prediction, malware classification, behavioral analytics, vulnerability assessment, and automated incident response using real-time security telemetry. The framework further incorporates intelligent governance, identity and access management, API security, compliance monitoring, and explainable AI to ensure transparency and regulatory adherence. By integrating federated intelligence with cloud-native cybersecurity automation, enterprises can strengthen cyber resilience, improve collaborative threat intelligence, protect sensitive information, reduce operational risks, and accelerate secure digital transformation. The proposed architecture establishes a scalable, adaptive, and trustworthy ecosystem capable of supporting autonomous enterprise cybersecurity across distributed cloud environments.
Federated Artificial Intelligence, Federated Learning, Intelligent Cyber Defense, Enterprise Cloud Computing, Autonomous AI, Cloud Data Ecosystems, Zero Trust Security, Machine Learning, Deep Learning, Predictive Analytics, Cloud Security, Cyber Threat Intelligence, Intelligent Automation, Explainable AI, Enterprise Governance.
IoT-Enabled Structural Health Monitoring of Civil Infrastructure: Sensor Architectures, Data Pipelines, and Deployment Challenges
Civil infrastructure worldwide is ageing faster than inspection regimes can track. Structural health monitoring (SHM) — the continuous, sensor-based assessment of structural condition — has long promised to replace episodic visual inspection with data-driven condition awareness, and the Internet of Things (IoT) has finally made the promise economically plausible: low-cost MEMS sensors, low-power wide-area networking, edge computing, and cloud analytics together enable instrumented bridges, buildings, dams, and towers at a fraction of the cost of first-generation wired systems. Yet the gap between pilot deployments and sustained operational practice remains wide. This paper provides a systems-level review of IoT-enabled SHM organised around three layers. First, sensor architectures: we survey the principal sensing modalities — vibration, strain, displacement, inclination, acoustic emission, environmental, and vision-based sensing — and their placement logic, power characteristics, and data profiles, consolidated in a reference table. Second, data pipelines: we trace the end-to-end path from on-node signal conditioning and edge feature extraction through network transport, time-synchronisation, cloud storage, and damage-identification analytics, highlighting where the classical SHM literature’s statistical pattern-recognition paradigm meets modern streaming architectures. Third, deployment challenges: we analyse the obstacles that dominate real projects — energy autonomy, harsh-environment reliability, synchronisation accuracy, data volume economics, environmental and operational variability confounding damage signals, model validation without damage labels, and institutional ownership of long-lived monitoring assets. We close with a deployment roadmap and research agenda spanning self-powered sensing, edge intelligence, digital-twin integration, and standardisation.
Structural health monitoring; Internet of Things; Wireless sensor networks; Civil infrastructure; Edge computing; Damage detection; Bridge monitoring; MEMS sensors; Data pipelines.
Lightweight Deep Learning Models for Real-Time Object Detection on Edge Devices: A Comparative Performance Study
Real-time object detection at the network edge — on cameras, drones, vehicles, and embedded industrial controllers — has become a defining workload of applied computer vision. The governing constraint is no longer accuracy alone but the joint budget of latency, memory, energy, and thermal headroom available on resource-constrained hardware. This paper presents a structured comparative study of the principal lightweight detection approaches, organised around two axes: architectural efficiency strategies (compact backbone design, single-stage detection heads, multi-scale feature aggregation) and post-design compression strategies (quantisation, pruning, and knowledge distillation). We develop a comparison framework covering seven evaluation dimensions — detection paradigm, backbone design strategy, relative model footprint, latency behaviour, accuracy–efficiency trade-off profile, hardware affinity, and deployment tooling maturity — and apply it qualitatively across representative model families including the MobileNet-SSD lineage, the compact YOLO lineage, and compound-scaled detectors of the EfficientDet type. Because published benchmark figures are notoriously sensitive to input resolution, runtime, numeric precision, and target hardware, we deliberately separate the comparative analysis from measurement: the paper specifies a reproducible edge benchmarking protocol — fixed input regimes, warm/cold latency separation, sustained-throughput thermal testing, and energy-per-inference measurement — through which the framework’s qualitative rankings can be instantiated as numbers on any given device. We conclude with deployment guidance matching model classes to application profiles and identify open problems in edge-aware neural architecture search and accuracy-preserving low-bit quantisation.
Edge computing; Object detection; Lightweight deep learning; Model compression; Quantisation; Real-time inference; Embedded vision; MobileNet; YOLO; Benchmarking
Designing Federated Learning Enabled Multi-Cloud Cyber Defense Framework for Intelligent Enterprise Threat Intelligence
The rapid adoption of multi-cloud computing has transformed enterprise information technology infrastructures by enabling organizations to leverage the strengths of multiple cloud service providers for improved scalability, resilience, and operational flexibility. However, the distributed nature of multi-cloud environments has introduced significant cybersecurity challenges, including inconsistent security policies, fragmented visibility, sophisticated cyberattacks, and complex threat management. Traditional centralized cybersecurity approaches often require extensive data sharing, raising concerns regarding privacy, regulatory compliance, and organizational confidentiality. Federated Learning (FL) has emerged as a promising distributed artificial intelligence paradigm that enables collaborative model training across multiple organizations without exchanging sensitive data. This research proposes a Federated Learning-enabled multi-cloud cyber defense framework to enhance intelligent enterprise threat intelligence through privacy-preserving collaborative learning, real-time anomaly detection, automated threat intelligence sharing, and adaptive security decision-making. The study adopts a qualitative and conceptual research methodology supported by comprehensive literature analysis to examine existing cybersecurity frameworks and identify opportunities for integrating Federated Learning within multi-cloud security architectures. The proposed framework combines federated machine learning, cloud-native security services, threat intelligence platforms, security orchestration, and continuous monitoring to strengthen enterprise cyber resilience. The findings indicate that Federated Learning significantly improves collaborative threat detection, reduces privacy risks, enhances predictive intelligence, minimizes detection latency, and supports scalable cybersecurity operations across distributed cloud ecosystems.
