Multidisciplinary Studies and Innovative Research
Multidisciplinary Studies and Innovative Research (MSIR) is a dynamic, peer‑reviewed, open‑access platform. Published bi‑monthly, the journal welcomes original research, reviews, short communications, case studies, and analytical papers across diverse disciplines—including sciences, technology, humanities, social sciences, and education. With a rigorous double‑blind review process and rapid turnaround, MSIR promotes high-impact work that spans theoretical inquiry and practical innovation. Aligned with international academic standards, the journal champions ethical publishing and full compliance with the Budapest Open Access Initiative. Indexed in platforms like Google .
Robust Policies for Multi-Unit Allocation and Lost-Sales Inventory Using Reinforcement and Online Learning
Efficient allocation of multiple units of scarce resources and management of inventory with potential lost sales are fundamental challenges in operations and supply chain management. Traditional methods often assume static demand and full observability, but real-world markets are inherently uncertain and dynamic. This paper explores the design and analysis of robust policies for multi-unit allocation and lost-sales inventory systems through reinforcement learning and online learning frameworks. We provide theoretical insights, practical algorithms, and empirical evaluations to demonstrate that learning-based strategies can adapt to stochastic demand and non-stationary environments while maintaining near-optimal performance.
Hierarchical Intent Modeling and Meta-Learning Integration in Deep Agentic Systems
The emergence of agentic behavior in artificial intelligence represents a critical turning point in the evolution of deep learning. As systems become increasingly autonomous, their capacity to formulate, refine, and pursue intentions defines the foundation of artificial cognition. This paper explores how hierarchical intent modeling, combined with meta-learning mechanisms, enables the development of deep agentic systems capable of self-directed adaptation and contextual reasoning. Hierarchical intent provides structural scaffolding for representing goals across multiple abstraction levels, while meta-learning endows systems with the capacity to generalize strategies across diverse environments. Together, these frameworks enable the synthesis of intent, action, and reflection — forming the cognitive backbone of artificial agency. The discussion analyzes how deep architectures internalize goal hierarchies, self-adjust their decision pathways, and evolve intent structures dynamically through recursive optimization. By merging cognitive theory with deep computational design, the paper proposes that hierarchical intent modeling and meta-learning together form the operational essence of self-adaptive, goal-aware artificial systems capable of evolving intelligence beyond human instruction.
Graph-Oriented Deep Learning Frameworks for Contextual Semantic Reasoning, Multi-Hop Knowledge Discovery, and Predictive Inference in LangGraph
Graph-oriented deep learning frameworks provide transformative capabilities for multi-agent and knowledge-intensive systems by integrating contextual semantic reasoning, multi-hop knowledge discovery, and predictive inference. This paper investigates the deployment of graph neural networks, attention-based mechanisms, and hierarchical representation learning within LangGraph environments. Graph-oriented architectures allow agents to encode relational structures, propagate information across nodes, and perform multi-hop reasoning to uncover latent connections in knowledge networks. Contextual semantic reasoning ensures coherent interpretation of information within complex, heterogeneous graph structures, while predictive inference enables anticipatory decision-making and knowledge synthesis. LangGraph orchestrates these processes at scale, providing a modular framework for integrating distributed reasoning, workflow coordination, and real-time updates. The study demonstrates how graph-oriented deep learning enables scalable, adaptive, and emergent intelligence in multi-agent systems, facilitating autonomous knowledge discovery, strategic reasoning, and robust prediction in dynamic environments.
Reflective Neural Architectures for Predictive Workload Orchestration and Self-Regulating Task Allocation in AI Systems
The management of dynamic workloads in modern AI systems requires architectures capable of predictive orchestration and autonomous task allocation. Reflective neural architectures integrate meta-cognitive reasoning, deep representation learning, and feedback-driven control mechanisms to enable AI systems to anticipate computational demand, optimize resource allocation, and adapt task scheduling in real time. These architectures employ hierarchical embeddings, self-monitoring modules, and adaptive connectivity to facilitate self-regulation, allowing nodes to evaluate their performance, detect bottlenecks, and reorganize task execution dynamically. By coupling predictive inference with reflective evaluation, AI systems achieve emergent intelligence that is both robust and scalable, capable of operating efficiently under complex and heterogeneous workload conditions. This paper examines the structural principles, learning dynamics, and emergent behaviors of reflective neural architectures, highlighting how predictive workload orchestration and self-regulating task allocation contribute to autonomous, adaptive, and context-aware AI systems.
