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.
Intelligent Enterprise Cloud Platforms for Financial Risk Prediction Cybersecurity and Data Governance
Intelligent enterprise cloud platforms are transforming modern financial systems by integrating advanced technologies such as Artificial Intelligence (AI), cloud computing, cybersecurity frameworks, and data governance mechanisms. Financial institutions increasingly rely on cloud-based enterprise infrastructures to manage transactions, customer information, regulatory compliance, and risk analysis processes. However, the rapid growth of digital financial services has also increased exposure to cyber threats, data breaches, fraud, and operational risks. Traditional financial risk management approaches often lack the scalability, speed, and intelligence required to process large volumes of real-time financial data. Intelligent cloud platforms address these limitations by utilizing machine learning, predictive analytics, and automated monitoring systems to improve financial risk prediction and cybersecurity management. These platforms support secure data storage, real-time fraud detection, governance compliance, and intelligent decision-making across enterprise operations. Furthermore, robust data governance frameworks ensure data integrity, privacy, accessibility, and regulatory adherence within cloud environments. This study explores the role of intelligent enterprise cloud platforms in financial risk prediction, cybersecurity enhancement, and data governance management. The research also examines existing technologies, implementation methodologies, operational benefits, challenges, and future implications associated with cloud-based intelligent financial systems in modern enterprise environments.
Intelligent Enterprise Systems, Cloud Computing, Financial Risk Prediction, Cybersecurity, Data Governance, Artificial Intelligence, Machine Learning, Financial Technology, Cloud Security, Predictive Analytics, Enterprise Platforms, Risk Management
Cloud-Native DevOps with Federated Learning and Scalable Enterprise Infrastructure Modernization
Cloud-native DevOps has become a foundational approach for organizations seeking scalable, resilient, and automated enterprise infrastructure solutions. Simultaneously, federated learning has emerged as an advanced decentralized machine learning technique that enables collaborative model training without transferring sensitive data to centralized repositories. This research explores the integration of cloud-native DevOps practices with federated learning to modernize enterprise infrastructure in distributed computing environments. The study investigates how technologies such as containerization, Kubernetes orchestration, microservices, Infrastructure as Code (IaC), and Continuous Integration/Continuous Deployment (CI/CD) pipelines can support secure and scalable federated learning ecosystems. The research further examines the role of automation, observability, edge computing, DevSecOps, and hybrid cloud deployment in enhancing operational efficiency and intelligent decision-making. A detailed literature review identifies critical challenges including scalability limitations, interoperability complexity, network latency, governance issues, and cybersecurity threats in modern enterprise systems. The proposed methodology introduces a cloud-native federated architecture capable of automating machine learning operations while preserving privacy and compliance requirements. The study concludes that integrating federated learning with cloud-native DevOps significantly improves infrastructure scalability, deployment flexibility, security resilience, and enterprise modernization capabilities, thereby supporting next-generation digital transformation initiatives across industries.
Cloud-native DevOps, Federated Learning, Enterprise Infrastructure Modernization, Kubernetes, CI/CD, Microservices, Infrastructure as Code, Hybrid Cloud, DevSecOps, Distributed Machine Learning, Automation, Cloud Computing, Edge Computing, MLOps, Scalable Systems.
Transforming Fragmented Enterprise Data into Actionable Insights Using Artificial Intelligence
The increasing volume of fragmented enterprise data across cloud platforms, legacy infrastructures, Internet of Things (IoT) systems, and distributed databases has created major challenges for organizations seeking efficient decision-making and operational intelligence. This study examines how artificial intelligence (AI) can transform fragmented enterprise data into actionable insights capable of improving organizational performance, scalability, and strategic innovation. The paper explores the integration of machine learning, cloud computing, predictive analytics, and intelligent automation in modern enterprise environments. It further evaluates how AI-driven architectures enhance data interoperability, real-time analytics, and enterprise-wide collaboration across sectors such as healthcare, biotechnology, manufacturing, and digital services. The study also discusses the role of scalable cloud infrastructures and advanced data engineering techniques in supporting intelligent business ecosystems. In addition, critical challenges relating to cybersecurity, data governance, organizational agility, and ethical AI implementation are examined. The findings indicate that AI-enabled enterprise intelligence systems significantly improve operational efficiency, support predictive decision-making, and strengthen digital transformation initiatives. The paper concludes that organizations adopting scalable AI frameworks and integrated enterprise architectures are better positioned to achieve sustainable competitiveness and long-term innovation in the evolving digital economy.
Artificial Intelligence, Enterprise Data Integration, Big Data Analytics, Cloud Computing, Digital Transformation, Machine Learning, Intelligent Enterprise Systems.
AI-Driven Enterprise System Integration: Improving Data Interoperability Across Complex Organizations
Artificial Intelligence (AI) has emerged as a transformative force in enterprise system integration, enabling organizations to improve data interoperability across increasingly complex digital environments. Modern enterprises often operate heterogeneous systems that generate fragmented and incompatible datasets, creating challenges in communication, coordination, and decision-making processes. This study examines the role of AI-driven integration technologies in enhancing interoperability among enterprise systems through intelligent automation, machine learning, cloud-based architectures, and real-time data synchronization. The paper explores how AI improves enterprise application integration, optimizes data governance, and facilitates secure information exchange across organizational units. It further analyzes the strategic implications of AI-enabled interoperability for operational efficiency, scalability, and digital transformation. Despite the significant benefits, challenges such as data quality issues, cybersecurity risks, ethical concerns, and legacy system incompatibility remain critical barriers to implementation. The study concludes that AI-driven enterprise integration provides a sustainable pathway for organizations seeking resilient, adaptive, and intelligent information infrastructures in rapidly evolving business ecosystems.
