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.
Global AI Skills Index: Creating an Open Benchmark for Tracking National AI Upskilling Readiness
Artificial intelligence continues to reshape global labor markets, amplifying the urgency for countries to develop scalable and equitable AI-related competencies. Yet, the absence of a unified global benchmark limits policymakers’ ability to compare national readiness or design targeted workforce development strategies. This study introduces the Global AI Skills Index (GASI), a comprehensive cross-national framework that evaluates AI upskilling readiness across four dimensions: AI Job Demand Density, Education-Industry Alignment, AI Skills Penetration Rate, and the Equity and Access Index. Drawing on standardized datasets from the OECD, UNESCO, ILO, World Bank, and LinkedIn covering 2018 to 2023, the index applies a weighted composite methodology to generate a harmonized 0-100 score for each country. Methodological reliability was assessed through cross-validation with three established international benchmarks: the OECD Digital Skills Indicators, the Stanford AI Index, and World Bank–LinkedIn labor-market analytics. Strong alignment across these sources (R² = 0.76 to 0.84) demonstrates that GASI accurately captures systemic national differences in AI competence, digital readiness, and workforce capability. Quantitative findings highlight pronounced global disparities: advanced economies such as the United States, Singapore, and Finland achieve scores above 80, while countries across Sub-Saharan Africa and South Asia consistently fall below 55. A significant positive association between national education expenditure and AI job demand (R² = 0.68) further underscores the central role of sustained human-capital investment. By integrating labor-market dynamics, education quality, skill prevalence, and equity considerations into a unified benchmark, the Global AI Skills Index offers an actionable, transparent framework for policymakers, educators, and international organizations. It enables evidence-based comparison, targeted intervention design, and long-term monitoring of national progress toward inclusive, AI-enabled workforce development.
Global AI Skills Index, artificial intelligence, workforce readiness, digital skills, education-industry alignment, equity in AI upskilling.
Measuring the Human Risk Factor: Developing Predictive Models for Insider Threat Detection Using Behavioral Analytics
Insider threats represent one of the most complex challenges in modern cybersecurity, as they originate from authorized users who exploit legitimate access for malicious or negligent purposes. Unlike external attacks, insider incidents often evolve gradually through subtle behavioral changes that traditional rule-based systems fail to detect. This study investigates the human risk factor underlying insider threats and develops predictive models that leverage behavioral analytics to identify early indicators of malicious intent. The research utilizes the Carnegie Mellon University CERT Insider Threat Dataset (v4.2), integrating system activity logs, communication patterns, and psychosocial proxies such as email sentiment and work-hour deviations. Data preprocessing involved normalization and feature selection across technical, behavioral, and psychological dimensions. Three machine learning models—Random Forest, Long Short-Term Memory (LSTM), and Autoencoder—were implemented to evaluate predictive performance. Model performance was assessed using precision, recall, F1-score, and ROC-AUC metrics. Results show that the LSTM model achieved the highest overall accuracy of 93.2 percent with an AUC of 0.96, outperforming both Random Forest and Autoencoder models. Behavioral deviations such as unusual file transfers, abrupt login time changes, and communication tone shifts emerged as strong predictors of insider risk. The findings highlight the value of integrating human behavioral analytics into cybersecurity frameworks to enhance proactive threat detection. This study contributes to the development of data-driven, ethically aligned security strategies that enable organizations to identify, quantify, and mitigate insider risks before they escalate into security incidents.
Insider Threats, Behavioral Analytics, Human Risk Factor, Predictive Modeling, Machine Learning, Cybersecurity, CERT Dataset, LSTM, Random Forest, Autoencoder.
Hybrid AI Models Combining Machine-Deep Learning for Botnet Identification
The botnet is considered a highly advanced vulnerability threat. Threats to smart systems and data integrity posed by botnet assaults have grown in importance alongside the proliferation of IoT networks, making them a major topic in cybersecurity discussions. Current detection systems frequently fail to accurately classify various attack types, manage data with high dimensions, or record attack trends over time. To efficiently detect botnets, this work aims to construct a robust hybrid AI model utilizing the BOT-IOT dataset, which encompasses both common and unusual attack types, including reconnaissance, DoS/DDoS, and information theft. The proposed methodology combines LSTM networks for temporal pattern recognition, Decision Trees (DT) for feature-based classification, and Logistic Regression (LR) for high-dimensional multiclass prediction, with aggregated final predictions to enhance stability and accuracy. During data preprocessing, missing value handling, categorical variable encoding, and consistent feature scaling using Min-Max normalization were all part of the procedure. The hybrid model achieved 99% accuracy, recall, and F1-score, with an AUC-ROC of 0.9895, surpassing established methods such as C4.5, KNN, and weighted ego network with LDA, as well as individual models (LSTM: 97%, DT: 94%, LR: 95%). These findings support improved, balanced, and real-time IoT botnet detection for smart city cybersecurity.
Cybersecurity, Cyber-Attacks, IoT, Botnet, AI, Machine learning, Deep Learning.