Federated Learning for Real Time Fraud Detection in Decentralized Exchanges
The rapid expansion of decentralized exchanges (DEXs) has amplified the need for real-time fraud detection mechanisms that ensure transparency and trust without compromising user privacy. Conventional centralized models for fraud analysis are increasingly ineffective in decentralized environments, where sensitive transaction data is distributed across multiple nodes. This paper proposes a federated learning-based framework for real-time fraud detection within DEX ecosystems, integrating privacy-preserving computation with adaptive intelligence. Drawing upon recent advancements in federated learning for credit card fraud detection [1], blockchain-enabled privacy protection [2], and communication-efficient anomaly detection in industrial IoT networks [3], the study develops a hybrid model that combines edge-level feature extraction with secure parameter aggregation. By leveraging blockchain-based consensus [4], [8] and decentralized model updates [5], the proposed architecture mitigates risks of single-point failure and data leakage. The framework is benchmarked against traditional centralized and semi-supervised approaches to evaluate performance under varying network latency and data heterogeneity conditions. Experimental simulations demonstrate significant improvements in detection accuracy, response latency, and model robustness. Furthermore, the study explores cross-domain applications in decentralized finance (DeFi), vehicular IoT, and cross-border payments [7], [9], highlighting federated learning’s potential as a cornerstone for future cyber-resilient financial systems. Ultimately, this research contributes to the growing discourse on secure, scalable, and transparent AI governance within decentralized trading infrastructures, advancing the intersection of FinTech, AI, and blockchain-driven risk management.
The Global Convergence of Patent Standards: Harmonization Challenges between Emerging and Developed Economies
The globalization of innovation and trade has intensified calls for a unified international patent system. However, the harmonization of patent standards remains a deeply contested process, marked by structural asymmetries between developed and emerging economies. Developed nations, equipped with mature intellectual property (IP) infrastructures, emphasize stringent patent protection to secure innovation and investment. Conversely, emerging economies seek flexibility to balance technology access, industrial growth, and public welfare. This paper examines the evolution of global patent harmonization efforts, from the Paris Convention to the TRIPS Agreement, and identifies key institutional, economic, and policy barriers that hinder convergence. It analyzes how divergent patent frameworks influence global innovation flows, economic inequality, and technology transfer. The study concludes by proposing an inclusive and adaptive approach to harmonization—one that integrates capacity-building, differentiated implementation, and policy coherence to ensure equitable participation in the global intellectual property regime.
Agentic Compliance-by-Design: An Interpretable Agent Architecture for Real-Time AML/KYC Actions in Fintech Platforms
Agentic Compliance-by-Design presents an interpretable, modular agent architecture for enabling real-time Anti-Money Laundering (AML) and Know-Your-Customer (KYC) actions within fintech platforms. The paper introduces agents that integrate rule-based legal constraints, probabilistic risk scoring, and ledger-aware verification to execute, explain, and audit intervention decisions with low latency. The architecture combines a compliance orchestration layer that encodes statutory and policy constraints, a trust layer for cryptographic provenance and smart-law triggers, and an explainability module that exposes causal rationales to auditors and regulators. We validate the approach using simulated transaction streams and SME banking scenarios, demonstrating high detection recall while preserving transaction throughput and offering concise human-readable justifications for automated holds, escalations, and customer re-onboarding. The design leverages principles from smart-law and blockchain compliance to reconcile programmable rules with regulatory intent, enabling adaptive governance and policy versioning. By embedding interpretability and auditability at the agent level, the system reduces operational burden, supports regulatory examinations, and facilitates lawful automation across jurisdictional regimes. We situate our contributions against contemporary frameworks for law-following AI and unified compliance intelligence. We extend key work on blockchain compliance, law-following AI, smart-law, and unified compliance.