Artificial Intelligence; Enterprise System Integration; Data Interoperability; Digital Transformation; Cloud Computing; Intelligent Automation.
Explainable AI Models for Cloud-Based Fraud Detection Risk Assessment and Secure Financial Decision Intelligence
The rapid adoption of cloud computing, digital banking, online financial services, and intelligent automation has significantly transformed modern financial ecosystems. However, the increasing complexity of cyber fraud, financial crimes, insider threats, and transaction anomalies has created major security and operational challenges for financial institutions. Traditional fraud detection systems often struggle to identify sophisticated attack patterns due to limited scalability, static rule-based mechanisms, and lack of adaptive intelligence. Explainable Artificial Intelligence (XAI) has emerged as a powerful solution capable of combining predictive intelligence with transparent and interpretable decision-making processes. This research presents an advanced framework for Explainable AI Models in cloud-based fraud detection, financial risk assessment, and secure financial decision intelligence. The proposed architecture integrates machine learning algorithms, deep learning models, explainability techniques, cloud-native infrastructure, anomaly detection systems, and cybersecurity frameworks to improve fraud detection accuracy, financial forecasting, and operational transparency. Experimental analysis demonstrates that explainable AI significantly enhances fraud detection precision, reduces false positive alerts, strengthens regulatory compliance, and improves trust in automated financial systems. The study further emphasizes the importance of secure cloud governance, ethical AI operations, and interpretable analytics in financial environments. The findings confirm that explainable AI-driven financial intelligence systems provide scalable, adaptive, secure, and transparent solutions for modern cloud-based financial ecosystems.
Explainable Artificial Intelligence, Cloud Computing,
Fraud Detection, Financial Risk Assessment
Machine Learning, Deep Learning, Cybersecurity, Predictive Analytics, Financial Data Security, Intelligent Automation, Cloud Security, Regulatory Compliance, Behavioral Analytics, Anomaly Detection
Resilient Enterprise Governance and AI-Driven Cloud Computing Architectures for Energy Utilities and Data-Driven Operations
The rapid digital transformation of energy utilities has significantly increased the adoption of cloud computing, artificial intelligence, and data-driven operational frameworks to improve efficiency, reliability, and sustainability. Modern energy enterprises rely on interconnected digital infrastructures to manage power generation, distribution, consumption analytics, and smart grid technologies. However, the growing dependence on cloud ecosystems and intelligent automation introduces challenges related to governance, cybersecurity, data privacy, operational resilience, and regulatory compliance. This study explores the role of resilient enterprise governance and AI-driven cloud computing architectures in supporting secure, scalable, and adaptive energy utility operations. The research examines how cloud-native platforms, distributed computing frameworks, and machine learning algorithms enhance operational decision-making, predictive maintenance, and real-time monitoring within energy systems. Furthermore, the study investigates governance mechanisms that ensure accountability, transparency, risk management, and business continuity across distributed infrastructures. The findings indicate that integrating adaptive governance models with AI-enabled cloud architectures improves system reliability, optimizes resource utilization, and strengthens cybersecurity resilience against evolving threats. The study concludes that resilient enterprise governance combined with intelligent cloud-based technologies forms the foundation for sustainable and future-ready energy utility ecosystems capable of supporting large-scale digital transformation and data-driven innovation.
Resilient enterprise governance, AI-driven cloud computing, energy utilities, smart grids, data-driven operations, cloud security, distributed systems, artificial intelligence, predictive analytics, cybersecurity resilience, digital transformation, enterprise architecture, machine learning, operational efficiency, data governance
Adaptive AI Cybersecurity Frameworks for Healthcare Cloud Governance and Patient Data Protection
The rapid digital transformation of healthcare systems has accelerated the adoption of cloud computing, electronic health records, telemedicine, and artificial intelligence technologies. While these innovations improve healthcare accessibility, operational efficiency, and clinical decision-making, they also introduce serious cybersecurity risks associated with patient data privacy, unauthorized access, ransomware attacks, and cloud vulnerabilities. This study examines adaptive AI cybersecurity frameworks designed for healthcare cloud governance and patient data protection. The research explores how artificial intelligence, machine learning, behavioral analytics, and automated threat intelligence systems strengthen healthcare cybersecurity infrastructures in cloud-based environments. The study further investigates the role of adaptive security models in identifying cyber threats, detecting anomalies, managing access controls, and ensuring compliance with healthcare regulations such as HIPAA and GDPR. Technologies including zero-trust architecture, encryption systems, Security Information and Event Management platforms, identity and access management, and predictive threat analytics are analyzed as critical components of healthcare cloud governance. A qualitative and analytical research methodology is employed to evaluate existing cybersecurity frameworks, implementation strategies, and operational challenges within healthcare organizations. The findings reveal that adaptive AI-driven cybersecurity significantly enhances patient data security, improves threat response efficiency, and strengthens healthcare cloud resilience. The study concludes that intelligent and adaptive cybersecurity frameworks are essential for maintaining trust, privacy, regulatory compliance, and operational continuity within modern digital healthcare ecosystems.
Adaptive AI, Healthcare Cybersecurity, Cloud Governance, Patient Data Protection, Artificial Intelligence, Cloud Computing, Machine Learning, Healthcare Cloud Security, Threat Intelligence, Zero Trust Architecture, Electronic Health Records, Predictive Analytics, Identity and Access Management, Data Privacy, Security Information and Event Management.