Body Percussion for Focused Starts: Links to Attention and Executive Function
Attention and executive functions represent cognitive processes underlying our ability to focus, remember, and self-regulate, which are vital for learning. In a classroom environment, these skills help determine how long it takes students to orient to learning tasks, assimilate verbal instructions, and stay engaged throughout an activity. New research indicates that rhythmic-motor coordination activities such as body percussion may help improve these attentional and executive processes. This article reviews how body percussion practices can enhance neural and behavioral mechanisms underlying attention and executive readiness in school settings. It also provides recommendations for integrating these activities as “start-of-lesson” activation strategies. The paper reviews literature from cognitive neuroscience, music education, and embodied learning research and describes mechanisms through which synchronous activation of the auditory and motor systems in rhythmic activities facilitates attention, working memory, inhibitory control, and task-switching functions. Regular engagement with body percussion supports prefrontal activation through repetition, timing, and cross-modal integration. The BAPNE technique and similar methods offer evidence for the positive impact of movement and rhythm on student concentration, accuracy, and classroom behavior. The article also proposes body percussion as a versatile and inexpensive tool for embodied learning, highlighting its accessibility to children with diverse abilities, the potential to synchronize group participation, and the benefits for awareness of rhythm and internal states. Furthermore, it underlines the importance of using body percussion and related movement activities to develop students’ cognitive abilities, as a break between classroom activities, or a “transition” or focus routine. The paper highlights body percussion as an innovative approach to facilitating students’ attention and executive readiness in a rhythmic and embodied way. It suggests that future research is needed to understand the longer-term effects of such practices on brain function, academic performance, and emotion regulation.
body percussion, executive function, attention, rhythmic movement, embodied learning, classroom engagement, cognitive readiness, BAPNE method.
A Survey of Blockchain-Enabled Supply Chain Processes in Small and Medium Enterprises for Transparency and Efficiency
Supply chain activities in small and medium-sized businesses (SMEs) are made possible by blockchain technology, which offers a revolutionary means of enhancing transparency, efficiency, and trust in global networks. Cooperation between manufacturers, suppliers, logistics companies and consumers is enhanced by blockchain because it provides decentralized, secure peer-to-peer transactions that cannot be altered. All through the supply chain, it enables supply chain traceability, real-time data passing, smart contract execution, and reduces costs, preventing fraud and guaranteeing product authenticity. The features are specifically useful to SMEs which tend to be limited in resources, operational capacity and access to progressive digital tools. This paper highlights the ability of blockchain to build sustainable benefits through reduced inefficiency and enhanced accountability among stakeholders and examines the potential of blockchain to develop competitiveness in other sectors (agriculture, manufacturing, and pharmaceuticals) where transparency and compliance are essential. This study has shown that blockchain can transform SME supply chain management in a way that allows it to become resilient, innovative, and create long-term value. Blockchain implementation in smaller supply chains can help the SMEs to compete successfully in the dynamic markets and be able to adapt to the digital transformation that the world is turning towards.
Blockchain, Supply Chain Management, Small and Medium Enterprises, Transparency, Efficiency, Smart Contracts.
Teacher-Vetted AI Feedback in Primary EFL Writing: A Classroom Cross-Over Study
The increased integration of artificial intelligence (AI) technologies in the English as a Foreign Language (EFL) context has led to significant changes in the learning and teaching paradigm. Automated writing evaluation (AWE) technologies support L2 writers by giving them instantaneous corrective feedback at the grammar, vocabulary, and sentence level. However, questions have been raised about the contextual accuracy of the automated writing feedback and its pedagogical suitability for younger learners who require scaffolding and guidance from their teachers. The current study assesses the impact of vetted teacher AI feedback compared to AI only (no human input) and teacher only feedback on primary EFL learners’ writing performance. The 6-week classroom cross-over study involved 60 primary aged learners, between the ages of 10 to 12, who completed 3 feedback conditions (AI only, teacher only, and teacher vetted AI feedback) for 2 weeks each. Quantitative and qualitative data were gathered from writing pre- and post-tests, rubric scoring of the essays, and a learner reflection journal. Quantitative results from an exploratory repeated measures design revealed that the human-in-the-loop condition (teacher vetted AI feedback) made a statistically the greatest improvement in accuracy, lexical diversity, and task completion. Learners also showed a higher level of motivation, self-confidence, and feedback acceptance when the teacher vetted, added a personal touch, and explained the AI-generated writing suggestions. Qualitative data demonstrated that human involvement enhanced learners’ trust in automated writing feedback, thus making it more pedagogically meaningful. The study results support the advantages of a human–machine partnership, in which teacher-vetted AI feedback is more time-efficient and instructionally effective without jeopardizing the pedagogical values manual feedback provides. The study findings also revealed the potential of AI technologies’ inclusion in primary EFL writing classrooms as a complementary pedagogical tool rather than replacing the teachers. It can help develop feedback literacy, learner agency, and sustainable digital disposition in young writers
AI feedback, teacher-vetted feedback, EFL writing, cross-over study, formative assessment, automated writing evaluation (AWE).
Artificial intelligence (AI) is transforming global labor markets, creating an urgent demand for new digital competencies and workforce readiness strategies. Yet, no unified framework currently exists to measure how prepared countries are to upskill their populations for an AI-driven economy. This study introduces the Global AI Skills Index (GASI), a comprehensive benchmarking tool designed to evaluate national AI upskilling readiness across education, labor, and inclusion dimensions. Using datasets from the OECD, UNESCO, ILO, World Bank, and LinkedIn spanning 2018 to 2023, the research employs a comparative, cross-national approach to construct a weighted composite index. Four indicators form the foundation of GASI: AI Job Demand Density, Education–Industry Alignment, AI Skills Penetration Rate, and Equity and Access Index. These metrics collectively assess how effectively nations integrate AI competencies within their education systems, labor markets, and institutional frameworks. Results reveal significant regional disparities. High-income countries such as the United States, Singapore, and Finland show strong AI integration in education and employment, while emerging economies in Africa and South Asia lag due to infrastructural and policy constraints. A positive relationship between education expenditure and AI job demand highlights the central role of investment in human capital. The Global AI Skills Index provides a transparent, evidence-based benchmark for comparing national AI readiness. It serves as a strategic tool for policymakers and educators to track progress, address inequality, and design inclusive pathways for sustainable, AI-enabled workforce development.
