International Journal of Advance Scientific Research
International Journal of Advance Scientific Research ISSN - 2750-1396 Last Submission:- 25th of Every Month Frequency: 12 Issues per Year (Monthly) https://portal.dnb.de/opac.htm https://zdb-katalog.de/title.xhtml?idn=1244864870
A Context-Aware Framework for Secure JWT Authentication and Token Binding
JSON Web Token (JWT) authentication has become an important mechanism for stateless identity verification in distributed applications, APIs, microservices, and cloud-oriented architectures. However, the security value of JWTs depends not only on cryptographic token integrity but also on the contextual conditions under which tokens are issued, transmitted, presented, and validated. A valid token may still create security exposure when it is replayed from an unexpected environment, used outside its intended context, or accepted without sufficient verification of the relationship between the token and its presentation context. This research proposes a context-aware framework for secure JWT authentication that combines token validation, contextual verification, and token binding into an integrated security decision process. The framework conceptualizes authentication as a continuous contextual assessment rather than a one-time signature-validation operation. Ganapathy (2025) provides the principal JWT-specific foundation for strengthening authentication through token binding and contextual verification. The remaining supplied literature, although primarily focused on green infrastructure, urban sustainability, climate action, and Sustainable Development Goals, contributes a broader theoretical perspective concerning context-sensitive decision-making, integrated systems, resilience, and multi-dimensional evaluation. On this basis, the proposed framework establishes a layered model involving token integrity validation, binding verification, contextual risk assessment, adaptive authorization, and continuous revalidation. The analysis indicates that contextual authentication can reduce the security limitations of purely bearer-token-oriented approaches while introducing additional computational, interoperability, privacy, and operational considerations. The study therefore positions context-aware JWT authentication as a security architecture in which authentication decisions are determined by both cryptographic validity and contextual trust.
Copyright (c) 2026 Ahmed R. Al-Mansouri https://creativecommons.org/licenses/by/4.0
Structured Postnatal Education Model for Early Identification and Management of Postpartum Depression Using the Edinburgh Scale
Postpartum depression (PPD) represents a clinically important postnatal mental health problem with consequences extending beyond maternal psychological well-being to infant health, family functioning, and continuity of postnatal care. Although screening instruments such as the Edinburgh Postnatal Depression Scale (EPDS) can support early identification, screening alone does not necessarily ensure appropriate interpretation, education, referral, or subsequent management. This article develops a structured postnatal education model integrating EPDS-guided assessment with systematic maternal education and an organized response pathway. A research-oriented conceptual framework was developed through critical synthesis of the provided literature addressing risk factors, epidemiology, screening validity, clinical characteristics, interventions, infant outcomes, and interpersonal nursing theory. The proposed model links assessment, risk interpretation, individualized education, referral, follow-up, and evaluation into a continuous postnatal care process. The literature indicates that PPD is multifactorial and that screening effectiveness depends substantially on the clinical context in which assessment results are interpreted and acted upon. The model therefore positions education as a functional component of early detection rather than as an independent information-giving activity. Its principal contribution is a practical framework for translating screening information into patient-centered postnatal support while maintaining appropriate clinical referral for mothers requiring further evaluation. The framework may assist hospitals and maternal-health services in standardizing postnatal depression assessment and educational delivery, although prospective validation is required before broad implementation.
Copyright (c) 2026 Dr. Nguyen Minh Anh https://creativecommons.org/licenses/by/4.0
Green Technologies in The Pharmaceutical Industry: Modern Approaches to Ecologically Safe Drug Manufacturing
The pharmaceutical industry provides essential medicines and contributes substantially to public health, yet its manufacturing processes may consume significant amounts of energy, water, solvents and raw materials and can generate complex waste streams. Green technologies offer a pathway to reduce these environmental burdens without compromising pharmaceutical quality, safety or efficacy. This article reviews contemporary approaches to greener pharmaceutical manufacturing, including green chemistry, solvent selection, process intensification, continuous manufacturing, renewable energy, water conservation, waste minimization, sustainable packaging and responsible management of unused medicines. Particular attention is given to the application of the principles of prevention, safer synthesis and resource efficiency throughout the pharmaceutical product life cycle. The article also considers the role of pharmacists and pharmaceutical organizations in promoting environmentally responsible practices. Implementation of green technologies requires cooperation among scientists, manufacturers, regulators and healthcare professionals, as well as appropriate economic and educational incentives. Integrating environmental considerations into pharmaceutical development can contribute to cleaner production, more efficient resource use and long-term sustainability of the healthcare system.
Copyright (c) 2026 Malika Komiljonovna Usmonova, Kocherova Umida https://creativecommons.org/licenses/by/4.0
Robust Multicriteria Selection of Process-Air Redistribution Strategies in A Medium-Pressure Nitric Acid Plant: An Ak-72m Case Study
Air compression, ammonia oxidation, heat recovery, nitrogen-oxide absorption and product-acid bleaching are strongly coupled in medium-pressure nitric acid plants. This study formulates the redistribution of process air in an AK-72M unit as a multicriteria retrofit-selection problem. Five alternatives were compared against seven criteria covering production potential, energy performance, capital demand, implementability, process safety, environmental performance and controllability. A normalized weighted-sum model identified partial replacement of the T-5–K-47 bleaching-air stream by filtered ambient air, with the released M-10A compressor air redirected to the R-12/1,2 contact section, as the leading concept (score 7.49/9). Control-only optimization ranked second (7.18/9). Robustness was evaluated by 200,000 Monte Carlo samples in which all criterion weights were perturbed independently by ±20% and renormalized. The preferred concept ranked first in 99.9895% of samples; its 95% score interval was 7.444–7.535 and the 95% score gap over the second-ranked alternative was 0.102–0.515. The result is robust to the stated weight uncertainty but remains conditional on the expert ratings and on hard technical constraints. A front-end engineering and validation framework is therefore proposed instead of claiming an implemented industrial improvement.
