International Journal of Technology, Management and Humanities
International Journal of Technology, Management and Humanities (IJTMH) The International Journal of Technology, Management and Humanities (IJTMH) is a peer-reviewed, open-access academic journal dedicated to the advancement of interdisciplinary research across the fields of technology, management, and the humanities. The journal, bearing the electronic International Standard Serial Number (e-ISSN) 2454-566X, was established in 2015 with the aim of fostering intellectual dialogue and innovative thinking across diverse disciplines. IJTMH publishes original research articles, case studies, technical notes, and review papers that contribute to the understanding and integration of technological innovations, management practices, and humanistic values. Its scope covers a wide range of topics, including but not limited to information technology, engineering management, business strategies, cultural studies, ethics, and societal impacts of technological change. Over the years, IJTMH has built a strong reputation for promoting research that bridges the gap between technical expertise and human-centered approaches. The journal welcomes contributions from scholars, practitioners, and industry experts worldwide, encouraging a global exchange of ideas and interdisciplinary collaboration. Committed to maintaining high academic standards, IJTMH ensures a rigorous peer-review process and strives for the timely dissemination of knowledge to its diverse readership, including researchers, academicians, and professionals.
Transforming Public and Enterprise Business Processes with Cloud AI Data Governance SAP and DevOps for Intelligent Secure and Inclusive Digital Ecosystems
Public institutions and enterprises are undergoing rapid digital transformation to improve efficiency, transparency, security, and inclusivity. This paper examines how the integration of Cloud computing, Artificial Intelligence (AI), Data Governance frameworks, SAP enterprise platforms, and DevOps practices can transform public and enterprise business processes to enable intelligent, secure, and inclusive digital ecosystems. Cloud technologies provide scalable and resilient infrastructure, AI enhances decision-making and service automation, and data governance ensures compliance, trust, and ethical data usage. SAP systems enable standardized and interoperable business processes, while DevOps accelerates innovation through continuous delivery and collaboration. By aligning technological capabilities with governance and process reengineering, organizations can deliver citizen-centric and customer-centric services, enhance operational resilience, and support sustainable digital growth across public and private sectors.
Public Governance, Enterprise Digital Transformation, Cloud Computing, Artificial Intelligence, Data Governance, SAP, DevOps, Business Process Transformation, Security, Inclusivity, Digital Ecosystems.
Advancing Data Consistency and Control Across Global Financial Institutions by Enterprise Master Data Platforms
This research examines how enterprise master data platforms enable data consistency and operational control across global financial institutions operating in complex, highly regulated environments. As financial institutions expand across jurisdictions, products, and legal entities, fragmented client and reference data has emerged as a persistent source of operational risk, reporting inconsistency, and governance failure. The purpose of this research is to examine enterprise master data management not as a technical integration initiative, but as a strategic control capability that underpins trusted business operations. The study adopts a qualitative, evidence-based approach, combining architectural analysis with structured evidence mapping derived from large-scale implementations within global financial institutions. The analysis identifies recurring design patterns in master and reference data platforms, including governance structures, stewardship models, control workflows, and lineage mechanisms, that collectively enable consistent enterprise-wide data representation. Key findings suggest that institutions achieving high levels of data consistency experience reduced reconciliation effort, improved auditability, and stronger alignment between operational processes and regulatory expectations. The study further demonstrates that operational control is strengthened when data governance is embedded into platform workflows and business decision points rather than managed as an isolated compliance function. From an academic perspective, this research extends existing data governance literature by explicitly linking enterprise data consistency to operational control outcomes. From a strategic industry perspective, it provides a practical framework for designing, governing, and sustaining enterprise master data platforms that support risk management, regulatory confidence, and decision integrity. The study concludes that enterprise master data platforms constitute a foundational capability for resilient, transparent, and controllable operations in global financial institutions.
Enterprise master data management, enterprise data governance, data consistency and control, global financial institutions, reference data standardization, client data integration, enterprise data platforms, operational risk management, regulatory reporting integrity, data lineage and traceability, data stewardship models, risk data aggregation.
Enterprise-Scale Data Stewardship Enablement Using Workflow-Driven Governance Mechanisms in Financial Services
Large financial enterprises face persistent challenges in governing reference data assets due to organizational fragmentation, regulatory pressure, and increasing dependency on shared data domains across business lines. This study examines how workflow-driven governance mechanisms can be operationalized to enable scalable and sustainable data stewardship models within complex financial services environments. Drawing on evidence from the BNY Mellon Reference Data Hub and QBE Insurance implementations using enterprise business glossary and metadata platforms, the paper analyzes how structured workflows, role-based accountability, and policy-driven controls contribute to improved data quality, lineage transparency, and governance enforcement. The study adopts a qualitative, design-oriented approach, synthesizing implementation artifacts, governance operating models, and stewardship processes observed across these large enterprises. Findings indicate that workflow-centric governance architectures significantly enhance stewardship effectiveness by formalizing decision rights, reducing manual intervention, and aligning business and technology stakeholders around shared data standards. The study contributes to a generalized enterprise stewardship framework that integrates workflow orchestration, reference data lifecycle management, and governance escalation models. This framework offers practical guidance for financial institutions seeking to mature their data governance capabilities while meeting regulatory expectations and operational scalability demands. The paper positions workflow-enabled stewardship not as a tooling initiative but as an institutional capability critical to enterprise data reliability and risk management.
