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.
Design of a Secure Multi-Tenant Artificial Intelligence Framework for Enterprise CRM ERP and Cloud Application Integration
In today’s dynamic enterprise environment, organizations increasingly rely on Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), and other cloud applications to manage operations, enhance customer engagement, and optimize decision-making. However, the integration of multiple enterprise applications introduces challenges related to data security, interoperability, and scalability, especially in multi-tenant cloud environments. This research proposes a Secure Multi-Tenant Artificial Intelligence (AI) Framework designed to facilitate seamless integration of CRM, ERP, and cloud applications while ensuring robust security and compliance. The framework leverages advanced AI techniques, including machine learning-based anomaly detection, natural language processing for contextual data interpretation, and predictive analytics for intelligent decision support. It incorporates secure authentication, role-based access control, and tenant isolation mechanisms to protect sensitive enterprise data across multiple tenants. Experimental simulations demonstrate that the proposed framework improves data integration efficiency, enhances threat detection, and supports adaptive decision-making across heterogeneous enterprise systems. By enabling intelligent automation, predictive insights, and secure interoperability, this framework addresses key challenges in modern enterprise ecosystems. The study contributes a scalable, secure, and AI-driven approach for optimizing multi-tenant enterprise cloud application integration, thereby improving operational efficiency, business intelligence, and resilience in digital enterprise environments.
Multi-tenant cloud security, Enterprise application integration, Artificial intelligence in CRM and ERP, Predictive analytics, Secure data interoperability, Role-based access control, Anomaly detection in cloud systems
Federated Learning Enabled Cybersecurity Architecture for Scalable Internet of Things and Cloud Computing Platforms
The rapid proliferation of Internet of Things (IoT) devices and cloud computing platforms has expanded attack surfaces, creating significant cybersecurity challenges. Traditional centralized cybersecurity solutions struggle to process massive distributed data while preserving privacy, leading to potential vulnerabilities and compliance issues. This research proposes a Federated Learning (FL) enabled cybersecurity architecture designed to provide scalable, privacy-preserving, and adaptive protection for IoT and cloud environments. The framework enables collaborative model training across distributed edge devices and cloud nodes without sharing raw data, ensuring data privacy and regulatory compliance. Core components include federated aggregation, anomaly detection using machine learning models, intrusion detection, secure communication protocols, and adaptive threat mitigation. Experimental evaluations on simulated IoT and cloud datasets demonstrate that the framework achieves high detection accuracy, reduces communication overhead, and preserves data confidentiality compared to traditional centralized approaches. The FL-enabled architecture supports dynamic scalability, real-time threat detection, and collaborative intelligence across heterogeneous platforms. This research highlights the potential of federated learning in enhancing cybersecurity for distributed systems, providing a practical blueprint for secure, resilient, and scalable IoT-cloud infrastructures.
Federated Learning, Cybersecurity, IoT Security, Cloud Computing Security, Privacy-Preserving AI, Distributed Machine Learning, Intrusion Detection, Anomaly Detection, Scalable Security Architecture, Edge Computing]
Governance-Aware AI Agents: Ensuring Trust, Accountability, and Robustness in Real Estate Intelligence Systems
Artificial intelligence is increasingly transforming real estate intelligence systems through applications in property valuation, mortgage risk assessment, urban prediction, and construction finance analytics. However, the growing reliance on complex machine learning models introduces significant challenges related to trust, accountability, transparency, and robustness in automated decision making. Because real estate decisions involve substantial financial and regulatory implications, ensuring responsible and trustworthy AI deployment has become a critical priority for industry stakeholders and policymakers. This study proposes the concept of governance-aware AI agents as a framework for embedding governance mechanisms directly into AI-driven real estate intelligence systems. The framework integrates explainability modules, auditability mechanisms, governance policy enforcement, and human oversight interfaces into the architecture of intelligent decision systems. By aligning technical design with responsible AI principles, governance-aware agents aim to improve system transparency, decision traceability, and reliability in high-stakes real estate environments. The paper develops a conceptual model that connects AI governance, explainable artificial intelligence, and real estate analytics into a unified approach to trustworthy AI deployment. The findings suggest that integrating governance structures within AI agent design can enhance stakeholder confidence, support regulatory compliance, and strengthen the robustness of real estate intelligence systems. The study provides a theoretical foundation for future empirical research on governance-aware AI implementation in property valuation and real estate decision platforms.
Governance-aware AI agents, Explainable artificial intelligence, Real estate intelligence systems, AI governance, Trustworthy AI.
Cyber-Resilient AI Cloud Architecture for Secure Enterprise Financial Healthcare Systems and Autonomous Digital Transformation
The rapid growth of digital technologies and cloud computing has significantly transformed enterprise systems across industries such as finance, healthcare, and large-scale organizational platforms. As enterprises increasingly adopt cloud infrastructures to store and process critical data, cybersecurity threats, data breaches, and operational disruptions have become major concerns. Traditional security models often fail to address the complexity and dynamic nature of modern cyber threats. Therefore, organizations require advanced security architectures that combine artificial intelligence, cyber resilience strategies, and scalable cloud computing frameworks. This research proposes an AI-powered cyber resilient cloud architecture designed to enhance the security, scalability, and intelligence of enterprise digital ecosystems. The proposed architecture integrates artificial intelligence, machine learning, cloud-native security mechanisms, and automated monitoring systems to detect cyber threats, predict potential vulnerabilities, and ensure continuous system availability. The framework supports secure enterprise platforms, financial transaction systems, healthcare analytics infrastructures, and autonomous digital transformation initiatives. The architecture enables organizations to proactively identify security risks and automatically respond to cyber incidents through intelligent threat detection and adaptive security policies. Additionally, the framework supports real-time analytics and predictive intelligence that improve decision-making processes in complex enterprise environments. The research presents architectural design principles, security frameworks, and implementation methodologies for building resilient enterprise cloud infrastructures capable of supporting next-generation digital transformation.