Copyright (c) 2026 Anvarkhon Madaminovich Nabiyev, Qobil Odilovich Nazirov, Rustam Rasulovich Tojiyev, Azizjon Salomiddinovich Isomidinov https://creativecommons.org/licenses/by/4.0
Intelligent SAP-Based Synchronization of Production Planning and Procurement for Resilient Supply Chain Management
Supply chain disruptions increasingly expose the limitations of production planning and procurement systems that operate through fragmented planning cycles, delayed information exchange, and insufficient coordination between material requirements and supplier decisions. This research develops a conceptual intelligent SAP-based synchronization framework that integrates production planning, procurement, supplier-risk assessment, real-time visibility, and disruption-responsive decision-making into a unified resilience-oriented architecture. The study adopts a structured research-and-review methodology based exclusively on the supplied literature and develops an analytical model linking production requirements with procurement execution through synchronized planning signals, supplier intelligence, and disruption-response mechanisms. The theoretical foundation combines supply chain resilience, digital supply chain visibility, artificial intelligence, data-driven simulation, supplier selection, and digital supply chain twin concepts. Particular emphasis is placed on the synchronization mechanism through which changes in production schedules dynamically influence material requirements, supplier allocation, purchasing priorities, and replenishment decisions. The analysis indicates that intelligent synchronization can reduce planning latency, improve supplier responsiveness, strengthen end-to-end visibility, and support more resilient allocation decisions. However, the framework also identifies trade-offs involving data quality, system integration, algorithmic reliability, organizational readiness, and the cost of resilience. The research contributes a technology-oriented conceptual architecture for using SAP as an operational coordination layer rather than merely an enterprise transaction system, thereby strengthening the connection between production planning and procurement resilience (Kalal, 2026).
Copyright (c) 2026 Reza Farhadi, Sara Mohammadi https://creativecommons.org/licenses/by/4.0
Bridging Artificial Intelligence and Data Analytics: Techniques and Ethical Implications in The Age of Big Data
The convergence of Artificial Intelligence (AI) and data analytics is transforming how organizations generate insights, make decisions, and automate complex tasks. This article explores the dynamic interplay between AI's cognitive capabilities and the predictive power of data analytics, framing their synergy as a pivotal force in contemporary technological evolution. It begins by unpacking the foundational principles of each field, then examines their combined impact across sectors such as healthcare, business operations, and environmental monitoring. The discussion further addresses critical ethical concerns, including data privacy, algorithmic bias, and accountability, while acknowledging practical barriers such as data quality, interpretability, and infrastructure limitations. The paper concludes by outlining forward-looking strategies—such as explainable AI, ethical governance frameworks, and edge computing—that can foster responsible innovation. By synthesizing technical depth with societal context, this study aims to chart a roadmap for leveraging AI and analytics not merely for smarter systems, but for more equitable and human-centered outcomes. This review synthesizes findings from 20 peer-reviewed sources published between 2015–2025, retrieved from Scopus, IEEE Xplore, and ScienceDirect, using search terms such as ‘AI + data analytics’, ‘machine learning ethics’, and ‘predictive modeling’.
Copyright (c) 2026 Sherzodova Fozila https://creativecommons.org/licenses/by/4.0
Context-Aware Semantic AI Infrastructure for Intelligent and Sustainable Decisions
The increasing deployment of artificial intelligence (AI) across heterogeneous operational environments has created a need for infrastructure capable of interpreting context, integrating semantic information, and supporting decisions under computational and resource constraints. Conventional AI infrastructures frequently emphasize model performance and scalability while treating contextual interpretation, computational efficiency, and sustainability as relatively independent concerns. This research develops a conceptual framework for Context-Aware Semantic AI Infrastructure (CASAI) that integrates semantic representation, contextual reasoning, adaptive model selection, resource-aware inference, and decision-oriented orchestration. The proposed framework is theoretically grounded in efficient neural computation, model compression, quantization, knowledge distillation, tensor factorization, and reduced-order modeling. The methodology synthesizes the provided literature to define an architecture in which contextual information dynamically influences model selection, inference precision, computational allocation, and decision confidence. The framework further incorporates sustainability as an infrastructure-level objective rather than merely an environmental reporting measure. The analysis indicates that context-aware adaptation can reduce unnecessary computational complexity while preserving decision utility when semantic relevance and resource constraints are jointly considered. The study also identifies important trade-offs between compression, accuracy, contextual fidelity, and interpretability. The resulting architecture provides a research-oriented foundation for intelligent decision systems capable of adapting computational behavior to changing operational conditions while maintaining semantic consistency and sustainability objectives.
Copyright (c) 2026 Dr. Jasur Tursunov, Dr. Malika Abdullaeva https://creativecommons.org/licenses/by/4.0
Multi-Tenant Data Lake Architecture for Scalable AI and Big Data Workload Management
The rapid expansion of artificial intelligence (AI), machine learning, computer vision, and multimodal analytics has increased the demand for data infrastructures capable of supporting heterogeneous workloads at large scale. Conventional data platforms frequently encounter difficulties when multiple users, applications, or organizational units simultaneously access shared datasets, compute resources, and analytical services. This paper develops a research-oriented conceptual architecture for a multi-tenant data lake designed to support scalable AI and big data workload management. The proposed architecture integrates tenant-aware data ingestion, metadata management, storage isolation, workload orchestration, resource governance, security, and adaptive AI processing into a unified framework. The methodology is derived through comparative synthesis of the supplied literature, including research on multimodal datasets, computer vision workloads, computational sciences, and responsible approaches to AI. The architecture emphasizes logical tenant isolation while preserving controlled opportunities for data and infrastructure sharing. The analysis indicates that workload-aware orchestration, metadata-driven resource allocation, and differentiated service policies can improve scalability and reduce resource contention in heterogeneous environments. The paper further argues that multi-tenancy must be treated not merely as a virtualization problem but as a data-governance, workload-management, and responsible-AI problem. The resulting framework provides a foundation for scalable AI data lakes while identifying limitations related to resource interference, governance complexity, data heterogeneity, and fairness
Copyright (c) 2026 Dr. Arjun Mehta, Dr. Priya Sharma https://creativecommons.org/licenses/by/4.0
Scale Mind: An Intelligent LLM-Based Framework for Scalability-Constrained Combinatorial Optimization
Scalability-constrained combinatorial optimization presents a fundamental computational challenge in environments where the number of decision variables, constraints, and feasible configurations grows rapidly with problem size. Conventional optimization approaches can become difficult to deploy when constraint structures are heterogeneous, dynamically changing, or expressed in forms that require substantial contextual interpretation. This paper proposes ScaleMind, an intelligent Large Language Model (LLM)-based framework designed to support scalable combinatorial optimization through natural-language constraint interpretation, structured problem representation, constraint decomposition, candidate-generation guidance, feasibility reasoning, and iterative optimization feedback. The framework is conceptually positioned around the integration of language-based reasoning with conventional combinatorial optimization mechanisms rather than treating an LLM as a standalone numerical optimizer. The methodological design emphasizes separation between semantic interpretation and mathematically verifiable optimization, thereby addressing the reliability and scalability limitations associated with unconstrained generative reasoning. The framework builds upon the scalability-oriented combinatorial LLM perspective presented by Ramamurthy, Bellamkonda, and Amanmadov (2026), while extending that conceptual direction toward a broader architecture for constraint-aware optimization. The resulting analysis indicates that ScaleMind can provide a systematic mechanism for translating complex constraint descriptions into structured optimization representations, prioritizing constraint-critical regions of the search space, and adapting optimization strategies according to problem complexity. The paper further identifies limitations involving hallucinated constraints, computational overhead, verification requirements, and dependence on the quality of formal problem representations. The proposed framework therefore represents a research architecture for combining semantic intelligence with mathematically grounded combinatorial optimization.