Data stewardship, workflow governance, reference data management, financial services data governance, enterprise data hubs, metadata management, business glossary, policy enforcement, stewardship operating model, data quality control, regulatory compliance, master data governance
The Economic Impact of Intermittent Fasting on Workforce Productivity in the United States
Intermittent fasting (IF) has already received considerable scientific interest as a potentially health-promoting diet, but there has been little discussion about the economic aspects of its feasibility to workforce productivity. In this paper, the researcher will analyze the correlation between IF and labor efficiency in the United States with reference to the effects of fasting protocols on cognitive performance, energy levels, absenteeism and the overall output of employees. Based on the findings of nutritional science, behavioral health, and labor economics, the study compares the possible economic benefits and expenses in terms of mass implementation of IF among working adults. The findings are expected to guide organizational wellness interventions, as well as, the policy on the role of fasting in the overall health of the population to inform the opportunities and constraints to implementing the fasting practice in the corporate health program. The research results add up to an emerging evidence concerning the connection of food consumption with economic productivity in modern labour markets.
Intermittent fasting, workforce productivity, labor efficiency, employee health, economic impact, United States
Ethical and Regulatory Challenges of AI in Finance
The recent accelerated incorporation of artificial intelligence (AI) in the financial industry has revolutionized the fundamental operations in determining creditworthiness, fraud detection, algorithmic trading and customer interaction. Although such applications can result in considerable efficiency, accuracy, and scalability, they also present difficult ethical and regulatory issues that should be criticized closely. This paper focuses on the ethical issues that are entailed in AI-based financial systems, such as algorithmic discrimination, absence of explainability and transparency, threats to the privacy of personal data, and whether automated decisions are accountable. Concurrently, it evaluates regulatory problems, which are posed due to the constraints of the current financial and data protection standards, cross-jurisdictional discrepancies, and the impossibility of regulating unclear and evolving AI models. The abstract displays the conflict between the promotion of innovation and consumer protection, financial stability, and trust on the part of the population. By summarizing the existing controversies and regulation strategies, the paper highlights the importance of powerful codes of ethics, dynamic regulation frameworks, and proper oversight systems to make AI implementation in finance fair, transparent, and socially accountable.
Artificial intelligence; financial services; ethics; regulation; algorithmic bias; transparency; data privacy; AI governance
Artificial Intelligence product management - identifying opportunities and developing AI product solutions
Artificial Intelligence (AI) is becoming a new reality of product management, as it will make possible the development of innovative solutions to solve difficult business and customer issues. Management of AI products should entail the identification of high-impact opportunities, conception of AI-based solutions, and the development and implementation of the solutions. It involves a thorough knowledge of market requirements, technical practicality and ethical aspects along with cross-functional teams that involve data scientists, engineers and stakeholders. Through a systematic integration of AI capabilities and business goals, companies are able to design products that can provide quantifiable value, improve user experiences and remain competitive. The current paper discusses the identification of opportunities, designing AI solutions, and overcoming the challenges in AI product management, illustrating the best practices and frameworks that enable to achieve a successful AI product lifecycle.
Artificial Intelligence, Product Management, AI Product Development, Opportunity Identification, AI Solutions, Ethical AI, Data-Driven Innovation.
Clinical Decision Support Systems and AI Regulation: Balancing Innovation, Patient Safety, and Legal Responsibility
Artificial intelligence (AI)-based Clinical Decision Support Systems (CDSS) are changing the face of healthcare by offering evidence-based information to improve the quality of diagnosis and treatment planning. Nevertheless, the fast adoption of AI in clinical practice poses some serious issues concerning patient safety, ethical duty, and legal liability. This study will be based on the regulatory frameworks of AI-enabled CDSS, and the way in which innovation can be achieved in balance with risk management and compliance needs. By comparing the available regulations, case analyses on the clinical implementation, and the opinions of the experts, the research points out the missing links in the current oversight systems and suggests the methods of responsible AI implementation in medicine. The results will inform the policymakers, healthcare professionals and developers in terms of ensuring that AI based decision support systems can drive innovative approaches in medical care without jeopardizing the well-being of patients and the legal requirements.
Clinical Decision Support Systems, Artificial Intelligence, Healthcare Regulation, Patient Safety, Legal Responsibility, AI Ethics, Risk Management
Governing High-Risk AI in Healthcare: Aligning Technical Robustness with Ethical and Legal Accountability
Clinical decision-making, diagnostics, and patient management have been transformed by the integration of high-risk artificial intelligence (AI) systems in healthcare, and complex technical, ethical, and legal issues have been generated simultaneously. These systems are typically implemented in situations involving safety-critical use, where a governance model is required that both provides technical resilience and ethical integrity and legal responsibility. This paper focuses on the regulation of high-risk AI in healthcare through the lens of the relationship between system reliability, transparency, and regulatory accountability. It discusses the main aspects of technical robustness, such as accuracy, explainability, bias reduction, and cybersecurity, and determines how these technical norms are in conflict with the ethical foundations of patient autonomy, fairness, and human control. The paper also evaluates current legal and regulatory frameworks to define accountability, liability and compliance loopholes in AI-driven healthcare applications. The research provides a combined governance framework that harmonizes the technical, ethical, and legal approaches to innovations by synthesizing the three aspects of action to attain patient safety, and trust in the population. The results add to the discussion of responsible adoption of AI in healthcare and offer the policy-related implications to the regulators, healthcare facilities, and AI developers aiming to establish trustful and responsible AI systems.