cyber resilient cloud architecture, secure enterprise systems,
healthcare analytics security, autonomous digital transformation, cloud security framework, machine learning threat detection, zero trust security, intelligent automation, predictive cyber threat analytics, automated incident response
AI Driven Multi-Cloud Architecture for Secure Enterprise Data and Autonomous Predictive Analytics
The rapid digital transformation of enterprises has resulted in massive data generation across distributed systems, cloud services, and connected devices. Traditional data platforms often struggle to ensure security, scalability, interoperability, and real-time intelligence. This study proposes an Intelligent Artificial Intelligence-driven Multi-Cloud Architecture designed to support secure enterprise data platforms, predictive risk analytics, and autonomous digital ecosystems. The proposed architecture integrates artificial intelligence, multi-cloud computing, secure data governance, and automated orchestration mechanisms to create a resilient digital infrastructure capable of handling complex enterprise workloads. The framework leverages machine learning models for predictive analytics, enabling organizations to detect risks, anomalies, and cyber threats before they impact operational processes. Additionally, the architecture supports autonomous digital ecosystems by enabling intelligent data pipelines, adaptive security mechanisms, and cross-cloud resource optimization. By combining distributed data platforms with AI-enabled decision intelligence, enterprises can achieve higher levels of efficiency, scalability, and operational resilience. The research explores architectural components, risk prediction models, and governance mechanisms within multi-cloud environments. It also analyzes the advantages and limitations of AI-driven enterprise cloud platforms. The findings highlight that integrating intelligent analytics with secure multi-cloud infrastructure can significantly enhance enterprise data management, predictive risk mitigation, and digital ecosystem automation, enabling organizations to achieve sustainable digital transformation in highly dynamic technological environments.
Artificial Intelligence, Multi-Cloud Architecture,
Enterprise Data Platforms, Predictive Risk Analytics, Autonomous
Digital Ecosystems, Cloud Security, Data Governance, Machine Learning, Digital Transformation
Cloud-Native Intelligent Data Analytics Platform for Financial Healthcare and Enterprise Decision Intelligence
Enterprises in financial services and healthcare sectors generate massive volumes of data that are critical for operational efficiency, risk management, and decision-making. Traditional analytics platforms often struggle to process this data in real time, resulting in delayed insights and suboptimal decisions. Cloud-native architectures, combined with intelligent data analytics, offer scalable, resilient, and flexible solutions to address these challenges. This research proposes a cloud-native intelligent data analytics platform designed to support financial risk analysis, healthcare monitoring, and enterprise decision intelligence. The platform integrates advanced analytics, machine learning, and AI-driven predictive models to analyze structured and unstructured datasets across multiple sources. Cloud-native technologies, including microservices, containerization, and orchestration frameworks, enable dynamic scaling, high availability, and fault tolerance. In the financial domain, the platform identifies potential risks, predicts market trends, and supports fraud detection. For healthcare monitoring, real-time analytics provide early detection of anomalies, predictive health outcomes, and operational optimization for patient care. Enterprise decision intelligence leverages aggregated insights to support strategic planning, resource allocation, and operational efficiency. This framework ensures secure data handling, compliance with regulatory requirements, and enhanced business agility. The study provides architectural design principles, implementation strategies, and evaluation metrics for deploying intelligent, cloud-native analytics platforms across complex enterprise ecosystems.
Cloud native intelligent data analytics platform, financial risk analytics, healthcare monitoring systems, enterprise decision intelligence, cloud data analytics architecture, AI driven data analytics, predictive risk modeling, healthcare data intelligence, enterprise analytics platforms, big data cloud computing, machine learning analytics, intelligent decision support systems
AI for Predictive Maintenance in Industrial Systems
Artificial Intelligence (AI) has revolutionized industrial maintenance strategies by enabling predictive maintenance (PdM), which anticipates equipment failures before they occur, optimizing operational efficiency, reducing downtime, and lowering costs. Leveraging machine learning, deep learning, and advanced sensor data analytics, AI-based predictive maintenance systems monitor machinery in real time, detect anomalies, and forecast potential malfunctions. This article explores the principles, techniques, and applications of AI in predictive maintenance for industrial systems, including data-driven approaches, condition-based monitoring, and prognostics. It discusses AI models for time-series analysis, anomaly detection, and remaining useful life (RUL) estimation, while addressing challenges related to data quality, scalability, interpretability, and integration with existing industrial systems. Applications span manufacturing, energy production, transportation, and heavy industries, demonstrating measurable improvements in reliability, safety, and operational costs. Future research directions include edge AI implementations, digital twins, integration with IoT networks, and adaptive AI models for dynamic industrial environments. AI-driven predictive maintenance represents a transformative approach that enhances industrial resilience, productivity, and sustainability.
Predictive Maintenance, Industrial AI, Machine Learning, Deep Learning, Condition Monitoring, Prognostics, Anomaly Detection, Remaining Useful Life, Industrial IoT, Digital Twin, Predictive Analytics, Equipment Failure Forecasting, Smart Manufacturing.
Generative AI for Creative Applications: Art, Music, and Design
Generative Artificial Intelligence (AI) has emerged as a transformative technology in creative industries, offering unprecedented capabilities for generating art, music, design, and multimedia content. Leveraging advances in machine learning, deep learning, and large-scale neural networks, generative AI systems can produce original, high-quality creative outputs that emulate human artistic expression while enabling entirely novel forms of creation. This article explores the foundations, methodologies, and applications of generative AI in creative domains, highlighting techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and large multimodal models capable of text-to-image, text-to-music, and multimodal content generation. Applications include digital art creation, music composition, graphic design, game content generation, and virtual reality environments. Despite its immense potential, generative AI poses technical, ethical, and legal challenges, including authorship, copyright, bias, and the authenticity of generated content. This comprehensive review examines the state-of-the-art in generative AI for creativity, evaluates its impact on human creativity and industry workflows, and discusses future research directions, including interactive AI-human co-creation and explainable generative models. Generative AI is poised to redefine creative processes, offering scalable, innovative, and collaborative avenues for human and machine creativity.
