Open Access Research and Innovation Journal
Open Access Research & Innovation Journal (OARIJ) is a peer-reviewed, international scholarly journal dedicated to publishing high-quality research across multidisciplinary fields. The journal provides a global platform for researchers, academicians, scientists, and industry experts to share innovative findings, emerging trends, and advanced methodologies that contribute to scientific and technological progress. OARIJ follows a strict editorial and peer-review process to ensure the originality, relevance, and scientific value of all published manuscripts. The journal welcomes submissions from diverse research areas including Engineering, Computer Science, Information Technology, Applied Sciences, Social Sciences, Business Studies, Environmental Research, and emerging interdisciplinary fields. As an open-access journal, OARIJ ensures that all published articles are freely available to readers worldwide without any subscription or access barriers, promoting global knowledge sharing and wider research visibility. The journal also supports high indexing potential, citation growth, and academic impact through continuous quality improvement and digital archiving practices. OARIJ is committed to maintaining academic integrity, ethical publishing standards (COPE guidelines), and long-term sustainability of scholarly communication. With a focus on originality, clarity, and research contribution, OARIJ serves as a trusted resource for students, researchers, and professionals seeking credible and impactful academic content.
AI-Enhanced Cloud DevOps Frameworks for Intelligent Automation and Operational Efficiency
The rapid expansion of cloud computing technologies has significantly increased the complexity of software development, deployment, and infrastructure management processes, making efficient DevOps practices essential for modern organizations. While DevOps methodologies support continuous integration, continuous delivery, and operational agility, managing large-scale cloud environments requires more intelligent and adaptive solutions to address performance optimization, automation, and scalability challenges. This study explores the integration of Artificial Intelligence (AI) into cloud-based DevOps frameworks as a strategic approach for improving operational efficiency and enabling intelligent automation across dynamic cloud ecosystems. The paper examines how AI-driven technologies, including machine learning, predictive analytics, anomaly detection, and intelligent orchestration, enhance DevOps workflows by automating repetitive tasks, optimizing resource allocation, predicting system failures, and supporting proactive infrastructure management. Furthermore, the research investigates the impact of AI-enhanced DevOps on deployment speed, system reliability, cloud scalability, cost optimization, and service resilience. The study also discusses key implementation challenges such as data dependency, integration complexity, cybersecurity risks, model transparency, and organizational adaptation requirements. In addition, the paper highlights emerging trends including autonomous DevOps pipelines, self-healing cloud infrastructures, AI-driven monitoring systems, and predictive performance management frameworks.
Copyright (c) 2026 Open Access Research and Innovation Journal
Contrastive Self-Supervised Learning for Multi-Modal Representation Learning
Self-supervised learning has emerged as a powerful paradigm for leveraging large-scale unlabeled data, particularly in multi-modal settings involving images, text, and audio. This study focuses on contrastive self-supervised learning to develop robust joint representations across multiple data modalities by maximizing agreement between semantically related inputs while distinguishing unrelated pairs. The proposed approach enables effective cross-modal alignment and representation fusion without reliance on extensive labeled datasets. By learning shared embedding spaces, the model enhances performance in downstream tasks such as classification, retrieval, and clustering. Experimental analysis demonstrates that contrastive strategies significantly improve generalization, robustness, and cross-modal understanding compared to traditional supervised and unimodal approaches, highlighting their potential for advancing intelligent multi-modal systems.
Digital Equity in Global Health AI: Why Low-Income Countries Are Left Behind by Pandemic Intelligence Systems
Artificial intelligence systems for pandemic surveillance and outbreak detection have demonstrated substantial technical capability in high-income countries, yet they remain largely absent from the public health systems of low and middle-income countries, precisely the settings that bear the highest infectious disease burden. This paper examines the structural reasons for this digital equity gap, drawing on global health literature, health informatics research, and case evidence from deployed AI surveillance systems. We identify six structural deficit dimensions spanning health data infrastructure, compute capacity, human capital, research funding, regulatory environment, and algorithmic representativeness. We present a comparative analysis of major pandemic intelligence systems against equity benchmarks, and propose a set of technical, institutional, and governance interventions grounded in published evidence. The analysis demonstrates that the AI equity gap is self-reinforcing: the absence of LMIC data in training sets produces models that perform poorly in LMIC settings, reducing adoption and perpetuating the absence. Breaking this cycle requires deliberate design choices, targeted funding, and WHO-led governance reform.