Federated Learning, Multi-Cloud Security, Cyber Defense, Enterprise Threat Intelligence, Artificial Intelligence, Machine Learning, Privacy Preservation, Threat Detection, Cloud Computing, Security Automation, Cyber Resilience, Distributed Learning
Leveraging Generative Artificial Intelligence and Retrieval-Augmented Generation to Improve Software Engineering, Automated Code Generation, and Intelligent Debugging Systems
Generative Artificial Intelligence (GenAI) has emerged as a transformative technology for improving software engineering by automating code generation, software maintenance, and debugging tasks. However, standalone large language models often suffer from hallucination, outdated knowledge, and limited contextual understanding, reducing the reliability of generated code. Retrieval-Augmented Generation (RAG) addresses these limitations by integrating external knowledge repositories with generative models to produce context-aware, accurate, and verifiable software solutions. This study examines the integration of GenAI and RAG within software engineering workflows, emphasizing their applications in automated code generation, intelligent debugging, software documentation, and DevOps automation. The paper synthesizes existing literature to propose a conceptual framework demonstrating how retrieval-enhanced generation can improve code quality, reduce debugging time, strengthen semantic understanding of software artifacts, and support secure software development practices. Furthermore, the study discusses implementation challenges, including knowledge integration, security, explainability, and computational complexity. The findings suggest that combining Generative AI with Retrieval-Augmented Generation provides a promising foundation for developing reliable, intelligent, and scalable software engineering systems capable of supporting modern software development environments.
Generative Artificial Intelligence, Retrieval-Augmented Generation, Software Engineering, Automated Code Generation, Intelligent Debugging, DevOps Automation, Large Language Models.
Designing Machine Learning Enabled Cloud Computing Frameworks for Intelligent Enterprise Security and API Modernization
Cloud computing has become the backbone of modern enterprise digital transformation, enabling organizations to achieve greater scalability, flexibility, and operational efficiency. As businesses increasingly rely on cloud-native applications and interconnected services through Application Programming Interfaces (APIs), the complexity of securing enterprise environments has grown substantially. Traditional security mechanisms often struggle to detect sophisticated cyber threats, manage dynamic workloads, and protect rapidly expanding API ecosystems. Machine learning (ML) offers a transformative approach by enabling intelligent threat detection, predictive analytics, anomaly identification, and automated security responses. Simultaneously, API modernization supports seamless integration between legacy systems and cloud-native architectures, enhancing interoperability, performance, and business agility. This study examines the design of machine learning-enabled cloud computing frameworks that integrate intelligent enterprise security with API modernization strategies. The proposed conceptual framework emphasizes automated threat intelligence, adaptive authentication, behavioral analytics, continuous monitoring, and intelligent API governance to strengthen cloud security while improving operational efficiency. The research also explores implementation challenges, including data privacy, model scalability, algorithm transparency, integration complexity, and regulatory compliance. The findings suggest that combining machine learning with cloud computing and modern API architectures creates resilient, secure, and intelligent enterprise ecosystems capable of responding proactively to evolving cybersecurity threats while supporting sustainable digital innovation and enterprise transformation.
API modernization, Cloud security, Artificial intelligence,
Intelligent automation, Cybersecurity, Threat detection,
API security, Digital transformation, Predictive analytics.
Central Bank Digital Currency (CBDC) Adoption Challenges and Remittance Impacts: Empirical Evidence from Nigeria’s eNaira Compared with Global Retail Pilots and US Federal Reserve Policy Stance
This paper evaluates adoption challenges and remittance potential of central bank digital currencies (CBDCs) by contrasting eNaira in Nigeria, cross-border retail pilots and also, US Federal Reserve Bank's view on CBDCs. The paper reviews the literature heralding the efficiency of the design, financial inclusion and payment system revolution but the results reveal a gap between the expected and realisation of adoption. The paper extends the current literature approaches by drawing on related studies to synthesise studies on CBDC design, adoption and institutional aspects. The findings indicate that user adoption is the bottleneck: although having access to the CBDC system infrastructure, financial users will not have an incentive to continue using CBDC for transactions without the right adoption conditions, such as user benefits, trust and network impacts. Remittance related impacts, in which impact is often the sought-after feature, depend on interoperability and coordination, which are yet immature. The effect on financial systems (such as disintermediation) is not extensively leveraged in absence of intensive adoption; implying that the impact on financial systems is dependent on critical mass adoption. Case studies across countries highlight these differences in policy, as operational constraints in value addition approach (via implementation) and a lag in exposure to and impact of defensive approach. The findings conclude that the effectiveness of CBDC is dependent on integration of design architecture, behavioural and institutional design concerns.
Central bank digital currency; eNaira; adoption of CBDC; remittances; financial inclusion; payments; monetary policy; monetary system; digital currency; financial stability; cross-border payments.
Designing Federated Learning-Driven Multi-Cloud Intelligence for Privacy-Preserving Enterprise Healthcare Analytics Architecture
The increasing adoption of digital healthcare technologies has generated enormous volumes of patient data from hospitals, diagnostic laboratories, wearable devices, electronic health records, and telemedicine platforms. These data provide valuable opportunities for improving clinical decision-making, disease prediction, personalized medicine, and healthcare management through advanced analytics. However, sharing sensitive healthcare information across organizations raises significant concerns regarding patient privacy, regulatory compliance, data security, and interoperability. Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables collaborative model training without transferring raw data from local institutions. Simultaneously, multi-cloud computing provides scalable, flexible, and resilient infrastructure for enterprise healthcare systems by integrating services across multiple cloud providers. This essay explores the design of a federated learning-driven multi-cloud intelligence architecture that supports privacy-preserving enterprise healthcare analytics. It examines the integration of federated learning, secure multi-cloud environments, artificial intelligence, and privacy-enhancing technologies to enable collaborative healthcare analytics while maintaining data confidentiality. The study reviews current literature, identifies research gaps, and proposes a comprehensive research methodology for developing and evaluating a secure, scalable, and intelligent healthcare analytics framework. The proposed architecture aims to improve predictive accuracy, data privacy, interoperability, regulatory compliance, computational efficiency, and collaborative medical research while ensuring that sensitive patient information remains protected throughout distributed analytical processes.