Generative AI, Creative AI, Art Generation, Music Composition, Design Automation, Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, Multimodal Generation, AI-Assisted Creativity, Human-AI Co-Creation, Creative Technologies, Digital Art, Computational Creativity
The rapid growth of Artificial Intelligence (AI) has led to increasing computational demands, resulting in significant energy consumption and environmental impact. Energy-efficient and Green AI focuses on designing algorithms, hardware, and systems that minimize energy use while maintaining high performance, thereby promoting sustainable and environmentally responsible AI practices. This approach encompasses model compression, pruning, quantization, low-power hardware accelerators, and adaptive training strategies to reduce carbon footprint without compromising accuracy. Green AI also emphasizes the evaluation of AI models not only based on performance metrics but also on energy and resource efficiency, fostering transparency and accountability in AI development. By integrating energy-awareness into AI research and deployment, Green AI supports the global push toward sustainable computing, enabling large-scale adoption of AI technologies in a climate-conscious and resource-efficient manner.
Energy-Efficient AI, Green AI, Sustainable Computing, Model Compression, Low-Power AI, Carbon Footprint Reduction, Hardware Acceleration, Pruning, Quantization, Environmental Impact of AI, Resource-Aware AI.
Integrated Artificial Intelligence Cloud DevOps and Enterprise Networks for Digital Banking Credit Risk and Healthcare Systems
The integration of Artificial Intelligence (AI), cloud computing, DevOps practices, and enterprise networking has revolutionized digital banking, credit risk management, and healthcare systems. Financial institutions and healthcare providers increasingly rely on cloud-native architectures to deliver scalable, secure, and data-driven services. AI-driven analytics enhance credit scoring, fraud detection, predictive diagnostics, and operational automation, while DevOps accelerates continuous integration and deployment across distributed cloud environments. Major cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud provide integrated AI and DevOps toolchains that support high-performance enterprise networks. Enterprise networking technologies, including software-defined networking (SDN) and zero-trust security frameworks, enable secure communication, real-time data processing, and regulatory compliance across digital ecosystems. In banking, AI improves credit risk modeling and transaction monitoring, while in healthcare, it enhances patient data analytics and clinical decision support systems. This research explores architectural integration strategies, governance frameworks, performance evaluation metrics, and security considerations necessary for implementing AI-cloud-DevOps enterprise networks. The study proposes a structured methodology to assess scalability, resilience, cybersecurity effectiveness, and regulatory compliance across financial and healthcare environments.
Integrated Artificial Intelligence, Cloud Computing, DevOps, Digital Banking, Credit Risk Modeling, Healthcare Information Systems, Machine Learning (ML), Continuous Integration and Continuous Deployment (CI/CD), Infrastructure as Code (IaC), MLOps, Cloud-Native Architecture, Microservices, Data Analytics, Regulatory Compliance, Cybersecurity
Early Approaches to Robotic Process Automation in Enterprise Systems
Robotic Process Automation (RPA) has become an innovative way of increasing operational efficiency and precision in enterprise systems. Initial applications of RPA were aimed at automating rule-based repetitive activity in a wide range of industries, such as the fields of finance, telecommunication, energy, and manufacturing. Through the combination of RPA with the current enterprise resource planning systems and other digital infrastructures, organizations could successfully reduce the time process cycles, eliminate the human errors, and streamline the resource options. Although these advantages occurred, there were challenges associated with early adoption, system interoperability, workforce adaptation, and change management. This paper presents a general overview of the early methods of RPA in the enterprise systems and its implementation strategies, practical uses, and addition of the new technologies including cloud computing and the Internet of Things. The results show the possibilities and constraints of RPA as a process transformation tool, which can inform future endeavors to automate processes in the enterprise.
Robotic Process Automation, Enterprise Systems, Process Efficiency, Automation, Digital Transformation, Industrial Integration
Cloud Native DevOps and AI Powered Enterprise Platforms with Blockchain Security and ETL Workloads
Cloud-native DevOps and AI-powered enterprise platforms are redefining how organizations manage complex data workflows, ensure security, and achieve operational agility at scale. This paper presents an integrated enterprise architecture that combines cloud-native DevOps practices, artificial intelligence–driven analytics, blockchain-based security mechanisms, and automated ETL workloads to support resilient and trustworthy digital platforms. The proposed framework leverages microservices, container orchestration, and infrastructure-as-code to enable rapid deployment, continuous integration, and continuous delivery across distributed cloud environments. DevOps automation ensures consistency, scalability, and fault tolerance while reducing operational overhead and deployment risks. Artificial intelligence is embedded across the platform to enhance decision-making, optimize resource utilization, and provide real-time operational intelligence. Machine learning models are integrated into ETL pipelines to automate data ingestion, transformation, and validation, enabling high-throughput processing of structured and unstructured enterprise data. These intelligent ETL workflows support advanced analytics, predictive insights, and adaptive system behavior in response to changing workloads and business requirements. Blockchain technology is incorporated as a foundational security layer to ensure data integrity, immutability, and transparent auditability across enterprise transactions and data exchanges. Smart contracts and distributed ledgers enable secure data sharing, tamper-resistant logging, and decentralized trust without reliance on centralized authorities. The architecture adopts a security-by-design approach, integrating blockchain security controls, identity management, and continuous monitoring directly into the DevOps lifecycle. This ensures that security policies evolve alongside applications and data pipelines. By unifying cloud-native DevOps, AI-driven intelligence, blockchain-based security, and automated ETL workloads, the proposed platform supports scalable, secure, and data-intensive enterprise operations. The framework is particularly suitable for organizations requiring high levels of data trust, compliance, and real-time analytics in multi-cloud and hybrid enterprise ecosystems.