Effectiveness of Photoactivated Disinfection Compared to Conventional Irrigation Techniques in Eliminating Resistant Endodontic Pathogens
Persistent endodontic infections are often caused by resistant microbial species that survive conventional irrigation techniques, posing significant challenges to successful root canal therapy. Traditional irrigants, including sodium hypochlorite, chlorhexidine, and EDTA, may be limited in their ability to fully eradicate these pathogens, particularly within complex root canal anatomies and biofilms. Photoactivated disinfection (PAD) has emerged as an innovative adjunctive approach, employing photosensitizers activated by specific light wavelengths to generate reactive oxygen species that effectively disrupt microbial cell walls and biofilms. Studies indicate that PAD can enhance microbial reduction, including resistant species, compared to conventional irrigation alone, while minimizing cytotoxic effects on periapical tissues. Despite promising results, considerations such as optimal photosensitizer selection, light delivery methods, and clinical standardization remain critical for routine application. Overall, PAD represents a novel and effective strategy for improving root canal disinfection and addressing persistent endodontic infections
Influence of Different Adhesive Systems on Marginal Integrity in Deep Margin Elevated Restorations: An In-Vitro Analysis
Achieving durable marginal integrity remains a crucial factor for the longevity and success of restorations involving deep margin elevation (DME). The choice of adhesive system plays a significant role in ensuring an optimal bond between the restorative material and tooth substrate, especially when margins are placed below the cemento-enamel junction. This in-vitro study aimed to evaluate the influence of different adhesive systems—etch-and-rinse, self-etch, and universal adhesives—on the marginal integrity of deep margin elevated restorations. Standardized Class II cavities with subgingival margins were prepared on extracted human molars and restored using resin composites following DME procedures. Marginal adaptation was assessed through dye penetration and scanning electron microscopy (SEM) after thermocycling to simulate clinical aging. Results indicated that universal adhesives demonstrated superior marginal sealing and reduced microleakage compared to self-etch and etch-and-rinse systems, likely due to their versatile bonding mechanism and improved chemical interaction with dentin. The findings suggest that adhesive selection significantly impacts the interfacial stability of DME restorations and should be guided by both substrate condition and restorative protocol.
Evaluation of Acoustic Streaming Patterns Generated by Different Irrigation Activation Systems Using CFD Analysis
Effective irrigation is essential for optimal root canal debridement, and activation systems such as ultrasonic, sonic, and laser-assisted techniques enhance irrigant penetration through acoustic streaming. Computational fluid dynamics (CFD) provides a powerful tool to analyze and visualize the complex fluid flow patterns generated by these systems, offering insights into velocity distribution, shear stress, and irrigant exchange within the root canal. This study evaluates the acoustic streaming patterns produced by different irrigation activation systems using CFD simulations, highlighting the influence of tip design, frequency, amplitude, and canal morphology on fluid dynamics. Understanding these patterns is critical for optimizing irrigation protocols, improving smear layer removal, and enhancing clinical outcomes in endodontic treatment.
Irrigant Interaction and Precipitate Formation: Chemical Stability and Clinical Implications in Endodontic Practice
Effective root canal disinfection is critical in endodontic therapy, with irrigants playing a central role in eliminating microbial biofilms and dissolving tissue remnants. However, chemical interactions between commonly used irrigants such as sodium hypochlorite, chlorhexidine, and chelating agents can lead to the formation of precipitates that compromise canal cleanliness, affect dentinal integrity, and pose potential cytotoxic risks. This review highlights the mechanisms of irrigant interaction, factors influencing chemical stability, and the clinical implications of precipitate formation. Strategies to minimize adverse reactions, including proper irrigation sequencing and intermediate flushing protocols, are discussed. Understanding these interactions is essential for optimizing endodontic outcomes and guiding the development of safer, more effective irrigation protocols.
Integrating Deep Learning Algorithms for Enhanced Detection of Periapical Pathologies in Endodontic Imaging
The integration of deep learning algorithms into endodontic imaging represents a transformative advancement in diagnostic dentistry. This study focuses on enhancing the detection accuracy of periapical pathologies using convolutional neural networks (CNNs) trained on digital radiographs. By employing automated image preprocessing and feature extraction, the system identifies periapical lesions with higher precision compared to conventional radiographic interpretation. The research further evaluates model performance through metrics such as accuracy, sensitivity, and specificity, aligning results with expert diagnostic assessments. Findings demonstrate that deep learning-based systems significantly improve diagnostic efficiency, reduce observer variability, and offer real-time decision support in clinical endodontics. This integration not only accelerates diagnostic workflows but also paves the way for more reliable, data-driven clinical interventions in dental radiography.