Optimized Data Swapping for High-Performance Computing: Techniques and Applications
Efficient data management is critical for achieving high performance in modern computing systems. This paper explores optimized data swapping as a key mechanism for enhancing system performance across computational tasks, memory management, and resource allocation. By enabling intelligent, real-time data exchange between memory hierarchies and processing units, optimized data swapping reduces latency, minimizes bottlenecks, and improves throughput. The study examines algorithmic strategies and hardware-accelerated solutions that facilitate efficient data movement in diverse environments, including high-performance computing, database systems, and computer vision applications. Experimental results and case studies demonstrate the effectiveness of these techniques in improving system efficiency and scalability. Additionally, the paper highlights implementation challenges, best practices, and future directions for integrating optimized data swapping into performance-driven computing architectures. The findings underscore the critical role of efficient data exchange mechanisms in unlocking peak system performance and enabling next-generation computing solutions.
Copyright (c) 2025 Open Access Research and Innovation Journal
Active Learning in Image Classification: A Comprehensive Review and Analytical
Active Learning (AL) has emerged as an effective strategy for improving image classification performance while minimizing the cost of manual data annotation. By selectively identifying the most informative and uncertain samples for labeling, AL reduces the dependency on large labeled datasets and enhances model efficiency. This paper presents a comprehensive review of active learning techniques applied to image classification, including uncertainty-based, query-by-committee, diversity-based, and hybrid approaches. It critically evaluates their performance across various datasets and deep learning architectures. Additionally, the paper discusses key challenges such as scalability, selection bias, and integration with modern deep learning pipelines. Future research directions are also highlighted, emphasizing the need for adaptive, efficient, and robust active learning frameworks for real-world applications.
From Cyber Risk to Intelligent Resilience: A Unified Security-Driven Program Management Framework Integrating
The increasing convergence of cybersecurity threats, artificial intelligence vulnerabilities, and complex program delivery environments has exposed significant limitations in traditional program management and governance frameworks. Organizations operating in high-compliance sectors such as healthcare, finance, and public infrastructure—particularly in emerging economies—face persistent challenges in balancing security assurance, operational agility, and regulatory compliance. Existing frameworks often treat cybersecurity, AI security, and program governance as isolated domains, resulting in fragmented risk visibility and reduced resilience. This paper proposes a unified, security-driven program management framework that integrates cybersecurity effectiveness, adversarial AI defense, DevSecOps governance, and data-driven system security within a single lifecycle-oriented model. The framework redefines program management by embedding cyber risk intelligence across all phases of program execution, enabling continuous monitoring, adaptive decision-making, and proactive threat mitigation. The proposed approach incorporates adversarial AI resilience mechanisms to address emerging machine learning vulnerabilities, while DevSecOps integration ensures secure and continuous delivery of digital systems. Additionally, the framework aligns governance structures with agile principles, enabling organizations to maintain compliance without compromising delivery speed. A qualitative evaluation and comparative analysis demonstrate that the proposed model improves cross-domain security alignment, enhances AI robustness, and strengthens governance efficiency. The framework is particularly relevant for complex, resource-constrained environments where traditional security models are insufficient to manage evolving digital risks. Overall, this research contributes a scalable and interdisciplinary approach that bridges cybersecurity, artificial intelligence, and program management, offering a foundation for intelligent resilience in modern digital ecosystems.
From Risk to Resilience: Embedding Cybersecurity and AI Defense Mechanisms into Modern Program Management
The increasing reliance on digital technologies and artificial intelligence (AI) has significantly expanded the risk landscape for modern organizations, particularly in medium and large-scale enterprises operating under strict regulatory requirements. Traditional program management approaches often fail to address the dynamic nature of cybersecurity threats and the growing vulnerability of AI systems to adversarial attacks. This paper presents a comprehensive framework that transforms risk-centric program management into a resilience-driven model by embedding cybersecurity practices and AI defense mechanisms across the entire program lifecycle. The proposed approach integrates security controls, adversarial robustness techniques, and agile governance principles to ensure continuous protection, adaptability, and compliance. By aligning DevSecOps practices with strategic program objectives, the framework enables proactive risk identification, real-time monitoring, and rapid response to emerging threats. The study further explores the role of program managers as key drivers of security integration, responsible for bridging technical and governance domains. Through conceptual analysis and cross-domain applicability, including healthcare, financial systems, and digital infrastructure, the findings demonstrate that resilience-oriented program management significantly enhances system reliability, operational efficiency, and stakeholder trust. The proposed model offers a scalable and forward-looking solution for organizations aiming to secure complex digital ecosystems in 2026 and beyond.
