ThinkTide Global Research Journal is an open-access, peer‑reviewed platform with a commitment to academic excellence and global collaboration. We elevate rigorous research—from emerging disciplines to cross‑disciplinary innovations—and make it freely accessible to scholars, practitioners, and policymakers worldwide. Utilizing a fast and transparent double-blind review process, we publish high-quality articles in fields ranging from sustainable development and global health to data science and policy innovation. Hosted on a streamlined online publishing platform (OJS/PKP), we ensure your research is accessible, discoverable, and archived for long-term impact. At ThinkTide, we believe that knowledge thrives when curiosity transcends borders. ThinkTide Global Research Journal serves as a bridge between theory and practice, offering a space where scholars, innovators, and professionals can contribute knowledge that addresses both local challenges and global issues. By embracing open access, the journal ensures that research outputs are not confined to subscription barriers but are instead available to all who seek to learn, adapt, and apply new insights. The scope of the journal is intentionally broad and interdisciplinary, welcoming original research articles, review papers, case studies, and policy analyses that advance understanding in sustainable development, education, technology, global health, data science, business, and public policy. We particularly encourage contributions that integrate multiple perspectives, highlight emerging fields, and propose innovative solutions to pressing global challenges. The journal aims to nurture a community of researchers who value inclusivity, diversity, and collaboration in knowledge creation. By upholding a rigorous peer-review process, we strive to maintain academic integrity and ensure that every published paper meets the highest scholarly standards. Our objective is not only to disseminate knowledge but also to spark dialogue, inspire critical thinking, and inform decision-making across sectors and regions. At its core, ThinkTide Global Research Journal envisions itself as more than a publication—it is a catalyst for change. By amplifying voices from both established and emerging research communities, we contribute to a more connected and informed world. We believe that the exchange of knowledge across borders empowers societies, strengthens innovation, and supports the pursuit of sustainable and equitable futures.
Unified Approaches to Dynamic Optimization: Tight Prophet Bounds, Non-Stationary Bandits, and Stochastic Knapsack Models
Dynamic optimization lies at the heart of modern decision-making, spanning applications from inventory management to online advertising. This paper explores unified approaches that connect three fundamental frameworks: tight prophet inequalities, non-stationary bandits, and stochastic knapsack models. By integrating insights from these seemingly disparate paradigms, we develop strategies that handle uncertainty, resource constraints, and evolving environments simultaneously. Our results provide tight performance guarantees and suggest new algorithmic principles for robust online decision-making.
Meta-Adaptive Neural Controllers for Predictive Harmonization of Divergent AI Workload Modalities across Distributed Platforms
The proliferation of distributed AI systems executing heterogeneous workloads necessitates advanced mechanisms for harmonizing divergent computational modalities. Meta-adaptive neural controllers offer a framework in which predictive modeling, self-adaptive learning, and cross-agent coordination converge to optimize workload distribution and execution across distributed platforms. These controllers dynamically assess task characteristics, forecast system bottlenecks, and reconcile competing computational demands to achieve harmonized performance while maintaining efficiency and latency constraints. By embedding meta-learning principles and adaptive control policies, the network continuously refines workload allocation strategies in response to emergent operational patterns. Predictive harmonization enables agents to anticipate modality conflicts, adjust execution priorities, and redistribute resources proactively, reducing performance variance and enhancing system resilience. This paper examines the architectural design, operational dynamics, and emergent properties of meta-adaptive neural controllers, highlighting their potential to unify heterogeneous AI workloads across distributed infrastructures while fostering autonomous, intelligent, and self-optimizing computational ecosystems.
Layered Deep Neural Systems for Dynamic Multi-Agent Interaction, Adaptive Workflow Optimization, and Autonomous Decision Execution in n8n
Layered deep neural systems provide advanced capabilities for managing dynamic multi-agent interactions, optimizing workflows adaptively, and executing autonomous decisions within n8n environments. By incorporating hierarchical and attention-driven neural architectures, agents can analyze multi-agent dependencies, predict task outcomes, and coordinate execution across complex workflows. Adaptive workflow optimization leverages predictive modeling to prioritize tasks, allocate resources efficiently, and adjust dynamically to changing operational conditions. Autonomous decision execution enables agents to respond to environmental fluctuations, inter-agent interactions, and workflow contingencies without manual intervention. n8n serves as a modular orchestration platform that integrates neural intelligence seamlessly, providing task monitoring, multi-agent coordination, and real-time execution control. This paper explores the principles, mechanisms, and applications of layered deep neural systems for intelligent automation in n8n, highlighting their potential to enhance efficiency, scalability, and resilience in modern multi-agent workflow systems.
