The Journal of Integrated Research (JIR) is a peer-reviewed, open-access multidisciplinary journal. It aims to inspire and support students, researchers, and professionals—particularly in pharmaceutical sciences, agriculture, and life sciences—by publishing innovative, science-based research that advances sustainable development. JIR focuses on improving plant and animal life, developing novel drugs, and producing nutritious, eco-friendly foods and medicines with minimal environmental impact. By fostering a collaborative, interdisciplinary research environment, the journal plays a pivotal role in shaping the future of scientists and researchers, promoting cutting-edge discoveries that contribute to both academic knowledge and real-world applications. We are proud to share that the Journal of Integrated Research (JIR) is fully indexed by Google Scholar, one of the most widely used academic search engines worldwide. Inclusion in Google Scholar significantly amplifies the visibility of our articles, making them discoverable to a broad, global audience across disciplines.
AI-Driven Cyber Threat Intelligence for Resilient Cloud Computing Environments
The rapid adoption of cloud computing has introduced new security challenges, requiring advanced approaches to protect digital assets and critical services. Artificial intelligence (AI) has emerged as a transformative technology for strengthening cyber threat intelligence and enhancing the resilience of cloud computing environments. This paper investigates the role of AI-driven techniques in identifying, analyzing, and mitigating cyber threats within cloud infrastructures. The study explores the application of machine learning, deep learning, and intelligent automation for anomaly detection, intrusion prevention, vulnerability assessment, and incident response. By enabling predictive analytics and adaptive security mechanisms, AI can significantly improve the accuracy and efficiency of cloud security operations. The paper further examines implementation challenges, including data quality, model interpretability, adversarial attacks, and regulatory compliance. Through an analysis of recent developments and practical deployment scenarios, the research highlights how AI-powered cyber threat intelligence contributes to proactive defense strategies, improved risk management, and the development of secure and resilient cloud ecosystems. The findings demonstrate that integrating AI into cloud security frameworks can enhance organizational preparedness against emerging and sophisticated cyber threats.
The Role of Enterprise System Integration in Advancing Digital Transformation in Healthcare
The rapid evolution of digital technologies has transformed healthcare organizations by improving operational efficiency, clinical decision-making, and patient-centered service delivery. Enterprise system integration has emerged as a critical component in advancing digital transformation within healthcare institutions through the coordination of electronic health records, enterprise resource planning systems, clinical management platforms, and data-sharing infrastructures. This study examines the role of enterprise system integration in supporting healthcare digital transformation by analyzing its impact on interoperability, workflow optimization, strategic decision-making, and healthcare service quality. The study further explores how integrated enterprise architectures enhance communication among healthcare departments, reduce data fragmentation, and support intelligent healthcare ecosystems. In addition, the paper identifies major challenges affecting integration processes, including cybersecurity risks, technological complexity, infrastructure limitations, and organizational resistance to change. The study argues that effective enterprise integration frameworks are essential for achieving sustainable digital healthcare transformation and improving institutional resilience. The findings highlight the importance of strategic governance, business intelligence systems, and emerging technologies such as artificial intelligence and cloud computing in modern healthcare environments.
AI-Powered Workflow Optimization in Enterprise Service Operations
Artificial Intelligence (AI) has emerged as a transformative force in enterprise service operations, enabling organizations to optimize workflows, improve operational efficiency, and strengthen decision-making processes. This study examines the role of AI-powered workflow optimization in enhancing enterprise service management through intelligent automation, machine learning, predictive analytics, and cloud-based operational systems. The article explores how AI technologies streamline repetitive processes, reduce operational costs, improve service delivery, and support real-time business process integration across enterprise environments. Furthermore, the study evaluates the integration of AI with enterprise architectures such as DevOps, DataOps, cloud computing, and enterprise resource planning systems to achieve operational agility and scalability. The research also highlights critical issues relating to cybersecurity, data privacy, governance, and organizational resilience in AI-driven operational ecosystems. Findings indicate that AI-powered automation significantly contributes to productivity enhancement, operational continuity, and strategic business transformation while creating opportunities for intelligent and adaptive enterprise systems. The article concludes that the successful adoption of AI-powered workflow optimization depends on effective governance frameworks, workforce readiness, and sustainable technological integration strategies within modern enterprise service operations.