Global AI Skills Index, artificial intelligence, workforce readiness, digital skills, education–industry alignment, equity in AI upskilling.
Predictive Analytics for Early Detection of Chronic Diseases Using Multimodal Healthcare Data
Timely intervention through early diagnosis of chronic conditions like diabetes, blood pressure and cancer relapse is essential to reduce the end outcomes of patients. The recent development in predictive analytics and machine learning has made it possible to extract meaningful patterns in complex healthcare data. The existing studies are however based mostly on single modality data which limits the predictability and the generality of their models. The proposed research is a multimodal predictive analytics model that incorporates various sources of healthcare data such as electronic health records (EHRs), laboratory tests, and medical imaging and wearable sensor data to help improve the process of early disease detection. There were different machine learning models created, such as the random forest and XGBoost or the deep neural networks and these have been tested on their publicly available datasets. Techniques of data preprocessing and fusion were used to deal with heterogeneity to enhance model robustness. The experimental findings prove that multimodal models are much better than unimodal models since they are more accurate and better calibrated to make early predictions of diabetes, hypertension and cancer relapse. In addition, interpretation analysis based on SHAP values offered clinical value on the most important risk factors affecting model decisions. The suggested scheme shows the opportunities of multimodal machine learning in identifying chronic disease at an early and accurate stage of early detection, and opens the path to data driven precision medicine and proactive healthcare management.
Predictive Analytics, Multimodal Healthcare Data, Machine Learning, Chronic Disease Prediction, Early Detection, Diabetes, Hypertension, Cancer Recurrence, Data Fusion, Explainable AI.
A Survey on Hybrid and Multi-Cloud Environments: Integration Strategies, Challenges, and Future Directions
The rapid evolution of cloud computing has led organizations to adopt hybrid and multi-cloud environments to meet increasing demands for scalability, flexibility, and resilience. While these environments provide significant benefits, they introduce unique challenges, including interoperability, data consistency, security, and vendor lock-in. This survey comprehensively reviews deployment architectures, integration strategies, middleware roles, and the challenges associated with multi-vendor cloud systems. The survey results reveal that middleware plays a critical role in enabling seamless communication, abstraction, orchestration, and maintainability across heterogeneous cloud platforms. Widely adopted integration strategies identified include API-driven integration, service-oriented architectures (SOA), containerization with orchestration, cloud brokers, and Middleware-as-a-Service (MWaaS). These strategies effectively mitigate heterogeneity, support workload portability, and enhance security, thereby enabling scalable and resilient multi-cloud deployments. Additionally, the survey highlights the operational complexities and open research challenges, emphasizing the need for standardized interfaces, unified governance frameworks, and automated management solutions. The findings provide a roadmap for enterprises to implement robust multi-cloud integration frameworks while addressing operational, security, and compliance requirements. This study contributes to a deeper understanding of hybrid and multi-cloud ecosystems, guiding future research toward adaptive middleware, AI-driven orchestration, and edge-cloud integration for enhanced performance, flexibility, and secure adoption.
Multi-Cloud, Cloud Computing, Hybrid Environments, Integration Approach, Vendor Lock-In, API-Driven Strategies.
Integrating Predictive Models into Public Health Policy: Forecasting Lead Exposure Risks Across the United States
In the United States, lead exposure has been one of the most persistent and avoidable environmental health risks, disproportionately impacting low-income and minority groups. Conventional risk assessment tools have been mainly reactive in nature and were used to detect the contamination when the exposure has already taken place. This paper combines predictive modeling with countywide environmental and census data to predict the communities that are most susceptible to lead exposure and provide interventions of active public health policy. Based on the data of the Environmental Protection Agency (EPA), U.S Census Bureau, and Centers of Disease Control and Prevention (CDC), several machine learning models including Random Forest and Gradient Boosting models were trained to examine the associations among the environmental quality indicators, socioeconomic variables, and demographic factors. The predictive models proved to be very accurate with key predictors such as the age of houses, median household income, racial makeup, and closeness to industrial discharge sites. The spatial mapping found that there were concentrated areas of high risks in older urban areas and post-industrial areas emphasizing structural inequalities in environmental protection. The results highlight the possibilities of using data-driven forecasting to inform targeted preventive actions, resource distribution, and infrastructure investment, allowing shifting the paradigm of response to proactive policy. Finally, the study offers a framework of incorporating predictive analytics into the national health decision-making systems, which facilitate fair and sustainable strategies to reduce lead exposure throughout the United States.
predictive modeling, lead exposure, public health policy, environmental data, census data, risk forecasting, preventive action, machine learning, GIS analysis, data-driven decision-making
Artificial Intelligence in Business Strategy: How AI Driven Analytics is Reshaping Decision Making
At a time when data is now a strategic asset, Artificial Intelligence (AI) is transforming the way organizations develop and execute business strategies. The paper discusses AI-based analytics as a way to change the managerial decision-making process, improving its accuracy, speed, and foresight. The study employs a mixed-method design to merge literature review and empirical findings of business leaders, data analysts, and technology strategists to examine the degree to which AI analytics models like predictive modeling, machine learning, and natural language processing can be used in data-driven decision-making processes. The conclusions indicate that AI implementation enhances operational efficiency, less cognitive bias in strategic decision-making, and real-time flexibility in competitive markets. Nonetheless, the research also pinpoints some serious issues such as privacy of data, ethical issues, and lack of skills that impede the best adoption of AI. The implications point to the need to strike a balance between the technological capacity and human control and ethical governance in organizations. This study adds to strategic management literature because it offers an in-depth insight into the role of AI-powered analytics in transforming decision-making frameworks and offers a prospective of sustainable, smart business strategies in the digital economy.