High-risk AI; Healthcare governance; Technical robustness; Ethical accountability; Legal regulation; Trustworthy AI
Zero Trust Security Architecture: Principles and Early Adoption
The rising complexity of cyber threats and the shortcomings of the old paradigm of security models in the form of perimeter security has compelled organizations to look into other security paradigms. Zero Trust Security Architecture (ZTSA) is one solution that can be viewed as a proactive solution, focusing on rigid identity checking, least privilege access, and constant patrol, instead of trusting things within network boundaries. This study will look at the core concepts of Zero Trust such as micro-segmentation, identity and access management, and device security, and its initial implementation history within the enterprise setting. The analysis of implementation strategies, challenges, and case studies help the study to identify practical considerations that organizations need to realize when implementing Zero Trust frameworks. The results indicate that technical integration and organizational preparedness are problematic, but early adoption shows great potential of reducing security risks and resiliency. The article offers a theoretical background to organizations that are interested in adopting Zero Trust that can be added to the growing debate on the topic of contemporary cybersecurity strategies.
Zero Trust Security, Cybersecurity, Identity and Access Management, Micro-Segmentation, Continuous Monitoring, Early Adoption, Enterprise Security.
Cybersecurity Risks and Threats in the Era of Pandemic-Induced Digital Transformation
The phenomenal pace of digital transformation occasioned by the prevalent disruptions greatly transformed how organizations operated, communicated, and delivered services within industries. Although it has allowed organizations to remain operational and sustained, the introduction of remote work patterns, cloud computing, and online platforms also predisposed companies to increased cybersecurity threats and novel threats. The given paper explores the qualities and extent of the cybersecurity risks and threats, which facilitated during the pandemic-driven digital transformation, focusing on the increased attack surfaces, human-factor vulnerabilities, and the vulnerabilities in the digital infrastructure. The study is a synthesis of the available literature and reports on the industry to determine the common cyber threats like phishing, ransomware, malware propagation, and data breaches, as well as the systemic problems, including governance, policy implementation, and user awareness. Moreover, the research paper shows that these threats affect key sectors, such as healthcare, finance, education, and social institutions, in a disproportionate way. The studies highlight adaptive cybersecurity frameworks, sound risk management, and ongoing user education, which is the responsiveness of the research through the analysis of mitigation measures and best practices implemented during this time. The results can be used to better understand the restructuring of the cybersecurity environment under the actions of accelerated digital transformation and offer a point of view to build more resilient and secure cyber-ecosystems in future crises-related settings.
Cybersecurity risks; Digital transformation; Pandemic-induced disruption; Cyber threats; Remote work; Information security; Risk management]
From Public Confidence to Civic Technology: Designing the Next Generation of Governance Analytics
The trust to governmental institutions in the society is now the critical factor to determine good governance where the citizens determine the level of legitimacy, accountability and transparency. Due to the rapid changes in civic technology, there are new possibilities to reinforce this relationship with the help of data-driven understandings and participatory digital solutions. This paper examines how to shift away from the conventional trust-building processes to the superior governance analytics that are able to track the real-time population mood, in constituency work, and patterns of civic participation. It offers a new-generation analytics system, combining ethical AI, people-centered design, and multidimensional data streams, to help to achieve responsive, transparent, and anticipatory governance. Analyzing the main design principles, possible applications, and systemic issues, the work gives the roadmap of how civic technology can be used to enhance the trust in the people and allow them to have more inclusive and intelligent governance systems.
Public Confidence, Civic Technology, Governance Analytics, Trust, Transparency, Ethical AI, Digital Participation, Citizen-Centric Design
Humanizing Automation: Lessons from Amazon’s Workforce Transition to Robotics
Automation and robotics have altered the previously known workplaces and organizations are struggling to strike a balance between efficiency and human practices. This paper will look at the employment shift of Amazon to the use of robotics and the social, operational, and ethical consequences of mass automation. The study, based on the review of workforce experiences, training programs, and organizational practices, draws major lessons in humanizing automation, such as the necessity of open communication, reskilling schemes, and employee involvement. The results highlight the importance of the fact that successful automation is not just a technological task but a socio-technical process that needs to carefully consider human needs. These lessons can be used by companies that want to implement robotics without interfering with the well-being and productivity of employees.