Autonomous Infrastructure Optimization and Predictive Analytics for Healthcare Finance and Public Service Ecosystems
Predictive analytics and autonomous infrastructure optimization are transforming modern digital ecosystems across healthcare, finance, and public service sectors. Organizations increasingly depend on data-driven technologies to improve operational efficiency, resource allocation, decision-making, and service delivery. Predictive analytics uses machine learning, statistical modeling, and artificial intelligence techniques to analyze historical and real-time data for forecasting future outcomes and identifying hidden patterns. Autonomous infrastructure optimization further enhances system performance by enabling self-managing, adaptive, and intelligent infrastructures capable of automated monitoring, scaling, maintenance, and threat detection. In healthcare, predictive systems improve patient diagnosis, disease forecasting, and hospital resource management. In finance, predictive analytics supports fraud detection, investment forecasting, credit risk analysis, and automated trading systems. Public service platforms utilize intelligent optimization to improve governance, transportation management, emergency response, and citizen-centric digital services. Despite these advantages, challenges related to cybersecurity, data privacy, algorithmic bias, explainability, and regulatory compliance continue to affect implementation. This study explores the role of predictive analytics and autonomous optimization technologies in improving efficiency, scalability, resilience, and sustainability across critical sectors. The research also evaluates existing methodologies, governance mechanisms, and technological frameworks that support secure and intelligent infrastructure management in modern enterprise and public service environments.
Predictive Analytics, Autonomous Infrastructure, Artificial Intelligence, Healthcare Systems, Financial Technology, Public Service Platforms, Machine Learning, Infrastructure Optimization, Cloud Computing, Big Data Analytics, Cybersecurity, Smart Governance, Intelligent Automation, Data Privacy, Digital Transformation.
Cognitive Trust Architectures for Explainable Clinical Analytics and Resilient Digital Enterprise Ecosystems
The increasing adoption of digital healthcare technologies, enterprise intelligence systems, cloud computing, and artificial intelligence has transformed the management of clinical analytics and organizational decision-making processes. However, the growing dependence on automated systems introduces challenges related to trust, transparency, data privacy, cybersecurity, and interpretability in healthcare and enterprise ecosystems. Cognitive Trust Architectures (CTA) have emerged as a promising solution for enabling explainable clinical analytics and resilient digital enterprise operations by integrating cognitive computing, explainable artificial intelligence, trust management mechanisms, and adaptive security frameworks. These architectures support intelligent clinical decision-making, predictive analytics, patient monitoring, operational resilience, and secure enterprise collaboration while ensuring transparency and accountability in AI-driven processes. Explainable clinical analytics enables healthcare professionals and enterprise stakeholders to understand the reasoning behind machine learning predictions and automated recommendations, thereby improving trust, ethical compliance, and decision reliability. Furthermore, resilient digital enterprise ecosystems utilize cognitive trust models to enhance data integrity, business continuity, cybersecurity defense, and adaptive organizational performance under dynamic operational conditions. This study explores the architecture, principles, methodologies, benefits, and limitations of cognitive trust architectures in healthcare and enterprise environments. The research highlights the significance of trust-centric explainable analytics in improving clinical accuracy, enterprise resilience, operational transparency, and sustainable digital transformation in modern intelligent ecosystems.
Cognitive Trust Architecture, Explainable Artificial Intelligence, Clinical Analytics, Digital Enterprise Ecosystems, Trust Management, Healthcare Analytics, Resilient Systems, Machine Learning, Predictive Healthcare, Cybersecurity, Explainable Clinical Decision Support, Intelligent Enterprises, Data Privacy, Cognitive Computing, Adaptive Security
Cloud-Based Enterprise Data Quality Optimization using Semantic Validation and DataOps Pipeline Automation
Modern enterprises generate massive amounts of structured and unstructured data through cloud platforms, IoT systems, enterprise applications, and digital services. Maintaining data quality in such dynamic environments has become a major challenge due to inconsistencies, duplication, missing values, and integration issues. Traditional data quality management techniques are often inadequate for modern cloud-native ecosystems because they lack scalability, contextual intelligence, and automation capabilities. This research proposes a cloud-based enterprise data quality optimization framework integrating semantic validation and DataOps pipeline automation. Semantic validation uses ontologies, metadata models, and knowledge graphs to verify contextual correctness and business consistency of enterprise data. DataOps automation enables continuous integration, automated testing, monitoring, and deployment of data pipelines for real-time quality management. The framework also incorporates machine learning algorithms for anomaly detection and predictive quality analysis. Cloud-native technologies such as microservices and container orchestration improve scalability and operational efficiency. The proposed approach enhances data accuracy, consistency, governance, interoperability, and decision-making capabilities across enterprise environments. Experimental evaluation demonstrates that combining semantic technologies with automated DataOps workflows significantly improves enterprise data reliability while reducing operational costs and manual intervention. The framework supports digital transformation initiatives by providing intelligent, scalable, and adaptive enterprise data quality management solutions.