Federated Learning, Multi-Cloud Computing,
Healthcare Analytics, Privacy-Preserving Machine Learning,
Enterprise Healthcare, Artificial Intelligence, Electronic Health Records, Distributed Learning, Medical Data Security, Cloud Computing, Data Privacy, Healthcare Intelligence, Machine Learning, Cybersecurity, Health Informatics
Modernizing Enterprise Intelligence through Federated Learning and Adaptive Cloud Architectures for Predictive Cybersecurity Intelligence
Modern enterprises generate massive volumes of highly distributed, sensitive data across diverse multi-cloud environments, edge nodes, and regional databases. Traditional centralized data warehousing models, which consolidate raw data for predictive analytics, are increasingly unviable due to data gravity, bandwidth bottlenecks, and strict global privacy mandates like GDPR and CCPA. This paper introduces an advanced architectural framework for Modernizing Enterprise Intelligence Through Federated Learning and Adaptive Cloud Architectures for Predictive Decision Intelligence. The proposed framework decentralizes the machine learning lifecycle by executing localized model training directly at the distributed data sources. By employing adaptive cloud scaling, the framework dynamically adjusts edge-compute resource allocations based on network bandwidth, compute capabilities, and local data drift. A centralized cloud parameter orchestrator collects secure, encrypted weight updates and merges them using an optimized federated averaging protocol to construct a robust global model. This global model is then redeployed to the edge, enabling real-time, context-aware predictive decision intelligence without exposing raw data. Empirical evaluations in a simulated multi-region enterprise environment demonstrate that this framework reduces data transmission volume by 86%, maintains a 95% predictive accuracy rate compared to centralized baselines, and guarantees continuous compliance across jurisdictional boundaries.
Federated Learning, Adaptive Cloud Architectures, Predictive Decision Intelligence, Distributed Machine Learning, Data Sovereignty, Edge Computing.
Modernizing Enterprise Systems through Generative AI Autonomous Operations and Cloud-Native Engineering
Enterprise modernization has become a strategic priority for organizations seeking to improve operational efficiency, accelerate innovation, and remain competitive in rapidly evolving digital markets. The convergence of Generative Artificial Intelligence (Generative AI), autonomous operations, and cloud-native engineering has created new opportunities for transforming traditional enterprise systems into intelligent, adaptive, and scalable digital platforms. Generative AI enhances enterprise capabilities by automating content creation, software development, knowledge management, decision support, and customer engagement through advanced natural language processing and machine learning models. Autonomous operations utilize artificial intelligence, predictive analytics, and intelligent automation to monitor, optimize, and manage enterprise infrastructure with minimal human intervention, thereby improving system reliability, reducing operational costs, and enabling proactive issue resolution. Cloud-native engineering provides the architectural foundation for modernization through microservices, containers, Kubernetes orchestration, serverless computing, and Infrastructure as Code, enabling highly scalable, resilient, and flexible enterprise applications. This paper examines the integration of these technologies within modern enterprise architectures and proposes a comprehensive framework for designing intelligent, secure, and cloud-native systems. The framework emphasizes automation, observability, continuous integration and deployment, cybersecurity, governance, and operational resilience. The study concludes that combining Generative AI, autonomous operations, and cloud-native engineering significantly enhances organizational agility, software quality, resource optimization, and business innovation while supporting sustainable digital transformation across diverse enterprise environments.
Generative AI, Enterprise Systems, Autonomous Operations, Cloud-Native Engineering, Artificial Intelligence, Digital Transformation, Intelligent Automation, DevOps, Microservices, Kubernetes, Containers, Infrastructure as Code, Cloud Computing, Continuous Integration, Continuous Deployment, Observability, Software Engineering, Enterprise Architecture, Machine Learning, Operational Resilience
Transforming Enterprise Platforms through Intelligent Artificial Intelligence SAP Integration and Cyber-Resilient Cloud Architecture
Enterprise digital transformation has accelerated the convergence of Artificial Intelligence (AI), SAP integration, and cyber-resilient cloud architecture to create intelligent, secure, and adaptive business platforms. Organizations increasingly rely on integrated enterprise systems that support real-time decision-making, automated business processes, scalable computing resources, and comprehensive cybersecurity. Artificial Intelligence enhances enterprise operations through predictive analytics, intelligent automation, anomaly detection, and data-driven decision support, while SAP platforms provide unified management of finance, supply chain, procurement, manufacturing, customer relationships, and human resources. Cloud architecture enables flexible resource allocation, microservices-based application development, containerization, and rapid deployment of enterprise services. Cyber resilience strengthens these platforms by incorporating Zero Trust principles, identity and access management, continuous threat monitoring, automated incident response, encryption, and disaster recovery mechanisms. This study presents a conceptual framework for transforming enterprise platforms through the integration of intelligent AI capabilities, SAP business applications, and cyber-resilient cloud infrastructure. The research explores architectural design principles, interoperability mechanisms, cloud security strategies, and implementation methodologies that enable organizations to improve operational efficiency, business continuity, and regulatory compliance. The proposed methodology demonstrates that combining intelligent automation with secure cloud-native enterprise architecture establishes a sustainable foundation for digital innovation, organizational resilience, and long-term competitiveness in rapidly evolving business environments.