Automation, Robotics, Workforce Transition, Human-Centered Design, Employee Engagement, Upskilling, Organizational Change
The Economics of Trust: Why Institutional Confidence Is the New Currency of Governance
Modern societies have come up with institutional trust as a key factor in proper governance. Due to the ineffectiveness of conventional economic indicators in capturing the dynamics of the population belief, trust is becoming a crucial asset of socio-economic capital. This paper examines the role of institutional confidence in the outcome of governance through compliance, lower transaction cost and stable policy environment. It looks at what drivers bolster or undermine trust, including transparency, accountability, equity in service delivery, and information integrity and looks at the impact of levels of trust on social cohesion, economic performance, and state legitimacy. In a conceptual analysis and using illustrative examples, the paper suggests that trust has become a new currency of governance, which is needed to overcome complicated situations in society and create robust relations between states and citizens. In sum, the results indicate the necessity of trust-based governance systems that emphasize ethical leadership and participatory procedures as well as stable institutional performance.
Institutional trust, governance, public confidence, transparency, accountability, social cohesion, policy effectiveness, trust economy.
Predictive Analytics for Reducing Title V Deviations in Chemical Manufacturing
The Title V operating permits require chemicals manufacturing facilities to comply with stringent air emissions limits, monitoring, recordkeeping, and reporting provisions. Despite robust Environmental Management Systems (EMS), deviations continue to occur due to manual monitoring limitations, complex regulatory obligations, and the variability of industrial processes. This study proposes a predictive analytics framework designed to anticipate Title V deviations before they occur. Using statistical modeling, machine learning (ML), historical deviation data, continuous emissions monitoring system (CEMS) inputs, and operational parameters, the framework estimates the probability of future deviations and identifies key drivers. Case simulations demonstrate that predictive modeling can reduce deviations by 30 55% by enabling proactive operational adjustments, improved recordkeeping, and targeted corrective actions. The approach is designed to complement existing facility specific permit conditions. Results show that predictive analytics can shift air compliance programs from reactive detection to proactive prevention, improving compliance consistency across multi unit chemical operations.
Title V, predictive analytics, emissions compliance, machine learning, environmental management, air permitting, EPA, data driven compliance
SAP-Based Digital Banking Architecture Using Azure AI and Deep Learning for Real-Time Healthcare Predictive Analytics
The digital transformation of banking has led to increasingly complex operational environments where security, real-time insight, and enterprise system integration are paramount. This research proposes a Secure Digital Banking Framework that tightly integrates core SAP systems with real-time AI-driven predictive analytics to enhance risk management, fraud detection, customer personalization, and operational efficiency. Leveraging SAP’s enterprise capabilities (e.g., SAP S/4HANA, SAP Fiori, SAP Cloud Platform) combined with streaming data platforms and machine learning models, the framework supports real-time data ingestion, contextual analytics, secure access control, and regulatory compliance. The architecture incorporates role-based security, end-to-end encryption, audit trails, and continuous monitoring, ensuring robust protections for sensitive financial data. Predictive analytics modules use advanced machine learning and deep learning techniques to forecast credit risk, detect anomalous transactions, and model customer lifetime value. A prototype implementation demonstrates that integrating SAP data sources with real-time AI models significantly improves predictive accuracy and response times compared to traditional batch analytics. Results indicate improvements in fraud detection precision, reduced false positive rates, and better operational visibility. The proposed framework provides a blueprint for financial institutions to modernize digital banking infrastructure with secure, integrated, and intelligent features that support compliance, resilience, and customer satisfaction.
Secure digital banking, SAP integration, predictive analytics, AI, real-time analytics, fraud detection, enterprise systems, risk management, machine
Text Classification: A Comprehensive Survey of Methods, Applications, and Future Directions
Text classification stands as a fundamental task in natural language processing, involving the automated assignment of predefined categories to textual documents, sentences, or phrases. This comprehensive survey examines the evolution of text classification methodologies from traditional machine learning approaches through deep learning innovations to contemporary transformer-based architectures. We analyze the progression from feature-engineered methods like Naïve Bayes and Support Vector Machines, through representation learning with CNNs and LSTMs, to pre-trained language models including BERT, RoBERTa, and GPT variants. Our analysis encompasses benchmark datasets, evaluation metrics, and quantitative performance comparisons across methodologies. We explore emerging paradigms including prompt-based learning, few-shot classification, and multilingual adaptation while addressing critical challenges in interpretability, fairness, and computational efficiency. The survey provides practitioners with decision frameworks for selecting appropriate approaches based on task requirements, data availability, and resource constraints. We conclude by identifying open research questions and future directions that will shape the next generation of text classification systems.
Text Classification, Natural Language Processing, Machine Learning, Deep Learning, Transformers, BERT, Neural Networks, Sentiment Analysis, Topic Categorization
AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments
The increasing adoption of digital banking platforms and SAP-based enterprise systems has significantly improved operational efficiency and financial service delivery, but it has also expanded the cyber threat landscape. Real-time detection of financial threats such as fraud, unauthorized access, and data breaches remains a critical challenge in highly interconnected cloud environments. This paper proposes an AI-driven cloud framework for real-time financial threat detection in digital banking and SAP environments, designed to enhance security, scalability, and responsiveness.The proposed framework integrates cloud-native architectures with machine learning and artificial intelligence techniques to continuously monitor transactional data, system logs, and user behavior across banking applications and SAP systems. Advanced analytics and anomaly detection models enable early identification of suspicious activities, while automated response mechanisms support rapid threat mitigation. Security controls such as encryption, identity and access management, and continuous compliance monitoring ensure adherence to financial and data protection regulations. Experimental evaluation demonstrates improved detection accuracy, reduced latency in threat response, and enhanced resilience against evolving cyber threats. The framework provides a practical and scalable solution for securing modern financial systems operating in cloud-based digital banking and SAP ecosystems.