Artificial Intelligence (AI), Business Strategy, Decision-Making, Predictive Analytics, Data-Driven Management, Strategic Intelligence, Machine Learning.
AI-Driven Software Ecosystem for Online Automated Applications: Integrating Oracle EBS, SAP, Cloud Computing, and Cyber-Physical Optimization with Secure Data Vaults and Firewall Intelligence
Modern enterprises increasingly link enterprise resource planning platforms such as Oracle E-Business Suite (EBS) with cloud services and cyber-physical assets (network infrastructure, edge controllers, and power subsystems). This coupling creates opportunity for operational optimization—automated firewall intelligence, asset-aware lifecycle management, and cyber-physical energy efficiency—but also introduces risks around availability, privacy, and forensic readiness. We propose an interpretable AI-driven software ecosystem that unites three core capabilities: (1) application-aware firewall intelligence that synthesizes safe, explainable rule refinements from correlated EBS telemetry and network flows; (2) privacy-preserving, cloud-native model training and inference (federated and encrypted patterns) to enable cross-site learning without centralizing sensitive telemetry; and (3) secure data-vault based redundancy (immutable snapshots, air-gapped forensic copies, and policy-as-code retention) to guarantee auditability and rapid recovery. The architecture is microservice-based: non-invasive EBS connectors normalize asset and workflow metadata; a unified feature store binds semantic EBS concepts (modules, integrations, maintenance records) to network and OT telemetry; an interpretable-model layer (rule lists, GAMs, small trees, and local explanation surfaces) powers SecOps and operations decisions; and a vault orchestration layer enforces retention, immutability, and staged recovery. Human-in-the-loop controls, canary enforcement, and simulation against historical traces are central to preventing disruption. Evaluation blends retrospective replay, synthetic fault/intrusion injection, shadow pilots, and staged production canaries with metrics for detection fidelity, rule-safety, recovery time objectives (RTO/RPO), and operator acceptance. Key contributions are a practical engineering blueprint for safe, explainable automation in EBS-linked estates; design patterns for reconciling immutable forensic needs with deletion/consent obligations (tokenization and layered metadata); and an MLOps + policy governance stack that supports reproducible, auditable model updates. We discuss trade-offs—latency and cost of privacy mechanisms, governance overhead, and model utility vs interpretability—and provide an operational roadmap for incremental adoption that minimizes operational risk while delivering measurable security and operational benefits.
Oracle E-Business Suite, interpretable AI, cloud-native, secure data vaults, firewall intelligence, cyber-physical optimization, privacy-preserving ML, policy-as-code, MLOps, forensic readiness.
AI-Enhanced Real-Time BMS Framework for Sustainable Healthcare and Digital Banking Transformation Using SAP and Cloud Technologies
The convergence of artificial intelligence (AI), cloud computing, and enterprise platforms like SAP is redefining the landscape of digital transformation across critical sectors such as healthcare and banking. This paper proposes an AI-enhanced ecosystem framework designed to drive sustainable innovation in both healthcare delivery and digital financial services. Leveraging the scalability and integration capabilities of SAP's intelligent enterprise systems and the flexibility of cloud-native infrastructure, the framework introduces a modular architecture that supports real-time decision-making, predictive analytics, and end-to-end process automation. In healthcare, the ecosystem enables enhanced patient care through AI-powered diagnostics, remote monitoring, and intelligent resource planning. In digital banking, it facilitates secure, personalized financial services including fraud detection, risk assessment, and smart customer engagement. The proposed solution emphasizes sustainability through efficient resource utilization, data-driven insights, and support for green IT initiatives. This interdisciplinary approach ensures compliance with regulatory standards (e.g., HIPAA, GDPR, Basel III), while promoting agility, security, and interoperability across domains. The framework not only enables operational excellence but also supports strategic goals such as resilience, scalability, and customer-centric transformation in the age of intelligent enterprises.
Artificial Intelligence (AI), Sustainable Digital Transformation, Healthcare Technology, Digital Banking, SAP Intelligent Enterprise, Cloud Computing, Predictive Analytics, AI in Healthcare, AI in Finance, Enterprise Architecture, Process Automation, Interoperability, Regulatory Compliance, Smart Ecosystems, Data-Driven Innovation
End-to-End SAP S/4HANA Rise Migration to SAP Cloud: Architecting a Secure and Scalable Landscape with Cloud Connector, Landscape Migration Server, SLT Server, Cloud Integration, and Governance Framework
Companies are moving away from the old SAP ECC into SAP S/4HANA on clouds to enjoy the agility, scaling ability, and innovation that come with cloud platforms. The RISE with SAP package further hastens this development by packaging the infrastructure, tools and managed services. Nevertheless, there are design and operational issues associated with an end-to-end migration, which must be secured, data replication in real-time, good integration, and powerful governance. The paper suggests an overall architecture of an end-to-end SAP S/4HANA RISE migrations, including Cloud connector, Landscape Migration Server (LMS), SAP Landscape transformation (SLT) server and SAP Cloud integration (CPI) under the coordination of a holistic governing framework. It is designed such that security is by design, that it is scalable and resilient and that it has modular layers and separation of concerns. We define component level roles, data flows, security controls and governance structures and test the architecture to various evaluation parameters like latency, throughput, fault tolerance and compliance. The outcome is a blueprint and best-practice guide of organizations looking to move SAP landscapes safely and in size to the cloud.