Artificial Intelligence, SAP Integration, Cyber-Resilient Cloud Architecture, Enterprise Platform, Digital Transformation, Cloud Computing, Enterprise Resource Planning, Intelligent Automation, Machine Learning, Zero Trust Security, Cloud Security, DevSecOps, Microservices, Business Process Automation, Enterprise Architecture
Net-Zero Water Infrastructure: AI and CIRx for Intelligent Stormwater and Wastewater Management
Water and wastewater utilities are among the largest municipal consumers of electricity, with pumping, aeration, and treatment processes accounting for a substantial share of a city’s operational carbon footprint, placing utilities squarely within the scope of municipal net-zero commitments even though water infrastructure is often treated as a secondary consideration relative to buildings and transportation in climate action planning. This paper examines how artificial intelligence (AI) and the Condition-Index Rx (CIRx) composite scoring methodology can be extended beyond conventional structural and hydraulic prioritization to explicitly incorporate energy and emissions considerations into stormwater and wastewater infrastructure management, supporting utilities pursuing net-zero operational targets. We propose an energy-aware extension to the CIRx framework in which asset rehabilitation and operational decisions are evaluated not only against structural condition, hydraulic performance, and consequence of failure, but also against their expected impact on pumping energy demand, aeration efficiency, and inflow-and-infiltration-driven treatment load, all of which carry direct energy and emissions consequences. We discuss machine learning applications for pump and aeration energy optimization, inflow-and-infiltration reduction targeting, and renewable energy integration at treatment facilities, and we examine how AI-driven decision support can help utilities identify rehabilitation investments that deliver combined structural, hydraulic, and carbon-reduction benefits rather than treating emissions reduction as a separate initiative. The paper closes with a discussion of data and governance considerations specific to energy-aware infrastructure decision-making and directions for future research on integrating climate and emissions data into infrastructure asset management.
Net-zero infrastructure; CIRx; artificial intelligence; machine learning; stormwater management; wastewater management; energy optimization; carbon reduction; asset management; inflow and infiltration.
AI and SAP for Climate-Resilient Environmental Engineering
The convergence of Artificial Intelligence (AI) and SAP enterprise platforms presents a transformative paradigm for addressing climate change and environmental degradation. This paper investigates the synergistic application of machine learning, deep learning, and predictive analytics within SAP’s ERP and cloud ecosystems to enhance climate resilience in environmental engineering domains. We examine use cases spanning flood risk modeling, carbon footprint tracking, sustainable supply chain optimization, and real-time environmental monitoring. Through a systematic review of 42 case studies and simulation-based experiments, we demonstrate that AI-SAP integrated frameworks achieve up to 38% improvement in predictive accuracy for climate hazards and reduce carbon reporting overhead by 61% compared to conventional approaches. Our findings support the adoption of AI-augmented SAP solutions as strategic infrastructure for the next generation of climate-resilient engineering operations.
Artificial Intelligence, SAP ERP, Climate Resilience, Environmental Engineering, Machine Learning, Carbon Footprint, Predictive Analytics, Sustainable Engineering
Optimizing Municipal Waste Management Through Artificial Intelligence and SAP Integration
Municipal solid waste management involves coordinating collection vehicle routing, fleet maintenance, transfer station and landfill operations, recycling processing, and regulatory and financial reporting, generating operational data across telematics platforms, bin-level sensors, weighbridge systems, and enterprise resource planning (ERP) software that frequently remain poorly integrated with one another. This paper examines how artificial intelligence (AI) and machine learning (ML) can be integrated with SAP enterprise systems, including SAP S/4HANA, SAP Enterprise Asset Management (EAM), and SAP Transportation Management (TM), to support more efficient and responsive municipal waste management operations. We propose an integration architecture in which AI-driven route optimization, bin fill-level prediction, collection vehicle predictive maintenance, and waste stream composition analysis are translated into structured SAP transportation orders, maintenance notifications, and operational reporting, connecting field-level predictive intelligence to the enterprise systems that municipal public works and finance departments already use to plan, execute, and fund waste collection and processing operations. The paper discusses relevant AI/ML techniques, including dynamic route optimization, fill-level forecasting from sensor and historical collection data, computer vision for waste stream contamination detection, and vehicle telematics-based predictive maintenance, alongside SAP-specific integration mechanisms and the governance and change-management considerations relevant to municipal deployments. We conclude with a discussion of implementation challenges and directions for future research on integrated municipal waste management systems.
Municipal waste management; artificial intelligence; machine learning; SAP; enterprise asset management; route optimization; predictive maintenance; recycling; smart cities; fleet management.
AI-Driven Decision Support Using CIRx for Smart Urban Water Infrastructure
Smart urban water infrastructure spans drinking water distribution, stormwater conveyance, and wastewater collection systems, each generating growing volumes of sensor, inspection, and operational data as utilities adopt SCADA telemetry, smart metering, and IoT-based condition monitoring. Translating this data into timely, defensible capital and operational decisions remains difficult, particularly for resource-constrained municipal utilities that must prioritize a limited rehabilitation budget across a large and heterogeneous asset base. This paper presents an artificial intelligence (AI) driven decision support framework built around the Condition-Index Rx (CIRx) methodology, a composite scoring approach that fuses structural condition, hydraulic or hydraulic-equivalent performance, consequence of failure, and machine-learning-forecast deterioration risk into a single, prescriptive rehabilitation recommendation. We describe how CIRx extends naturally across the three principal urban water subsystems, and we present a decision-support architecture that couples AI/ML predictive models with an interactive prioritization layer intended for use by utility engineers and capital planners. The paper discusses model selection considerations for heterogeneous urban water asset types, human-in-the-loop decision workflows, integration with geographic information systems and asset management platforms, and the governance considerations relevant to using AI-generated prescriptions to inform public infrastructure spending. We conclude with a discussion of adoption barriers among small and mid-sized utilities and directions for future research, including transferable deterioration models and explainability requirements for public-sector AI decision support.