Artificial Intelligence, Cloud Computing, Financial Cybersecurity, Digital Banking, Threat Detection, SAP Environments, Real-Time Analytics
AI-Powered Cloud Architecture for Secure Real-Time Financial Analytics in Banking and Healthcare
The increasing digitalization of banking and healthcare systems has generated massive volumes of financial and operational data that require real-time analytics for effective decision-making. While cloud computing enables scalable and cost-efficient data processing, it also introduces critical challenges related to security, privacy, and regulatory compliance. This paper proposes an AI-Powered Cloud Architecture for Secure Real-Time Financial Analytics in Banking and Healthcare, designed to deliver intelligent, scalable, and cyber-resilient analytics capabilities. The proposed architecture integrates cloud-native services with advanced AI and machine learning models to support real-time data ingestion, predictive analytics, and anomaly detection. Security is embedded across all architectural layers through encryption, role-based access control, continuous monitoring, and compliance enforcement aligned with standards such as PCI-DSS, HIPAA, and GDPR. Experimental evaluation demonstrates improved analytical accuracy, low-latency processing, and enhanced protection against financial fraud and cyber threats. The results indicate that the proposed solution effectively supports secure, real-time financial analytics while maintaining data integrity and system reliability in cloud-based banking and healthcare environments.
AI, Cloud Computing, Financial Analytics, Banking Systems, Healthcare Systems, Cybersecurity, Real-Time Processing.
Next-Generation Security Operations Center (SOC) Resilience: Autonomous Detection and Adaptive Incident Response Using Cognitive AI Agents
The increasing scale, sophistication, and velocity of cyber attacks has shown that there are fundamental flaws in the current Security Operations Centers (SOCs) that are founded upon manual analysis and hardened rules and ad-hoc procedures. The current paper aims to address these issues with the help of Next-Generation SOC resilience framework, which is built upon autonomous detection and adaptation to incidences or accidents with the help of Cognitive Artificial Intelligence (AI) agents. The proposed system will introduce multi-agent AI structures, where deep learning-based anomaly detection is used, the optimization of responses can be achieved with the reinforcement learning process, and the contextual reasoning is supported with the help of knowledge graphs. Some of the ways include unsupervised and semi-supervised model of learning unknown threats, cognitive agents to correlate the alert messages using heterogeneous data sources, and adaptive playbooks based on feedback to develop. Experimental evaluation is conducted with a simulated environment of SOC on the enterprise, where real-world data, including network traffic and endpoint telemetry and security logs are taken into account. The results indicate that the accuracy of the detection (as much as 18 per cent higher than the base SIEM systems) is much higher, the mean time to detect (MTTD) by 35 per cent, the mean time to respond (MTTR) by 42 per cent. These findings affirm that cognitive AI agents enhance the resilience of SOC by leading to independent decisions, enhancing fatigue of analysts, and boosting the efficiency and scale of incident response efforts.
Intelligent SOC, Cognitive AI Agents, Adaptive Incident Response, Autonomous Detection, Security Operations Center, Cognitive AI Agents.
AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery
The systems of AI customer service have advanced very far in the field of Conversational AI, Natural Language Processing (NLP) and Intelligent Virtual Assistance, but complete systems still cannot handle complex, ambiguous or emotive customer requests. The article proposes a Hybrid Human-Machine Collaboration Model (HHMCM) of the real-time service delivery depending on the speed and adaptability of the AI-driven automation with the degree of judgment, empathy, and awareness of situations in human agents. The model includes four main layers starting with the AI-driven Interaction Engine, which recognizes an intent and customizes chatbots using transformer-based NLP models and identifying high-risk or ambiguous cases; a Knowledge Orchestration Layer, which dynamically retrieves product, policy, and contextual information; a Human-in-the-Loop (HITL) workflow, which routes high-risk or unclear cases to expert agents and a Continuous Feedback Reinforcement Module, which corrects and improves the system using agent feedback and customer feedback. The HHMCM was applied on a 50,000 anonymized interaction log dataset of a financial services helpdesk and compared to a baseline of fully automated chatbot. The metrics that were measured were five and these included average resolution time, first-contact resolution (FCR), customer satisfaction (CSAT), accuracy of intent classification, and frequency of escalation. The results have shown that the hybrid model allowed reducing the mean resolution time by 46, FCR by 38, CSAT by 29 and reducing escalations by 41. Another advantage that the model had was a stronger accuracy of intent detection particularly where query has multiple intent and context-intensive query. The findings can validate the concept of a hybrid AI-human solution as the potential to enhance the customers-delivering service in real time, and offer a scalable, reliable, and customer-centric automation solution.