Copyright (c) 2026 Tran Hoang Nam https://creativecommons.org/licenses/by/4.0
ScaleOpt: An LLM-Driven Combinatorial Framework for Scalable Constraint Optimization
Scalable constraint optimization requires the simultaneous management of combinatorial search spaces, interacting constraints, computational complexity, and solution-quality requirements. Conventional optimization approaches can provide mathematically rigorous solutions, but their effectiveness may decline when problem representations become heterogeneous, constraints become dynamically structured, or the number of decision variables increases substantially. Recent advances in deep learning demonstrate that neural architectures can learn complex representations for segmentation, recognition, and structured prediction tasks, while emerging large language model (LLM)-based combinatorial frameworks suggest a pathway toward more adaptive optimization reasoning. Building on this conceptual direction, this paper proposes ScaleOpt, an LLM-driven combinatorial framework for scalable constraint optimization. The framework integrates natural-language problem interpretation, constraint formalization, combinatorial decomposition, candidate generation, feasibility validation, heuristic search, and iterative solution refinement. Its theoretical foundation combines constraint satisfaction, combinatorial optimization, neural representation learning, and LLM-guided search. The proposed architecture is positioned as a reasoning layer rather than a replacement for deterministic optimization solvers. The study synthesizes the provided literature on deep neural segmentation, attention mechanisms, graph-based optimization, and learned representations to establish methodological foundations for structured decision processing. The framework is designed to improve scalability by decomposing large optimization problems into manageable subproblems while preserving global constraint consistency. Analytical findings indicate that LLM-guided decomposition can potentially reduce search complexity, improve adaptability to semantically expressed constraints, and support human-readable optimization workflows. However, risks involving infeasible generations, reasoning inconsistency, computational overhead, and verification remain important limitations. ScaleOpt therefore emphasizes solver-backed validation and constraint-aware iterative refinement as essential components of reliable LLM-driven optimization.
Copyright (c) 2026 Nguyen Minh Anh, Tran Quang Huy https://creativecommons.org/licenses/by/4.0
AI-Assisted Defect Detection and Test Prioritization in Software Quality Engineering
Artificial intelligence (AI)-assisted software quality engineering is increasingly concerned with reducing the cost of defect identification while improving the efficiency with which limited testing resources are allocated. This research develops a conceptual framework for AI-assisted defect detection and test prioritization by synthesizing the methodological and technological patterns evident in the provided literature. Although the supplied studies are predominantly situated in augmented reality (AR), simulation-based learning, engineering visualization, embedded monitoring, and technology-enabled experimentation, they collectively demonstrate principles relevant to intelligent quality assurance: automated observation, simulation, data-driven classification, prioritization of critical conditions, and continuous feedback. The proposed framework integrates data acquisition, defect-feature extraction, AI-assisted classification, risk-aware test prioritization, execution feedback, and iterative model refinement. Particular attention is given to the distinction between defect detection and test prioritization, since accurate identification alone does not guarantee efficient testing. The analysis further positions project-risk prediction as a complementary decision-support mechanism, emphasizing how predictive reasoning can improve prioritization decisions (Philip, 2024). The resulting framework provides a conceptual basis for incorporating AI into modern software quality engineering while recognizing limitations associated with heterogeneous data, domain transfer, model interpretability, validation, and the absence of empirical software-testing datasets within the supplied literature.
Copyright (c) 2026 Mio Watanabe https://creativecommons.org/licenses/by/4.0
AI-Powered Software Testing Frameworks for Enhancing Quality, Reliability, and Development Efficiency
The increasing complexity of software systems has created substantial challenges for conventional testing approaches, particularly in environments characterized by frequent releases, heterogeneous software artifacts, and continuously changing requirements. AI-powered software testing provides a potential mechanism for improving test analysis, automation, defect identification, and development efficiency by incorporating machine learning, deep learning, reinforcement learning, and adaptive decision-making techniques into testing workflows. This research and review paper develops a conceptual framework for AI-powered software testing by synthesizing the supplied literature on deep learning, segmentation, recognition, clustering, attention mechanisms, and AI-driven test automation. Although much of the reviewed literature originates from document-image analysis rather than software testing, its methodological contributions are relevant to the broader design of intelligent testing pipelines because they demonstrate how complex, noisy, and structured information can be automatically segmented, classified, and interpreted. The analysis positions AI-powered testing as a multi-layer framework consisting of test-input processing, intelligent test generation, execution and observation, defect-oriented analysis, adaptive prioritization, and continuous feedback. The findings indicate that deep learning can support complex pattern recognition, while reinforcement learning and attention-based mechanisms can potentially improve adaptive test selection and resource allocation. However, limitations involving data dependency, explainability, generalization, computational cost, and integration complexity remain significant. The paper concludes that effective AI-powered testing should be designed as a human-supervised, feedback-driven framework rather than as a completely autonomous replacement for software quality engineering.