SAP S/4HANA Cloud Migration, Cloud Connector Architecture, SLT Data Replication, SAP Cloud Integration, Cloud Governance Framework
AI-Enabled Smart Connect Ecosystem for Real-Time Optical Wireless Mobile Communication: A Cloud-Based Scalable ERP Framework for Intelligent and Sustainable Software Maintenance
Smart connect ecosystems integrate devices, networks, sensors, and software to deliver continuous, reliable services in mobile communications. With the growing interest in Optical Wireless Communication (OWC) as a high‐bandwidth, low-interference, spectrum-efficient complement or alternative to RF, new architectures are needed to ensure real-time performance, reliability, scalability, and maintainability of software components. This paper proposes a framework combining real-time OWC for mobile platforms with a scalable Enterprise Resource Planning (ERP)–based system for intelligent software maintenance. The aim is to provide a unified ecosystem where optical wireless links deliver high throughputs and low latency, while the ERP backbone supports monitoring, versioning, fault-handling, and predictive maintenance of the software stack across devices and nodes. We first survey the state of real-time OWC technologies, examine the constraints (mobility, alignment, latency, reliability), and then describe how ERP systems can be adapted to support software maintenance: configuration management, deployment orchestration, logging, diagnostics, and predictive analytics. Experimental or simulation results using a prototypical OWC link (mobile transmitter/receiver, visible light or free-space optical channel) show that our approach achieves end-to-end latency under 2 ms in ideal conditions, with software rollback time reduced by ~50% in maintenance scenarios and defect detection earlier via monitoring. Key challenges include environmental interference in optical paths, alignment issues during mobility, and ensuring the ERP system scales without becoming a bottleneck. The paper contributes a reference architecture, a set of evaluation metrics, and comparison with baseline RF-based mobile communication systems. We conclude that a smart connect ecosystem combining real-time OWC and ERP-driven software maintenance can deliver superior responsiveness and robustness, if the system is carefully designed with redundancy, predictive analytics, and modular software components.
Optical Wireless Communication; Real-Time Communication; Mobile Platforms; ERP; Intelligent Software Maintenance; Scalability; Low Latency; Reliability; Predictive Monitoring.
Copyright (c) 2025 Atharva Kishore Sonawane (Author)
Machine Learning–Enhanced Citrix Framework for Zero-Downtime Data Exchange and DC–DC Converter Optimization in Mobile Cloud Ecosystems
The convergence of mobile cloud computing, machine learning (ML), and power electronics offers a transformative approach to data exchange and resource optimization. This paper proposes a Machine Learning–Enhanced Citrix Framework designed to achieve zero-downtime data exchange and adaptive DC–DC converter optimization within distributed mobile cloud ecosystems. The framework integrates Citrix virtualization for seamless workload migration and ML-driven predictive analytics to ensure continuous data flow and energy efficiency across edge and cloud nodes. Using dynamic voltage regulation and converter control algorithms, the model minimizes latency and enhances reliability in high-demand data environments. The study further evaluates system scalability, energy efficiency, and network resilience under varying load conditions. Experimental results demonstrate that the proposed framework significantly reduces downtime, improves data throughput, and achieves optimal power utilization, making it suitable for next-generation intelligent cloud infrastructures.
Machine Learning, Citrix Framework, Mobile Cloud Computing, Zero Downtime, Data Exchange, DC–DC Converter, Power Optimization, Virtualization, Edge Computing, Predictive Analytics, Cloud Ecosystem, Energy Efficiency, Fault Tolerance, Dynamic Resource Management, Intelligent Infrastructure.
Copyright (c) 2025 Uma Rajendra Chawla, Urvashi Sanjay Joshi (Author)
Deep Learning-Driven Modernization of Smart Connect Ecosystems: Advancing Cloud Data Governance, Sustainable Operations, and Intelligent Software Maintenance
The modernization of Smart Connect ecosystems through deep learning offers transformative potential for data-driven, cloud-integrated environments. This paper presents a comprehensive framework that leverages deep learning architectures to enhance cloud data governance, promote sustainable operations, and enable intelligent software maintenance. By integrating predictive analytics and automated decision-making, the framework ensures improved system reliability, optimized energy efficiency, and compliance with data privacy standards. The proposed approach emphasizes real-time data orchestration, adaptive learning models, and scalable cloud deployment strategies to strengthen interoperability across connected systems. Experimental evaluations demonstrate that the adoption of deep learning methodologies within Smart Connect infrastructures significantly improves system resilience, reduces maintenance overhead, and supports long-term sustainability goals. This study contributes to the development of intelligent, self-optimizing Smart Connect platforms that align with modern enterprise and environmental objectives.
Deep Learning, Smart Connect Ecosystems, Cloud Computing, Data Governance, Sustainable Operations, Software Maintenance, Predictive Analytics, Artificial Intelligence, Cloud Integration, Automation, System Reliability, Energy Efficiency, Intelligent Systems, Scalability, Data Privacy.