Artificial Intelligence (AI), Conversational AI, Customer Experience Automation, Natural Language Processing (NLP), Chatbot Interactions (Personalized), Artificial Intelligence (AI) Customer Support, Intelligent Virtual Assistants.
Artificial Intelligence as a Tool for Enhancing Transparency in Public Administration
Artificial intelligence (AI) has become an increasingly accepted tool for enhancing transparency in government. AI assists governments in reducing corruption, enhancing service delivery, and improving accountability by enabling automated data analysis, real-time monitoring, and greater citizen engagement. The use of machine learning and natural language processing can provide tools to identify anomalies in government expenditure, ease access to government information, and facilitate the use of evidence in decision-making. The introduction of AI into public administration is fraught with difficulties, despite its potential, including the risk of data loss, algorithmic bias, and the need for a robust regulatory framework. These issues will have to be tackled in order to guarantee that AI not only improves transparency but also promotes ethical governance. This paper examines AI-based solutions that may promote open, responsible, and transparent institutions of the people.
Artificial intelligence, Transparency, Public administration, Accountability, Governance, Machine learning, Open data.
Evaluating the Role of Citizen Science in Improving Spatial Data Quality for Health Planning in the USA
Spatial data can be instrumental in health planning in the sense in which it helps policymakers to recognize inequalities, distribute resources, and create interventions according to geographic trends in disease and access to services. Nevertheless, standard sources of spatial data in the United States namely government health surveys, administrative records, and satellite data are frequently associated with issues of timeliness, completeness and local accuracy. Citizen science has also become a complementary method over recent years, where the public is involved in the data collection process, mapping, and validation that have the potential to add more information to the spatial resolution and contextual richness of health-related data. This paper analyzes the contribution of citizen science to the quality of spatial data on health planning in the USA. It analyzes the most important platforms of citizen science, including OpenStreetMap and HealthMap, and contrasts their outputs with authoritative datasets to evaluate the difference in their completeness, location accuracy, and topicality. The research utilizes both spatial data analysis and the interviews of stakeholders to determine the ways in which citizen-generated data can provide essential information on missing links in the knowledge of environmental exposures, most accessible health services, and health risks at a community level. The results underscore the possibility of the participatory data ecosystems to enhance the power of the intelligence of the population, the equity of data, and the evidence-based decision-making in various communities.
Citizen Science, Spatial Data Quality, Health Planning, Public Health GIS, Data Equity, Crowdsourcing, USA.
Cybersecurity and the Future of Global Peace: International Norms for Digital Conflict Prevention
One of the biggest threats to the global stability in the twenty-first century is the growing militarization of cyberspace. This essay analyses the way in which the development of cybersecurity regulation and establishment of global normative can help in the prevention of digital conflicts and long-term peace. It addresses the issue of intersection of technology, international law and global security and addresses the lack of internationally accepted frameworks used to check the conduct of the states in cyberspace. Based on the policy materials, international treaties, and recent cyber-attacks, the research assesses the current diplomatic efforts and the effectiveness in addressing responsible cyber behavior. The paper suggests a multidimensional approach to enhance trust, transparency, and accountability by promoting international cooperation, capacity-building, and improving the institution. The results indicate that the threat posed by cyber escalation will persist in the absence of explicit and binding international norms to bring about peace and security in the international system. The researchers conclude that the digital peace architecture built by consensus is critical in order to protect the future of the world order.
Cybersecurity, Global peace, International norms, Digital conflict prevention, Global governance.
Democratizing Vocational Training: AI-Enabled Skill Development for Gig and Informal Workers
More than sixty percent of the global workforce operates in informal or gig-based economies, yet these workers remain systematically excluded from structured upskilling systems that are increasingly essential in the AI-driven labor market. This study addresses this persistent exclusion by proposing and validating a tri-layer AI-enabled vocational training model designed to strengthen skill acquisition, learning efficiency, and employability outcomes for informal workers. The model integrates three mutually reinforcing components: a generative simulation layer that uses synthetic visual environments to replicate trade tasks; an adaptive feedback layer driven by transformer architectures that personalizes learning trajectories; and a labor analytics layer that connects performance metrics to workforce demand patterns. To assess the expected effectiveness of this model, the study synthesizes empirical findings from VR-based training, AI-assisted learning systems, and international datasets from the ILO, UNESCO-UNEVOC, and World Bank meta-analyses. Benchmark evidence demonstrates that AI-enabled simulations substantially outperform conventional workshop training, yielding higher learning efficiency (78 percent compared to 42 percent), improved task accuracy (84 percent compared to 55 percent), and stronger adherence to safety procedures (83 percent compared to 48 percent). These improvements highlight the potential of generative and transformer-based systems to replicate the instructional value of apprenticeship-style mentoring at scale. The findings underscore that AI-driven vocational ecosystems can democratize access to high-quality technical learning, reduce skills inequality, and support national strategies aligned with SDG 4. However, achieving equitable deployment requires prioritizing low-cost delivery models, localized content, multilingual interfaces, and strong governance frameworks to safeguard learner data and ensure inclusion. Overall, the study provides a validated conceptual and analytical foundation for integrating generative AI into informal workforce training, demonstrating its promise as a scalable and socially transformative instrument for future-ready skill development.