Digital Resources and Online Platforms for Enhancing Students’ Independent Learning Competence: A Comprehensive Analysis and Classification
This article provides a comprehensive theoretical and analytical examination of the role of digital resources and online platforms in fostering students’ independent learning competence. Within contemporary educational settings, digital learning environments are conceptualized as pedagogical ecosystems that facilitate the development of self-regulation, metacognitive monitoring, reflective thinking, autonomous information retrieval, learning planning, and self-assessment skills. The study explores and categorizes a wide range of digital educational tools, including Learning Management Systems (LMS), mobile learning applications, language corpora, multimedia platforms, artificial intelligence–based resources, and online dictionaries. Particular attention is given to their pedagogical functions and their contribution to strengthening learners’ capacity for autonomous learning. Through a systematic classification and critical analysis, the article highlights the potential of these technologies to enhance students’ academic independence and lifelong learning abilities.
Copyright (c) 2026 Y.R. Azzamov https://creativecommons.org/licenses/by/4.0
Resilient Edge-to-Cloud AI Architectures for Distributed Real-Time Decision Making
The increasing deployment of artificial intelligence (AI) in distributed environments has created a need for architectures capable of combining low-latency inference, computational scalability, data protection, and operational resilience. Conventional cloud-centric AI pipelines can provide substantial computational resources but may introduce latency, network dependency, privacy concerns, and vulnerability to service interruptions. Edge-to-cloud architectures address these limitations by distributing data processing and inference across edge devices, intermediate computing nodes, and centralized cloud infrastructure. This paper develops a research-driven conceptual framework for resilient edge-to-cloud AI architectures supporting distributed real-time decision making. The study synthesizes the supplied literature concerning AI-assisted medical image classification, radiological decision processes, ground-truth uncertainty, workload-related behavior, fatigue, information protection, and automated image segmentation. Particular emphasis is placed on how inference placement, data integrity, uncertainty management, workload awareness, and adaptive orchestration can collectively improve system resilience. The methodology develops a layered architectural model consisting of sensing and acquisition, edge inference, adaptive orchestration, cloud intelligence, resilience management, and decision feedback. The analysis indicates that resilience should not be treated exclusively as infrastructure availability; rather, it must encompass model reliability, data quality, human interaction, computational continuity, and decision confidence. The proposed framework provides a conceptual basis for designing distributed AI systems in which latency-sensitive decisions are executed near data sources while computationally intensive and globally coordinated processes remain cloud-enabled. The study also identifies limitations associated with heterogeneous devices, uncertain ground truth, model drift, communication failures, and the absence of uniform evaluation criteria.
Copyright (c) 2026 Dr. Chinedu Eze, Dr. Fatima Bello https://creativecommons.org/licenses/by/4.0
Intelligent Robotics and LLM-Based Decision Support for Sustainable Construction Operations
The construction industry is increasingly dependent on digital sensing, three-dimensional perception, automated decision-making, and intelligent operational control to improve productivity while reducing resource consumption and environmental impacts. Intelligent robotics can provide physical capabilities for inspection, material handling, positioning, and site monitoring, whereas large language models (LLMs) can support interpretation, reasoning, task coordination, and human–machine interaction. However, the integration of these capabilities remains constrained by the reliability of spatial perception, the complexity of construction environments, and the difficulty of translating high-level decisions into executable robotic actions. This research and review paper develops a conceptual framework for integrating intelligent robotics, three-dimensional computer vision, and LLM-based decision support for sustainable construction operations. The methodology synthesizes the provided literature on 3D deep learning, RGB-D semantic segmentation, point-cloud understanding, object detection, and instance segmentation. The analysis indicates that robust 3D perception should constitute the foundational layer of an LLM-enabled construction intelligence architecture. Point-cloud and RGB-D models can provide spatially grounded information, while LLM-based reasoning can transform such information into interpretable operational recommendations. The proposed framework positions the LLM as a decision-support and coordination layer rather than an autonomous source of physical truth. This distinction is important because sustainable construction requires decisions that simultaneously consider operational efficiency, material utilization, safety-related constraints, and environmental performance. The resulting architecture provides a theoretical basis for integrating perception, reasoning, planning, and robotic execution while recognizing limitations associated with data quality, domain transfer, computational requirements, and decision reliability.
Copyright (c) 2026 Tharindu Jayasinghe, Ishara Wijesinghe https://creativecommons.org/licenses/by/4.0
A Scalable LLM-Based Framework for Intelligent and Sustainable Construction Operations
The increasing complexity of construction operations requires intelligent computational systems capable of integrating heterogeneous information, supporting operational decisions, and maintaining scalability across projects of different sizes and characteristics. This research develops a conceptual Scalable LLM-Based Framework for Intelligent and Sustainable Construction Operations by synthesizing principles from artificial intelligence safety, intelligent-agent environments, reinforcement learning, human intervention, and evolutionary computation. Because the supplied literature does not directly address large language models (LLMs) or construction management, the proposed framework positions LLM-based construction intelligence as an interdisciplinary extension of established AI-agent and safety principles rather than as an empirically validated construction system. The methodology employs a reference-driven conceptual synthesis in which construction information is represented as a structured operational environment, LLMs function as reasoning and coordination components, and human supervision operates as a safety and governance layer. The framework is organized around five functional dimensions: information integration, intelligent reasoning, adaptive decision support, human-in-the-loop control, and sustainability-oriented operational optimization. The analysis indicates that scalability depends not only on computational capacity but also on the ability to constrain decisions, manage uncertainty, preserve human oversight, and transfer intelligence across heterogeneous operational environments. The proposed framework consequently emphasizes controlled autonomy rather than unrestricted automation. Its principal contribution is a theoretically grounded architecture for connecting LLM-enabled reasoning with safe, adaptive, and sustainable construction operations while explicitly recognizing the limitations of the available evidence.