Copyright (c) 2025 Deepa Narayan Ghosh (Author)
AI-Driven Supply Chain Optimization and Zero-Touch Workforce Management in Pediatric Healthcare: A Privacy-Preserving Framework Using Natural Language Processing and SAP
The integration of Artificial Intelligence (AI) with enterprise resource planning (ERP) systems such as SAP is transforming the way pediatric healthcare organizations manage their operational ecosystems. This paper presents a comprehensive, privacy-preserving AI framework designed to optimize the supply chain and enable zero-touch workforce management in pediatric healthcare environments. By leveraging Natural Language Processing (NLP), the proposed framework facilitates intelligent automation across procurement, inventory control, clinical logistics, and workforce scheduling processes. The system employs advanced predictive analytics to forecast demand for medical supplies, pharmaceuticals, and human resources, thereby minimizing shortages and inefficiencies. Through real-time insights from SAP S/4HANA and AI-driven data interpretation, the framework supports evidence-based decision-making and adaptive resource allocation. The zero-touch workforce management model eliminates repetitive administrative tasks by automating approval workflows, shift allocation, and compliance tracking, allowing healthcare professionals to focus more on patient care and less on operational overhead. A key contribution of this study is the implementation of privacy-preserving mechanisms, including federated learning, differential privacy, and homomorphic encryption, to ensure secure collaboration among hospitals, suppliers, and workforce management systems without sharing raw patient or institutional data. The use of NLP-based conversational agents further enhances communication efficiency among clinical and administrative teams by interpreting and responding to unstructured queries while maintaining data confidentiality. The proposed AI–SAP integration demonstrates significant potential to achieve operational resilience, cost reduction, and sustainable healthcare delivery in pediatric settings. Experimental evaluations and scenario-based simulations indicate measurable improvements in supply chain transparency, reduced workforce downtime, and enhanced regulatory compliance under frameworks such as HIPAA and GDPR. Ultimately, this research contributes to the ongoing digital transformation of pediatric healthcare by providing a scalable, intelligent, and secure model for AI-driven operational excellence.
Keywords: AI-driven healthcare, pediatric supply chain optimization, zero-touch workforce management, SAP, privacy-preserving framework, natural language processing, federated learning, intelligent automation.
Copyright (c) 2025 John Paul Rangasamy (Author)
AI-Driven Cloud Migration Framework for Distributed Oracle Databases: A Privacy-Preserving and Zero-Touch Automation Approach
Migrating enterprise Oracle databases to the cloud remains a high-value but high-risk activity: it delivers scalability, elasticity, and operational efficiency, yet exposes sensitive data, requires complex application and schema transformations, and often demands lengthy, costly manual orchestration. This paper proposes an AI-driven, privacy-preserving, zero-touch automation framework for Oracle database cloud migration that reduces manual effort, preserves data confidentiality, and accelerates safe cutover. The framework combines (1) an intelligent discovery and dependency-mapping layer that uses static analysis, runtime telemetry, and natural language signals from runbooks to build a rich dependency graph; (2) an ML-based transformation planner that predicts schema and code change patterns, recommends data-motion strategies (online replication, logical replication, or bulk transfer), and estimates resource needs and risk; (3) a privacy stack that integrates data classification, automated redaction/de-identification, policy-driven tokenization, and differential-privacy-aware analytics to limit exposure during testing and model training; (4) a secure orchestration plane that implements zero-touch workflows (infrastructure provisioning, schema migration, data sync, application cutover, rollback) via infrastructure-as-code, policy engines, and automated verification gates; and (5) a human-in-the-loop governance console for audit, manual checkpoints, and safety overrides. We describe algorithms for dependency inference, a training regimen for migration outcome prediction, and techniques for generating synthetic or privacy-preserving test datasets. An evaluation plan (retrospective replay on historical migrations, controlled pilot migrations, and privacy leakage assessment) is presented along with key metrics (migration time, downtime, data fidelity, number of manual interventions, privacy leakage bounds). We discuss trade-offs between automation aggressiveness and safety, and show how staged zero-touch adoption (assist → advise → automate) preserves operational continuity. The proposed architecture aims to shorten migration cycles, reduce human error, and protect sensitive data while providing traceable, auditable automation for enterprise Oracle migrations.
Oracle database migration; cloud migration; zero-touch automation; privacy-preserving data transformation; natural language processing; dependency mapping; infrastructure-as-code; differential privacy; synthetic data; migration orchestration
Copyright (c) 2025 Pooja Gaikwad Sawant (Author)
Scalable AI Framework Using SAP, Oracle Cloud, SDN, and BMS Upgrades for Intelligent and Sustainable Healthcare
This paper proposes a scalable AI framework for intelligent patient monitoring that integrates SAP healthcare modules and Oracle Cloud AI services to create a unified, data-driven ecosystem for healthcare delivery and financial inclusion. The framework leverages real-time data analytics, machine learning pipelines, and IoT-enabled patient monitoring systems to support predictive diagnostics, personalized treatment recommendations, and proactive health interventions. By embedding AI-driven financial analytics and secure digital payment mechanisms, the system ensures equitable access to healthcare services, especially for underserved populations. Furthermore, the architecture emphasizes sustainable IT operations through optimized cloud resource utilization, green computing strategies, and compliance with environmental, social, and governance (ESG) goals. This integration of SAP and Oracle Cloud provides seamless interoperability, data transparency, and end-to-end automation, enhancing both clinical decision-making and operational sustainability in healthcare ecosystems.
AI framework, Intelligent patient monitoring, SAP integration, Oracle Cloud, Financial inclusion, Sustainable IT operations, Predictive healthcare analytics, IoT-enabled monitoring, Cloud interoperability, ESG compliance, Machine learning pipelines, Real-time diagnostics, Green computing, Healthcare automation.