Artificial intelligence, generative models, informal workforce, vocational training, gig economy, transformer networks, VR simulation, upskilling, digital inclusion, competency-based education, digital divide, SDG 4.
Exploring the Influence of ERP-Supported Business Intelligence on Customer Relationship Management Strategies
In the modern business environment, organizations face the dual challenge of managing complex operations and meeting rising customer expectations. Traditional systems alone are no longer sufficient to ensure efficiency, insight, and customer satisfaction. There is a growing need to explore how integrated enterprise solutions supported by analytical tools can improve customer-oriented strategies and ensure sustainable competitiveness. This paper addresses that need by examining the role of system integration in strengthening decision-making and customer engagement. In this study, ERP-BI integration is analysed as a key enabler for aligning marketing, sales, and service strategies to achieve customer-centric outcomes. Enterprise Resource Planning (ERP) provides an integrated backbone for managing resources and operations, as BI turns raw data into insights that may be used for action. Together, these systems reinforce Customer Relationship Management (CRM) by enhancing customer engagement, improving decision-making, and increasing retention. The use of BI technologies like QlikView, Power BI, and Tableau is also highlighted in the study, along with challenges like data quality, implementation costs, and awareness gaps. Findings indicate that effective integration drives customer satisfaction, loyalty, and long-term organizational competitiveness.
ERP, Customer Relationship Management (CRM), Customer Service Quality, Business Intelligence (BI), BI Tools, Customer Retention, Service Strategies, Data Integration, CRM Strategies.
A Review of Cyber Threat Detection in Software-Defined and Virtualized Networking Infrastructures
The convergence of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has transformed the design and management of modern networks by enabling programmability, flexibility, and scalability. While these technologies bring significant advantages for cloud computing, IoT, and 5G, they also expand the cyber-attack surface and expose critical vulnerabilities in control, data, and virtualization layers. Attackers are increasingly exploiting centralized controllers, open APIs, and hypervisors, resulting in advanced persistent threats, malicious virtual functions, denial-of-service (DoS/DDoS) campaigns, and intrusions targeting multi-tenant infrastructures. Detecting such threats is particularly challenging because traditional methods, such as signature-based and rule-driven intrusion detection, often fail against novel, stealthy, or polymorphic attacks. Consequently, adaptive and intelligent mechanisms such as machine learning models, real-time monitoring, and distributed detection systems have emerged to mitigate risks effectively. This paper reviews cyber threat detection in SDN and NFV environments, analyzing vulnerabilities, detection methods, and research contributions. It also highlights the advantages and limitations of traditional and modern techniques while emphasizing the growing role of AI-enabled approaches in enhancing network resilience against evolving threats. Future research must focus on intelligent, automated, and scalable security frameworks for next-generation SDN/NFV ecosystems.
SDN, Cyber Threat Detection, NFV, Intrusion Detection, Network Security, Deep Learning, Anomaly Detection, Machine Learning, Attack Mitigation.
GEIOS EQG for Retrofit Wells : Repowering well with Advanced Metamaterials for Heat transfer and Advance Nano Stimulation
This study presents a technical assessment of GEIOS Enhanced Quantum Geothermal (EQG) technology applied to a non-commercial geothermal well in a high-enthalpy, silica-rich system in South America. The subject asset intersects stable reservoir temperatures of 270–295°C at a measured depth of 2,540 m, but delivered only 1.8 MWe during initial testing due to sub-commercial permeability and severe silica scaling of up to 21 mm on internal surfaces. The EQG system implements a sealed, non-extractive circulation architecture that replaces permeability-dependent fluid production with engineered heat transfer mechanisms. The design focuses on enhanced thermal conductivity at the wellbore–formation interface via advanced materials, nano-engineered stimulation strategies, and optimized internal fluid dynamics, while explicitly treating silica fouling as a primary design load. Thermohydraulic modeling, constrained by field temperature logs, completion geometry, and measured silica deposition, reproduces the in-situ temperature profile across the 500–2,540 m interval in close agreement with measured logs. Under EQG retrofitting and nanofoam-assisted stimulation, steady-state operation achieves 4.6–5.4 MWe net electrical output (90% CI), corresponding to a ~155–200% uplift relative to the historical 1.8 MWe baseline. System stability is maintained over modeled multi-year horizons with controlled parasitic loads and bounded fouling impacts. Results confirm that repowering underperforming assets in chemically aggressive, low-permeability environments requires shifting performance dependence from stochastic transmissivity to controlled, architecture-driven heat transfer efficiency. The EQG configuration evaluated here demonstrates a technically viable, permeability-agnostic route to monetize previously stranded high-enthalpy resources in silica-rich South American fields while preserving operational resilience in severe scaling conditions.