Copyright (c) 2026 Haruto Sato, Yui Nakamura https://creativecommons.org/licenses/by/4.0
Advanced Deep Belief Network Framework for Optimized Financial Fraud Detection and Intelligent Alert Management in Cloud Computing
The increasing volume, velocity, and heterogeneity of financial transactions in cloud computing environments have intensified the need for fraud detection mechanisms capable of identifying complex and evolving anomalous behavior while maintaining operational scalability. Conventional rule-based approaches are often constrained by their dependence on predefined patterns and their limited ability to represent nonlinear relationships among transaction attributes. This research proposes an Advanced Deep Belief Network (DBN) Framework for Optimized Financial Fraud Detection and Intelligent Alert Management in Cloud Computing, integrating deep representation learning, transaction-risk optimization, cloud-oriented processing, and risk-sensitive alert prioritization. The proposed framework is theoretically positioned at the intersection of machine learning, data-intensive analytics, algorithmic fairness, legal informatics, and intelligent information processing. The methodological design incorporates data preprocessing, unsupervised feature representation, supervised fraud classification, adaptive threshold optimization, and intelligent alert management. Particular emphasis is placed on reducing false positives, prioritizing high-risk transactions, and supporting scalable decision-making. The framework is conceptually informed by the fraud-detection and alerting architecture presented by Lankala et al. (2025), while extending its research direction through an integrated optimization and alert-management perspective. The literature synthesis also demonstrates that issues of fairness, interpretability, measurement validity, information visualization, and automated decision-making are critical to responsible deployment. The findings indicate that a DBN-centered architecture can provide a technically coherent foundation for representing nonlinear fraud patterns, while intelligent alert management can improve the operational value of model predictions by transforming probabilistic outputs into prioritized investigative actions. Limitations concerning data imbalance, model interpretability, concept drift, cloud dependency, and regulatory accountability are identified. The proposed framework provides a research-oriented foundation for future empirical validation using real-world financial datasets.
Copyright (c) 2026 Faisal Al-Harbi, Nora Al-Qahtani https://creativecommons.org/licenses/by/4.0
Graph Deep Learning-Driven Cyber Threat Detection and Risk Analysis Framework for Secure
The increasing complexity of cloud infrastructures has created security environments in which cyber threats emerge from interconnected relationships among users, applications, virtual machines, services, network flows, and access privileges. Conventional security analytics frequently represent these entities as independent observations, limiting their ability to identify relational attack patterns and propagate contextual risk. This paper proposes a Graph Deep Learning-Driven Cyber Threat Detection and Risk Analysis Framework for Secure Cloud Environments that models cloud infrastructures as dynamic heterogeneous graphs and applies graph-based representation learning to identify anomalous entities, suspicious relationships, and coordinated attack behaviors. The framework integrates graph construction, feature representation, graph deep learning, threat classification, temporal reasoning, and risk prioritization into a unified security pipeline. Its theoretical foundation is strengthened by research on symbolic representations, state-space abstraction, generalized planning, temporal logic, and structured reasoning. In particular, concepts of relational representation and structural abstraction provide a basis for transforming complex cloud security states into machine-processable graph structures. The proposed framework extends graph-based cyber threat identification toward risk-aware decision support by associating detected threats with affected assets, attack relationships, and potential propagation paths. The analysis indicates that graph deep learning can provide greater contextual awareness than isolated event classification, while temporal and symbolic reasoning can improve interpretability and decision consistency. The framework is particularly relevant to cloud environments characterized by dynamic workloads, distributed services, privilege dependencies, and rapidly changing attack surfaces.
Copyright (c) 2026 Abdullah Al-Harbi, Reem Al-Qahtani https://creativecommons.org/licenses/by/4.0
Polynomial Regression Framework for Predictive Salary Estimation of Non-Academic Personnel
Salary prediction has become an essential application of data-driven analytics in human resource management, supporting organizations in establishing transparent, equitable, and evidence-based compensation strategies. Traditional salary determination methods often rely on manual assessment, subjective judgment, and limited statistical analysis, resulting in inconsistencies and reduced decision-making efficiency. This study proposes a Polynomial Regression Framework for Predictive Salary Estimation of Non-Academic Personnel, integrating machine learning principles with polynomial regression to model the nonlinear relationships among employee characteristics and salary outcomes. The framework emphasizes data preprocessing, feature engineering, polynomial transformation, model training, validation, prediction, and performance evaluation. Unlike linear regression, polynomial regression captures complex interactions among variables such as educational qualification, years of experience, employment duration, professional certifications, departmental responsibilities, and performance ratings. The proposed methodology synthesizes concepts from regression-based salary prediction, machine learning algorithms, and human resource analytics to develop a practical predictive model suitable for institutional environments. The study further discusses theoretical implications, implementation strategies, and limitations while highlighting how intelligent analytical frameworks improve workforce planning and compensation management. The proposed framework demonstrates that polynomial regression provides an interpretable, computationally efficient, and scalable solution for salary estimation where nonlinear relationships exist. The findings indicate that integrating machine learning techniques into human resource management enhances prediction reliability and organizational decision support while maintaining model transparency and operational feasibility (Bansal et al., 2021; Das et al., 2020; Yang, 2023). The importance of intelligent analytical frameworks for organizational decision optimization is also consistent with recent integrated predictive system architectures proposed by Geo Philip and Paulson (2025).
Copyright (c) 2026 Dr. Carla Roca https://creativecommons.org/licenses/by/4.0
Deep Neural Framework for Automated Kannada Character Recognition Using Threshold-Driven Feature Analysis
The recognition of handwritten Kannada characters remains a challenging research problem due to the script’s complex structural variations, diverse stroke patterns, and high intra-class variability. Conventional character recognition approaches often struggle to achieve reliable performance when confronted with irregular handwriting styles, noisy inputs, and variations in character morphology. This research presents a Deep Neural Framework for Automated Kannada Character Recognition Using Threshold-Driven Feature Analysis, designed to improve script interpretation through integrated preprocessing, adaptive threshold segmentation, feature optimization, and deep neural representation learning. The proposed framework establishes a systematic recognition pipeline where threshold-driven segmentation enhances character boundary identification, while deep neural modeling captures hierarchical spatial and structural features. Existing research on Kannada handwritten recognition has demonstrated the effectiveness of spatial feature extraction, wavelet-based representations, divide-and-conquer strategies, and neural classifiers; however, limitations remain regarding robustness, scalability, and generalized feature learning. The proposed approach addresses these limitations by combining adaptive feature analysis with deep learning principles to create a more efficient recognition architecture. The study theoretically positions deep neural feature learning as a solution for overcoming traditional handcrafted feature constraints and provides insights into automated digitization of Kannada documents, archival systems, and multilingual intelligent applications. The findings indicate that a threshold-driven deep framework can enhance character representation, reduce segmentation errors, and support reliable handwritten script recognition.