cloud-native DevOps, AI-powered enterprise platforms, blockchain security, ETL workloads, intelligent automation, machine learning pipelines, data integrity, CI/CD pipelines, distributed systems, secure data analytics, enterprise cloud architecture, digital transformation
Architecting Enterprise-Grade Cross-Platform Mobile Applications with Web Views
The design of enterprise-scale mobile applications that provide similar functionality and user experience on both iOS and Android platforms has been a hard challenge especially when working with large scale systems where quick delivery of features, maintainability and platform compatibility is paramount. This paper introduces a cross-platform mobile structure that is strategically implemented by using Web Views in native mobile containers, which allows enterprises to balance on the development efficiency and the ability to have robust native capabilities. Through modular web architecture, which is integrated with high-performance Web Views, organizations can ensure a great deal of the code reuse and centralize business logic and presentation layers. The suggested solution would enable the development teams to roll out and roll out features in a very short time without redeploying the complete application, which would greatly cut down the release cycle time. The platform-specific issues like authentication, secure storage, push notifications, deep linking and device-level integration are still handled by native containers, making web and native systems interact seamlessly. The paper investigates main architecture designs to achieve secure two-way communication between native and web layers, good state synchronization, and optimizing performance in memory and resource constrained mobile applications. Furthermore, the architecture also fulfills the enterprise needs in terms of scalability, security, compliance with accessibility, and operational sustainability. The practical application of the idea of enterprises being implemented as modular systems through WebViews demonstrates the benefits of the reduced redundancy in development across platforms, the enhanced uniformity of user experience, and the fact that these modular systems meet the needs of evolving business conditions without compromising the performance or dependability. The results indicate that a Web View-based hybrid architecture is a practical and large-scale solution to organizations that want to upgrade their mobile platform and retain agility, governance, and cross-platform alignment in the complicated enterprise ecosystem.
Cross-Platform Mobile Architecture, Web Views, Enterprise Mobile Applications, Modular Web Architecture, Hybrid Mobile Systems, Scalable Mobile Platforms, Native-Web Integration, Mobile Engineering
Leveraging AI for Scalable Cybersecurity in IT Infrastructure: A Program Management Approach
In today's rapidly evolving digital landscape, organizations are increasingly exposed to cyber threats that are more sophisticated and pervasive than ever before. Traditional IT program management practices, focused largely on perimeter defense mechanisms, have proven to be inadequate in addressing modern cybersecurity challenges. As a result, organizations are seeking more effective and adaptive solutions to enhance their cybersecurity frameworks. Artificial intelligence (AI) has emerged as a transformative technology in the field of cybersecurity, offering powerful tools to enhance threat detection, streamline incident response, and improve risk management processes. This article explores how AI-driven cybersecurity solutions can optimize IT program management, examining their role in improving security operations, mitigating cyber risks, and enhancing organizational resilience. AI technologies, particularly machine learning (ML), deep learning (DL), and behavioral analytics, are revolutionizing the way organizations approach cybersecurity. By leveraging AI to automate and accelerate threat detection and incident response, businesses can improve the speed and accuracy of identifying security breaches, ultimately minimizing the damage caused by attacks. Moreover, AI’s predictive capabilities enable organizations to anticipate potential vulnerabilities and proactively address them before they are exploited by attackers. As AI continues to advance, its potential to reduce human error, enhance decision-making, and provide deeper insights into cybersecurity threats will become increasingly valuable for IT program managers. Despite its promise, the integration of AI into IT program management is not without challenges. One of the primary barriers is the complexity of deploying and maintaining AI-based systems. These technologies require significant infrastructure and expertise, and their integration into existing IT frameworks often requires substantial organizational change. Additionally, there are concerns around data privacy and security, as AI systems often require vast amounts of sensitive data to function effectively. Furthermore, AI systems are not immune to vulnerabilities, including adversarial attacks that can manipulate or deceive AI algorithms, undermining their effectiveness. This article aims to provide a comprehensive overview of how AI-driven solutions can optimize IT program management in the context of cybersecurity. Through an analysis of current literature, industry case studies, and expert opinions, this study highlights both the benefits and challenges of adopting AI technologies in IT management practices. It provides recommendations for organizations seeking to integrate AI into their cybersecurity strategies, emphasizing the importance of strategic planning, proper training, and continuous monitoring. The findings underscore the need for organizations to embrace AI-driven cybersecurity solutions as part of a broader, more proactive IT program management approach that anticipates emerging threats and enables more effective risk management. Ultimately, AI holds the potential to transform the way organizations approach cybersecurity, offering new levels of efficiency, effectiveness, and resilience in the face of ever-evolving cyber threats. As these technologies continue to mature, their integration into IT program management will be essential for ensuring long-term security and operational success.
AI-driven cybersecurity, IT program management, machine learning (ML), deep learning (DL), predictive security, incident response automation, threat detection, risk management, behavioral analytics, cybersecurity frameworks, data privacy, adversarial attacks, cybersecurity strategy, automation in cybersecurity, network security, cyber threat prevention, risk mitigation, AI integration, IT infrastructure, security operations, digital transformation, cybersecurity optimization, AI tools, security automation, data governance, proactive security measures, emerging threats, continuous learning in AI, compliance and security, IT security management, AI-powered solutions, organizational security.