Reinforcement Learning in Intelligent Accounting and Tax Systems: Challenges, Applications, and Future Directions
Reinforcement Learning (RL) has emerged as a powerful branch of artificial intelligence that enables systems to learn optimal decision-making strategies through continuous interaction with dynamic environments. In the domain of accounting and tax systems, RL offers significant potential for automating complex financial processes, improving compliance management, optimizing resource allocation, and enhancing strategic financial decision-making. This paper reviews the current landscape of Reinforcement Learning applications in intelligent accounting and tax management systems, examining its role in automated financial reporting, tax risk assessment, fraud detection, audit planning, cash flow optimization, and regulatory compliance monitoring. The study highlights key implementation challenges, including data quality, regulatory constraints, interpretability requirements, and integration with existing accounting information systems. Furthermore, successful applications of RL-based financial optimization frameworks are discussed to demonstrate their effectiveness in improving operational efficiency and decision accuracy. The paper concludes by identifying emerging research opportunities and future directions for integrating Reinforcement Learning into next-generation accounting and tax systems, enabling organizations to achieve greater financial intelligence, adaptability, and governance in an increasingly digital business environment.
Real-Time Financial Process Optimization in Accounting and Tax Systems Using Ant Colony Optimization
This paper explores the application of Ant Colony Optimization (ACO) for real-time financial process optimization in modern accounting and tax systems. Inspired by the collective foraging behavior of ants, ACO is employed to identify optimal pathways for transaction processing, tax assessment workflows, resource allocation, and financial data routing within complex organizational environments. The study investigates the efficiency, adaptability, and practical implementation of ACO in comparison with traditional accounting process management and rule-based optimization techniques, particularly in scenarios requiring rapid decision-making and dynamic responses to changing financial conditions. The proposed framework enables intelligent prioritization of accounting tasks, automated tax compliance processing, and enhanced allocation of financial resources across interconnected accounting information systems. Experimental evaluations demonstrate that ACO effectively manages complex financial operations, minimizes processing delays, improves workflow efficiency, and adapts to evolving regulatory and business requirements. The findings indicate that Ant Colony Optimization provides a promising approach for developing intelligent accounting and tax management systems capable of supporting real-time financial decision-making, operational efficiency, and regulatory compliance in modern enterprises.
Blockchain and AI Integration for Advanced Information Security in Enterprise Systems
The increasing sophistication of cyber threats targeting enterprise systems demands a paradigm shift from conventional perimeter-based security models to more resilient, intelligent, and adaptive frameworks. This paper explores the synergistic integration of blockchain technology and artificial intelligence (AI) as a dual-layered approach for advanced information security. Blockchain provides an immutable, decentralized ledger that ensures data integrity, provenance, and non-repudiation, while AI contributes predictive analytics, anomaly detection, and automated response capabilities. Their convergence addresses critical vulnerabilities in enterprise architectures, including single points of failure, insider threats, and scalable trust management. We analyze architectural models such as federated learning on blockchains and smart contract-driven security orchestration. The findings indicate that while challenges in computational overhead and algorithmic transparency persist, the blockchain-AI fusion offers a transformative pathway for zero-trust security, decentralized identity management, and real-time threat intelligence sharing across enterprise ecosystems.
Digital Transformation Strategies for Organizational Resilience and Sustainable Growth in a Rapidly Evolving Global Environment
In the contemporary digital era, organizations are increasingly required to adapt to rapid technological advancements and dynamic market conditions to remain competitive and sustainable. This study explores strategic approaches that enable organizations to effectively harness the power of digital transformation and thrive in an evolving global environment. Digitalization is no longer limited to technological adoption; rather, it has become a comprehensive organizational strategy that reshapes business models, operational processes, and customer engagement practices. The paper emphasizes the importance of cultivating a culture of innovation, agility, and continuous learning to support successful digital transformation initiatives. Organizations that embrace a digital-first mindset are better positioned to respond to changing consumer demands, emerging technologies, and competitive disruptions. Furthermore, the study highlights the critical role of data-driven decision-making, where advanced analytics, artificial intelligence, and machine learning technologies facilitate the extraction of actionable insights from large-scale data sources. These capabilities enhance operational efficiency, improve forecasting accuracy, and enable personalized customer experiences. Additionally, the research discusses the significance of strategic collaboration and ecosystem partnerships among industries, technology providers, startups, and academic institutions. Such collaborations accelerate innovation, strengthen digital capabilities, and promote sustainable value creation. The study concludes that organizations adopting integrated digital transformation strategies can achieve improved resilience, long-term growth, enhanced competitiveness, and greater adaptability in a rapidly changing world.