Budget Accountability and Strategic Financial Control in Emerging Banking Systems: Empirical Evidence from Nigeria
This study examines the role of budget accountability and strategic financial control in strengthening the stability, efficiency, and institutional discipline of emerging banking systems, with a specific focus on Nigerian commercial banks. In many emerging economies, banking institutions operate under conditions of macroeconomic uncertainty, regulatory pressure, operational risk, fraud exposure, and volatile lending environments. Under such conditions, the quality of budgeting systems and the seriousness of financial control practices often determine whether banks merely survive or achieve sustainable performance. This paper investigates how accountability in budget preparation, budget execution, expenditure monitoring, internal audit compliance, and strategic control mechanisms shape financial outcomes in Nigerian banks. The study adopts a simplified empirical research design suitable for practical banking analysis rather than a purely technical or highly mathematical approach. Primary and secondary insights are combined to build a realistic picture of how internal financial discipline influences organizational performance.
Advances in Food Fortification Strategies to Combat Micronutrient Deficiencies
Micronutrient deficiencies represent a persistent global health challenge that disproportionately affects low- and middle-income populations, particularly children and women of reproductive age. This research paper explores recent advances in food fortification strategies, examines the efficacy of biofortification and industrial fortification, and evaluates programmatic and technological innovations aimed at improving nutritional outcomes. We conducted a multi-country intervention study that analyzed the impact of fortified staple foods and nutrient-enhanced crops on micronutrient status indicators such as serum ferritin, vitamin A levels, and anemia prevalence. Results demonstrate significant improvements in micronutrient biomarkers in intervention groups compared to controls, with effects varying by nutrient type and baseline deficiency levels. The study reveals critical insights into formulation optimization, consumer acceptance, and scalable implementation frameworks. The findings support accelerated adoption of evidence-based fortification policies and highlight future research directions for maximizing nutritional equity.
Sustainable Food Processing Technologies for Enhancing Nutrient Retention and Shelf Stability
Sustainable food processing technologies are emerging as a transformative pathway to simultaneously address global nutritional deficiencies, food waste, and environmental degradation. Traditional thermal and chemical preservation techniques, while effective in extending shelf life, often compromise nutrient density and sensory quality. In response, this study explores the integration of advanced non-thermal and energy-efficient processing methods to enhance nutrient retention and shelf stability while minimizing ecological impact. Technologies including high-pressure processing, pulsed electric fields, cold plasma, and solar-assisted dehydration were systematically evaluated across diverse food matrices. A controlled experimental framework was designed to quantify micronutrient retention, antioxidant stability, microbial inactivation efficiency, and energy consumption metrics. Results demonstrate that sustainable technologies can achieve up to 30–45% higher vitamin preservation compared to conventional heat treatments, while reducing energy usage by approximately 20–35%. The findings highlight the viability of sustainable processing models for scalable industrial implementation, contributing to food security and public health resilience. This research provides a systems-level perspective on aligning technological innovation with environmental and nutritional sustainability objectives.
Functional Food Development Using Plant-Based Bioactive Compounds for Improved Metabolic Health
Abstract Metabolic disorders, including obesity, insulin resistance, dyslipidemia, and type 2 diabetes, are among the most pressing public health challenges of the 21st century. Conventional pharmacological approaches, while effective, are often associated with long-term side effects and limited accessibility. Functional foods enriched with plant-based bioactive compounds offer a complementary and preventive strategy for improving metabolic health. This study proposes a systematic framework for the development, characterization, and validation of functional foods formulated with polyphenols, flavonoids, dietary fibers, and plant sterols derived from selected botanical sources. A prototype plant-based nutritional formulation was developed and evaluated through in vitro antioxidant and anti-inflammatory assays, followed by a 12-week randomized controlled trial involving prediabetic participants. Results demonstrated significant improvements in fasting glucose, HOMA-IR index, LDL cholesterol levels, and inflammatory biomarkers compared to a control group. Mechanistic analyses revealed modulation of AMPK signaling and downregulation of pro-inflammatory cytokines. These findings highlight the translational potential of plant-derived bioactives in functional food design for metabolic health enhancement.