Trust, Transparency, and AI: Building Digital Bridges for U.S.–Global Business Collaborations
Artificial Intelligence (AI) is redefining the foundations of trust, transparency, and international collaboration in today’s digital economy. As businesses increasingly depend on algorithmic decision-making to guide trade, finance, and consumer interactions, ensuring transparency and building trust have become essential for sustainable global partnerships. The United States, with its advanced digital infrastructure and leadership in shaping global standards, serves as a critical bridge for harmonizing diverse regulatory frameworks and promoting responsible AI adoption. This paper examines how trust and transparency function as the twin pillars of AI-enabled business collaborations between the U.S. and global markets. It highlights case studies of Black Vitriol LLC, which employs AI-driven solutions to enhance digital trade security, and Eatsbueno AI, which leverages explainable AI to foster consumer confidence in food-tech innovation. These examples illustrate how startups can serve as models of responsible practice, reinforcing adoption, legitimacy, and cross-border cooperation. The study further explores ethical and regulatory challenges across jurisdictions, emphasizing the need for convergence in data governance, algorithmic accountability, and digital trade policies. By synthesizing insights from theoretical foundations, practical applications, and policy landscapes, the research underscores that building digital bridges requires more than technological innovation. It also demands cross-cultural trust, ethical alignment, and strategic governance. The paper concludes with forward-looking policy recommendations for emerging markets, stressing capacity building, alignment with trade frameworks, and leveraging startups as innovation catalysts. Balancing innovation with transparency and trust is positioned as the defining challenge for the future of U.S.–global AI collaborations.
Artificial Intelligence, Trust, Transparency, Global Business, U.S. Collaborations, Regulation, Black Vitriol LLC, Eatsbueno AI.
AI- Driven Anti-Money Laundering Systems for Cybersecurity Resilience in U.S. Financial Infrastructure: A Framework for Real-Time Threat Detection, Regulatory Compliance and National Security
The recent avalanche of digitalization of the financial industry in the United States has increased the complexity of cyber-enabled money laundering as well as its prevalence, which is a significant level of threat to national security and regulatory stability. More than 300 million dollars of money laundered can be detected only after the fact whereas traditional Anti-Money Laundering (AML) systems, in many cases, are based on the static rule-based framework, which fails to be responsive to threats in real-time. The paper outlines an AI-based system that will promote the resilience of cybersecurity, regulatory standards, and real-time adversarial detection in the American financial system. The framework is based on machine learning, natural language processing (NLP), and predictive analytics to combine anomaly detection, behavioral modeling, and automated compliance reporting to minimize false positives and enhance detect accuracy. The mixed-method design, which involves the use of expert interviews, institutional surveys, and a simulation, based on which the study is conducted, assesses the effectiveness of AI-enhanced AML systems in the context of major indicators of accuracy in detection, speed of response, and efficiency in compliance. Results indicate that AI-based AML systems enhance early-warning systems significantly, enhance inter-institutional intelligence disclosure, and create consistency with regulatory requirements such as the Bank Secrecy Act (BSA) and FinCEN regulations. Moreover, the AI-based approach improves national security by reducing the risks of illegal finance and online terrorism. The study highlights the strategic necessity of incorporating AI governance and transparency systems to guarantee accountability, minimize the bias of the algorithms, and maintain the trust of the population. In the end, it is a framework that can be used by policymakers, regulators, and financial institutions to strike a balance between innovation and compliance in the dynamic environment of digital finance.
Artificial Intelligence (AI), Anti-Money Laundering (AML), Cybersecurity Resilience, U.S. Financial Infrastructure, Real-Time Threat Detection, Regulatory Compliance, National Security, Predictive Analytics, Ethical AI Governance.
Fusion of LiDAR and HDR Imaging in Autonomous Vehicles: A Multi-Modal Deep Learning Approach for Safer Navigation
The growing interest in driverless vehicles, calls for improved safety and navigation for these systems, especially in challenging and hidden conditions. In this paper, we propose a new multi-modal deep learning model that combines LiDAR and HDR imagery for enhanced perception. LiDAR offers accurate 3D spatial data, HDR imaging provides high quality visual information under challenging lighting. The proposed method adopts a two-stream neural network architecture including the CNN (for spatial feature extraction) and RNN (for temporal sequence analysis) to fuse LiDAR point clouds and HDR images at the feature level. The model is first trained and validated on a custom dataset covering various real-world driving conditions, such as lighting and weather. The quantitative results demonstrate that the proposed fusion model is able to achieve 91% object detection accuracy and reduce the MAE of distance estimation to 0.33 meters, which outperforms LiDAR-only model by 9% in detection accuracy and HDR-only model by 16%. The method also shows the improvement of obstacle detection and avoidance in low light or fog. These results verify the beneficial effect of sensor fusion on enhancing perception robustness and navigation safety, paving a solid way for further development in autonomous driving techniques.
LiDAR, High Dynamic Range (HDR) Imaging, Sensor Fusion, Deep Learning, Autonomous Vehicles, Object Detection, Obstacle Avoidance
Real-Time Machine Learning for SAP Order Fulfillment: Leveraging AI on GKE
Efficient order fulfillment is a cornerstone of modern supply chain performance, requiring fast, accurate, and adaptive decision-making. This paper presents a real-time machine learning framework for SAP order fulfillment, leveraging AI on Google Kubernetes Engine (GKE) to enhance operational efficiency and responsiveness. The proposed system integrates SAP transactional and inventory data with distributed ML models to optimize order processing, inventory allocation, and delivery scheduling. Deploying on GKE provides scalability, high availability, and fault-tolerant orchestration, enabling the system to handle large volumes of real-time data across complex supply chain networks. Machine learning models deliver predictive insights on demand patterns, inventory shortages, and potential fulfillment delays, while prescriptive recommendations support proactive decision-making. Experimental results demonstrate significant improvements in order accuracy, processing speed, and overall supply chain efficiency. This research underscores the potential of combining AI, real-time analytics, and cloud-native infrastructure to advance next-generation SAP order fulfillment processes.