Machine Learning-Driven Inventory Optimization for Intelligent Supply Chain Management: A Case Study Approach
Efficient inventory management is a critical component of modern supply chain operations, directly influencing organizational profitability, customer satisfaction, and operational resilience. Traditional inventory management approaches often struggle to accurately respond to fluctuating market demand, supply chain disruptions, and complex operational uncertainties. This study presents a comprehensive case study on the application of machine learning algorithms for optimizing inventory management and enhancing supply chain performance. The research investigates how advanced machine learning techniques can be utilized to analyze historical sales patterns, forecast product demand, optimize stock levels, and support data-driven inventory decision-making processes. By integrating predictive analytics and intelligent forecasting models, the proposed approach enables organizations to minimize stock shortages, reduce excess inventory costs, improve order fulfillment efficiency, and enhance overall supply chain responsiveness. The paper further examines the implementation of machine learning methods such as regression models, time-series forecasting, classification techniques, and neural networks in inventory optimization frameworks. In addition, the study discusses major challenges associated with deploying machine learning solutions in supply chain environments, including data integration complexity, scalability issues, model interpretability, and real-time decision synchronization.
DeepFM-Based Intelligent Orchestration for Optimizing Resource Management in Cloud DevOps Environments
The increasing complexity of cloud-based DevOps environments necessitates advanced data-driven approaches for efficient orchestration and resource management. This study proposes a Deep Factorization Machine (DeepFM)-based framework to enhance intelligent decision-making in DevOps pipelines by capturing both low-order feature interactions and high-order nonlinear relationships within system telemetry data. The model is designed to analyze deployment patterns, workload dynamics, and infrastructure metrics to predict optimal resource allocation and improve system performance. By integrating DeepFM into orchestration workflows, the proposed approach enables more accurate predictions, reduces operational inefficiencies, and enhances scalability in dynamic cloud environments. Experimental insights suggest that this method significantly improves deployment reliability and resource utilization compared to traditional rule-based and machine learning approaches, highlighting its potential for building adaptive and efficient DevOps ecosystems.
Decentralized Model Governance for Federated AI: Reputation Systems, Smart Contract Orchestration, and Accountability Without Trust
Federated learning enables distributed AI model training without centralizing raw data, but the conventional federated learning architecture concentrates governance authority in a central aggregation server whose trustworthiness is assumed rather than verified. This assumption is violated routinely in practice through poisoning attacks, Sybil attacks, free-rider exploitation, and Byzantine gradient injection, none of which the central server architecture can prevent by design. This paper proposes a decentralized model governance framework for federated AI that eliminates the central trusted server by replacing it with a permissioned blockchain governance layer comprising seven interoperable smart contracts: registration, update submission, validation, reputation scoring, aggregation, slashing, and audit. Participant influence on model aggregation is determined by a dynamic reputation score computed from the history of their contribution quality and stored immutably on-chain, making governance accountable by architectural construction rather than by institutional policy. The framework draws on the Edge AI Consensus system of Gupta et al., which demonstrated that blockchain-secured federated learning with reputation-weighted consensus achieves 94.5% model accuracy while tolerating up to 30% malicious participants and withstanding three simultaneous adversarial attack types without a central trusted server, establishing the empirical foundation on which the governance framework proposed here is built. We present formal specifications for each contract, analyze the game-theoretic properties of the staking and slashing mechanism, compare the proposed framework against conventional and hybrid blockchain-federated learning architectures, and discuss regulatory alignment with GDPR Article 22, the EU AI Act high-risk system requirements, and emerging AI governance standards.