Synergistic Coevolution of Cognitive Agency and Deep Learning Architectures for Self-Adaptive Intelligence
The emergence of cognitive agency within artificial systems has transformed deep learning from a representational paradigm into a dynamic ecosystem of coevolving intelligence. As deep neural architectures grow increasingly complex, they exhibit behaviors akin to cognitive self-organization, enabling adaptive reasoning, contextual learning, and autonomous decision-making. This paper examines the synergistic relationship between cognitive agency and deep learning architectures, proposing that their coevolution forms the foundation of self-adaptive intelligence. It explores how neural architectures evolve through feedback-driven optimization and how agentic behavior arises from meta-learning, hierarchical abstraction, and continuous environmental interaction. The discussion traces this evolution from static data-driven models to self-regulating systems capable of reflective cognition and self-directed learning. By integrating cognitive theory with computational design, the paper argues that deep learning’s future lies not in greater scale but in its capacity to internalize agency — a process that redefines autonomy, adaptability, and the ontology of machine intelligence.
Reassessing Intellectual Property Valuation: Economic Models for AI-Generated Assets and Patent Portfolios
The rapid evolution of generative artificial intelligence (AI) has reshaped the landscape of intellectual property (IP) by introducing a new class of intangible assets—AI-generated creations. These assets challenge traditional economic valuation models that were designed for human-generated works and discrete patents. Conventional approaches such as discounted cash flow, market comparables, and option-based frameworks often fail to capture the unique characteristics of AI-generated assets, including ambiguous authorship, rapid obsolescence, near-zero marginal reproduction costs, and high interconnectivity within patent portfolios. This paper reassesses intellectual property valuation by proposing refined economic models tailored for AI-driven innovation. It examines the limitations of traditional valuation frameworks and introduces integrated models that incorporate stochastic revenue forecasts, legal attribution risks, real-options flexibility, and portfolio interdependencies. Furthermore, it explores how data-driven techniques and machine learning can enhance valuation accuracy and transparency. The study also evaluates the implications for accounting, taxation, mergers and acquisitions, and public policy, suggesting that a unified framework linking law, economics, and computation is essential for valuing AI-generated assets and patent portfolios in the digital era.
Securing Cloud Infrastructure using Federated Machine Learning Frameworks
With the rapid expansion of cloud computing services, data security and privacy have become pressing challenges in modern digital ecosystems. Traditional centralized machine learning approaches for threat detection and anomaly analysis in cloud environments often require aggregating sensitive data from distributed nodes, increasing the risk of exposure and data breaches. Federated Machine Learning (FML) offers a transformative solution by enabling collaborative model training across multiple cloud nodes without sharing raw data. This paper explores how federated machine learning can be integrated into cloud infrastructure to enhance security, privacy, and resilience. By distributing intelligence across multiple tenants and service layers, federated frameworks enable real-time detection of insider threats, intrusions, and misconfigurations while maintaining compliance with data protection regulations. The discussion further highlights architectural strategies, communication optimization, and the integration of secure aggregation protocols to prevent model poisoning and adversarial attacks. The paper concludes that federated learning not only preserves privacy but also establishes a scalable foundation for proactive, autonomous, and self-healing cloud security systems.
