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.
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
Copyright (c) 2026 Open Access Research and Innovation Journal
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.
Copyright (c) 2025 Open Access Research and Innovation Journal
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.
DeepBiometricFusion: Multi-Modal Defense Against DeepFake Payment Attacks
DeepFake-driven payment fraud has rapidly evolved into one of the most serious threats in biometric authentication ecosystems, particularly across digital banking, fintech wallets, eCommerce checkout systems, and remote KYC workflows. Modern generative models can synthesize highly convincing facial videos, overlay spoofed identities, manipulate voiceprints, and exploit biometric vulnerabilities to trigger fraudulent financial transactions. This research introduces DeepBiometricFusion, a unified multi-modal defense architecture that integrates facial dynamics, voiceprint signatures, micro-texture liveness cues, and cross-modal temporal coherence to detect DeepFake payment attacks with high reliability. The proposed system leverages multi-stream deep neural networks, cross-attention fusion, and adversarial anomaly detection to jointly validate biometric consistency across modalities. Experiments conducted on three large-scale datasets—DFSecurePaySet, MultiModal-LiveID, and VidVoice-FraudBench—demonstrate the superior robustness of our framework compared to existing uni-modal and dual-modal solutions. The results show that DeepBiometricFusion achieves a 98.4% attack detection accuracy with a substantially reduced false acceptance rate (FAR), establishing it as a practical and secure solution for financial-grade authentication.
Learning Algorithms for Resource-Constrained Markets: Pricing, Inventory, and Stochastic Optimization
Resource-constrained markets, characterized by limited inventory, budget restrictions, and dynamic demand, present significant challenges for decision-makers. Traditional optimization approaches often fail to account for uncertainty and temporal changes in demand, leading to suboptimal allocation and pricing decisions. Recent advances in learning algorithms, particularly those based on stochastic optimization, multi-armed bandits, and reinforcement learning, offer powerful frameworks for adapting to these uncertainties. This paper explores the application of these learning algorithms to pricing, inventory management, and resource allocation in dynamic markets. By integrating adaptive learning strategies with stochastic optimization models, businesses can enhance revenue, minimize wastage, and better respond to changing market conditions. Through detailed analysis and illustrative examples, this work highlights how learning-driven approaches can transform traditional resource-constrained market operations.
Trustless AI Agents Verifiable Decision Logs via Blockchain and Federated Governance
The integration of Artificial Intelligence (AI) in high-stakes, decentralized ecosystems necessitates a new paradigm of trust, transparency, and verifiability. In critical domains such as finance, healthcare, and edge computing, AI agents must operate autonomously while maintaining transparent and auditable decision-making processes. This paper introduces the concept of trustless AI agents autonomous systems that function in federated environments with verifiable decision logs recorded on blockchain infrastructure. We propose a hybrid architecture where federated AI models perform localized training across distributed nodes, and their decision outcomes are hashed, time-stamped, and immutably recorded using blockchain mechanisms. This design ensures tamper-resistant audit trails, decentralized validation, and enhanced accountability. The proposed model addresses challenges such as privacy leakage, model poisoning, and traceability of policy enforcement, while promoting regulatory compliance and system transparency. The architecture leverages consensus protocols like Proof-of-Authority and Delegated Proof-of-Stake for efficient and scalable log validation. Advanced cryptographic tools such as zero-knowledge proofs and homomorphic encryption are explored to preserve data confidentiality without compromising verifiability. Our experimental evaluation validates the framework’s feasibility across metrics including model accuracy, decision latency, and blockchain throughput in federated learning setups. The system is designed to be adaptable with emerging AI ethics guidelines and supports public trust through decentralized oversight. This work establishes the foundation for a new generation of AI governance frameworks that are secure, explainable, and inherently auditable, without relying on centralized authorities.