Advancing Endodontic Treatment Planning Through 3D Reconstruction and Volumetric Analysis: The Future of Precision Dentistry
The integration of three-dimensional (3D) reconstruction and volumetric analysis is transforming endodontic treatment planning, paving the way for precision dentistry. Traditional two-dimensional imaging often limits the clinician’s ability to fully assess complex root canal anatomies and periapical pathologies. With advancements in cone-beam computed tomography (CBCT) and digital reconstruction technologies, practitioners can now visualize and quantify anatomical structures with greater accuracy and depth. This innovation enhances diagnostic precision, improves procedural predictability, and reduces the incidence of treatment errors. Moreover, volumetric data allows for individualized treatment approaches, facilitating better outcomes and long-term tooth preservation. Despite challenges related to cost, data interpretation, and radiation exposure, the ongoing integration of artificial intelligence and machine learning is expected to further optimize 3D diagnostic workflows. As technology continues to advance, 3D reconstruction and volumetric analysis stand as essential tools in the evolution toward more data-driven and patient-specific endodontic care.
Comparative Assessment of MTA, Biodentine, and New-Generation Bioceramics in Vital Pulp Therapy: A Randomized Controlled Study
Background: Vital pulp therapy (VPT) remains an essential procedure in conservative endodontics, aiming to maintain pulp vitality and function after exposure due to caries or trauma. The development of bioceramic materials such as Mineral Trioxide Aggregate (MTA), Biodentine, and newer-generation formulations has significantly improved treatment outcomes by enhancing biocompatibility, sealing ability, and dentin bridge formation. However, clinical comparisons under standardized conditions remain limited. Aim: This randomized controlled study aimed to comparatively assess the clinical and radiographic outcomes of MTA, Biodentine, and new-generation bioceramics used in vital pulp therapy of cariously exposed permanent teeth. Materials and Methods: A total of ninety patients aged 18–40 years presenting with carious pulp exposure in permanent teeth with positive vitality tests were randomly assigned into three groups: MTA, Biodentine, and new-generation bioceramics. Standardized partial pulpotomy procedures were performed, and the treated teeth were evaluated clinically and radiographically at 1 week, 3 months, 6 months, and 12 months. Parameters assessed included postoperative pain, continued pulp vitality, dentin bridge formation, and periapical healing. Statistical analysis was conducted using ANOVA and chi-square tests with significance set at p < 0.05. Results: All three materials demonstrated favorable clinical and radiographic success rates at the 12-month follow-up. Biodentine and the new-generation bioceramics showed superior handling characteristics and faster setting times compared to MTA. The new-generation bioceramics exhibited slightly enhanced radiographic evidence of dentin bridge formation and fewer postoperative symptoms, although differences among the groups were not statistically significant (p > 0.05). MTA, Biodentine, and new-generation bioceramics are all effective materials for vital pulp therapy, showing high clinical success and biocompatibility. While MTA remains the benchmark for biological performance, Biodentine and newer bioceramics offer improved handling, reduced setting time, and comparable healing outcomes, supporting their use as reliable alternatives in vital pulp preservation (Singh, 2019; Ward, 2002).
Long-Term Clinical Outcomes of Bioceramic-Based Vital Pulp Therapy in Mature Permanent Teeth: A Prospective Study
Vital pulp therapy (VPT) has emerged as a conservative and biologically oriented alternative to root canal treatment for preserving pulp vitality, even in mature permanent teeth. The introduction of bioceramic-based materials has enhanced the predictability of VPT by combining excellent biocompatibility, sealing ability, and bioactivity. This prospective study aims to evaluate the long-term clinical and radiographic outcomes of bioceramic-based VPT in mature permanent teeth over a 3–5 year follow-up period. A total of 80 mature teeth diagnosed with reversible pulpitis or mechanical/caries-induced pulp exposure were treated using mineral trioxide aggregate (MTA) and Biodentine under standardized protocols. Clinical assessments included the absence of spontaneous pain, swelling, tenderness, or sinus tract formation, while radiographic evaluations focused on dentin bridge formation and periapical health. Preliminary findings indicate a high clinical success rate, with more than 90% of treated teeth maintaining vitality and functional integrity at the 3-year mark. Radiographic results revealed consistent dentin bridge formation and stable periapical conditions across both material groups. Biodentine demonstrated faster initial healing, while MTA exhibited superior long-term radiographic stability. These outcomes reinforce the efficacy of bioceramic materials in promoting sustained pulp healing and dentinogenesis in mature permanent teeth. The study concludes that bioceramic-based materials provide predictable, long-term success in vital pulp therapy, representing a reliable clinical choice for preserving natural dentition. Ongoing follow-up and multicenter studies are essential to confirm their long-term clinical durability and optimize treatment protocols in contemporary endodontic practice.