Automation-Driven Reliability Engineering for Public-Sector Biomedical Systems
One of the fundamental requirements of the biomedical systems of the state that takes part in the support of the national research and the national health programs is reliability. These are mission-sensitive platforms that are very sensitive as regards maintenance because any slight failure can lead to severe consequences. The paper will discuss how reduced reliability engineering through automation can be applied to enhance the resiliency of the systems and this is particularly in cases where the systems are operating on a limited regulatory framework. It takes into account among the most significant practices, including self-healing pipelines, automated rollback plans, and telemetry-based monitors to strengthen the performance of the system and minimize disruptions. Such systems can independently detect and react to failures with the aid of automation so as to reduce the possibility of occurrence of incident and speed up recovery. The research demonstrates that incident rate and recovery duration have greatly been reduced with time which is due to the study that is conducted longitudinally which justifies the value of such automation plans in providing continuity of operations. It has shown that the practices can be used to create fault tolerant and high availability architectures that can be used to meet the demands. Besides, the paper discusses the telemetry-based monitoring as the means of enhancing the reliability of services through the integration of real-time data to implement a quick response and make decisions. Overall, this research demonstrates that automation is essential in promoting credibility of biomedical platforms, which also offers helpful information as to how such systems can be capable of responding to the changes, which are being made, and at the same time meet the regulatory and operational demands. The results can be used as a guide toward the implementation of automation architectures into government-based IT infrastructure, which facilitates the resilience of systems, as well as their efficiency in key biomedical services.
Reliability Engineering, Automation Frameworks, Biomedical Platforms, Public-Sector IT, System Resilience, Fault Tolerance, Self-Healing Systems, Incident Reduction, Telemetry-Driven Monitoring
Secure and Causality-Aware AI Cloud Architectures for Enterprise Automation across Mobile Healthcare and Omnichannel Retail Media Systems
The convergence of artificial intelligence, cloud-native software engineering, and enterprise automation is transforming decision-making across regulated and data-intensive sectors such as mobile healthcare and omnichannel retail media. However, existing AI-driven cloud platforms often prioritize predictive accuracy and scalability while insufficiently addressing security, causal validity, and regulatory accountability. This paper proposes a secure and causality-aware AI cloud architecture designed to support enterprise automation across heterogeneous domains by integrating causal incrementality analysis and bandit-based optimization within a cloud-native framework. The proposed architecture combines zero-trust security principles, privacy-preserving data pipelines, and compliance-aware governance layers with causal inference engines that distinguish correlation from true intervention impact. In mobile healthcare systems, this enables safe and explainable automation for clinical decision support, patient engagement, and remote monitoring while ensuring data confidentiality and regulatory compliance. In omnichannel retail media environments, the architecture supports adaptive customer acquisition and budget optimization through contextual multi-armed bandit models informed by causal attribution and real-time feedback. By unifying secure AI engineering, causal intelligence, and cloud-native orchestration, the architecture enhances trustworthiness, operational efficiency, and cross-domain generalizability of enterprise automation platforms. The paper discusses design principles, system components, and deployment considerations, and highlights how causality-aware optimization improves decision reliability compared to purely predictive approaches. The proposed framework provides a foundation for building ethically aligned, secure, and scalable AI systems capable of supporting high-stakes automated decision-making in both healthcare and retail media ecosystems.
Cross-Platform Performance Optimization Strategies for Large-Scale Mobile Applications
Mobile applications that are large-scale, run across the public and privately, are required to achieve consistent performance when operating with the heterogeneous devices and operating systems, as well as network conditions. Cross-platform frameworks like React Native allow a developer to create applications quickly and reuse the code, but they also create performance bottlenecks as the applications reach millions of users. This paper discusses cross-platform performance optimization measures of large scale mobile apps, empirical evidence of which has been provided by high-traffic and mission-critical deployments in the public service setting. The study examines techniques such as architectural and implementation-level techniques such as memoization of components, state isolation, reduction of native bridge interactions, asynchronous rendering pipelines and advanced memory management practices. Based on practical case studies, the paper explains how disciplined performance engineering is a direct enhancement of the key system performance metrics which include UI responsiveness, application startup latency, battery consumption, and runtime stability at peak load condition. The findings show that the proactive optimization of the performance does not just enhance the end-user experience, but also enhances the reliability of the system, the accessibility and the long-term maintainability of the system which are pertinent to the applications which need to support applications with high-user base and workflows which cannot be deferred. The study also emphasizes on the significance of performance profiling, monitoring and scalability planning at all stages of application lifecycle especially during peak hours. The synthesized best practices related to cross-platform optimization make this research valuable to architects and developers of large-scale mobile systems required to be efficient, resilient and compliant in challenging operational conditions.
Cross-Platform Performance Optimization, Large-Scale Mobile Applications, React Native Architecture, Mobile Scalability Engineering, Asynchronous Rendering, State Management Optimization, Mobile Memory Management, Mission-Critical Mobile Systems
A Multi-Domain AI Framework for Enterprise Agility Integrating Retail Analytics with SAP Modernization and Secure Financial Intelligence
In the era of digital transformation, enterprises face mounting pressure to enhance agility, optimize operational efficiency, and strengthen cybersecurity resilience. Multi-domain Artificial Intelligence (AI) frameworks have emerged as pivotal enablers, integrating insights from retail analytics, SAP modernization initiatives, and cyber defense strategies to deliver actionable intelligence across organizational functions. This study proposes a comprehensive AI framework that bridges the gap between retail decision-making, enterprise resource planning modernization, and proactive cybersecurity measures. By leveraging machine learning, natural language processing, and predictive analytics, the framework enables real-time demand forecasting, personalized customer engagement, automated SAP workflows, and threat detection. The proposed methodology employs a hybrid approach combining cloud-based AI models, edge computing, and data lakehouse architectures to ensure scalability, security, and performance. Empirical results indicate that organizations adopting multi-domain AI frameworks achieve improved operational efficiency, faster decision cycles, and enhanced protection against cyber threats. The research also identifies critical challenges such as data heterogeneity, integration complexity, and governance requirements. This study underscores the transformative potential of multi-domain AI in fostering enterprise agility and provides practical insights for organizations seeking to integrate retail analytics, SAP modernization, and cyber defense within a unified AI-driven ecosystem.