A Survey on Blockchain-Enabled ERP Systems for Secure Supply Chain Processes and Cloud Integration
Modern businesses are significantly reliant on integrated technologies to enhance the performance of their supply chains, operational efficiency, and decision-making capabilities. To manage client relationships, finances, inventory, procurement, and other tasks, businesses require Enterprise Resource Planning (ERP) systems. But in complicated and dispersed supply chains, typical ERP systems struggle with transparency, security, and scalability. A new answer has arisen: blockchain technology. It provides immutability, decentralization, and safe data interchange. Smart contracts built on the blockchain enable automated processes, enforce compliance, and foster improved confidence among stakeholders. Cloud computing has revolutionized ERP implementation with its scalable, affordable, and real-time accessible solutions. However, it does come with some new integration and security problems. The convergence of blockchain, cloud computing, and ERP systems provides a framework for secure, transparent, and adaptable supply chain processes, enhancing procurement, logistics, financial management, and cross-organizational collaboration. This paper presents a systematic survey of blockchain-enabled ERP systems, examining technology foundations, integration architectures, application domains, and critical challenges such as interoperability, scalability, and user adoption. By synthesizing recent literature, this study highlights research gaps, offers insights into practical implementation, and identifies opportunities for future research, including the integration of AI and IoT to further enhance automation, predictive analytics, and trust in supply chain ecosystems.
Block chain, Enterprise Resource Planning (ERP), Cloud Integration, Supply Chain Management, Smart Contracts, Digital Transformation.
AI for Informal Workforce Development: Leveraging Generative Models to Teach Practical Trade Skills to Gig and Blue-Collar Workers
The accelerating diffusion of artificial intelligence (AI) is reshaping global approaches to workforce training, yet informal and gig-economy workers remain largely excluded from structured upskilling systems. This study investigates how generative AI models—notably Generative Adversarial Networks (GANs), transformer architectures, and large language models—can be leveraged to teach practical trade skills to blue-collar and informal workers. Building on frameworks from the International Labour Organization (ILO) and UNESCOUNEVOC, the research adopts a conceptual–analytical design that integrates empirical evidence and meta-analytic findings to develop a tri-layer AI upskilling model. The model comprises: (1) a Generative Simulation Layer that replicates complex trade procedures through synthetic data and VR demonstrations; (2) an Adaptive Feedback Layer utilizing transformer-based learning analytics to personalize training; and (3) a Labor Analytics Layer linking performance data to employment and income outcomes. Comparative analysis reveals that AI-enabled simulations substantially enhance learning efficiency (78% vs 42%), task accuracy (84% vs 55%), and safety compliance (83% vs 48%) relative to conventional workshop training. Regional adoption trends between 2018 and 2022 show significant growth in Asia, Europe, and North America, demonstrating the scalability of AI-driven vocational systems. Furthermore, cost-effectiveness assessments indicate higher performance gains per training dollar when AI tools are integrated with VR environments. The findings affirm that generative AI can democratize skill acquisition, strengthen employability, and align informal workforce competencies with Industry 4.0 imperatives. Nevertheless, equitable deployment demands inclusive digital infrastructure, affordable access, and robust governance frameworks to ensure that AI-mediated upskilling contributes to sustainable and just economic growth.
Artificial intelligence, generative models, informal workforce, vocational training, gig economy, transformer networks, VR simulation, upskilling, digital inclusion.
Enhancing Incident Response Efficiency Through Automated Dynamic Threat Case Generation
The exponential rise in cyber-attacks has created unprecedented challenges for Security Operations Centers (SOCs) that must respond to incidents with speed, accuracy, and adaptive intelligence. Traditional incident response (IR) workflows rely heavily on static playbooks and manual decision-making, which struggle to keep pace with the rapidly changing attack landscape. This study proposes an automated dynamic threat case generation framework that enhances incident response efficiency through context-aware automation and continuous learning. The framework integrates structured threat intelligence sources, including MITRE ATT&CK, CVE, and CAPEC, with real-time SOC telemetry to automatically generate, prioritize, and adapt response cases according to evolving threat behaviors. A hybrid quantitative and experimental methodology was adopted, simulating over 200 attack scenarios to evaluate improvements in key SOC performance indicators. Metrics such as mean detection time, response accuracy, analyst workload, and case adaptation rate were benchmarked against baseline manual response processes. Results revealed a 61% reduction in detection time, 25% increase in response accuracy, and over 50% reduction in analyst workload, demonstrating significant efficiency gains achieved through automation. Statistical correlation analysis confirmed strong positive relationships between automation depth and operational accuracy (r = 0.79, p < 0.005), and a negative correlation between alert volume and case generation time (r = −0.84, p < 0.002), validating the scalability of the proposed approach. The findings underscore that automated dynamic threat case generation represents a practical advancement in incident response automation, enabling SOCs to respond more quickly and intelligently to sophisticated threats. By reducing human dependency in repetitive triage tasks while maintaining analyst oversight, the model contributes to building resilient, adaptive, and data-driven security ecosystems. Future research should explore integrating reinforcement learning for predictive case generation and extending this framework across multi-organizational cyber defense networks.
Incident Response, Security Operations Center, Cyber Threat Intelligence, Dynamic Case Generation, SOAR Automation, Attack Graphs, Efficiency Optimization.