A Cross-Domain Deep Learning Approach Using Sparrow and Tree Growth Algorithms for Cyber Threat Detection and Renewable Energy Forecasting
The rapid proliferation of Internet of Things (IoT) devices and the simultaneous global expansion of renewable energy systems have introduced a critical need for intelligent, adaptive, and secure computational frameworks. This study proposes a cross-domain deep learning model integrating Sparrow Search Algorithm (SSA) and Tree Growth Algorithm (TGA) for dual objectives: detecting cyber threats in IoT environments and forecasting renewable energy generation. The hybrid model leverages deep learning’s nonlinear mapping capability with metaheuristic optimization’s adaptive exploration-exploitation dynamics, offering improved generalization across heterogeneous datasets. The SSA component enhances feature selection by identifying high-impact features that maximize classification performance in cyber threat detection, while TGA optimizes neural weights to improve prediction accuracy in renewable energy forecasting. Experimental evaluations on NSL-KDD and solar-wind energy datasets reveal the model’s superior accuracy, robustness, and convergence speed compared to conventional deep learning and standalone optimization methods. The proposed cross-domain approach not only enhances cybersecurity in IoT but also supports smart energy management, illustrating the power of hybrid intelligence in bridging two vital industrial sectors.
Adaptive Feature Selection and Deep Learning Synergy for IoT Security and Energy Prediction Using Tree Growth and Rooster Optimization
The rapid evolution of the Internet of Things (IoT) ecosystem has introduced new dimensions of intelligent connectivity, enabling seamless integration between smart devices and critical energy systems. However, this advancement has simultaneously expanded the attack surface, exposing IoT networks to security breaches, intrusions, and data manipulation that threaten both privacy and energy management systems. This paper presents an innovative hybrid framework that combines adaptive feature selection with deep learning models, optimized through Tree Growth Optimization (TGO) and Rooster Optimization Algorithm (ROA). The proposed model efficiently identifies relevant features from heterogeneous IoT data streams, enhancing detection precision and forecasting accuracy. TGO facilitates structured feature extraction by modeling environmental adaptation, while ROA ensures global optimization by simulating rooster hierarchy and dominance behavior. The synergy between these algorithms optimizes a deep learning architecture comprising a hybrid LSTM-DBN network for intrusion detection and renewable energy forecasting. Experimental analysis on benchmark IoT and energy datasets demonstrates that the proposed hybrid model significantly outperforms conventional optimization and learning approaches in accuracy, computational efficiency, and robustness. Results confirm that this adaptive synergy framework can serve as a foundation for secure, sustainable, and intelligent IoT infrastructures.
Lightweight Deep Learning Models for Edge-Based Cyber Threat Detection
The rapid proliferation of Internet of Things (IoT) devices and distributed networks has shifted the cybersecurity landscape toward the edge, where real-time threat detection is essential. However, deploying deep learning models on edge devices presents challenges due to limited computational power, memory constraints, and energy efficiency requirements. This paper explores the development and implementation of lightweight deep learning models for edge-based cyber threat detection, emphasizing architectural optimizations, compression techniques, and adaptive intelligence. By leveraging model pruning, quantization, and knowledge distillation, researchers have enabled high-performance models that operate effectively in resource-constrained environments. Furthermore, integrating these optimized models into edge networks facilitates faster detection of malicious activities, reduces data transmission to centralized servers, and enhances privacy by processing sensitive information locally. The paper also examines challenges such as adversarial robustness, model update synchronization, and data heterogeneity across edge nodes. Ultimately, lightweight deep learning represents a pivotal advancement in creating scalable, privacy-preserving, and energy-efficient cyber defense systems for next-generation edge infrastructures.
Intelligent Threat Detection: Leveraging Machine Learning for Cybersecurity Automation
As the digital landscape grows increasingly complex, traditional cybersecurity systems are struggling to keep pace with evolving threats, sophisticated malware, and large-scale data breaches. Machine learning (ML) has emerged as a pivotal technology in modern cybersecurity, providing automation, adaptability, and predictive intelligence for detecting and mitigating cyber threats. This paper explores how ML techniques are revolutionizing cybersecurity by enabling real-time anomaly detection, intelligent intrusion prevention, and automated incident response. The discussion encompasses both supervised and unsupervised approaches for threat identification, the integration of ML with security orchestration, and the challenges of data imbalance, adversarial attacks, and explainability. By examining current advancements and future trends, this paper underscores how ML-driven cybersecurity automation enhances detection accuracy, response efficiency, and resilience against emerging digital threats.