From Protection to Monetization: Intellectual Property Financing in the Digital Economy
In the digital economy, intellectual property (IP) has evolved from a mechanism primarily designed for legal protection into a strategic financial asset capable of generating significant economic value. As technology-driven enterprises increasingly rely on intangible assets such as patents, copyrights, trademarks, and trade secrets, the role of intellectual property financing has become central to innovation-led growth. IP financing enables companies—particularly startups and technology firms—to leverage their intangible assets for capital generation, investment attraction, and market expansion. This paper examines the transition from traditional protection-oriented IP frameworks to modern monetization strategies that support financial innovation in the digital era. It explores how businesses utilize IP-backed loans, licensing agreements, securitization, and IP-based venture funding to transform intellectual assets into economic capital. The study also discusses the challenges associated with valuation, legal uncertainty, and regulatory gaps in IP financing. By analyzing emerging financial models and industry practices, this paper highlights the importance of developing robust IP governance structures that facilitate efficient monetization while protecting innovation. The findings suggest that effective IP financing frameworks can enhance investor confidence, accelerate technological development, and strengthen the global digital economy.
Platform Liability, AI Training Data, and the Limits of Safe Harbour Protection
The rapid expansion of artificial intelligence (AI) systems has transformed how digital platforms collect, process, and utilize vast amounts of data. A significant portion of this data, including copyrighted materials, personal information, and user-generated content, is used to train AI models that power recommendation systems, generative AI, and predictive analytics. Traditional legal frameworks governing platform liability, particularly safe harbour protections, were designed primarily for passive intermediaries that host or transmit content rather than actively analyze and transform it for machine learning purposes. This evolution raises complex legal questions about whether platforms that collect and use content for AI training should continue to benefit from immunity provisions. This paper examines the intersection of platform liability, AI training data practices, and the limitations of safe harbour doctrines. It explores how the increasing role of platforms in curating and processing large datasets challenges the foundational assumptions behind intermediary liability protections. The study further analyzes emerging legal debates regarding responsibility for copyright infringement, privacy violations, and algorithmic accountability. By evaluating regulatory developments and judicial interpretations, the paper highlights the growing tension between innovation incentives and the need to protect creators, users, and data subjects. Ultimately, it argues that existing safe harbour frameworks may require significant reform to address the new realities of AI-driven platforms.
Building Investor Trust in AI and Blockchain Ventures through Cooperative IP Governance
In the rapidly evolving domains of artificial intelligence (AI) and blockchain technologies, investors face unique challenges related to intellectual property (IP) management, regulatory uncertainty, and technological risk. Trust in these ventures is a critical factor influencing capital allocation, partnership formation, and long-term sustainability. This paper explores the role of cooperative IP governance frameworks in fostering investor confidence in AI and blockchain ventures. Cooperative IP governance emphasizes collaboration between investors, innovators, and regulatory bodies to ensure transparent, equitable, and enforceable IP practices. The study analyzes mechanisms such as joint IP ownership, shared licensing models, and cross-border compliance strategies that enhance transparency, reduce litigation risks, and encourage responsible innovation. Findings suggest that structured IP cooperation not only mitigates investment risk but also contributes to sustainable value creation, ethical innovation, and market credibility. This research highlights best practices for venture managers and investors seeking to navigate the complex intersection of technology, law, and finance.
Behind the Lens: Diversity, Inclusion, and Race in Contemporary Media Production Industries
The contemporary media production industries, encompassing film, television, digital streaming, and advertising, play a pivotal role in shaping societal perceptions of race, culture, and identity. Despite growing advocacy for diversity and inclusion, systemic disparities persist in both on-screen representation and behind-the-scenes production roles. This paper examines the current state of racial and cultural diversity within media industries, identifies barriers to equitable participation, and evaluates initiatives aimed at fostering inclusive practices. By analyzing case studies, industry reports, and scholarly literature, the research highlights the complex interplay between institutional structures, creative decision-making, and audience reception in promoting or hindering diversity. The study concludes with recommendations for policy, organizational, and creative strategies to enhance inclusivity and equitable representation in media production.