Privacy-Preserving Federated Learning for Global Infectious Disease Surveillance: Challenges, Architectures, and Governance
The COVID-19 pandemic exposed critical gaps in global infectious disease surveillance infrastructure, including the inability to rapidly share patient data across jurisdictions due to sovereignty, privacy, and regulatory constraints. Federated learning offers a compelling paradigm in which local health institutions collaboratively train shared AI models by exchanging only model gradients rather than raw patient data. This paper provides a comprehensive review of privacy-preserving federated learning architectures for global disease surveillance, examining key technical challenges including data heterogeneity, statistical non-IID distributions, adversarial threats, and communication overhead. We propose a four-layer reference architecture encompassing local institution nodes, national federation coordinators, a global aggregation hub, and a governance layer. We further present a taxonomy of aggregation algorithms with suitability assessments for cross-border public health contexts, and propose a governance framework aligned with WHO International Health Regulations, GDPR, and HIPAA. Our analysis establishes federated learning as a viable foundation for a more resilient, privacy-respecting global pandemic intelligence infrastructure.
From Data Silos to Intelligent Insights: The Strategic Role of Master Data Management
Data silos remain a persistent barrier to effective decision-making and operational agility in modern enterprises. This paper investigates the role of Master Data Management (MDM) as a strategic enabler for integrating fragmented data environments and transforming isolated datasets into actionable insights. It begins by analyzing the impact of siloed data on organizational performance, including inconsistencies, redundancy, and limited visibility across business functions. The study then explores key MDM components—data governance, data quality management, and data integration—demonstrating how they collectively establish a unified and reliable data foundation. Building on this foundation, the paper highlights how MDM supports advanced analytics and insight generation by enabling seamless data accessibility and consistency across systems. It further examines implementation challenges such as legacy system integration, organizational resistance, and governance complexity, while proposing best practices including scalable architectures, cross-functional collaboration, and adaptive governance models. Through illustrative case-based insights, the research positions MDM as a critical framework for bridging the divide between fragmented data and strategic intelligence, ultimately empowering organizations to enhance decision-making, uncover hidden patterns, and sustain competitive advantage in a data-driven landscape.
Data-Centric Transformation: Leveraging Master Data Management for Intelligent Organizational Growth
In the era of data-driven decision-making, organizations increasingly rely on structured and reliable data ecosystems to remain competitive. This paper examines the critical role of Master Data Management (MDM) in enabling data-centric transformation by ensuring the accuracy, consistency, and integrity of core organizational data across distributed systems. Drawing upon recent literature and practical case insights, the study demonstrates how effective MDM frameworks enhance operational efficiency, improve data governance, and support advanced analytics and AI-driven decision processes. Furthermore, the paper explores the challenges associated with MDM adoption, including data silos, integration complexity, and governance constraints, while outlining best practices such as scalable architecture design, cross-functional alignment, and agile data governance strategies. Ultimately, the study positions MDM as a foundational pillar for intelligent enterprise growth, empowering organizations to unlock actionable insights, respond to dynamic market conditions, and sustain long-term digital innovation.
Machine Learning Frameworks for Automated Legal Compliance
The increasing complexity of regulatory environments across industries has made legal compliance a challenging and resource-intensive process. Machine Learning (ML) has emerged as a transformative technology capable of automating compliance monitoring, risk detection, and regulatory interpretation. This research presents a comprehensive exploration of machine learning frameworks designed for automated legal compliance, focusing on their architecture, methodologies, and real-world effectiveness. By integrating Natural Language Processing (NLP), predictive analytics, and hybrid rule-based systems, modern frameworks can interpret legal texts, identify compliance gaps, and provide actionable insights. Experimental evaluation demonstrates that ML-driven compliance systems significantly reduce legal risks, improve accuracy, and enhance operational efficiency compared to traditional manual approaches. The study also addresses ethical considerations, explainability challenges, and regulatory constraints that influence system design. Ultimately, this research highlights the growing role of intelligent systems in shaping the future of legal governance and compliance automation.