Market-Aware Multi-Agent Systems: Preventing Emergent Collusive Behavior in Reinforcement-Learning Trading Agents
As reinforcement learning (RL) continues to advance multi-agent systems for algorithmic trading, a pressing concern has emerged: the tendency of autonomous agents to develop tacit collusive behaviors that undermine market competitiveness. This paper addresses the challenge of emergent collusion in market-facing multi-agent reinforcement learning (MARL) environments by proposing a novel market-aware framework. Drawing on organizational paradigms in agent coordination, game-theoretic principles under uncertainty, and recent findings on algorithmic collusion, we develop a MARL architecture that incorporates macroeconomic signals—such as price elasticity, demand shifts, and aggregate agent behavior—into each agent’s policy optimization. Empirical simulations in synthetic financial markets demonstrate that standard MARL agents often converge toward supra-competitive pricing equilibria, echoing findings from recent studies on self-reinforcing AI collusion. By contrast, our market-aware agents adaptively adjust to dynamic environments and maintain competitive pricing even in the absence of explicit regulation. We further discuss the role of platform-level incentives and shared market feedback as regulatory substitutes, offering architectural and governance guidelines for mitigating systemic risks posed by advanced AI in digital economies. This work contributes to both AI safety and computational economics by bridging algorithmic design and policy-aware learning mechanisms to ensure fair, efficient, and collusion-resistant trading systems.
Advancements in Irrigation Delivery Systems: Assessing the Cleaning Efficiency of Negative Pressure versus Positive Pressure Techniques
Effective irrigation remains a critical determinant of success in root canal therapy, as it ensures the removal of debris, biofilm, and microorganisms from complex canal anatomies. This study aimed to evaluate and compare the cleaning efficiency of negative pressure and positive pressure irrigation delivery systems. Forty extracted human teeth with standardized canal preparations were divided into two groups: one irrigated using a negative pressure system (EndoVac) and the other with a conventional positive pressure syringe–needle technique. Both groups utilized sodium hypochlorite and EDTA as irrigants under controlled conditions. The extent of debris removal, irrigant penetration, and apical extrusion were analyzed using stereomicroscopic and digital imaging methods. The results demonstrated that the negative pressure system achieved deeper irrigant penetration and superior apical cleanliness while minimizing irrigant extrusion beyond the apex. In contrast, the positive pressure technique showed limited irrigant exchange in the apical third and higher extrusion risk. These findings suggest that negative pressure irrigation offers improved safety and cleaning efficiency compared to traditional positive pressure systems, representing a significant advancement in modern endodontic irrigation technology.
A Micro-CT Analysis of Apical Debris Extrusion Using Different Irrigation Activation Protocols
Apical debris extrusion remains a major concern in endodontic therapy, as it can trigger postoperative pain and periapical inflammation. This study aimed to evaluate and compare the extent of apical debris extrusion associated with different irrigation activation protocols using Micro-Computed Tomography (Micro-CT) analysis. Forty extracted single-rooted human teeth were prepared and randomly divided into three experimental groups: ultrasonic activation, sonic activation, and conventional syringe irrigation. All specimens were irrigated with sodium hypochlorite under standardized conditions. Following instrumentation and irrigation, the extruded debris was collected and analyzed using Micro-CT imaging to obtain volumetric data without altering specimen integrity. The results revealed that the ultrasonic activation group exhibited the least amount of apical debris extrusion, followed by the sonic activation group, while conventional syringe irrigation produced the highest extrusion levels. Micro-CT visualization confirmed that activation techniques improved debris suspension and fluid exchange, thereby minimizing apical extrusion. These findings suggest that advanced irrigation activation systems enhance cleaning efficiency and safety by reducing debris displacement beyond the apical foramen.
Bioactive Properties of Calcium Silicate–Based Bioceramics in Maintaining Pulp Vitality: An In Vitro and In Vivo Analysis
The preservation of pulp vitality is a critical objective in contemporary endodontic practice, emphasizing the importance of bioactive materials that promote healing and regeneration. This study evaluates the bioactive properties of calcium silicate–based bioceramics in maintaining pulp vitality through both in vitro and in vivo analyses. In vitro investigations using human dental pulp stem cells revealed that materials such as mineral trioxide aggregate (MTA) and Biodentine exhibit excellent biocompatibility, enhancing cell viability, proliferation, and odontoblastic differentiation. In vivo studies on animal models demonstrated that calcium silicate–based bioceramics induced the formation of a homogeneous dentin bridge, reduced inflammatory response, and effectively maintained pulp vitality. The release of calcium and silicate ions played a key role in stimulating mineralization and tissue repair. Compared with conventional calcium hydroxide, these materials provided superior sealing ability, bioactivity, and long-term stability. The findings suggest that calcium silicate–based bioceramics represent a reliable biomaterial class for vital pulp therapy, bridging the gap between biological compatibility and clinical performance.