Reframing Race: Cultural Identity and Representation in Participatory Media
This paper explores how participatory media platforms reshape cultural identity and representation, challenging traditional narratives of race. With the rise of social media, digital storytelling, and collaborative content creation, marginalized communities gain agency to construct, contest, and disseminate their own narratives. This study examines the intersection of race, media, and identity, analyzing how participatory media empowers users to challenge stereotypes, negotiate cultural meaning, and cultivate transnational communities. Through case studies, theoretical frameworks, and critical analysis, this paper highlights the potential of participatory media to transform public discourse on race while also addressing the limitations and challenges inherent in digital platforms.
API-Driven Enterprise Integration Architectures: Strategic Frameworks for Scalable Digital Transformation in Contemporary Organizations
Enterprise integration has become a central pillar of digital transformation strategies as organizations increasingly operate within complex, distributed, and data-intensive technological ecosystems. Application Programming Interfaces (APIs) now function not merely as technical connectors but as strategic assets enabling interoperability, scalability, and innovation across enterprise systems. This research examines contemporary API-driven integration architectures, focusing on governance frameworks, scalability considerations, cloud interoperability, and security optimization. Recent developments indicate that structured API strategies significantly enhance enterprise agility by facilitating modular system design, real-time data exchange, and collaborative digital ecosystems. Prior studies highlight how enterprise integration frameworks supported by robust API governance improve operational resilience while enabling seamless connectivity between legacy infrastructures and emerging cloud-native platforms [1]. This paper extends that perspective by synthesizing recent architectural trends, strategic integration models, and governance practices expected to influence enterprise digital transformation through 2026 and beyond. The study proposes a conceptual strategic framework for API-centric enterprise integration, emphasizing lifecycle governance, hybrid cloud coordination, and security-first design principles. The findings suggest that organizations adopting mature API integration strategies demonstrate improved innovation capacity, reduced operational complexity, and enhanced ecosystem collaboration. The research contributes to ongoing academic discourse by offering a forward-looking analysis of enterprise integration architectures aligned with evolving technological landscapes
API-Centric Enterprise Integration: Strategies, Governance, and AI-Driven Digital Transformation
Enterprise integration has undergone a significant transformation with the widespread adoption of Application Programming Interfaces (APIs) as foundational components of digital ecosystems. API-centric integration enables organizations to achieve seamless interoperability between legacy systems, cloud platforms, and mobile applications while supporting modularity, scalability, and operational efficiency. This paper investigates contemporary enterprise integration strategies, emphasizing API-led architectures, governance frameworks, security mechanisms, and emerging AI-driven automation. Drawing upon recent research and best practices, the study demonstrates that structured API lifecycle management, zero-trust security, and AI-enhanced orchestration collectively enable sustainable digital transformation. The findings provide a comprehensive framework for organizations seeking to implement secure, scalable, and innovative enterprise integration strategies.
Periapical Osseous Healing Following Non-Surgical Endodontic Retreatment: A Cone-Beam CT–Based Comparative Evaluation of Nano-Hydroxyapatite With and Without Platelet-Rich Fibrin
Background: Successful periapical healing after non-surgical endodontic retreatment is critical for long-term tooth survival. Nano-hydroxyapatite (nHA) is widely used as a bone graft material, and its regenerative potential may be enhanced by platelet-rich fibrin (PRF), an autologous source of growth factors. Cone-beam computed tomography (CBCT) allows precise volumetric assessment of periapical bone healing. Objective: To compare periapical osseous healing following non-surgical endodontic retreatment using nHA alone versus nHA combined with PRF, assessed via CBCT. Methods: A randomized controlled study was conducted on patients requiring non-surgical endodontic retreatment for periapical lesions. Participants were allocated into two groups: Group A received nHA alone, and Group B received nHA combined with PRF. CBCT scans were obtained preoperatively and at 3, 6, and 12 months postoperatively to evaluate volumetric changes in periapical lesions. Healing outcomes were analyzed using appropriate statistical methods. Results: Both groups demonstrated significant periapical bone regeneration over time. However, Group B (nHA + PRF) showed accelerated and greater volumetric reduction of periapical lesions compared to Group A (nHA alone), indicating enhanced osteogenic healing. Conclusion: The adjunctive use of PRF with nHA in non-surgical endodontic retreatment significantly improves periapical osseous healing. CBCT-based volumetric evaluation provides a reliable method to assess bone regeneration. Incorporating PRF with nHA may offer a promising strategy for optimizing endodontic retreatment outcomes.