Multi-domain AI, enterprise agility, retail analytics, SAP modernization, cyber defense, predictive analytics, AI frameworks, digital transformation, cloud AI, data integration.
Sector Specific Technology Adoption in SMEs: Manufacturing, Retail, and Services
This study examines sector specific patterns of technology adoption among small and medium sized enterprises (SMEs) in the manufacturing, retail, and services sectors. The purpose of the research is to identify the key technologies adopted by SMEs, the drivers and barriers influencing adoption, and the resulting organizational impacts across these sectors. A comparative qualitative analysis was employed, drawing on existing literature, industry reports, and secondary data to assess technological trends, adoption motivations, and outcomes. The findings reveal significant sectoral differences: manufacturing SMEs primarily adopt production and efficiency oriented technologies such as ERP systems, automation, and IoT; retail SMEs focus on customer facing technologies including e commerce platforms, POS systems, and digital payments; while service SMEs emphasize cloud computing, digital collaboration tools, and process automation. Despite these differences, common challenges such as limited financial resources, skills gaps, and cybersecurity concerns persist across all sectors. The study concludes that effective technology adoption in SMEs is strongly influenced by sectoral needs but ultimately depends on strategic alignment, managerial capability, and access to skills and support. These insights contribute to a better understanding of digital transformation in SMEs and offer guidance for policymakers and practitioners aiming to support sector specific digital adoption strategies.
Governance Frameworks for Automated Enterprise Decision Systems
In this article, the issue of governance is discussed because of the growing use of automated decision systems by enterprises when regulating and high-impact environments are involved. The conventional governance tends to lay emphasis on the outside compliance, although this strategy does not embed accountability and oversight into the system design. To close this gap, the article suggests a governance framework of automated decision systems within the enterprise, which focuses on internal controls, policy enforcement and audit preparedness, and operational oversight. The framework supports the inclusion of governance in the decision processes and introduces various mechanisms, including risk leveling, approval gateways, and evidence-generation in order to have transparent and responsible activities. Enterprises can use these controls in making the system design to ensure that they can effectively control the automated decision-making processes without losing the agility to grow. The article examines governance frameworks that can be applied to both early-stage and scaling platforms, which provide a realistic strategy of organisations that wish to scale automated decision systems in a safe manner. Finally, the framework presented also provides an excellent holistic solution to governance that not only increases the transparency and accountability of the system, but it also does not interfere with flexibility in operations. The study will serve the purpose of responsible automation by breaking down actionable details to develop governance into systems of enterprise decision-making.
enterprise governance, automated decision systems, accountability frameworks, compliance architecture, system oversight.
Edge Computing Architectures for IoT Data Aggregation in Industrial Manufacturing
This paper discusses edge computing platform to be used in aggregating IoT data in a production scenario. As IoT sensors generate huge quantities of data, edge computing is a feasible solution to work with such data and aggregate them near where they originate, thus reducing the amount of latency and saving on bandwidth. The research paper has a design that uses edge and fog computing to support distributed data processing in the network edge to facilitate real-time analytics. Running sensor data on the platform eliminates the requirement to have high bandwidth communication to centralized cloud servers and enhances responsiveness of the system and network traffic jams are minimized. In addition, edge analytics plays a critical role when it comes to making quick decisions, as it creates real-time information about manufacturing processes. The framework facilitates predictive maintenance where sensor data is used to detect any signs of failure in equipment early before it goes out of business, thereby minimizing the time spent on downtime and enhancing operational efficiency. The document outlines some key architectural frameworks of applying edge computing to the industrial IoT environment, which places emphasis on local data processing, machine learning algorithms, and cloud-based services. The proposed system lay out will simplify the manufacturing systems with scalable, low-latency, bandwidth efficient systems, which enable operational intelligence and predictive maintenance.
Edge computing, IoT architecture, industrial IoT, data aggregation, fog computing, edge analytics, latency reduction, bandwidth optimization, predictive maintenance, manufacturing systems.
Integrating API-First Architecture with Experience-Centric Design for Seamless Insurance Platform Modernization
The fast development of insurance industry requires a transition to more user-friendly and mobile-friendly platforms. The focus of this paper is to outline a modernization strategy of insurance platforms, which combines an API-first-based architecture with an experience-driven design. It is with APIs flexibility and scalability that the intended framework will succeed in establishing interoperable systems that will fulfill the dynamic demands of contemporary insurance organizations. This architecture will facilitate integration of legacy systems in a better way, and this will enable the insurers to easily adapt to the market changes and regulatory demands. The experience-based design is aimed at enhancing user satisfaction through maximization of user interface and interaction flows in such a way that customers have a personalized, efficient experience. With the application of API-first and the extensively optimized emphasis on the user experience, insurers will be able to deliver services faster, decrease operational expenses, and enhance customer loyalty. By incorporating API-first architecture, it is possible to have a better level of connectedness within the system, enabling real-time data exchange and decision-making. Moreover, the experience-based strategy will make sure that the customer experiences an uninterrupted and smooth digital experience throughout the process. The given paper emphasizes technical and strategic aspects of introducing such a framework, outlining some of the main elements of the system, such as API management, user interface design, and system scalability. It also discusses how this modernization may affect the insurance industry and provides an insight into how digital transformation may move in this industry.