AI-Driven Regulatory Compliance in LegalTech Systems
Artificial Intelligence (AI) has emerged as a transformative force in LegalTech, particularly in automating and enhancing regulatory compliance processes. As regulatory environments grow increasingly complex across financial, healthcare, and data governance domains, organizations face significant challenges in ensuring consistent adherence to evolving rules. This research explores the design and implementation of AI-driven regulatory compliance systems that leverage machine learning, natural language processing, and knowledge representation techniques to interpret, monitor, and enforce compliance requirements. The study proposes an intelligent framework capable of extracting regulatory obligations, mapping them to organizational processes, and detecting potential violations in real time. Experimental evaluation demonstrates significant improvements in compliance accuracy, processing speed, and cost reduction compared to traditional rule-based systems. The findings highlight the potential of AI-driven LegalTech systems to reduce human error, enhance transparency, and provide scalable compliance solutions in dynamic regulatory landscapes.
Development of Functional Food Products Enriched with Plant-Based Bioactive Compounds
Functional foods enriched with plant-based bioactive compounds have emerged as a transformative area in food science due to their potential to deliver health benefits beyond basic nutrition. The increasing prevalence of lifestyle-related disorders such as obesity, diabetes, cardiovascular diseases, oxidative stress, and inflammatory conditions has intensified the demand for food products that not only nourish but also actively contribute to disease prevention and health promotion. Plant-derived bioactive compounds, including polyphenols, flavonoids, carotenoids, anthocyanins, phytosterols, dietary fibers, alkaloids, glucosinolates, and essential oils, have demonstrated strong antioxidant, antimicrobial, anti-inflammatory, anti-diabetic, cardioprotective, and anticancer potential in both in vitro and in vivo studies. However, the successful incorporation of these compounds into functional food matrices requires careful consideration of extraction efficiency, compound stability, bioavailability, sensory acceptance, processing compatibility, and shelf-life performance. This research paper presents a comprehensive investigation into the development of functional food products enriched with plant-based bioactive compounds, with emphasis on ingredient selection, formulation strategies, experimental optimization, physicochemical analysis, sensory profiling, storage stability, and health-related functionality. The findings confirm that plant-based bioactive enrichment represents a practical and scalable route toward the next generation of preventive, personalized, and health-promoting foods.
AI-Driven Prediction of Food Spoilage Using Smart Sensor Data and Machine Learning Models
Food spoilage remains one of the most persistent challenges in modern food systems, affecting public health, retail economics, supply chain efficiency, and environmental sustainability. Conventional spoilage assessment methods—such as manual inspection, microbiological culturing, and laboratory-based chemical analysis—are often slow, destructive, expensive, and impractical for continuous real-time monitoring. This paper presents a complete AI-driven framework for predicting food spoilage using smart sensor data and machine learning models. The proposed system integrates environmental and biochemical sensing variables, including temperature, relative humidity, pH, ammonia concentration, carbon dioxide concentration, volatile organic compounds (VOCs), and storage duration, to classify food freshness and estimate spoilage progression over time. The study focuses on a sensor-rich intelligent monitoring architecture designed for perishable food products such as meat, dairy, fruits, and vegetables. A structured experimental dataset was developed using simulated and realistic spoilage trajectories under cold-chain and ambient storage conditions. After preprocessing, feature engineering, and temporal labeling, multiple machine learning models were trained and compared, including Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Long Short-Term Memory (LSTM) networks.
Evaluating the Integration of AI-Driven Cybersecurity Frameworks in Agile Program Management for Medium and Large Organizations
The increasing complexity of cybersecurity threats, combined with the widespread adoption of AI technologies, necessitates robust program management frameworks in medium and large organizations. This study evaluates the integration of AI-based cybersecurity solutions within Agile program management practices, focusing on the balance between operational speed, assurance, and compliance. A mixed-method approach is employed, combining quantitative analysis of open-source cybersecurity datasets with qualitative survey insights from program managers. The research examines adversarial attacks and the effectiveness of AI-driven defense mechanisms, including Recurrent Neural Network (RNN) models for anomaly detection. Results indicate that organizations adopting AI-integrated frameworks achieve higher threat detection accuracy and maintain compliance standards without compromising program delivery speed. The findings provide actionable guidance for embedding security-first practices into Agile program lifecycles, offering valuable insights for program managers, cybersecurity teams, and organizational leadership.