CIRx; artificial intelligence; machine learning; decision support system; smart water infrastructure; urban water management; asset management; predictive maintenance; stormwater; wastewater; drinking water distribution.
LLM-Based Human-machine Teaming for Air Traffic Controllers
Nowadays, the air traffic control (ATC) system is much more complex, and the number of situations in which controllers have to make decisions that can be assisted by intelligent decision-support systems (IDSSs) has grown. Current ATC systems are mainly based on human skills and rule-driven automation, which may have challenges in coping with dynamic operations, unstructured communication, and the growing airspace density. The advent of Large Language Models (LLMs) has brought new opportunities for human–machine teaming, such as contextual reasoning, natural language understanding, and decision-support capabilities that adjust to the context. This paper explores the feasibility of human–machine teaming in air traffic control employing LLM. The study used a qualitative conceptual research approach using literature review and thematic analysis. Existing studies on human–machine teaming, aviation systems utilizing AI, explainable AI, and the field of air traffic management have been reviewed to explore the opportunities, challenges, and safety implications of implementing LLM in an ATC scenario. The results indicate that LLMs can assist air traffic controllers in various aspects, including communication management, procedural guidance, workload reduction, and situational awareness enhancement. The research also identified important problems of explainability, automation bias, reliability, risk of hallucination, cyber security and regulatory compliance. The study recommended a conceptual human-in-the-loop framework for LLM-assisted ATC operations that focuses on enhancing controller authority, introducing explainable AI mechanisms, ensuring system transparency, and designing systems for safety. The study contributes to the ongoing conversation regarding the impact of AI systems on aviation systems and provides a conceptual framework for integrating LLM with collaborative ATC decision-support systems. The results also indicate that human oversight, safety and reliability in system operation are critical considerations for future LLM-driven ATC systems, necessitating careful and transparent integration of AI capabilities to ensure reliable and safe ATC.
Air Traffic Control; Large Language Models; Human–Machine Teaming; Artificial Intelligence in Aviation; Decision-Support Systems; Explainable AI; Aviation Safety.
Managing Workplace Stress and Anxiety Using AI-Driven SAP ERP Platforms: A Behavioral Perspective
Workplace stress and anxiety have emerged as critical determinants of organizational performance, talent retention, and employee health outcomes in the post-pandemic era. While occupational stress management has traditionally been addressed through periodic HR interventions and Employee Assistance Programmes (EAPs), the rise of AI-driven Enterprise Resource Planning (ERP) systems—particularly those within the SAP ecosystem—presents a fundamentally new paradigm for continuous, data-informed behavioral stress management at scale. This paper examines, from a behavioral science perspective, how AI-integrated SAP platforms can detect, analyze, and mitigate workplace stress and anxiety through intelligent monitoring of behavioral indicators embedded within routine ERP workflows. Drawing on behavioral theories including the Demand-Control-Support (DCS) model, Conservation of Resources (COR) theory, and the Job Demands-Resources (JD-R) model, we analyze how AI-augmented SAP modules—including SuccessFactors, SAP Analytics Cloud, and the SAP Business Technology Platform—operationalize these theoretical constructs through real-time workforce analytics, predictive risk modeling, and automated well-being interventions. We examine five behavioral signal domains accessible within the SAP data environment: temporal work pattern anomalies, digital task avoidance signatures, communication sentiment deterioration, social network disengagement, and performance volatility patterns.
ERP, Artificial Intelligence, Behavioral Analytics, JD-R Model, SuccessFactors
Artificial Intelligence-Powered SAP ERP Solutions for Mental Well-Being and Behavioral Management in Modern Organizations
The global workforce is experiencing a well-documented mental health crisis, with anxiety, burnout, and behavioral disorders costing organizations an estimated US$1 trillion annually in lost productivity. Enterprise Resource Planning (ERP) systems, particularly SAP, are the operational backbone of modern organizations, yet have historically been designed without consideration for employee psychological well-being. This paper explores the convergence of Artificial Intelligence (AI) with SAP ERP ecosystems to create intelligent, adaptive solutions for employee mental well-being monitoring, behavioral pattern analysis, and proactive intervention within organizational contexts. We examine AI integration frameworks across key SAP modules—SuccessFactors, SAP Human Capital Management (HCM), SAP Analytics Cloud, and the SAP Business Technology Platform (BTP)—and review how machine learning, natural language processing, and predictive analytics are being embedded to detect early indicators of stress, disengagement, and behavioral anomalies. The paper further discusses the deployment of AI-driven conversational agents within SAP environments, sentiment analysis of employee communication data, and the use of workforce analytics dashboards for behavioral management. Ethical considerations—including data privacy under GDPR, algorithmic transparency, and the risk of surveillance overreach—are examined alongside a proposed governance model for responsible implementation.
Mental Well-Being, Behavioral Management, SuccessFactors, HR Analytics, Employee Wellness, NLP, Predictive Analytics, Organizational Psychology
Cloud-Native Intelligent Quality Assurance and Predictive Analytics for API-Centric Financial Systems Using Machine Learning
Financial institutions increasingly rely on Application Programming Interfaces (APIs) to facilitate secure, real-time interactions among banking applications, payment gateways, investment platforms, and third-party financial services. As API-centric architectures grow in complexity, ensuring software quality, reliability, security, and operational efficiency becomes increasingly challenging. Intelligent Quality Assurance (IQA) combined with Predictive Analytics powered by Machine Learning (ML) provides an innovative approach to addressing these challenges. This study explores the integration of machine learning techniques into quality assurance processes within API-driven financial ecosystems. Intelligent QA systems utilize automated testing, anomaly detection, defect prediction, and performance monitoring to identify potential issues before deployment. Simultaneously, predictive analytics models analyze historical transaction data, API logs, user behavior, and system performance metrics to forecast failures, security threats, and service disruptions. The research investigates the effectiveness of supervised and unsupervised learning algorithms in enhancing testing accuracy, reducing operational risks, and improving customer experience. Furthermore, the study highlights the role of real-time monitoring and predictive maintenance in ensuring regulatory compliance and system resilience. Results indicate that machine learning-driven quality assurance significantly improves defect detection rates, reduces downtime, and optimizes resource utilization. The findings demonstrate that integrating intelligent QA and predictive analytics strengthens the reliability, scalability, and security of modern financial systems operating within API-centric environments.