The Role of Bioceramic-Based Materials in Promoting Odontoblastic Differentiation During Vital Pulp Therapy
Vital pulp therapy (VPT) represents a biologically driven approach aimed at preserving the vitality and function of the dental pulp following injury or exposure. Central to the success of this therapy is the promotion of odontoblastic differentiation, which facilitates reparative dentin formation and pulp healing. In recent years, bioceramic-based materials have emerged as promising agents due to their excellent bioactivity, biocompatibility, and sealing ability. This study explores the role of bioceramic materials—such as mineral trioxide aggregate (MTA), Biodentine, and EndoSequence Root Repair Material—in stimulating odontoblastic differentiation during vital pulp therapy. These materials release calcium and silicate ions that enhance the expression of odontogenic markers, including dentin sialophosphoprotein (DSPP), dentin matrix protein-1 (DMP-1), and alkaline phosphatase (ALP), thereby supporting mineralization and dentin bridge formation. Evidence from recent in vitro and in vivo studies indicates that bioceramics outperform traditional agents like calcium hydroxide in achieving predictable pulp healing outcomes. The biological mechanisms underlying their effectiveness involve the activation of signaling pathways such as BMP, Wnt/β-catenin, and TGF-β, which guide stem cell differentiation toward an odontoblastic phenotype. Therefore, bioceramic-based materials play a critical role in advancing regenerative endodontics by combining clinical reliability with biologically favorable responses. Continued research into their molecular mechanisms and long-term clinical performance is essential to further enhance outcomes in vital pulp therapy.
Reimagining Data Migration: A Fusion of Algorithmic Models and Practical Insights
Data migration has emerged as one of the most complex yet critical processes in the digital transformation era. Enterprises shifting from legacy systems to modern cloud or hybrid infrastructures must address challenges in scalability, performance, and integrity. Traditional migration strategies often rely on rigid ETL (Extract, Transform, Load) pipelines that lack adaptability when confronted with unstructured data or heterogeneous architectures. This paper explores a reimagined approach to data migration that combines algorithmic models with practical insights. By leveraging predictive algorithms, heuristic optimization, and anomaly detection, organizations can minimize downtime, mitigate data loss, and improve accuracy in migration workflows. Furthermore, embedding practical methodologies such as incremental migration, automated validation, and business process alignment ensures sustainable adoption. The fusion of computational intelligence with hands-on practices redefines how enterprises plan, execute, and maintain large-scale data migrations, paving the way for resilient and future-ready digital ecosystems.
Driving Digital Transformation with Sales force: Strategies for Modern Enterprises
Digital transformation has become the cornerstone of modern enterprise competitiveness, enabling organizations to harness data, cloud platforms, and artificial intelligence for innovation and growth. Yet, the complexity of integrating multiple technologies, aligning workflows, and delivering seamless customer experiences presents significant challenges. Salesforce, as a leading cloud-based ecosystem, offers enterprises the tools and frameworks to accelerate digital transformation. This paper examines strategies for driving digital transformation with Salesforce, focusing on its technological enablers, including CRM, automation, analytics, and AI, as well as its role in redefining enterprise strategies for customer engagement, agility, and innovation. By exploring Salesforce’s capabilities in creating a connected digital ecosystem and highlighting best practices for enterprise adoption, this paper demonstrates how Salesforce empowers organizations to thrive in the digital-first economy.
Multi-Objective Optimization in Federated Learning for Balancing Employee Privacy, Accuracy, and Organizational Utility
The rise of data-driven decision-making in workforce management has made employee performance analytics an essential part of modern organizational ecosystems. However, the sensitive nature of employee data necessitates solutions that respect privacy while maintaining model accuracy and ensuring organizational utility. Federated learning (FL) has emerged as a promising paradigm that enables collaborative model training without centralized data aggregation, thus protecting employee confidentiality. Despite its potential, FL presents trade-offs between employee privacy, predictive accuracy, and the organization’s need for actionable insights. This research paper investigates multi-objective optimization (MOO) approaches within federated learning to balance these competing goals. Using a case study of federated workforce analytics, we analyze the integration of privacy-preserving mechanisms such as differential privacy with optimization frameworks that simultaneously account for accuracy and utility. Experimental results demonstrate that incorporating multi-objective optimization not only improves fairness in workforce assessment but also achieves a more sustainable balance among privacy, performance, and organizational value. The study provides theoretical and empirical insights, offering practical guidelines for deploying FL systems in sensitive employee evaluation environments.