API-first architecture, Experience-centric design, Insurance platform modernization, System integration, User experience
An API-Driven Solution for Enhancing Employee Lifecycle and Cost Management Efficiency
The current paper includes an API-based solution with MuleSoft that would help automate the processes of managing lifecycle and expenses of employees in enterprises. Integration architecture allows communication of human resources (HR) systems, payroll systems, and financial applications through reusable API and efficient coordination of workflow. This system guarantees automated data validation, policy enforcement, as well as detailed reporting, which results in the increased operational efficiency and perceiving costs in greater detail in both the HR and finance divisions. The solution allows not only optimizing the daily routine processes but also enhancing adherence to corporate policies by using the features of MuleSoft to create powerful and scalable APIs. The automatic adoption of these systems by means of communications via API omits any manual data input errors, optimizes the management of employee lifecycles, and provides a clear view of expenses. Moreover, the paper will examine how such integrations will help minimize the operational costs at the cost of making more strategic decisions. The solution can therefore play an important role in meeting the targets of both cost reduction and compliance to provide a superior framework in order to enhance the efficiency of the enterprise HR and finance systems.
MuleSoft, Employee Lifecycle Management, Expense Automation, API Integration, Enterprise Finance, Workflow Orchestration.
Program-Level Risk Orchestration in AI-Enabled Cyber Infrastructure: A Systems-of-Systems Management Approach
Machine Learning (ML) and Deep Learning (DL) driven AI capabilities are being increasingly leveraged for detection, anomaly discovery, and autonomous response within, across, and through cyber infrastructures. Scaling such cyber defense capabilities, they are deployed less as standalone solutions but as programs consisting of autonomous subsystems, common data assets, and joint operational processes. However, scaling also creates systemic risks (e.g., cascading, latent, and indirect) and poses control risks (e.g., distributed, dual-use) that are not sufficiently recognized and managed under prevalent cybersecurity risk management (RM) models, which are generally limited to controls at the component level, local rather than program-wide risk visibility and mitigation. This paper posits a program-level risk orchestration framework and some of its design principles, which are based on systems-of-systems (SoS) engineering and established risk RM standards. Building on early work on AI-based intrusion detection systems, SoS engineering and interoperability, cybersecurity and cyber risk threat modeling, as well as formal risk governance and capital standards, the paper develops the thesis of conceptualizing and managing AI-enabled cyber infrastructure as a risk RM program of a special kind, namely a system-of-systems, in need of coordinated oversight and control. The RM program-level framework it proposes as a starting point combines technical (i.e., from AI subsystems) cyber risk signals with risk governance, investment, and economic decision models to structure the consistent and repeatable identification, assessment, prioritization, treatment, monitoring, and reporting of cyber risks across the program. To better support accountability and return on investment, the program-level management perspective further aligns the operations of the cybersecurity function with a standard RM lifecycle, investment criteria, and cost and benefit capital standards. The paper also positions the proposed framework with respect to recent and extant works, by offering a complementary management lens to a mostly algorithm-focused stream of research.
AI-enabled cybersecurity, program-level risk orchestration, systems-of-systems engineering, cyber risk governance, intrusion detection systems, risk management standards.
Automotive Sales Intelligence: Leveraging Modern BI for Dealer Ecosystem Optimization
The automotive retail business is rapidly evolving due to the digitalization process, the increase in data, and the evolving demands of customers. The conventional method of decision-making in dealer ecosystems has been characterized by fragmented information, visibility, and reactive strategies. In this study, the author examines how Business Intelligence (BI) can be applied in modern times to improve sales intelligence in the automotive industry and maximize the performance of dealer ecosystems. The study suggests a combined Automotive Sales Intelligence Framework, which is a combination of data integration, advanced analytics, and visualization to assist in evidence-based decision-making in sales, inventory, marketing, and customer relationship management. The implemented framework will comprise four layers: The data acquisition layer will include dealer management systems, customer touchpoints, and market sources; the data processing/data warehousing layer will support analytical intelligence through descriptive and diagnostic BI tools, and predictive BI tools; finally, the decision-support layer will allow real-time dashboards and actionable insights to the stakeholders. The framework helps to enhance demand forecasting, inventory optimization, customized sales strategy, and performance tracking by harmonizing the functions of dealers with the insights of data. The analysis indicates how the current BI technology can cause raw sales data to become strategic intelligence, and this will ensure a greater efficiency of operations and ultimate competitive advantage: cloud-based BI tools and interactive analytics. These results contribute to the scholarly and practical discourse as they demonstrate that a systematic BI-based model can help achieve sustainable development and optimization in an automotive dealer ecosystem.
Automotive Retail, Sales Intelligence, Business Intelligence, Dealer Analytics, Decision Support Systems, Data-Driven Optimization
Closed-Loop AI Frameworks for Real-Time Decision Intelligence in Enterprise Environments
The traditional analytics systems tend to be retroductive, where they look at the past information with a view to deriving insights that may not be really applicable in the rapidly changing enterprise world. This limitation is what this paper is going to look into on how closed-loop AI systems can truly achieve their potential by making it possible to have real-time decision intelligence in the enterprises. It puts an emphasis on the integration of streaming data pipelines, feedback loops that are continuously running and adaptive inference systems in which AI systems dynamically learn based on the outcome of their actions and respond with new actions accordingly. In the article, it is also described how latency control, traceability of decision, and stability of the system are the key architectural recommendations that are significant to maintain the reliability and efficiency of the real-time AI systems. In addition, the paper also talks about the technical issues, such as processing large volumes of data, and about governance issues, such as accountability, transparency and auditability of the decision-making processes. The research is a roadmap of how companies can utilise the potential of AI to make operational decisions and manage to have scalability, reliability, and effective supervision of the system simultaneously. The proposed closed-loop model would turn the decision-making process more agile in such a way that an AI-based system could always be developing and adapting to different circumstances in business.