Influence of Advanced Endodontic Irrigation Dynamics on Coronal Dentin Energy States and Their Impact on Prosthodontic Adhesion Longevity
Advancements in endodontic irrigation technologies have significantly altered the physicochemical characteristics of coronal dentin, with emerging evidence suggesting a critical influence on prosthodontic adhesion longevity. This study explores the impact of advanced endodontic irrigation dynamics—including sonic, ultrasonic, laser-activated, and negative-pressure systems—on coronal dentin surface energy states and their subsequent effects on adhesive performance. Variations in irrigant activation, fluid shear forces, and cavitation phenomena modify dentin wettability, surface free energy, and chemical composition, thereby influencing resin infiltration and hybrid layer formation. Altered dentin energy states may enhance immediate bond strength; however, they can also predispose adhesive interfaces to hydrolytic and enzymatic degradation over time. Understanding the interplay between irrigation-induced surface modifications and prosthodontic adhesive systems is essential for optimizing long-term restorative outcomes. This abstract highlights current insights (2025 perspective) into irrigation–adhesion interactions and underscores the need for clinically integrated protocols that balance effective canal disinfection with durable coronal adhesion.
Three-Dimensional Irrigant Flow–Induced Microstructural Changes in Root Dentin and Their Consequences on Post-Core Stress Distribution Under Functional Loading
Background: The effectiveness of endodontic irrigation depends not only on chemical action but also on the three-dimensional (3D) hydrodynamics of irrigant flow within the root canal system. While advanced irrigation techniques enhance debris removal and disinfection, they may also induce microstructural alterations in root dentin, potentially influencing its mechanical behavior. These changes are clinically relevant in teeth restored with post-core systems, where stress distribution under functional loading plays a critical role in long-term survival. Objective: This study investigates 3D irrigant flow–induced microstructural changes in root dentin and evaluates their consequences on post-core stress distribution under functional loading conditions. Methods: A combined computational and imaging-based approach was employed. Three-dimensional computational fluid dynamics (CFD) modeling was used to simulate irrigant flow patterns and associated shear stresses within prepared root canals. Microstructural changes in dentin following irrigation were characterized using high-resolution micro-computed tomography (micro-CT), focusing on tubule morphology, surface erosion, and mineral density variations. These experimentally derived changes were incorporated into finite element models (FEM) of restored teeth with post-core systems to assess stress distribution under simulated masticatory loads. Results: Regions exposed to higher irrigant velocity and wall shear stress demonstrated notable dentin microstructural alterations, including tubule enlargement and localized surface erosion. Finite element analysis revealed that these changes significantly influenced stress distribution, with increased stress concentrations observed at the post–dentin interface and in the cervical root region under functional loading. Conclusion: Three-dimensional irrigant flow can induce measurable microstructural changes in root dentin that alter its biomechanical response when restored with post-core systems. Understanding the interplay between irrigant hydrodynamics, dentin integrity, and stress distribution may inform the optimization of irrigation protocols and restorative strategies to reduce fracture risk and improve clinical outcomes.
Privacy-Aware Performance Evaluation in Cloud HR Platforms Using Federated Learning
In the era of digital transformation, Human Resource (HR) platforms increasingly rely on cloud computing to process and analyze vast amounts of employee performance data. While cloud-based systems offer scalability and accessibility, concerns regarding data privacy and regulatory compliance remain significant. This paper proposes a privacy-aware framework for performance evaluation in cloud HR platforms using Federated Learning (FL). By enabling decentralized model training, the framework preserves sensitive employee data while allowing the extraction of meaningful performance insights. Experimental evaluations demonstrate the framework’s effectiveness in achieving high predictive accuracy with minimal privacy risk. The results indicate that federated models can match the performance of centralized approaches while adhering to stringent privacy requirements, offering a scalable and secure solution for modern HR analytics.