Differential Privacy-Enhanced Federated Learning for Fair and Transparent Workforce Assessment
The increasing adoption of workforce analytics has made organizations more reliant on data-driven decision-making for employee performance assessment, career progression, and organizational development. However, centralized workforce data collection poses critical risks to employee privacy, fairness, and transparency. To address these challenges, this research explores a novel framework that integrates Differential Privacy (DP) within Federated Learning (FL) to enable secure, equitable, and explainable workforce assessment. By combining the decentralized collaborative training of FL with the rigorous privacy guarantees of DP, the proposed model safeguards sensitive employee information while minimizing bias in performance metrics. Experiments conducted on simulated workforce performance datasets reveal that the DP-enhanced FL framework achieves competitive accuracy compared to centralized models while significantly improving privacy protection and fairness indices. The findings demonstrate that DP-FL can serve as a scalable and ethical solution for workforce analytics, balancing organizational utility with employee trust.
Federated Meta-Learning Approaches for Secure and Privacy-Preserving Employee Performance Analytics
The rise of digital workplace environments has introduced new opportunities and challenges in analyzing employee performance data. Conventional centralized machine learning models often demand sensitive data aggregation, raising concerns regarding privacy, security, and organizational trust. This paper explores the design, implementation, and evaluation of federated meta-learning approaches in the domain of employee performance analytics. The study provides a comprehensive analysis of privacy-preserving protocols, adversarial threat models, and secure aggregation strategies that ensure confidentiality. Experiments conducted on synthetic and enterprise-like datasets demonstrate how federated meta-learning significantly improves generalization capabilities, provides personalization for different organizational settings, and achieves higher accuracy in performance predictions compared to traditional federated learning. The results highlight the potential of federated meta-learning as a cornerstone in building secure, adaptive, and privacy-preserving performance evaluation systems for modern enterprises.
Scalable AI Infrastructure for Real Time Payment Processing and Big Data Handling
Instant payment rails, open banking, and 24×7 settlement are driving a step change in throughput, tail-latency, and reliability requirements for AI-enabled risk controls in financial systems. This paper proposes reference architecture for scalable AI infrastructure that unifies real-time payment processing with big-data handling while meeting stringent consistency, governance, and regulatory constraints. Building on advances in hybrid/edge–cloud scale-out design, streaming analytics, and AI-driven decisioning, we architect a stream-first stack that couples (i) exactly-once event ingestion and CDC-based ledger integration with Kafka/Flink; (ii) an online–offline feature store synchronized by feature-time contracts; (iii) low-latency model serving on auto scaled GPU/CPU pools with canary/shadow deployment; and (iv) a lake house backbone for repayable training, auditability, and continuous learning. Methodologically, we decompose the latency budget from ingress to decision (SLO p99.9 ≤ 50 ms) and introduce two mechanisms: risk-adaptive autoscaling, which modulates concurrency using model uncertainty and event mix; and consistency-preserving stream/model synchronization, which binds inference to versioned features, schemas, and watermark-aware windows to prevent training/serving skew. We incorporate concept-drift alarms, active learning for rare-event enrichment, and rule-graph/ML co-execution for explainable fraud, AML, and sanctions screening. The work delivers (1) a vendor-agnostic blueprint for regulated, real-time AI at payment scale; (2) formal contracts for data/feature/version governance; and (3) an evaluation methodology that ties risk metrics (precision/recall, alert fatigue) to systems metrics (p99/p99.9, backpressure, recovery time). We discuss implications for interoperable instant-payment schemes, cross-border corridors, and future integration with programmable settlement and privacy-preserving consortium analytics.
A Theoretical Perspective on Strategic Investment Decision Making in Modern Enterprises
Strategic investment decision-making is one of the most critical determinants of long-term organizational success in modern enterprises. In an era characterized by technological transformation, globalization, and heightened competition, firms are increasingly challenged to make complex investment choices that balance risk, return, and sustainability. This paper provides a comprehensive theoretical perspective on how enterprises conceptualize and execute strategic investment decisions. It explores the intersection of classical decision-making models, behavioral finance, organizational psychology, and modern computational methods. The study further incorporates an experimental framework simulating enterprise investment decisions using a game-theoretic model with multiple competing scenarios. The results reveal that organizations relying on structured, evidence-based models outperform those using purely intuitive or short-term approaches. This research contributes to the growing body of literature by integrating theoretical models with simulated experimentation, offering both practical and academic insights.