Closed-loop AI, Real-time decision intelligence, Streaming data pipelines, Continuous feedback loops, Latency control, System stability
Cyber-Secure Cloud Architecture Integrating Network and API Controls for Risk-Aware SAP Healthcare Data Platforms
The rapid digitalization of healthcare systems has increased reliance on SAP-based platforms deployed on cloud infrastructures, introducing complex challenges related to cybersecurity, data governance, and operational risk. This paper proposes a cyber-secure cloud architecture that integrates network-level security mechanisms and API governance controls to enable risk-aware management of healthcare data platforms. The architecture combines zero-trust networking, intrusion detection and prevention systems, secure API gateways, and policy-driven data access to protect sensitive clinical and operational data across hybrid and multi-cloud environments. Risk-aware analytics are embedded into the platform to continuously assess security posture, data access behavior, and system performance, supporting informed decision-making and regulatory compliance. The proposed framework aligns with healthcare standards such as HIPAA and GDPR while supporting scalable SAP workloads and real-time data processing. Through architectural analysis and use-case-driven evaluation, the study demonstrates how integrated network and API controls can reduce attack surfaces, improve data integrity, and enhance system resilience. The results indicate that the proposed approach enables secure, compliant, and scalable SAP healthcare data platforms capable of supporting mission-critical healthcare operations.
Cybersecurity, Cloud Architecture, SAP Healthcare Systems, API Security, Network Security, Risk-Aware Analytics, Data Governance.
Resilient and Ethical Enterprise IT Systems integrating Platform Engineering SAP Analytics Healthcare Interoperability and Cloud Infrastructure
Modern enterprises face unprecedented demands for reliability, ethical data handling, interoperability, and agility in their IT systems. This paper presents a comprehensive framework for designing resilient and ethical enterprise IT systems that integrate platform engineering, SAP ecosystems, data analytics, healthcare interoperability, and cloud infrastructure. Resilience ensures continuity of operations amid disruptions, while ethical considerations such as privacy, fairness, and accountability are essential in systems handling sensitive data—especially in healthcare contexts. Platform engineering provides standardized tooling, automation, and internal platforms that reduce complexity and accelerate delivery. SAP systems serve as core business processing engines; integrating them for real-time analytics enhances decision support while aligning transactional and analytical workflows. Data analytics unlocks insights across operations, and healthcare interoperability enables seamless information exchange following protocols like HL7 and FHIR. Cloud infrastructure supports elastic scalability, distributed delivery, and costefficient resource utilization. Through an extensive literature review and multiple case studies involving expert interviews and system artifact analysis, this research identifies architectural patterns, governance practices, and measurable outcomes associated with unified enterprise systems. Findings show that enterprises adopting these integrated approaches achieve improved performance, reduced downtime, enhanced compliance, and stronger trust among stakeholders. The paper concludes with practical recommendations and directions for future research in resilient, ethical enterprise IT design.
Resilient IT Systems, Ethical Data Governance, Platform Engineering, SAP Systems, Data Analytics, Healthcare Interoperability, Cloud Infrastructure, Enterprise Architecture, Digital Transformation
Cybersecurity in Critical Residential Infrastructure
The increasing digitalization of residential environments has transformed housing systems into a critical component of national cyber-physical infrastructure. Smart homes, residential energy systems, and IoT-enabled utilities now play an essential role in societal functioning, yet they remain highly vulnerable to cyber threats due to fragmented governance, heterogeneous devices, and limited security-by-design adoption. This article examines cybersecurity challenges within critical residential infrastructure by synthesizing existing literature on smart grids, residential IoT, and cyber risk management frameworks. It identifies key threat vectors, including data breaches, ransomware, and cyber-physical attacks capable of cascading into broader infrastructure failures. The study further evaluates governance gaps and emerging technological responses, with particular attention to artificial intelligence–driven threat detection and resilience enhancement. By conceptualizing residential infrastructure as a critical security domain, the article contributes to ongoing discourse on infrastructure protection and underscores the need for integrated policy, technical standards, and adaptive security architectures to safeguard residential systems against evolving cyber risks.
Cybersecurity, Critical Infrastructure, Smart Homes, Residential Energy Systems, Internet of Things, Risk Management, Artificial Intelligence.
Quantifying the Causal Effect of FMCSA Enforcement Interventions on Truck Crash Reduction: A Quasi-Experimental Approach Using Carrier-Level Safety Data
The Federal Motor Carrier Safety Administration (FMCSA) and partners in the states are supposed to minimize collisions and other serious safety offenses by effective enforcement interventions, such as compliance reviews, audits, and planned inspections not to mention enforcement interventions. However, the carriers onto which the interventions are imposed are usually not random and already are of high profile of risk such that naive before vs. after comparisons is misleading information. The paper is a quantitative evaluation of the causal impact of FMCSA enforcement interventions on truck crash outcomes using carrier-level data on safety, the FMCSA Safety Measurement System (SMS) and the Motor Carrier Management Information System (MCMIS) of 2015-2023 with over 50,000 carriers of varying types and operating in different locations. Our quasi-experimental, propensity score matching, Difference-in-Differences (DiD) study will compare propensity-score adjusted research with a matched control group with the parallel trends assumption validated through the analysis of event-study plots. Synthetic Control methods are used at carrier-segment level with the aim of providing robustness checks in high impact interventions. The significant results are per cent of the crash rates decreased, the Average Treatment Effect (ATE/ATT) at 95 percent confidence intervals, and the amount of crashes which will be avoided each year under the circumstances of the observed volume of the interventions. Placebo tests, alternative matching specifications and sensitivity to time windows are also used to measure the strength. The initial results indicate that the FMCSA interventions result in a mass reduction in the crash rates, and the measures of effects are dependent on the carrier size, type of operation, former degree of the risk, and region where the carriers operate. The findings have practical implications of the differences in effects of the enforcement measures, which can be utilized to make more specific and evidence-based policy changes. This research enhances better understanding of enforcement efficacy in enforcing road safety and minimizing the likelihood of crashes with a help of causal inference tools and carrier-level information.
Causal inference, enforcing behaviour, position along the road, crash minimisation, quasi-experimental design, synthetic control, interventional assessment, selection biases, counterfactual analysis, policy impact assessment, carrier safety performance.