A Unified Program Management Perspective on Cybersecurity, AI Adversarial Resilience, and Risk-Integrated Governance Across High-Compliance and Emerging Systems
The rapid adoption of digital technologies, artificial intelligence (AI), and agile methodologies has transformed program management in medium and large organizations, creating new challenges in cybersecurity, risk management, and regulatory compliance. This paper presents a unified framework for embedding cybersecurity, adversarial AI defenses, and DevSecOps practices into program management lifecycles while maintaining agility and assurance in high-compliance environments. We examine the effectiveness of existing cybersecurity program management frameworks, analyze AI adversarial threats and mitigation strategies, and propose security-first agile playbooks that integrate governance, risk management, and outcome-driven execution. Cross-domain applications, including telehealth, liquid biopsy diagnostics, banking, and public infrastructure programs, illustrate the framework’s scalability and practical relevance. The study emphasizes the evolving role of the program manager as a strategic enabler, bridging technical and business domains to enhance organizational resilience. Findings highlight the necessity of early, continuous, and integrated security measures supported by automation, stakeholder alignment, and adaptive governance. This comprehensive approach provides a foundation for resilient, efficient, and secure program management in complex and evolving digital environments.
A Unified Multi-Domain Deep Learning Framework for Predictive Modeling and Adaptive Recommendation in Healthcare, Finance, Autonomous Systems, and Media
This paper proposes a unified multi-domain deep learning framework for predictive modeling and adaptive recommendation across healthcare, finance, autonomous systems, and media platforms. The framework integrates multiple advanced models, including recurrent neural networks (RNN-LSTM), Deep Factorization Machines (DeepFM) with attention, YOLOv8 for object detection, Graph Neural Networks (GNNs), and Large Language Model (LLM)-based recommenders, to address diverse real-world challenges. The proposed architecture adopts a modular design that enables seamless integration of time-series prediction, computer vision, and recommendation modules within a single scalable system. Experimental results demonstrate that the framework achieves high accuracy, robustness, and adaptability, with strong performance in ventilator pressure prediction, financial forecasting, real-time vehicle detection, and personalized recommendation tasks. The findings highlight the effectiveness of combining heterogeneous deep learning techniques to support intelligent decision-making across domains. Furthermore, the study emphasizes the importance of scalability, flexibility, and cross-domain interoperability in modern AI systems. Future work will focus on enhancing model interpretability, incorporating multimodal data fusion, and developing resource-efficient architectures for real-time deployment in practical environments.
Assessing Strategic Budget Control as a Driver of Financial Stability in Nigerian Commercial Banking Institutions
This study examines whether strategic budget control can serve as a practical driver of financial stability in Nigerian commercial banking institutions. In many emerging banking environments, financial instability is rarely caused by one dramatic event alone; instead, it often grows quietly through weak planning, poor expenditure discipline, delayed performance review, and a lack of coordination between financial targets and operational decisions. Nigerian commercial banks operate in a setting shaped by regulatory oversight, macroeconomic volatility, liquidity pressure, inflation exposure, and rising expectations for risk governance. Within such a context, budget control is not merely an accounting routine. It becomes a management system that influences how institutions allocate funds, respond to shocks, control operating costs, and protect earnings quality over time. This paper therefore investigates the relationship between strategic budget control and financial stability, focusing on how structured planning, variance monitoring, internal review, and corrective decision-making affect institutional resilience.
Budgetary Control Practices and Financial Stability in Emerging Banking Systems: A Study of Nigerian Commercial Banks
This study examines how budgetary control practices influence financial stability in emerging banking systems, with a specific focus on Nigerian commercial banks. In many emerging economies, banks operate under difficult conditions shaped by inflationary pressures, policy uncertainty, exchange-rate instability, governance weaknesses, and growing regulatory demands. Within such an environment, budgetary control is no longer a routine accounting exercise; it becomes a strategic managerial tool for cost discipline, liquidity planning, risk containment, operational coordination, and long-term institutional survival. The paper investigates how planning, monitoring, expenditure control, variance review, and managerial accountability contribute to the stability and resilience of banks. The study adopts a simple empirical research design based on structured survey responses and managerial observations collected from staff members of selected Nigerian commercial banks. The analysis is supported by a conceptual review of regulatory and institutional literature on Nigerian banking and financial stability.