Efficient Performance Prediction in Cloud-Based HR Systems Using Federated Learning
Cloud-based Human Resource (HR) systems have transformed how organizations manage employee data and performance evaluation. While centralized systems offer convenience, they also pose challenges related to data privacy and security. Federated Learning (FL) has emerged as a promising paradigm that allows collaborative learning across distributed datasets without exposing sensitive employee information. This paper investigates the application of FL for efficient performance prediction in cloud-based HR platforms, focusing on privacy preservation, computational efficiency, and predictive accuracy. Experimental results demonstrate that the FL-based approach significantly improves model generalization and resilience to attacks, providing organizations with reliable and secure performance insights. The study also evaluates performance under different data distributions and simulated attack scenarios, emphasizing the role of FL in safeguarding both analytics quality and data privacy.
Cloud HR Performance Analytics Enabled by Federated Learning
Cloud-based Human Resource (HR) platforms have become central to modern workforce management, enabling organizations to collect, process, and analyze employee performance data at scale. While these platforms offer unprecedented analytical capabilities, they also introduce significant privacy, security, and compliance challenges due to the sensitive nature of employee information. Traditional centralized analytics architectures often require raw data aggregation, increasing the risk of data breaches and regulatory violations. Federated Learning (FL) emerges as a compelling paradigm that allows collaborative model training across distributed HR data sources without exposing raw employee records. This paper presents a comprehensive federated learning–enabled framework for cloud HR performance analytics, emphasizing privacy preservation, scalability, and resilience. Through a detailed system design, experimental evaluation, and performance analysis, we demonstrate how federated learning can achieve high analytical accuracy while maintaining strict data locality constraints. The findings suggest that FL-based HR analytics not only improve trust and compliance but also enhance the robustness and sustainability of cloud HR ecosystems.
Data-Private Employee Analytics in Cloud HR Systems Through Federated Learning
The rapid adoption of cloud-based Human Resource (HR) systems has enabled organizations to collect, process, and analyze employee performance data at unprecedented scale. While these systems enhance workforce planning, talent management, and productivity forecasting, they also introduce critical privacy and security concerns due to centralized data aggregation. Employee data, which often includes sensitive behavioral, productivity, and evaluation metrics, becomes an attractive target for breaches and misuse. This paper proposes a data-private employee analytics framework for cloud HR systems using Federated Learning (FL), where predictive models are trained collaboratively across distributed organizational nodes without transferring raw employee data. The proposed approach preserves privacy by design while maintaining analytical accuracy and scalability. We present a complete system architecture, learning methodology, and experimental evaluation using synthetic and real-world HR datasets. The results demonstrate that federated models achieve performance comparable to centralized approaches while significantly reducing data exposure risks. This study highlights how federated learning can serve as a foundational technology for trustworthy, scalable, and regulation-compliant employee analytics in modern cloud HR environments.
Enhancing User Trust and Payment Security in eCommerce Transactions Using Behavioral Biometrics
The rapid expansion of eCommerce has introduced unprecedented convenience for consumers while simultaneously amplifying risks associated with fraudulent activities and identity theft. Traditional security mechanisms, such as passwords and OTPs, often fail to provide robust protection against sophisticated cyber threats. Behavioral biometrics, which analyze unique patterns in user behavior—such as typing rhythm, mouse movement, and device handling—emerge as a promising approach to enhance both security and trust in online transactions. This paper presents an in-depth study on the integration of behavioral biometrics within eCommerce platforms, analyzing their effectiveness in mitigating fraud and improving user confidence. Experimental results demonstrate significant improvements in fraud detection accuracy and a measurable increase in perceived trust among users. Finally, the study outlines practical considerations for deploying behavioral biometric systems, highlighting the balance between security, user experience, and privacy compliance.