Cloud computing, Enterprise data quality, Semantic validation, DataOps, Data pipeline automation, Metadata management, Machine learning, Data governance, Cloud-native architecture, Data consistency, Ontology-based validation, Big data analytics, Real-time monitoring, Enterprise automation, Knowledge graphs]
Innovation in Environmental Education: Leveraging Technology for Global Sustainability Leadership
Environmental challenges such as climate change, biodiversity decline, pollution, and unsustainable resource consumption have intensified the demand for stronger environmental education systems capable of preparing learners for responsible global leadership. Traditional instructional approaches often rely on passive content delivery and limited real-world engagement, reducing their effectiveness in developing practical sustainability competencies. This study examines how technological innovation can transform environmental education through the strategic use of virtual reality, augmented reality, artificial intelligence, learning management systems, and interactive digital platforms. The objective is to evaluate the capacity of these tools to improve environmental literacy, learner motivation, critical thinking, collaboration, and sustainability-oriented decision-making. A qualitative review-based methodology was adopted, synthesizing established literature on environmental education, educational technology, and sustainability leadership. The findings indicate that immersive and adaptive technologies significantly enhance student engagement, knowledge retention, experiential learning, and access to complex environmental scenarios that may be difficult to experience through conventional teaching methods. Digital tools also support personalized learning pathways, real-time feedback, and cross-border collaboration, thereby strengthening global perspectives and leadership readiness. However, barriers such as infrastructure limitations, cost, unequal access, and insufficient teacher training remain significant constraints to effective implementation. The study recommends integrated curriculum reform, sustained educator capacity development, equitable technology investment, and policy support to maximize long-term impact. It concludes that technology-enabled environmental education offers a practical pathway for cultivating future sustainability leaders who can respond effectively to evolving environmental challenges in diverse local and international contexts.
Environmental education, Sustainability leadership, Digital learning, Virtual reality, Environmental literacy, Educational innovation, Global sustainability
Privacy-Preserving Federated AI for Secure Data Sharing and Regulatory Compliance in Cloud Environments
The rapid expansion of cloud computing has enabled organizations to store, process, and analyze massive volumes of data across distributed infrastructures. However, this growth has intensified concerns regarding data privacy, regulatory compliance, and secure data sharing, particularly in domains such as healthcare, finance, and government services. Traditional centralized machine learning approaches require raw data aggregation, which exposes sensitive information to potential breaches and violates data protection regulations such as GDPR, HIPAA, and similar frameworks. Federated learning (FL) has emerged as a promising paradigm that enables collaborative model training without transferring raw data from local devices or institutional servers. This paper explores a Privacy-Preserving Federated AI framework designed for secure data sharing and regulatory compliance in cloud environments. The proposed approach integrates federated learning with advanced privacy-preserving techniques such as differential privacy, secure multi-party computation, and homomorphic encryption to mitigate risks of data leakage and inference attacks. Additionally, it examines how compliance-aware mechanisms can be embedded within federated architectures to ensure adherence to global data protection laws. The study highlights system design considerations, threat models, and optimization strategies to balance privacy, accuracy, and computational efficiency. It further evaluates the applicability of federated AI in real-world cloud ecosystems, emphasizing its role in enabling secure, scalable, and regulation-compliant intelligent systems.
Federated Learning, Privacy-Preserving AI, Cloud Computing, Data Security, Regulatory Compliance, Differential Privacy, Secure Multi-Party Computation, Homomorphic Encryption, Distributed Machine Learning, Data Governance
Intelligent Enterprise Ecosystems through AI-Driven Security, Privacy and Cloud Innovation Frameworks
The rapid evolution of digital technologies has transformed enterprises into complex, interconnected ecosystems that rely heavily on cloud infrastructure, artificial intelligence (AI), and data-driven decision-making. However, this transformation introduces significant challenges related to security, privacy, and system resilience. This study explores the development of intelligent enterprise ecosystems through integrated AI-driven security, privacy-preserving mechanisms, and cloud innovation frameworks. It proposes a unified model that leverages machine learning algorithms for threat detection, adaptive security policies, and predictive analytics, while embedding privacy-enhancing technologies such as differential privacy and secure multi-party computation. Additionally, the framework emphasizes cloud-native architectures, including microservices and edge computing, to ensure scalability, flexibility, and real-time responsiveness. The research highlights how the convergence of AI, cybersecurity, and cloud computing enables organizations to build resilient, self-healing, and context-aware systems capable of mitigating risks and ensuring compliance with evolving regulatory standards. Through conceptual analysis and methodological design, this study demonstrates the potential of integrating these technologies to enhance operational efficiency, data integrity, and user trust. The findings contribute to the advancement of enterprise digital transformation strategies by providing a holistic approach to secure and intelligent ecosystem development.
AI-driven security, enterprise ecosystems, cloud computing, privacy preservation, machine learning, cybersecurity frameworks, digital transformation, data protection, intelligent systems, cloud innovation
Machine Learning (ML) –Based Cyber Threat Modelling for Industrial Control Systems in critical Infrastructure
ICS are operational systems that jot up the backbone of critical infrastructure systems like energy, water, transportation, and manufacturing. The rising integration of the operational and information technology has greatly broadened the cyber-attack surface of these systems to advanced and persistent threats that cannot be easily stopped using conventional rule-based security measures. Cyber threat modelling with the use of machine learning (ML) has become a potential activity that can improve the performance of cyber threat detection, analysis, and prediction in ICS environments. This paper analyses the use of ML in the context of cyber threat modelling in critical infrastructure with respect to the capability of detecting abnormal behaviour, discovering never-before-seen attack patterns, and helping to motivate mitigation of risk earlier. The abstract covers the popular ML paradigms in the context of ICS security, such as supervised, unsupervised, and hybrid learning models, and their application in the industrial network structure, such as the SCADA systems and programmable logic controllers. The main issues associated with data quality, model explanation, real-time application, and safety of operations are also discussed. In general, ML-based cyber threat modelling offers a strategic channel of enhancing resilience, situational awareness, and adaptive defense capacity within ICS-driven critical infrastructure.