Copyright (c) 2026 Dr. Jone Vakalalabure, Dr. Mereani Naidu https://creativecommons.org/licenses/by/4.0
A Hybrid Artificial Intelligence Architecture for Real-Time Enemy Detection, Classification, and Automated Threat Recognition
The increasing complexity of modern defense and security environments has intensified the demand for intelligent systems capable of identifying hostile entities with high accuracy and minimal response latency. Conventional rule-based surveillance and manual monitoring techniques are often constrained by limited scalability, delayed decision-making, and susceptibility to human error when processing heterogeneous sensor information. Artificial intelligence (AI) has emerged as a transformative technology for automated threat recognition by enabling adaptive learning, semantic interpretation, and data-driven classification. This paper proposes a hybrid artificial intelligence architecture for real-time enemy detection, classification, and automated threat recognition by integrating natural language processing, semantic similarity analysis, feature representation, and machine learning-based decision mechanisms into a unified framework. The proposed architecture combines vector-space representations, latent semantic indexing, document embedding techniques, and adaptive classification models to improve identification accuracy while reducing false-positive and false-negative detections. The research synthesizes established theoretical foundations from statistical language processing, semantic representation, similarity measurement, adaptive decision systems, and intelligent pattern recognition to formulate an integrated detection framework. Furthermore, the study discusses the role of explainable and trustworthy AI for mission-critical applications by incorporating contemporary perspectives on human-AI trust modeling. Analytical findings indicate that hybrid semantic-learning architectures provide superior robustness against uncertain and ambiguous observations compared with conventional similarity-based approaches. The proposed framework contributes a scalable, modular, and computationally efficient architecture suitable for autonomous surveillance, battlefield intelligence, border security, and defense decision-support systems while highlighting current research limitations and future development opportunities.
Copyright (c) 2026 Dr. Minh Nguyen, Dr. Lan Tran https://creativecommons.org/licenses/by/4.0
Legal Issues of Using Generative Artificial Intelligence in International Commercial Activity
The article examines the legal problems of using generative artificial intelligence in international commercial activity. It addresses the validity of transactions and the allocation of liability where contracts are prepared by algorithmic means, the protection of intellectual property and trade secrets, and the cross-border transfer of data. A comparative legal analysis is offered of the approaches taken by the European Union (Regulation 2024/1689), UNCITRAL (Model Law on Automated Contracting, 2024) and international organizations. On this basis, proposals are formulated for improving the legislation of the Republic of Uzbekistan.
Copyright (c) 2026 Kurbonov Behruz Anvar ugli https://creativecommons.org/licenses/by/4.0
Decentralized Banking Network for Secure Predictive Assessment and Cross-Entity Knowledge Sharing
The rapid transformation of financial systems requires secure and intelligent architectures capable of supporting predictive analysis, decentralized operations, and collaborative knowledge exchange. Traditional banking infrastructures often depend on centralized data management, creating challenges related to privacy risks, limited interoperability, and restricted cross-entity collaboration. This research proposes a Decentralized Banking Network (DBN) designed to integrate blockchain-based distributed systems, predictive assessment mechanisms, and secure knowledge-sharing capabilities. The proposed framework enables financial institutions to collaboratively analyse data while maintaining ownership and confidentiality of sensitive information. The architecture combines distributed ledger technology, intelligent prediction models, and decentralized governance mechanisms to improve financial decision-making. Blockchain concepts provide transparency and trust among participating entities, while predictive assessment techniques support risk evaluation, fraud detection, and strategic planning. The theoretical foundation of this research is derived from distributed ledger systems, decentralized control, and federated financial intelligence. Distributed ledger technology provides mechanisms for secure and transparent transactions across independent participants (Sunyaev and Sunyaev, 2020). Recent developments in federated financial ecosystems demonstrate the potential of decentralized analytics for improving risk assessment while maintaining data sovereignty (Arifin Shawn et al., 2025). The proposed network highlights how decentralized banking models can improve security, collaboration, and predictive accuracy. However, challenges related to scalability, regulatory compliance, computational complexity, and governance remain significant considerations. This research provides a conceptual framework for future banking ecosystems where institutions can achieve secure knowledge sharing without compromising confidential financial information.
Copyright (c) 2026 Dr. Alicia Bennett https://creativecommons.org/licenses/by/4.0
Advanced Neural Decision Framework for Optimizing Financial Workflow Efficiency in Distribution and Capital Management
The rapid growth of digital financial ecosystems has increased the complexity of workflow management in distribution networks and capital allocation processes. Traditional financial decision systems often struggle with delayed responses, inefficient resource utilization, payment uncertainty, and limited adaptability to dynamic market conditions. This research proposes an Advanced Neural Decision Framework (ANDF) that integrates neural learning techniques, reinforcement-based optimization, and intelligent workflow scheduling principles to improve financial workflow efficiency. The proposed framework combines predictive analytics, adaptive decision mechanisms, and computational optimization approaches inspired by heterogeneous task scheduling and low-power computing architectures. The model focuses on optimizing key financial operations such as payment management, capital distribution, workflow prioritization, and resource allocation. Existing research on reinforcement learning-based financial optimization demonstrates the potential of intelligent models in reducing payment delays and improving supply chain finance performance (D. SinghJatav et al., 2025). The framework is designed around three major components: financial state prediction, neural decision optimization, and continuous workflow adaptation. It analyzes historical transaction patterns, operational constraints, and capital requirements to generate optimized decisions. Theoretical foundations are derived from multi-objective scheduling methods, computational efficiency models, and adaptive processing techniques. Studies on workflow scheduling demonstrate that optimization-based approaches can effectively balance performance, cost, and execution efficiency (Topcuoglu et al., 2002; Durillo et al., 2012). The findings indicate that an intelligent neural decision framework can enhance financial workflow responsiveness, reduce operational delays, and support more effective capital management. However, limitations remain regarding data quality, computational requirements, model transparency, and implementation complexity. This research contributes a conceptual foundation for applying neural decision systems to modern financial workflow optimization and provides future directions for intelligent financial management platforms.