Building Resilient AI Models Against Data Poisoning Attacks
The rapid deployment of artificial intelligence across critical domains such as healthcare, finance, and cybersecurity has heightened concerns about data poisoning attacks that compromise model reliability. Data poisoning refers to the intentional manipulation of training data to degrade model performance, induce targeted misclassifications, or implant hidden backdoors. These attacks exploit the fundamental reliance of machine learning systems on data quality, making them difficult to detect and potentially devastating in practice. This paper investigates strategies for building resilient AI models against data poisoning. It synthesizes research on threat taxonomies, including clean-label poisoning, backdoor insertion, and gradient-based adversarial contamination, highlighting how these methods undermine training pipelines. Defensive techniques are then evaluated across three stages: data sanitization and anomaly detection before training, robust optimization and adversarial training during model development, and runtime monitoring with post-training auditing. The study emphasizes hybrid approaches that integrate robust statistical learning with uncertainty estimation and federated data validation, thereby reducing single points of failure. Beyond technical defenses, the paper situates resilience within broader frameworks of AI governance, proposing that explainability, accountability, and continuous validation are essential to building trust in poisoned-data-prone environments. The results underscore that while no single defense is sufficient, layered strategies combining pre-processing, in-processing, and post-processing interventions can substantially mitigate risks.
Beyond Firewalls: Proactive Cyber Defense Using Behavior-Based Anomaly Detection
Traditional firewalls and rule-based intrusion prevention systems (IPS) have become inadequate in dealing with sophisticated and evolving cyber threats. As modern attacks increasingly evade signature-based detection, there is a pressing need for more dynamic, adaptive, and intelligent defense mechanisms. This research presents a comprehensive study on behavior-based anomaly detection systems (ADS) as a proactive layer of cybersecurity. The approach focuses on monitoring user behavior, network traffic patterns, and system-level activities to identify deviations from established norms. By integrating machine learning algorithms, particularly unsupervised models, the framework improves anomaly detection accuracy and minimizes false positives. We conducted experiments using the NSL-KDD and CIC-IDS 2017 datasets to validate the effectiveness of the proposed methodology. The results demonstrated that behavior-based anomaly detection systems significantly outperformed conventional firewalls in identifying zero-day exploits and insider threats. This paper argues for a paradigm shift in cybersecurity strategies—from reactive to proactive—emphasizing the necessity of deploying behavior-aware anomaly detection tools in modern digital infrastructures.
A Comprehensive Framework for Adaptive Cybersecurity in Cloud-Native Infrastructures
Cloud-native infrastructures have revolutionized the deployment and scaling of applications by enabling modularity, automation, and flexibility through microservices, containers, and orchestration tools. However, this shift has also introduced new and complex cybersecurity challenges. Traditional security models struggle to keep up with the dynamic, ephemeral nature of cloud-native environments. This paper proposes a comprehensive, adaptive cybersecurity framework designed specifically for cloud-native infrastructures. It incorporates real-time threat intelligence, behavioral analytics, continuous monitoring, and AI-driven decision-making to secure services across distributed architectures. The framework is validated using experimental simulations on Kubernetes clusters with real-world threat emulation tools. The results demonstrate significant improvements in threat detection latency, false positive reduction, and overall security posture. This research bridges the gap between cloud-native computing and adaptive cybersecurity, offering a scalable, intelligent, and resilient solution.
Against the Odds: Achieving Low Regret in Network Revenue Management with Indiscrete and Degenerate Distributions
This paper explores the problem of regret minimization in Network Revenue Management (NRM) under the rarely examined but practically relevant settings where demand distributions are either indiscrete (i.e., continuous but not smoothly distributed) or degenerate (i.e., having mass concentrated at specific points). Traditional NRM algorithms rely on assumptions such as demand smoothness and discretization to provide performance guarantees. We challenge these assumptions and develop a robust learning framework that adapts to discontinuities and concentrated demand masses. Our approach leverages distribution-agnostic techniques and adaptive thresholds to achieve low regret over time. We demonstrate, both theoretically and through simulations,that our model maintains performance guarantees even in worst-case demand configurations, thereby extending the applicability of NRM methods in real-world logistics and pricing systems.