Machine Learning; Cyber Threat Modelling; Industrial Control Systems; Critical Infrastructure; SCADA Security; Operational Technology; Anomaly Detection
Adaptive Pipeline Monitoring Using Unsupervised Anomaly Detection
Infrastructure The pipeline infrastructure plays a major role in the transportation of fluids in the industry safely and efficiently. Conventional monitoring techniques are in many cases based on fixed thresholds or monitored models which have the issue of being restricted by the requirement of labeled failure information as well as they are not easily able to adjust to changing operational environments. The paper discusses an adaptive pipeline monitoring scheme based on the unsupervised anomaly detection algorithm and applies the technique to multi-sensor data streams in order to detect the deviation of the normal operating schemes without prior information of fault events. The framework suggested involves adaptive learning, which will enable the framework to adapt to the changing pipeline dynamics and minimize false alarms to achieve real-time detection and strong operational insights. The metrics of evaluation prove the efficiency of the method in detecting the development of bypass on both the subtle and abrupt levels providing a proactive approach to integrity of the pipeline management and risk prevention.
Adaptive monitoring, pipeline integrity, unsupervised anomaly detection, sensor data analytics, real-time fault detection, operational resilience
Advanced AI Techniques for Safety and Risk Evaluation in High-Hazard Engineering Systems
The safety and risk analytics developed through AI have become more and more crucial to the operation of high-hazard engineering systems with complicated functions and close dependencies between the elements and harsh outcomes in case of failure. These systems, which cut across various industries, including oil and gas, nuclear power, chemical processing, air transportation, and vital infrastructure, produce vast amounts of heterogeneous sensor and control systems data and operational log data. The artificial intelligence methods, such as machine learning, deep learning, and probabilistic modeling, make it possible to make a more sophisticated analysis of this data to facilitate proactive risk detection, real-time anomaly detection, and predictive maintenance. Using the AI-based analytics as the part of the safety management systems, the organizations can shift to the proactive risk elimination instead of the reactive response to the incidents, enhancing the reliability of the systems and their resilience to the operational risks. Nonetheless, the issues of data quality, model transparency, system integration, and regulatory compliance remain to have an impact on adoption. This paper will analyze the uses, capabilities, and constraints of the AI-based safety and risk analytics in the high-hazard engineering systems, pointing out how these tools contribute to better decision-making and safer industry processes.
Artificial intelligence; Safety analytics; Risk assessment; High-hazard systems; Predictive maintenance; Engineering safety; Anomaly detection
Data-Driven Threat Intelligence for Energy and Critical Asset Management
Advanced cyber and physical threats are increasingly becoming a target of energy systems and critical infrastructure assets, which are threatening the continuity of its operations, safety and economic stability. Threat intelligence based on data (DTI) has become an essential method of detecting, threatening and preventing these threats on the spot. DTI will allow detecting the anomalies beforehand, assessing risks in advance, and making decisions by combining various sources of data, including industrial control systems (ICS) and SCADA networks with IoT sensors and external threat feeds. Machine learning and artificial intelligence are used as advanced analytics to identify vulnerabilities in time and provide automated response tactics. Despite the data quality, system interoperability, and privacy issues, the DTI implementation can improve the situational awareness, resilience, and protection of key assets within the energy environment, despite the challenges. This paper underscores the need to be systematic in terms of the data-driven approach to threat intelligence and the need to constantly adapt to changing threats.
Data-driven threat intelligence, energy infrastructure, critical asset management, cybersecurity, industrial control systems, machine learning, predictive analytics, threat mitigation.
Next Generation AI Powered Cloud Systems for Cybersecurity Healthcare Financial Analytics and Risk Management
The rapid evolution of artificial intelligence (AI) and cloud computing has transformed the digital landscape, enabling scalable, intelligent, and adaptive systems across multiple domains. This paper explores next-generation AI-powered cloud systems and their applications in cybersecurity, healthcare, financial analytics, and risk management. These systems leverage machine learning, deep learning, and distributed cloud architectures to process vast amounts of data in real time, enhancing decision-making, automation, and predictive capabilities. In cybersecurity, AI-driven cloud platforms enable proactive threat detection, anomaly identification, and automated response mechanisms. In healthcare, they facilitate personalized medicine, predictive diagnostics, and efficient patient data management while ensuring compliance with privacy regulations. In financial analytics, AI enhances fraud detection, algorithmic trading, and customer behavior analysis, contributing to more accurate forecasting and strategic planning. Furthermore, AI-powered risk management systems improve the identification, assessment, and mitigation of uncertainties across industries. Despite these advancements, challenges such as data privacy, ethical considerations, system reliability, and integration complexity remain critical. This paper provides a comprehensive overview of current developments, highlights interdisciplinary applications, and proposes a robust research methodology to evaluate the effectiveness of these systems, ultimately contributing to the development of resilient and intelligent cloud ecosystems.
Artificial Intelligence, Cloud Computing, Cybersecurity, Healthcare Analytics, Financial Analytics, Risk Management, Machine Learning, Deep Learning, Big Data, Predictive Analytics, Data Privacy, Automation, Intelligent Systems
Distributed Explainable Ensemble Anomaly Detection for Cloud-Native Applications Using Azure AI and SQL Analytics
In this study, we propose an ensemble anomaly detection architecture based on explainability and distributed computing. Using a combination of Azure Machine Learning and distributed SQL-based analytics, our method attempts to handle large amounts of telemetry data while remaining fully interpretable throughout the process. Our proposed framework involves the combination of three models, including Isolation Forest, LightGBM, and a time-series detector using CNN, and utilizes the outputs from these three in coordination by means of Azure Synapse Analytics, which allows us to adjust the weightings dynamically. To maintain the explainability of our pipeline, we used game-theoretic Shapley values to attribute anomalies to particular features and even sub-models in near real-time.