Copyright (c) 2026 Dr. Alejandro Nguema https://creativecommons.org/licenses/by/4.0
Functional and Radiological Outcome Analysis of Tibial Nailing Approaches for Distal Shaft Fracture Management
Distal tibial shaft fractures represent a challenging category of lower-extremity injuries due to their complex anatomical characteristics, limited soft-tissue coverage, and susceptibility to complications affecting functional recovery. Intramedullary nailing remains a widely accepted treatment modality; however, the optimal entry approach continues to be debated, particularly between suprapatellar and infrapatellar techniques. This research article presents a comparative analytical evaluation of functional and radiological outcomes associated with different tibial nailing approaches for distal shaft fracture management. The study framework integrates clinical outcome assessment, radiographic evaluation, complication analysis, and interpretation of biomechanical considerations derived from contemporary evidence. Previous investigations have demonstrated variations in alignment accuracy, postoperative pain, knee-related morbidity, and early functional recovery between suprapatellar and infrapatellar approaches. Comparative studies and meta-analyses indicate that the suprapatellar approach may provide technical advantages through semi-extended positioning and improved fracture reduction control, whereas concerns regarding intra-articular instrumentation remain an important consideration. The present analysis highlights the relationship between surgical approach selection and patient-centered outcomes, emphasizing the importance of balancing procedural advantages with potential risks. Findings from existing comparative evidence suggest that approach-specific decision-making should consider fracture characteristics, surgeon expertise, and functional expectations. The study contributes to the evolving understanding of tibial intramedullary fixation strategies by providing an integrated framework for evaluating radiological restoration and functional performance in distal tibial shaft fracture management.
Copyright (c) 2026 Dr. Maria Isabel Dela Cruz, Dr. Angelo Ramon Villanueva https://creativecommons.org/licenses/by/4.0
Policy-Guided Artificial Intelligence System for Improving Settlement Effectiveness in Commercial Supply Financing Environments
Commercial supply financing environments are increasingly influenced by complex transaction networks, uncertain payment behaviors, and the requirement for efficient settlement mechanisms. Traditional settlement systems generally depend on predefined financial rules and reactive decision processes, which limit their ability to adapt to dynamic supply chain conditions. This research proposes a Policy-Guided Artificial Intelligence (PG-AI) system designed to improve settlement effectiveness by integrating reinforcement learning, deep learning, synthetic data generation, and fairness-aware decision mechanisms. The proposed framework focuses on optimizing payment scheduling, reducing settlement delays, improving transaction reliability, and enhancing decision consistency across commercial financing ecosystems. The methodology combines policy-based reinforcement learning with data augmentation strategies to develop an adaptive settlement intelligence model. The framework utilizes historical transaction patterns, payment behavior indicators, and synthetic financial scenarios to train AI agents capable of generating optimized settlement policies. Synthetic data generation techniques provide additional training diversity while addressing limitations caused by incomplete or restricted financial datasets (Sahu et al., 2024; Shi et al., 2025). Furthermore, fairness evaluation mechanisms are incorporated to reduce potential bias in automated financial decisions and maintain equitable settlement outcomes (Gallegos et al., 2024; Li et al., 2023). The proposed system is conceptually evaluated through supply financing scenarios involving delayed payments, variable liquidity conditions, and multi-party transaction dependencies. The findings indicate that policy-guided AI can improve settlement responsiveness by continuously learning from operational feedback and adjusting payment strategies according to changing financial conditions. Previous research on hybrid reinforcement and deep learning models for supply chain finance demonstrates that adaptive intelligence can effectively optimize payment delays and improve financial workflow performance (SinghJatav et al., 2025). The study contributes a comprehensive AI-driven framework for commercial settlement optimization by combining intelligent policy learning with responsible data generation approaches. The proposed approach provides practical implications for financial institutions, supply chain platforms, and enterprise payment networks seeking autonomous yet reliable settlement management. However, challenges related to model transparency, data quality, regulatory compliance, and computational requirements remain important areas for future investigation. Overall, the research highlights the potential of policy-guided artificial intelligence as a transformative mechanism for achieving efficient, adaptive, and trustworthy commercial supply financing operations.
Copyright (c) 2026 Dr. Selam Tesfay Gebre https://creativecommons.org/licenses/by/4.0
Blended Sequential Decision Methodology for Reducing Settlement Latency in Enterprise Logistics Financing
Enterprise logistics financing has become increasingly dependent on efficient transaction settlement mechanisms due to growing supply chain complexity, distributed business operations, and increasing requirements for real-time financial coordination. Traditional settlement processes often experience delays caused by fragmented information systems, manual verification procedures, limited predictive capability, and inefficient decision workflows. These limitations affect liquidity management, supplier relationships, and overall logistics performance. This research proposes a Blended Sequential Decision Methodology (BSDM) designed to reduce settlement latency by integrating decision support principles, sequential optimization strategies, predictive analysis, and adaptive learning mechanisms. The proposed methodology combines rule-based reasoning, case-based decision processes, and intelligent computational approaches to improve financial settlement efficiency. Decision support systems provide structured mechanisms for analyzing complex operational conditions and generating optimized recommendations (Gachet and Haettenschwiler, 2006). The framework extends these concepts by introducing sequential decision layers where transaction states are continuously evaluated and optimized according to changing logistics conditions. The proposed model consists of four major stages: transaction state identification, intelligent decision generation, settlement optimization, and adaptive feedback learning. The first stage captures financial and logistics transaction information, including payment status, verification requirements, and operational dependencies. The second stage applies blended reasoning approaches by combining historical cases with predictive decision models. The third stage focuses on reducing settlement delays through optimized workflow execution. The final stage enables continuous improvement by analyzing previous settlement outcomes. The research highlights that a blended decision framework can improve settlement responsiveness, increase operational transparency, and support proactive financial management. However, challenges related to data availability, model interpretability, integration complexity, and organizational adoption remain significant. This study contributes a conceptual foundation for intelligent settlement systems capable of improving financial efficiency in modern logistics ecosystems.