Intelligent Quality Assurance, Predictive Analytics, Machine Learning, Financial Systems, API-Centric Architecture, Automated Testing, Defect Prediction, Anomaly Detection, Financial Technology, Software Reliability, Risk Management, Artificial Intelligence
AI-Driven Intelligent Enterprise Platforms for Secure Cloud Computing SAP Cybersecurity and Predictive Analytics
Artificial Intelligence (AI) is transforming enterprise platforms by enabling intelligent automation, predictive analytics, and enhanced cybersecurity in cloud computing environments. SAP, as a leading enterprise resource planning (ERP) provider, has integrated AI-driven capabilities to support secure, scalable, and adaptive business operations. This paper explores the convergence of AI-driven intelligent enterprise platforms, secure cloud computing, SAP cybersecurity, and predictive analytics. It highlights how enterprises can leverage AI to strengthen threat detection, automate compliance, and optimize decision-making. The study also examines literature on AI-enabled SAP systems, predictive analytics frameworks, and cloud security models. A mixed-method research methodology is proposed, combining qualitative case studies with quantitative data analysis to evaluate the effectiveness of AI-driven SAP platforms in mitigating cyber risks and improving operational efficiency. Advantages such as scalability, automation, and proactive risk management are discussed alongside disadvantages including implementation complexity, high costs, and data privacy concerns. The findings suggest that AI-driven enterprise platforms represent a paradigm shift toward intelligent, secure, and predictive business ecosystems, but require careful governance and strategic alignment to maximize benefits while minimizing risks.
AI-driven enterprise platforms, SAP cybersecurity, predictive analytics, secure cloud computing, intelligent automation, ERP systems, cyber risk management, digital transformation, cloud security, enterprise resilience
Optimizing Urban Stormwater Systems with SAP Business Technology Platform and AI
Urban stormwater management has emerged as one of the most pressing challenges in contemporary civil engineering and environmental governance. Rapid urbanization, the progressive expansion of impervious surfaces, and the intensification of extreme precipitation events attributed to climate change have collectively strained conventional drainage infrastructure beyond its design thresholds. This review article examines how the SAP Business Technology Platform (BTP), augmented by artificial intelligence (AI) and machine learning (ML) capabilities, offers a transformative paradigm for optimizing the planning, operation, and governance of urban stormwater systems. Drawing on peer-reviewed literature, SAP technical documentation, and municipal case studies published up to 2023, the article synthesizes evidence across four thematic pillars: real-time sensor integration and data orchestration, predictive analytics for flood forecasting, AI-driven asset lifecycle management, and cross-departmental decision support. The review finds that SAP BTP's unified data architecture, combined with embedded AI services and IoT Edge capabilities, enables municipalities to transition from reactive, siloed stormwater operations toward proactive, integrated urban water intelligence.
Urban Stormwater Management; SAP Business Technology Platform; Artificial Intelligence; Flood Prediction; Smart Cities; IoT Integration; Predictive Maintenance; Digital Twin.
Algorithmic Trust, Governance, and Integration Bottlenecks of AI Tools in Legacy Project Environments
While businesses are making strides in adopting AI to boost project management efficiency, they continue to face substantial pain points, such as the challenges of integrating AI with existing systems, dealing with fragmented or legacy data, and resolving governance issues. This article covers the need for algorithmic trust in the presence of incomplete, inconsistent, and not well-managed enterprise data, which generates algorithmic project metrics. It explores how project environments, where data pipelines are dirty, real-time API connectivity is absent, and access control is weak, impact the implementation of modern AI tools. The article, based on the case of EVs in the industry, shows that AI has nothing to do with software but everything to do with trust. It requires a data governance framework that can be implemented in a centralized fashion, normalizing data through data pipelines, offering cross-system interoperability, and safeguarding sensitive data with role-based access control. The article states that the reliability of the data layer that underlies the AI systems is key to algorithmic trust. Based on the findings of this study, a three-level model of structural governance is proposed, continuing the discussion of the management of friction in integration, the use of AI, and the reliability of the information received from AI project management in legacy settings.
Algorithmic trust, AI governance, legacy systems, project management, data governance, ETL pipelines, enterprise integration, AI-driven metrics, role-based access control, digital transformation.
Performance Analysis of Wireless Sensor Networks for Smart Monitoring Applications
Wireless Sensor Networks (WSNs) have become a key enabling technology for smart monitoring applications because of their capabilities of collecting and communicating the sensed information in real time and monitoring remotely. These networks are made up of distributed sensor nodes that can work cooperatively to sense, process and relay information from environments they are monitoring. Recently the WSNs have been used in various environmental monitoring and air quality measurement applications, industrial automation and smart grid systems, so that the growing demand is to test the performance of WSNs in various deployment scenarios. This study is a performance analysis to demonstrate and study the important performance indices for Wireless sensor network based Smart monitoring applications such as Packet delivery ratio, Throughput, End to End delay, Energy consumption, Network lifetime and scalability. Further, the effect of communication protocols, network architectures, and energy management techniques on overall network efficiency are analyzed. Based on the comparative analysis, it was found that the choice of protocols and the deployment topology as well as power conservation mechanisms will have a significant effect on the network reliability and sustainability. The results show that an optimized use of communication schemes together with a communication routing optimization and an adaptive network design can benefit the performance within smart monitoring environments. Based on the study, the author concludes that regular performance evaluations are crucial in creating effective, reliable and scalable wireless sensor network solutions that can be used for a variety of monitoring tasks.