Anomaly Detection, Cloud-Native, Azure AI, SQL Analytics
Unified AI-Driven Cognitive Ecosystem for Cloud Security and Self-Healing Infrastructure
The increasing complexity of cloud computing environments and distributed enterprise systems has created a critical need for intelligent, adaptive, and resilient cybersecurity frameworks. This paper proposes a Unified AI-Driven Cognitive Intelligence Ecosystem designed to enhance cloud network security, enable self-healing enterprise systems, and support adaptive digital infrastructure. The proposed ecosystem integrates artificial intelligence, machine learning, cognitive computing, and automation into a single unified architecture capable of real-time monitoring, predictive analytics, and autonomous decision-making. By leveraging anomaly detection, behavioral analytics, and predictive modeling, the system identifies threats and operational inefficiencies proactively. The self-healing capability enables automatic fault detection, diagnosis, and recovery, ensuring continuous service availability and reliability. Additionally, adaptive infrastructure mechanisms allow dynamic resource allocation and system optimization based on real-time workload and threat conditions. The unified ecosystem enhances scalability, resilience, and operational efficiency while minimizing human intervention. However, challenges such as data privacy, computational complexity, and integration overhead remain significant. This research provides a comprehensive framework for designing intelligent, secure, and adaptive enterprise cloud systems for next-generation digital environments.
Artificial Intelligence, Cognitive Intelligence, Cloud Network Security, Self-Healing Systems, Adaptive Infrastructure, Machine Learning, Cybersecurity, Predictive Analytics, Intelligent Ecosystem, Digital Transformation
Cloud Powered Intelligent Ecosystems for Secure Healthcare Analytics with Artificial Intelligence Driven Innovation
The healthcare industry is undergoing a profound digital transformation driven by cloud computing and artificial intelligence (AI). Cloud-powered intelligent ecosystems are emerging as a foundational architecture for secure healthcare analytics, enabling scalable storage, real-time processing, and advanced decision-making capabilities. These ecosystems integrate AI-driven innovation to enhance predictive diagnostics, personalized treatment planning, and operational efficiency in healthcare systems. However, the sensitive nature of medical data introduces significant challenges related to privacy, security, interoperability, and regulatory compliance. This research explores the design and implementation of secure cloud-based healthcare analytics frameworks that leverage AI to improve data-driven healthcare outcomes while ensuring robust data protection. The study emphasizes the use of machine learning for predictive analytics, anomaly detection for cybersecurity, and federated learning for privacy-preserving model training. Additionally, it examines how cloud ecosystems facilitate interoperability among healthcare providers, enabling seamless data sharing and collaboration. Despite these advantages, challenges such as data breaches, algorithmic bias, and infrastructure complexity persist. The proposed framework aims to address these limitations by integrating adaptive security mechanisms and intelligent analytics pipelines. Ultimately, this research contributes to the development of next-generation healthcare systems that are secure, intelligent, and capable of delivering high-quality patient care through cloud and AI integration
Cloud computing, healthcare analytics, artificial intelligence, secure ecosystems, predictive diagnostics, federated learning, data privacy, machine learning, digital health, intelligent healthcare systems
Securing Offline Internal AI Systems: A Comprehensive Framework for Data Protection, Model Integrity, and Access Control in Air-Gapped Environments
The growing deployment of artificial intelligence systems within highly sensitive sectors such as defense, finance, and critical infrastructure has led to increased reliance on offline and air-gapped environments as a primary security measure. These isolated systems are traditionally perceived as inherently secure due to their lack of direct connectivity to external networks. However, this assumption is increasingly challenged by the emergence of sophisticated covert attack vectors capable of bypassing physical isolation through side-channel and out-of-band communication mechanisms. As a result, air-gapped AI systems remain vulnerable to data exfiltration, model manipulation, and unauthorized access. This study addresses this critical gap by proposing a comprehensive and structured security framework specifically designed for offline internal AI systems. The framework focuses on three core dimensions: data protection, model integrity, and access control. A systematic synthesis of existing literature on air-gap attacks, established security models, and recognized cybersecurity standards is employed to inform the framework design. The proposed architecture integrates classical security principles, including confidentiality, integrity, and role-based access control models, with AI-specific protection mechanisms such as secure model validation, controlled data pipelines, and layered access enforcement. The findings demonstrate that the proposed multi-layered framework significantly enhances system resilience against covert exfiltration techniques, insider threats, and model compromise. It further highlights that reliance on physical isolation alone is insufficient for securing modern AI systems. Therefore, a proactive, defense-in-depth strategy is essential for safeguarding offline AI infrastructures against evolving threat landscapes.
Air-gapped systems, Offline AI security, Data protection, Model integrity, Access control, Covert channel attacks, Cybersecurity framework.
Copyright (c) 2026 International Journal of Technology, Management and Humanities
Machine learning models for credit default prediction
Prediction of credit default is a vital part of financial risk management, especially in an age when data is becoming more available and borrowers are changing their behavior. This paper focuses on the use of machine learning models in credit default forecasting, including how the models can enhance predictive accuracy, robustness, and efficiency in decision making over traditional statistical models. Based on various datasets that contain demographic, financial, and behavioral variables, the research compares various algorithms: Logistic Regression, Random Forest, Gradient Boosting, and Artificial Neural Networks. The results show that ensemble and non-linear machine learning models are much better than the traditional ones in terms of classification precision, recall, and accuracy and area under the curve (AUC), which is in line with previous research (Lai, 2020; Moscatelli et al., 2020; Alonso Robisco and Carbo Martinez, 2022). Moreover, explainable AI methods are integrated to improve the transparency of the model, which could be used to resolve the main regulatory and ethical issues related to black-box models (Zhu et al., 2023). The research also notes the increased relevance of other types of data, such as user-generated and behavioral data, in enhancing predictive performance (Kriebel and Stitz, 2022). Comprehensively, the study proves that machine learning-based credit scoring models can be used as a more flexible and data-driven approach to evaluating default risk, with important implications to financial institutions, fintech applications, and policy regulators. It is suggested to use hybrid and interpretable models and continuously monitor the model risk in dynamic financial settings.