A Multi-Layered Zero-Trust Intrusion Detection Framework for Secure Software-Defined Networks Using Attention-Enhanced Deep Learning
Software-Defined Networking (SDN) has emerged as a transformative networking paradigm by separating the control plane from the data plane, enabling centralized programmability, scalability, and dynamic traffic management. Despite these advantages, the centralized control architecture introduces significant security challenges, including controller-targeted attacks, lateral movement threats, and sophisticated intrusion patterns. Traditional perimeter-based security models are no longer sufficient to protect SDN infrastructures against evolving cyber threats. This paper proposes a comprehensive multi-layered zero-trust security framework for SDN environments that integrates attention-enhanced deep learning for intrusion detection and incorporates adversarial robustness mechanisms. The proposed architecture enforces strict identity verification, continuous monitoring, and least-privilege access across all SDN layers, including application, control, and data planes. An attention-based Convolutional Neural Network (CNN) model is deployed for traffic classification and anomaly detection, improving feature discrimination and reducing false positive rates. Furthermore, adversarial defense strategies are embedded into the detection pipeline to enhance robustness against cross-domain and evasion-based attacks. Experimental validation using benchmark intrusion datasets demonstrates improved detection accuracy, enhanced resilience to adversarial perturbations, and scalable performance in dynamic SDN environments. The proposed framework provides a secure and adaptive solution for next-generation programmable networks, aligning with zero-trust security principles and intelligent threat mitigation strategies.
A Hybrid Zero-Trust and Attention-Driven Deep Learning Model for Intrusion Detection in Software-Defined Networks
Software-Defined Networking (SDN) has transformed modern network architecture by decoupling the control and data planes, enabling centralized management and programmability. However, this architectural flexibility introduces new attack surfaces, particularly at the controller layer, making SDN environments highly attractive targets for sophisticated cyber threats. Traditional perimeter-based security mechanisms are insufficient to protect dynamic and distributed SDN infrastructures. In response, Zero-Trust Security (ZTS) has emerged as a robust paradigm that assumes no implicit trust within the network and enforces continuous verification. This paper proposes a multi-layered zero-trust intrusion detection framework for SDN that integrates controller-level verification, flow-level behavioral analysis, and an attention-enhanced Convolutional Neural Network (CNN) model for anomaly detection. The proposed model builds upon the multi-layered defense strategy introduced in “Towards Zero Trust Security in SDN: A Multi-Layered Defense Strategy” and extends deep learning-based intrusion detection inspired by “Enhancing Intrusion Detection with CNN Attention using NSL-KDD Dataset.” Unlike traditional IDS approaches, the proposed framework integrates zero-trust principles with intelligent traffic analysis to ensure adaptive and real-time threat mitigation. Experimental evaluation conducted using the NSL-KDD dataset demonstrates that the proposed attention-based CNN achieves improved detection accuracy, reduced false positive rates, and enhanced robustness against evolving attack patterns. The results confirm that integrating zero-trust enforcement with intelligent intrusion detection significantly strengthens SDN security resilience. The proposed framework is suitable for next-generation enterprise, cloud, and 5G-enabled SDN deployments and provides a scalable foundation for secure programmable networks in 2026 and beyond.
Integration of Food Science Innovations into Public Nutrition Programs for Long-Term Health Outcomes
The growing burden of malnutrition, micronutrient deficiency, and diet-related chronic diseases has intensified the need for advanced strategies that integrate scientific innovation into public nutrition systems. Food science innovations, ranging from biofortification and functional food engineering to smart processing and digital nutrition monitoring, provide promising pathways to improve long-term population health outcomes. However, translating laboratory-driven food technology into large-scale public nutrition programs remains challenging due to socio-economic barriers, policy fragmentation, and implementation inefficiencies. This paper explores the structural integration of food science advancements into public nutrition frameworks, emphasizing sustainable health impact rather than short-term supplementation approaches. Using conceptual experimentation models and synthesized observational outcomes from intervention simulations, the study evaluates the effectiveness of innovation-driven nutrition programs in improving dietary quality, micronutrient retention, and community-level health indicators.