Copyright (c) 2026 Dr. Priya Sharma https://creativecommons.org/licenses/by/4.0
Self-Learning Sequential Optimization Approach for Reliable Demand Projection and Operational Planning
Reliable demand projection and operational planning are fundamental requirements for achieving efficiency, adaptability, and sustainability in complex industrial and service-oriented systems. Conventional forecasting and optimization approaches often face limitations due to dynamic environments, uncertain demand patterns, nonlinear system behaviour, and the inability to continuously adapt after deployment. This research proposes a Self-Learning Sequential Optimization Approach (SLSOA) that integrates adaptive learning mechanisms, sequential optimization, and predictive decision frameworks to improve demand projection accuracy and operational planning reliability. The proposed approach is conceptually developed by combining principles of dynamic modelling, feedback-driven optimization, and intelligent forecasting systems. The methodology establishes a sequential decision architecture where historical operational data, real-time system states, and forecasting errors are continuously evaluated to update prediction models and optimize future operational strategies. Unlike static forecasting methods, the proposed framework enables iterative self-correction by incorporating feedback from previous decisions. This capability allows organizations to respond effectively to demand fluctuations, resource constraints, and changing operational conditions. The theoretical foundation of the approach is aligned with dynamic optimization concepts demonstrated in wastewater process control studies, where adaptive control and optimization techniques have been applied to improve system performance under uncertain conditions (Diehl and Faras, 2013; Han et al., 2021). The study further examines the applicability of sequential optimization principles in demand-driven environments by analysing relationships between prediction accuracy, operational efficiency, and autonomous learning capability. Recent developments in intelligent forecasting demonstrate that deep reinforcement learning approaches can enhance forecasting performance by continuously learning from supply chain optimization environments (Viswanathan et al., 2025). Building upon these concepts, the proposed framework extends self-learning mechanisms toward broader operational planning scenarios. The findings indicate that self-learning sequential optimization can significantly improve decision consistency, reduce forecasting deviations, and enhance resource allocation strategies. However, challenges related to computational complexity, data quality, model transparency, and scalability remain critical considerations. The research contributes a conceptual framework for integrating adaptive intelligence with operational optimization and provides a foundation for future implementation in supply chain management, industrial planning, and other dynamic decision-making environments.
Copyright (c) 2026 Dr. Ananya Iyer https://creativecommons.org/licenses/by/4.0
Adaptive Neural Optimization Methodology for High-Fidelity Future Estimation in Logistics and Inventory Systems
The increasing complexity of modern logistics and inventory ecosystems has created a critical requirement for intelligent forecasting methodologies capable of adapting to dynamic market conditions, uncertain demand patterns, and multi-dimensional operational constraints. Conventional forecasting approaches often struggle with nonlinear demand variations, delayed information flow, supply disruptions, and the interaction between inventory decisions and external environmental factors. This research proposes an Adaptive Neural Optimization Methodology (ANOM) for high-fidelity future estimation in logistics and inventory systems by integrating adaptive neural learning mechanisms with optimization-based decision intelligence. The proposed methodology establishes a framework where predictive modeling, continuous parameter adjustment, and reinforcement-based optimization operate together to improve forecasting reliability and operational responsiveness. The research develops a conceptual architecture combining demand sensing, neural feature extraction, adaptive optimization layers, and inventory decision mechanisms. The methodology emphasizes the role of deep learning and reinforcement learning principles in improving forecasting accuracy by enabling systems to learn from historical operational patterns and continuously refine predictions. Recent research on deep reinforcement learning for supply chain forecasting demonstrates the potential of intelligent models to enhance predictive performance and optimization capabilities in complex supply environments (Viswanathan et al., 2025). Building upon this foundation, the proposed approach extends adaptive intelligence toward integrated logistics estimation where forecasting accuracy and inventory optimization are treated as interconnected processes rather than isolated tasks. The study synthesizes concepts from intelligent forecasting, adaptive computational models, and optimization-driven decision systems. The findings indicate that adaptive neural methodologies can reduce forecasting uncertainty, improve inventory planning decisions, and support proactive responses to supply chain volatility. The proposed framework also identifies practical limitations, including computational requirements, dependence on high-quality operational data, model interpretability challenges, and the necessity of continuous system calibration. This research contributes a structured theoretical and technical foundation for developing next-generation logistics intelligence platforms. By combining neural adaptability with optimization strategies, the methodology provides a pathway toward more resilient, accurate, and autonomous inventory management systems capable of supporting future-oriented decision-making in complex supply networks.
Copyright (c) 2026 Dr. Esteban Morales Vargas https://creativecommons.org/licenses/by/4.0
Detecting Internal Consumer Relationships Through Enhanced Data Aggregation Methodologies
Understanding consumer relationships has become increasingly complex due to the growing diversity of consumer interactions, behavioral patterns, and transactional data generated across multiple channels. Traditional relationship analysis approaches often focus on observable consumer attributes such as purchase frequency, demographic characteristics, or direct feedback, while overlooking hidden relational structures that influence loyalty, engagement, and long-term behavioral intentions. This research paper proposes an enhanced data aggregation methodology for detecting internal consumer relationships by integrating multi-dimensional consumer information and advanced analytical frameworks. The study conceptualizes consumer relationships as dynamic internal structures formed through behavioral similarities, relational tendencies, brand interactions, and contextual factors. The proposed framework combines consumer relationship theory with enhanced aggregation mechanisms to identify latent relationship patterns among consumers. The methodology emphasizes the transformation of fragmented consumer data into meaningful relational clusters through systematic data integration, feature representation, behavioral mapping, and relationship inference. Advanced clustering-based approaches support the identification of hidden consumer groups by analyzing similarities in behavioral characteristics and interaction patterns. Previous research on latent behavioral pattern discovery demonstrates the importance of analytical segmentation methods in revealing underlying consumer structures that remain invisible through conventional approaches (Jatav et al., 2025). The research synthesizes existing consumer relationship theories and extends them through a data-driven perspective. The framework incorporates concepts related to relationship benefits, consumer relationship proneness, brand relationship formation, and multi-level consumer-retailer interactions. The findings indicate that enhanced aggregation methodologies can improve the understanding of consumer relationship networks by identifying internal similarities, predicting behavioral tendencies, and supporting personalized relationship management strategies. The study contributes theoretically by connecting relationship marketing concepts with computational consumer analytics. Practically, it provides organizations with a structured approach for developing more accurate segmentation models, improving customer engagement strategies, and optimizing relationship-oriented decision-making. However, limitations remain regarding data quality, privacy concerns, contextual changes in consumer behavior, and the interpretability of algorithmic relationship detection. Future research can expand this framework through real-time data processing, adaptive learning systems, and cross-industry validation.
Copyright (c) 2026 Sofia Almeida https://creativecommons.org/licenses/by/4.0