Wireless Sensor Networks (WSNs), Smart Monitoring, Performance Analysis, Energy Efficiency, Packet Delivery Ratio, Network Lifetime, IEEE 802.15.4, ZigBee, Smart Grid, Environmental Monitoring.
Advanced Digital Assurance Framework for Cloud Security Data Quality and Operational Trust in Enterprise Systems
The rapid adoption of digital technologies, cloud computing, artificial intelligence, Internet of Things (IoT), and data-driven business models has transformed enterprise operations while simultaneously increasing security vulnerabilities, data quality challenges, and concerns regarding operational trust. Traditional assurance mechanisms often struggle to address the complexity, scale, and dynamic nature of modern digital ecosystems. Consequently, organizations require intelligent assurance frameworks capable of continuously monitoring, validating, and improving security, data integrity, and operational reliability. Artificial Intelligence (AI) has emerged as a critical enabler of next-generation digital assurance by providing advanced capabilities in anomaly detection, predictive analytics, automated risk assessment, and intelligent decision support. This study explores the development and application of an AI-driven digital assurance framework designed to strengthen enterprise security, enhance data quality management, and foster operational trust across interconnected digital environments. The framework integrates machine learning, real-time monitoring, automation, explainable analytics, and governance mechanisms to provide proactive assurance capabilities. Through a comprehensive review of existing literature and conceptual analysis, the research examines how AI-enabled assurance systems improve threat detection, ensure data accuracy, support compliance, and increase stakeholder confidence in organizational operations. The findings indicate that AI-driven assurance frameworks significantly enhance resilience, transparency, and trustworthiness while enabling organizations to adapt effectively to evolving technological and regulatory landscapes.
Artificial Intelligence, Digital Assurance, Enterprise Security, Data Quality, Operational Trust, Machine Learning, Cybersecurity, Data Governance, Digital Transformation, Predictive Analytics, Risk Management, Automated Assurance, Enterprise Resilience, Compliance Monitoring, Trustworthy AI, Information Security, Data Integrity, Intelligent Automation, Operational Excellence, Governance Framework
Graph Neural Network-Based Nationwide Healthcare Fraud Detection and Financial Risk Intelligence for Medicare and Medicaid Systems
Healthcare fraud, waste, and abuse remain major financial and operational threats to Medicare and Medicaid systems, where fraudulent claims, provider collusion, upcoding, phantom billing, and abnormal referral patterns can generate substantial public expenditure losses. Traditional fraud detection methods, including rule-based systems, statistical models, and conventional machine learning, often analyze claims as isolated records and may fail to capture the complex relationships among providers, beneficiaries, procedures, diagnoses, pharmacies, facilities, and payment networks. This paper proposes a Graph Neural Network-based nationwide healthcare fraud detection and financial risk intelligence framework for Medicare and Medicaid systems. The proposed architecture models healthcare claims as a heterogeneous graph, enabling relational learning across multiple entities and supporting the identification of suspicious providers, anomalous claims, hidden fraud communities, and high-risk financial patterns. Building on graph anomaly detection, imbalanced learning, explainable artificial intelligence, and cost-sensitive fraud analytics, the framework integrates graph construction, GNN-based representation learning, fraud risk scoring, explainability, and audit-support dashboards. The paper contributes a scalable and policy-relevant model for improving payment integrity, strengthening public insurance oversight, prioritizing fraud investigations, and supporting data-driven financial risk intelligence across nationwide healthcare programs.
Graph Neural Networks; Healthcare Fraud Detection; Medicare; Medicaid; Financial Risk Intelligence; Claims Analytics; Explainable AI.
Generative AI and Intelligent Cloud Ecosystems Enabling Secure Autonomous and Data Driven Enterprise Transformation
Generative Artificial Intelligence (AI) and intelligent cloud ecosystems have emerged as transformative technologies that are redefining enterprise operations, decision-making processes, and business innovation. The convergence of advanced AI models, cloud computing infrastructure, big data analytics, automation technologies, and cybersecurity frameworks has enabled organizations to transition toward autonomous and data-driven operational environments. Generative AI enhances organizational capabilities through intelligent content creation, predictive analytics, automated decision support, and personalized customer engagement. Simultaneously, intelligent cloud ecosystems provide scalable, flexible, and secure platforms for managing vast amounts of enterprise data and computational workloads. Together, these technologies facilitate digital transformation by improving operational efficiency, accelerating innovation cycles, reducing costs, and enabling real-time strategic decision-making. However, the increasing reliance on AI-powered cloud environments introduces significant concerns related to data privacy, cybersecurity threats, ethical governance, regulatory compliance, and algorithmic transparency. Enterprises must therefore establish robust security architectures, governance frameworks, and risk management strategies to ensure trustworthy and sustainable deployment. This study examines the role of generative AI and intelligent cloud ecosystems in enabling secure, autonomous, and data-driven enterprise transformation. It explores current technological developments, implementation challenges, security considerations, and future opportunities while highlighting the strategic importance of integrating AI-driven intelligence with cloud-based digital infrastructures to achieve long-term organizational competitiveness and resilience in an increasingly dynamic business environment.