Dietary Diversity and Its Association with Socioeconomic Determinants of Health
Dietary diversity is a fundamental indicator of nutritional adequacy and population health, reflecting the quality and variability of food consumption across different food groups. This study explores the association between dietary diversity and socioeconomic determinants of health using a mixed analytical framework combining observational simulation, statistical correlation modeling, and hypothetical community-level experimental assessment. The research evaluates how income level, education, urbanization, food accessibility, and household stability influence dietary diversity patterns in developing populations. Results indicate that socioeconomic gradients strongly shape dietary behavior, with higher education and income levels correlating positively with dietary diversity scores. Experimental simulations using community dietary datasets suggest that improving socioeconomic empowerment and food accessibility infrastructure can significantly enhance nutritional outcomes and reduce micronutrient deficiency prevalence. The findings highlight the importance of integrated public health strategies that address both economic inequality and food system resilience to promote sustainable nutrition security.
Post-Harvest Loss Reduction Strategies to Improve Food Availability in Developing Regions
Post-harvest losses represent one of the most persistent barriers to achieving global food security, particularly in developing regions where agricultural supply chains remain fragile and resource-constrained. Significant quantities of produced food are lost between harvest and consumption due to inadequate storage, transportation inefficiencies, microbial spoilage, and mechanical damage during handling. These losses directly reduce food availability, increase market prices, and weaken nutritional access for vulnerable populations. Addressing post-harvest loss reduction requires a multi-layered strategy combining technological innovation, infrastructure improvement, and community-level awareness programs. This research investigates post-harvest loss mitigation approaches through a synthesis of experimental modeling, preservation technology assessment, and supply chain optimization. The study demonstrates that integrated preservation systems can significantly enhance food retention rates and improve regional food resilience.
Blockchain-Driven Data Integrity Models for Collaborative Intelligent Systems
Collaborative intelligent systems increasingly rely on distributed data generated by multiple agents, devices, and organizations. While such collaboration enhances decision-making capabilities and system intelligence, it simultaneously introduces significant challenges related to data integrity, trust, and transparency. Traditional centralized data validation mechanisms are often insufficient in decentralized environments where multiple stakeholders contribute and consume information. This paper presents a blockchain-driven data integrity model designed to ensure trustworthy data exchange in collaborative intelligent systems. The proposed approach integrates blockchain-based verification mechanisms with intelligent system architectures to create tamper-resistant, transparent, and auditable data pipelines. The research investigates how blockchain can be integrated with intelligent systems such as machine learning-based collaborative platforms, IoT-driven analytics environments, and distributed decision-making networks.
Decentralized Learning Architectures for Intelligent Systems Using Blockchain-Based Data Verification
The rapid growth of intelligent systems across domains such as healthcare, finance, autonomous transportation, and industrial automation has created an increasing demand for reliable and trustworthy data-sharing mechanisms. Traditional machine learning architectures often rely on centralized data repositories, which introduce vulnerabilities including data tampering, single points of failure, and privacy concerns. As distributed environments become more prevalent, there is a need for decentralized learning frameworks that ensure both data integrity and collaborative intelligence. Blockchain technology, with its decentralized ledger and immutable record-keeping capabilities, provides a promising foundation for secure and transparent data verification in intelligent systems.This research explores a decentralized learning architecture that integrates blockchain-based data verification with distributed machine learning processes. The proposed framework allows multiple intelligent agents to collaboratively train models while ensuring that all shared data and model updates are verified through blockchain consensus mechanisms
Beyond Safe Harbours: Rethinking AI Liability and Copyright Immunity Frameworks
The rapid evolution of artificial intelligence (AI) technologies has exposed significant limitations in existing legal frameworks, particularly regarding copyright immunity and liability protections. Traditional “safe harbour” provisions, designed to shield intermediaries from liability for user-generated content, are increasingly insufficient in addressing the complexities of autonomous AI decision-making and content creation. This paper critically examines current AI liability and copyright immunity regimes, highlighting gaps and inefficiencies, and proposes a reimagined framework that balances innovation, accountability, and rights protection. By analyzing comparative legal systems, case studies, and emerging policy proposals, this study provides actionable insights for lawmakers, industry stakeholders, and researchers to navigate the evolving AI landscape responsibly.