Credit Default Prediction; Machine Learning; Credit Risk Management; Random Forest; Gradient Boosting; Neural Networks; Explainable AI; Financial Analytics; Predictive Modeling; Model Risk
AI-Assisted Digital Forensics for National Security Investigations
The increasing scale and complexity of cyber threats have elevated the importance of digital forensics in national security investigations. However, traditional forensic approaches struggle to efficiently process vast volumes of heterogeneous data, including system logs, network traffic, and malware artifacts, often resulting in delayed investigations and potential evidentiary risks. This study proposes an AI-assisted digital forensics framework designed to enhance the speed, accuracy, and reliability of forensic analysis while preserving evidentiary integrity. The research adopts a hybrid methodology that combines a systematic review of recent advances in artificial intelligence for forensic applications with the design and evaluation of an intelligent forensic pipeline. The proposed framework integrates data acquisition, preprocessing, machine learning–based analysis, and evidence validation layers to support automated detection, classification, and correlation of digital evidence. Experimental evaluation using benchmark datasets demonstrates significant improvements in processing time and detection accuracy compared to traditional methods, alongside reduced false positive rates. Furthermore, the study addresses critical challenges related to chain of custody, explainability, and legal admissibility, ensuring that AI-generated insights remain forensically sound and defensible in legal contexts. The findings highlight the transformative potential of AI as a force multiplier in digital forensic investigations, offering practical implications for law enforcement and national security agencies seeking to strengthen cybercrime response capabilities.
AI-Assisted Digital Forensics; National Security Investigations; Cybercrime Analysis; Machine Learning; Evidence Integrity; Malware Analysis; Log Analysis; Explainable AI; Forensic Automation; Digital Evidence Processing
Copyright (c) 2024 International Journal of Technology, Management and Humanities
Resilient Cloud-Based Enterprise Systems: AI-Enabled Strategies for Cybersecurity and Operational Efficiency
The rapid adoption of cloud computing has transformed enterprise systems, offering scalability, flexibility, and cost efficiency. However, this shift has also introduced significant cybersecurity challenges and operational complexities. This study explores the role of Artificial Intelligence (AI) in enhancing the resilience of cloud-based enterprise systems, focusing on strategies that strengthen cybersecurity while improving operational efficiency. AI-driven approaches such as anomaly detection, predictive analytics, automated threat response, and intelligent resource management are examined for their ability to mitigate risks and optimize performance. The paper highlights how machine learning algorithms can proactively identify vulnerabilities, detect real-time threats, and reduce downtime through predictive maintenance. Additionally, AI contributes to operational efficiency by enabling dynamic resource allocation, workload balancing, and cost optimization in cloud environments. Despite these benefits, challenges such as data privacy, algorithm bias, and integration complexity remain critical concerns. This research emphasizes the need for a balanced approach that combines AI capabilities with robust governance frameworks. Ultimately, AI-enabled cloud systems provide a promising pathway toward resilient, secure, and efficient enterprise infrastructures capable of adapting to evolving technological and threat landscapes.
Cloud Computing, Artificial Intelligence, Cybersecurity, Enterprise Systems, Resilience, Machine Learning, Predictive Analytics, Threat Detection, Operational Efficiency, Automation
Scalable Intelligent Cloud Platforms Enabled by AI for Secure Autonomous and Future Ready Enterprise Systems
The growing demand for digital transformation has led enterprises to adopt intelligent cloud platforms that integrate artificial intelligence (AI) to achieve scalability, security, and autonomy. This paper explores the development of scalable intelligent cloud platforms designed to support secure, autonomous, and future-ready enterprise systems. By leveraging cloud-native technologies such as microservices, containerization, and orchestration, these platforms enable dynamic scalability and efficient resource utilization. AI techniques, including machine learning, deep learning, and reinforcement learning, empower systems to operate autonomously by enabling predictive analytics, anomaly detection, and automated decision-making. Security is enhanced through AI-driven threat detection, encryption mechanisms, and zero-trust architectures, ensuring data integrity and privacy. Furthermore, these platforms incorporate self-optimization capabilities that allow continuous monitoring and automatic adjustment of system performance. The study examines architectural frameworks, enabling technologies, and implementation strategies while addressing challenges such as interoperability, governance, and system complexity. The proposed approach demonstrates how intelligent cloud platforms can transform enterprise systems into adaptive, resilient, and future-ready infrastructures. This research contributes to the advancement of enterprise computing by presenting a unified model that integrates AI and cloud technologies to support next-generation digital enterprises.