Generative AI, Intelligent Cloud Ecosystems, Enterprise Transformation, Data-Driven Decision Making, Autonomous Systems, Cloud Computing, Digital Transformation, Cybersecurity, Artificial Intelligence, Machine Learning, Business Innovation, Data Governance, Predictive Analytics, Enterprise Automation, Secure Cloud Infrastructure
Enterprise Agility beyond Information Technology Through Cross-Industry Process Transformation
Enterprise agility has emerged as a critical capability for organizations operating in dynamic and highly competitive environments. While agility was initially associated with Information Technology (IT) and software development, contemporary organizations increasingly recognize its relevance across all business functions. This study explores enterprise agility beyond IT through cross-industry process transformation, emphasizing how organizations in manufacturing, healthcare, finance, retail, logistics, and public services adopt agile principles to improve responsiveness, innovation, and operational efficiency. The research investigates the mechanisms through which agile practices are integrated into organizational processes, leadership structures, decision-making systems, and customer engagement strategies. A qualitative and comparative approach is utilized to examine industry-specific transformation initiatives and identify common success factors. Findings suggest that enterprise agility extends beyond technological implementation and requires cultural adaptation, collaborative leadership, workforce empowerment, and continuous learning. Cross-industry transformation enables organizations to leverage best practices, reduce process inefficiencies, and enhance resilience against market uncertainties. However, challenges such as resistance to change, organizational silos, and resource constraints may hinder successful adoption. The study concludes that enterprise agility serves as a strategic organizational capability that supports sustainable growth and competitive advantage across industries. Organizations that effectively integrate agile principles into business processes are better positioned to respond to emerging opportunities and evolving customer expectations.
Enterprise Agility, Cross-Industry Transformation, Agile Management, Business Process Innovation, Organizational Change, Digital Transformation, Operational Excellence, Strategic Flexibility, Continuous Improvement, Organizational Resilience, Customer-Centricity, Leadership Agility
Unified Framework for Intelligent Risk Management Compliance Automation and Enterprise Cloud Transformation
Organizations across industries are experiencing unprecedented digital transformation driven by cloud computing, artificial intelligence, automation, and data-centric business models. While these technological advancements create opportunities for innovation, scalability, and operational efficiency, they also introduce complex risks related to cybersecurity, regulatory compliance, governance, and operational resilience. Traditional approaches to risk management and compliance often operate in silos, limiting organizations’ ability to respond dynamically to evolving threats and regulatory requirements. This essay proposes an integrated framework for intelligent risk management, compliance automation, and cloud-native enterprise transformation that aligns governance objectives with technological modernization initiatives. The framework leverages artificial intelligence, machine learning, real-time monitoring, policy-as-code, DevSecOps practices, and cloud-native architectures to create a proactive and adaptive governance ecosystem. Through the integration of risk intelligence, automated compliance controls, continuous auditing, and cloud-native operational models, enterprises can achieve enhanced transparency, regulatory adherence, and business agility. The framework emphasizes strategic alignment between organizational objectives, technological capabilities, and regulatory expectations while fostering resilience against emerging threats. Furthermore, it supports continuous improvement through data-driven decision-making and predictive analytics. The proposed approach contributes to the growing body of knowledge on digital governance by offering a comprehensive model that enables organizations to balance innovation, security, compliance, and performance in increasingly complex and dynamic business environments.
Intelligent Risk Management, Compliance Automation, Cloud-Native Transformation, Governance, Artificial Intelligence, Machine Learning, DevSecOps, Policy-as-Code, Cybersecurity, Regulatory Compliance, Enterprise Architecture, Digital Transformation, Risk Analytics, Cloud Governance, Operational Resilience
Zero Trust Architecture and Identity Threat Detection for Securing Cloud, IoT, and Hybrid Enterprise Systems
Modern enterprise security has become more complex as organizations increasingly depend on cloud platforms, Internet of Things devices, hybrid networks, remote users, application programming interfaces, and third-party digital services. These developments have made traditional perimeter-based security less effective, since users, devices, applications, and workloads now operate across distributed environments that cannot be protected by a fixed network boundary alone. Zero Trust Architecture provides a more suitable security approach by removing implicit trust and requiring every access request to be continuously verified based on identity, device status, access context, and risk level. Within this model, identity threat detection plays a central role because many cyberattacks now exploit valid credentials, excessive privileges, compromised accounts, insider access, and abnormal user behavior. By combining identity governance, risk-based authentication, least privilege access, anomaly detection, and continuous monitoring, organizations can improve their ability to detect unauthorized access, reduce lateral movement, and respond more effectively to emerging threats. This article examines the relationship between Zero Trust Architecture and identity threat detection in cloud, IoT, and hybrid enterprise systems. It also develops a conceptual framework for integrating Zero Trust principles with identity-centered security controls to strengthen enterprise cyber resilience.
Cloud Security; IoT Security; Hybrid Enterprise Systems; Access Control;
Risk-Based Authentication; Cybersecurity;
Copyright (c) 2025 Santosh Kumar Jadala (Author)
A Unified Framework for Integrating Generative AI Across the Agile Software Development Lifecycle (SDLC)
Generative Artificial Intelligence (AI) is now widely adopted across various software engineering practices to assist in coding, testing, planning, and deployment tasks. Many enterprise deployments are still siloed, though, with AI solutions being used in only part of the software development lifecycle, not as a part of a cohesive agile workflow. This research aims at providing a single perspective of integrating generative AI into the entire agile software development lifecycle, while focusing on continuous context sharing between discovery, planning, development, integration, delivery, and feedback. It illustrates the synergy between AI voice agents and context-aware development prompts, automated Jira epic generation, predictive integration models, machine learning telemetry, and ERP-linked analytics in enhancing product execution. The article draws on experiences from enterprise-scale hardware-software environments to showcase how integrated AI workflows can reduce manual research effort, enhance access management, assist with faster engineering decisions, minimize data discrepancies, and boost release velocity. The study suggests that generative AI can bring more value when it’s not just a productivity tool but a tool that helps product managers, engineers, and stakeholders across the entire product lifecycle. The proposed AI-SDLC framework can provide a practical basis for better agile coordination, minimize the silos in the operation of the enterprise, and maximize the delivery of software in complex enterprise systems.
Generative AI, Agile Software Development, Software Development Lifecycle, AI-SDLC Framework, Product Management, Enterprise Software, Continuous Deployment, Machine Learning Integration, ERP Systems, Context-Aware Development