Unmasking Black-Box Bias: Interpretable ML for Socioeconomic Inequality in Urban-Rural U.S. Income Prediction
Income inequality between urban and rural populations in the United States remains a persistent socio-economic challenge, with significant implications for public policy and equitable resource distribution. This study investigates the use of interpretable machine learning (ML) models to predict income disparities across urban and rural settings while uncovering potential algorithmic biases inherent in traditional black-box models. The primary aim is to enhance both predictive performance and fairness in classifying income levels by leveraging socio-demographic and geographic features. To achieve this, we utilized a range of traditional machine learning classifiers, including Logistic Regression, Random Forest, and Gradient Boosting, alongside interpretable counterparts such as Decision Trees and Post-hoc explanation tools including SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). These models were evaluated not only on standard classification metrics such as precision, recall, and F1-score, but also on fairness and bias-oriented measures, including disparate impact and demographic parity. This dual focus enables a holistic understanding of both model performance and ethical robustness. The results demonstrate that while black-box models offer superior predictive power, interpretable models reveal nuanced patterns of income stratification linked to geographic and demographic variables. SHAP and LIME explanations exposed critical features influencing predictions, such as employment type, education level, and location category, thereby illuminating latent structural inequalities. Moreover, interpretable models provided more transparent decision-making pathways, making them valuable for stakeholders interested in diagnostic and prescriptive analytics. In conclusion, this study underscores the importance of integrating interpretable ML in socioeconomic modeling, not merely as a technical enhancement but as a necessary step toward ethical and accountable AI systems. These findings support the adoption of interpretable ML frameworks for socially impactful applications, particularly where fairness, trust, and transparency are paramount. Policymakers can leverage these insights to guide data-driven decisions that promote equity across geographic boundaries
Artificial Intelligence and the Future of Decision-Making: Challenges in Autonomy, Accountability, and Alignment
Artificial Intelligence (AI) is rapidly reshaping the landscape of decision-making across industries, ranging from healthcare to finance, manufacturing, and public policy. As AI systems increasingly assume roles that require autonomous decision-making, concerns about accountability, ethical alignment, and control mechanisms have become critical. The growing complexity of AI models, particularly those based on deep learning and large-scale machine learning, raises questions about how such systems make decisions, how biases may influence their outcomes, and how to ensure their objectives align with human values. This research paper explores the intricate challenges of autonomy, accountability, and alignment within AI decision-making frameworks, presenting experimental insights on algorithmic transparency and human-AI collaboration. Furthermore, it provides a critical examination of the current limitations in AI governance, highlights the risks posed by misaligned objectives, and proposes a roadmap for developing trustworthy AI systems. Through an in-depth analysis and experiment-driven results, this paper emphasizes the need for interdisciplinary approaches to balance innovation with ethical responsibility.
Beyond Ratings: Integrating Spatial Data and Behavioral Cues in Modern Prediction Systems
Modern prediction systems, especially those driving recommender engines and intelligent decision tools, have long relied on ratings as the primary indicator of user preference. However, this approach fails to capture the complexity of real-world behavior, particularly in dynamic environments where context, location, and user interactions offer richer predictive signals. This study introduces a hybrid predictive framework that integrates spatial data and behavioral cues alongside traditional rating systems. Drawing on a multilayered dataset collected from a real-world application ecosystem, including user ratings, geolocation logs, and clickstream behavior, we developed an ensemble deep learning model combining convolutional neural networks (CNNs), attention mechanisms, and gradient-boosted decision trees (GBDT). The model was evaluated using AUC, F1-score, and precision-recall metrics against standard collaborative filtering baselines. Results show a significant improvement in predictive accuracy, with a 14.7% increase in F1-score and 11.2% gain in AUC over baseline models. The inclusion of spatial embeddings and temporal-behavioral sequences contributed to better personalization, reduced prediction sparsity, and improved responsiveness to context shifts. The findings suggest that spatial-behavioral integration not only enhances accuracy but also provides deeper insights into user intent and preference volatility. This paper demonstrates the feasibility and effectiveness of expanding beyond rating-based paradigms and offers a blueprint for next-generation prediction systems in sectors such as healthcare, smart retail, and personalized services.