A Comparative Study of Biometric Techniques for Secure and Fraud‑Resistant eCommerce Transactions
Biometric authentication has emerged as a pivotal security paradigm in enabling secure, fraud‑resistant eCommerce transactions. As online commerce expands globally, traditional security measures such as passwords and tokens remain vulnerable to attacks, driving the need for more reliable identity verification mechanisms. This paper presents a comprehensive comparative study of key biometric techniques — including fingerprint recognition, facial recognition, iris scanning, and behavioral biometrics — in the context of eCommerce security. We explore their strengths, limitations, real‑world applicability, and performance metrics under various attack scenarios. Experimental results highlight trade‑offs between security, usability, and system overhead, demonstrating that multimodal biometric systems offer significantly higher resilience against spoofing and fraud. Finally, we discuss future research trajectories and practical deployment challenges for next‑generation biometric authentication systems in eCommerce ecosystems.
Efficient Keypoint Generation from Unstructured Qualitative Data via Large Language Models
The exponential growth of unstructured qualitative data across domains—ranging from customer feedback and interviews to social media posts and open-ended survey responses—poses significant challenges for data-driven decision-making. Traditional methods for qualitative analysis often rely on manual coding, thematic categorization, or statistical text mining, which are time-consuming and lack contextual precision. Recent advancements in Large Language Models (LLMs) such as GPT-4 and Gemini have introduced new possibilities for efficiently extracting meaningful keypoints from massive text datasets. This research investigates an LLM-driven framework for keypoint generation, focusing on optimizing interpretability, accuracy, and efficiency in processing unstructured qualitative information. Using a comparative evaluation between transformer-based architectures and fine-tuned LLMs, this study demonstrates that LLM-assisted keypoint extraction not only enhances contextual relevance but also achieves up to 87% semantic fidelity compared to human-coded benchmarks. Experimental results indicate that integrating prompt optimization and self-reflective reasoning improves extraction quality while maintaining computational efficiency. The study concludes with an in-depth discussion of the implications for research, business intelligence, and knowledge management systems, paving the way for scalable qualitative analysis through AI-driven automation.
AI-Assisted Qualitative Data Interpretation Using LLMs for Key Point Detection
Qualitative data interpretation plays a pivotal role in understanding human perceptions, behaviors, and contextual nuances across domains such as social sciences, healthcare, education, and marketing. However, the manual coding and interpretation of qualitative data remain labor-intensive, subjective, and time-consuming. This research introduces an AI-assisted approach utilizing Large Language Models (LLMs) to automate key point detection in qualitative data. The study explores how transformer-based architectures, specifically GPT-style LLMs, can identify, cluster, and summarize key insights from unstructured textual data with high interpretive accuracy. Through empirical evaluation on multiple open-ended survey datasets and interview transcripts, the system demonstrates its capability to emulate human-level thematic analysis while maintaining scalability and consistency. The proposed framework combines semantic embeddings, context-sensitive reasoning, and probabilistic filtering to enhance interpretability and trustworthiness. Results reveal that the LLM-based interpretation mechanism achieves a 91.4% alignment rate with human-coded themes, suggesting its potential for revolutionizing qualitative data analysis.
From Broadcast to Blockchain: A Historical Analysis of Television’s Socio-Cultural Influence in Saudi Arabia and Its Transition into the Digital Age
Television has played a foundational role in shaping socio-cultural norms, collective identity, and public discourse in Saudi Arabia since its formal introduction in the mid-twentieth century. As a centralized broadcast medium, television historically functioned as both a cultural mirror and a regulatory instrument, reflecting state values while simultaneously influencing social behavior, religious interpretation, and national cohesion. Over time, technological evolution, globalization, and digital convergence have fundamentally altered the role of television, transforming it from a unidirectional medium into an interactive, decentralized, and increasingly personalized ecosystem. This paper presents a historical and analytical examination of Saudi television’s socio-cultural influence, tracing its evolution from traditional broadcasting to digitally mediated platforms and blockchain-enabled media infrastructures. Through qualitative historical analysis and a small-scale experimental audience engagement study, the research evaluates how shifts in media technology have reshaped cultural representation, identity formation, and power structures in Saudi society. The findings suggest that while traditional television once reinforced unified cultural narratives, the digital transition has enabled pluralism, audience agency, and decentralized content validation, marking a critical redefinition of media authority in the Kingdom’s digital age.