Real-Time Machine Learning, SAP, Order Fulfillment, Artificial Intelligence (AI), Google Kubernetes Engine (GKE), Predictive Analytics, Prescriptive Insights, Supply Chain Optimization, Inventory Management, Cloud-Native Infrastructure.
LiDAR, High Dynamic Range (HDR) Imaging, Sensor Fusion, Deep Learning, Autonomous Vehicles, Object Detection, Obstacle Avoidance.
Ethical and Governance Challenges of Agentic AI
The speed with which agentic AI is developed and implemented has brought about serious ethical and governance questions, especially when it comes to accountability, openness and the human element of supervision. The necessity to have robust structures that will guarantee the ethical use of AI systems is of critical importance as they make more and more autonomous decisions. This paper discusses these issues, including the complications of making AI systems responsible towards their activities and the openness of the processes of their decision-making. It also looks at the significance of the human control in reducing the risk of agentic AI. The research design employed is a qualitative one in which cases studies are analyzed and the effectiveness of the available governance structures examined. The main conclusions are that the existing models of governance cannot be used to overcome the ethical challenges unique to agentic AI as more powerful control mechanisms and more transparent accountability frameworks are needed. This study offers useful information about enhancing the implementation of AI and the recommendations to policy makers, AI developers, and the general population interested in the responsible use of technology.
Ethical implications, Governance structures, Accountability issues, Human control, Transparency challenges, Autonomous decision-making.
AI-Based Threat Modeling for Secure Software Design
The introduction of Artificial Intelligence (AI) software design is a revolution in cybersecurity. This paper discusses how AI algorithms can be used to predict the attack surface of a software application at the design stage and thus improve vulnerabilities at the initial stage of the software development life cycle. The analysis of AI potential to detect patterns and anomalies that could be otherwise missed using traditional security methods is conducted using machine learning models. These techniques involve using some AI methods, including supervised and unsupervised learning, on historical software data to predict outliers. The findings indicate that AI-based threat modeling can be an effective approach to predicting vulnerabilities, and it provides more precise and timely information than traditional techniques. Finally, the research points out the significance of constructive countermeasures in the development lifecycle, and how AI may lead to a more secure software design by predicting threats prior to their occurrence. The evidence highlights how AI can be critical in the future of secure software engineering.
Threat Modeling, Artificial Intelligence, Software Design, Vulnerability Detection, Machine Learning, Active Security, Computer Security, Predictive Algorithms, Software Vulnerability, Secure Design.
Copyright (c) 2025 Nishad Kane, Dhvani Kansara (Author)
Threat Modeling, Artificial Intelligence, Software Design, Vulnerability Detection, Machine Learning, Active Security, Computer Security, Predictive Algorithms, Software Vulnerability, Secure Design
Predictive Risk Analytics in Banking Using Blockchain-Validated Translational And Data AI
Being an era of unforeseen financial cottonwool, risk management has thus become a pillar of stability and competitiveness in banking. On one side, worldwide digital-transaction growth-oriented customer behavior poses its own unique aspects, while, on the dark-and-sinister side, evolving fraud methods demand banks' transitioning from old static insulted bank rules modeling on risk assessment to a smart system that learns dynamically. This budding AI, birthed in predictive analytics, thus invites newer vulnerabilities, such as those regarding the reliability of the data, the inability to explain models, and the inability to comply with regulations. While working infinitely well, these models have gone, quite metaphorically, and have been touted as "black-box" models, trained on datasets whose very provenance is questionable, opening grounds for questioning trust, bias, and auditability. Given the multidimensional nature of the problems at hand, this paper puts forth a new hybrid framework comprising blockchain-validated data pipelines and a bi-layered AI system consisting of data-centric and translational AI modules. This architecture aims to provide secured, explainable, and adaptively predictive risk assessment in its very design, catering to high-stake banking environments. Blockchain is considered as a decentralized trust infrastructure that guarantees the immutability, timestamping, and cryptographic verification of financial datasets (customer transaction logs, loan portfolios, or KYC documents), providing verified data input, protection against any form of hacking, and hence an audit-worthy AI pipeline. On approval, the financial datasets undergo scrutiny in the data AI layer for credit risk patterns, early signs of fraud, and financial stress signals through time-series neural networks with LSTM and Transformer models. The translational AI layer further aids generalization of these insights over banking units and customer cohorts by transferring the acquired knowledge to new domains without requiring retraining, striving toward a unified risk measure over regions and financial products. The intelligence pipeline is benchmarked using several datasets, including opensource credit default datasets, simulated transactional logs, and simulated blockchain-audited trails of transactions. The experimental results showed a statistically considerable gain in predictive capability, where blockchain-validated models showed up to 6.5% higher F1-score and 7.2% higher AUC score than their non-validated counterparts for risk types. Moreover, the system's fraud detection latency remains under a satisfactory 1.5 seconds while maintaining traceability of the data. The application of blockchain also boosts trust in the AI predictions while simplifying compliance reporting under Basel III, GDPR, and the FATF AML framework. Most importantly, the hybrid approach supports explainable risk scores and traceable decision paths, which are necessary in upholding consumer trust and institutional accountability. In essence, the study presents a scalable, secure, and regulator-oriented architecture for AI-powered risk assessment in financial services. Further extensions intend to investigate the possibilities of incorporating privacy-preserving technologies such as zero-knowledge proofs and federated learning, that would allow cross-bank collaborations without violating data sovereignty agreements and/or customer confidentiality.
Predictive Risk Analytics, Blockchain Validation, Translational AI, Data-Centric AI, Financial Fraud Detection, Decentralized Auditability, Secure Banking Systems, Explainable AI, Regulatory Compliance, AI Trustworthiness.