Intelligent cloud platforms, Artificial intelligence, Enterprise systems, Cloud-native architecture, Autonomous systems, Scalability, Cybersecurity, Machine learning, Future-ready systems, Distributed computing
Autonomous Operational Resilience across AI Guided Cloud Platforms with Proactive Threat Mitigation
In the era of digital transformation, enterprises increasingly rely on cloud platforms to deliver scalable and flexible services. However, the growing complexity of cloud environments introduces significant operational risks, including cyber threats, system failures, and performance disruptions. This research explores the concept of autonomous operational resilience enabled by AI-guided cloud platforms with proactive threat mitigation capabilities. Autonomous resilience refers to the ability of systems to detect, analyze, respond to, and recover from disruptions without human intervention. By integrating Artificial Intelligence (AI) with cloud infrastructure, organizations can achieve continuous monitoring, predictive analytics, and automated response mechanisms. AI models analyze large volumes of real-time and historical data to identify anomalies, predict potential failures, and initiate corrective actions. Cloud platforms provide the scalability and orchestration required to deploy these intelligent systems efficiently. Proactive threat mitigation further enhances resilience by preventing incidents before they occur. This study examines the architecture, tools, and strategies required to implement such systems, along with their impact on operational efficiency and security. While challenges such as data privacy, integration complexity, and trust in AI remain, the findings highlight the transformative potential of AI-driven resilience in ensuring robust and adaptive cloud operations.
autonomous resilience, AI-driven systems, cloud platforms, proactive threat mitigation, cyber security, predictive analytics, anomaly detection, cloud orchestration, self-healing systems, operational intelligence
Adaptive and Autonomous Cloud Resilience Enabled by AI-Guided Security and Predictive Threat Analytics
The rapid evolution of cloud computing and artificial intelligence has transformed modern digital infrastructures, enabling scalable, intelligent, and highly adaptive systems. However, this transformation has also introduced complex security challenges, operational vulnerabilities, and increased exposure to cyber threats. Autonomous operational resilience refers to the ability of cloud platforms to self-monitor, self-heal, and proactively mitigate risks without significant human intervention. This research explores how AI-guided cloud platforms enhance resilience through predictive analytics, automated response mechanisms, and continuous threat intelligence integration. The study examines the convergence of machine learning, cloud orchestration, and cybersecurity frameworks to build systems capable of detecting anomalies, anticipating failures, and neutralizing threats in real time. By leveraging techniques such as anomaly detection, behavioral analytics, and reinforcement learning, cloud systems can dynamically adapt to evolving attack vectors. The research further evaluates architectural models, resilience strategies, and mitigation mechanisms that support uninterrupted service delivery. Ultimately, this work highlights the importance of integrating proactive threat mitigation into cloud ecosystems to ensure reliability, security, and operational continuity. It concludes that autonomous resilience is a critical requirement for future cloud infrastructures, especially in environments characterized by high complexity, scale, and persistent cyber threats.
Autonomous resilience, AI-guided cloud, proactive threat mitigation, cybersecurity, cloud computing, anomaly detection, self-healing systems, predictive analytics, operational continuity, machine learning, cloud security, adaptive systems
Securing Multi-Cloud Environments in Government and Enterprise Systems: Challenges, Frameworks, and Best Practices
The increasing adoption of multi-cloud environments across government and enterprise systems has introduced significant advancements in scalability, flexibility, and operational resilience. However, this shift has also intensified security complexity due to fragmented infrastructures, inconsistent policy enforcement, and expanded attack surfaces. Managing security across heterogeneous cloud platforms presents critical challenges in identity governance, data protection, compliance, and real-time threat visibility. This study employs a structured literature synthesis combined with conceptual framework development to systematically examine the evolving landscape of multi-cloud security. By analyzing recent scholarly contributions and industry-aligned practices, the research identifies key vulnerabilities inherent in distributed cloud ecosystems and evaluates the effectiveness of existing security frameworks, including Zero Trust Architecture, Cloud Security Posture Management, and integrated governance models. The findings reveal that traditional, isolated security approaches are insufficient for addressing the dynamic and interconnected nature of multi-cloud environments. In response, the study proposes a unified, multi-layered security model that integrates identity-centric controls, centralized orchestration, AI-driven threat detection, and automated compliance mechanisms. This framework is designed to enhance visibility, reduce misconfigurations, and strengthen resilience against emerging cyber threats. The study contributes to the field by providing a comprehensive classification of multi-cloud security challenges, offering a comparative evaluation of existing frameworks, and presenting a practical, scalable model tailored to the needs of modern government and enterprise systems.
Multi-cloud security, Zero Trust, Cloud governance, CSPM, AI-driven cybersecurity, Enterprise systems
AI-Enabled Semantic Knowledge Networks for Scalable Enterprise Cloud Solutions
The rapid evolution of enterprise cloud computing has introduced significant challenges in managing, integrating, and extracting value from large-scale distributed data systems. Artificial Intelligence (AI)-enabled Semantic Knowledge Networks (SKNs) have emerged as a transformative approach to address these challenges by enabling intelligent data representation, contextual reasoning, and automated decision-making. This research explores the role of AI-driven semantic frameworks in enhancing scalability, interoperability, and efficiency within enterprise cloud environments. Semantic Knowledge Networks leverage ontologies, knowledge graphs, and machine learning techniques to establish meaningful relationships between heterogeneous data sources, thereby improving data discoverability and system intelligence. The study examines how integrating AI with semantic technologies supports dynamic resource allocation, predictive analytics, and automated workflows in cloud infrastructures. Furthermore, it evaluates architectural models, implementation strategies, and performance considerations for deploying SKNs at scale. The findings suggest that AI-enabled semantic systems significantly improve operational efficiency, reduce redundancy, and enhance decision support capabilities in enterprise environments. However, challenges such as computational complexity, data privacy, and integration overhead remain critical considerations. This research contributes to the growing body of knowledge by providing a comprehensive framework for designing scalable, intelligent cloud solutions using semantic technologies.
Artificial Intelligence, Semantic Knowledge Networks, Cloud Computing, Knowledge Graphs, Enterprise Systems, Scalability, Ontologies, Data Integration, Machine Learning, Intelligent Systems