Impact of Cone-Beam Computed Tomography on the Detection of Missed Canals and Periapical Lesions: A Clinical Perspective
Accurate detection of missed canals and periapical lesions remains a major challenge in endodontic diagnosis and retreatment. Conventional two-dimensional radiographs often fail to reveal the complex internal anatomy of teeth due to image distortion and anatomical overlap. The advent of Cone-Beam Computed Tomography (CBCT) has provided a significant advancement by enabling three-dimensional visualization of dental structures, thereby improving diagnostic precision.This study aims to evaluate the clinical impact of CBCT on the detection of missed root canals and periapical lesions, emphasizing its diagnostic superiority over traditional radiographic techniques. A clinical assessment was conducted comparing conventional periapical radiographs and CBCT scans in patients with previously treated teeth presenting with persistent periapical symptoms. Both imaging modalities were analyzed by calibrated endodontists to identify missed canals and periapical pathologies. Statistical comparisons were made to determine the diagnostic differences between the two methods Findings demonstrated that CBCT significantly increased the detection rate of missed canals and periapical lesions compared to conventional radiography. The enhanced diagnostic information obtained from CBCT led to improved treatment planning and more predictable clinical outcomes.: CBCT serves as an invaluable diagnostic tool in endodontics, allowing for a comprehensive assessment of root canal morphology and periapical pathology. Its application enhances clinical decision-making, particularly in retreatment cases, by minimizing diagnostic uncertainties. While considerations regarding radiation dose and cost remain, the benefits of CBCT in improving endodontic diagnostic accuracy are substantial and clinically relevant.
Cross-Border Model Training and Data Sovereignty: Conflicts-of-Law Across the AI Supply Chain
Artificial Intelligence (AI) model training increasingly depends on vast, globally distributed datasets, creating unprecedented challenges for cross-border governance. Data sovereignty laws such as the EU’s General Data Protection Regulation (GDPR), China’s Data Security Law, and emerging U.S. state privacy statutes—directly impact how AI developers collect, process, and transfer training data across jurisdictions. This article examines conflicts-of-law issues that emerge in the AI supply chain, particularly regarding data localization, algorithmic accountability, intellectual property, and trade secrecy. By engaging with case law, treaties, and academic scholarship, it highlights the tension between innovation and sovereignty in transnational AI model training. Drawing on comparative examples from the United States, European Union, and Asia, the paper explores theoretical frameworks of extraterritoriality, conflict-of-laws doctrines, and corporate liability in the AI ecosystem. It also considers ethical implications, including fairness, transparency, and the impact of surveillance-driven AI. The article concludes by recommending policy reforms—such as harmonized treaties, stronger international cooperation, and accountability mechanisms—to balance sovereignty with innovation.
Towards Privacy-Aware Causal Inference in Cloud Security: Detecting Hidden Threat Paths in Multi-Cloud Ecosystems
The growing adoption of multi-cloud ecosystems has revolutionized the delivery of computing resources, offering flexibility, scalability, and resilience. However, this distributed architecture introduces significant challenges for security monitoring, particularly in detecting stealthy or hidden threat paths that traverse across heterogeneous platforms. Traditional rule-based intrusion detection and anomaly detection frameworks often fail to uncover complex interdependencies across cloud services, leaving organizations vulnerable to advanced persistent threats (APTs). This research proposes a privacy-aware causal inference framework designed to detect hidden threat paths in multi-cloud environments by modeling causal dependencies within anonymized or obfuscated logs. The approach leverages graph-based causal reasoning combined with privacy-preserving techniques to balance security observability with user data confidentiality. Experimental validation conducted using obfuscated CloudTrail and Azure Activity logs demonstrates that the proposed method achieves high detection accuracy while reducing the risk of privacy leakage. Comparative results against conventional anomaly detection methods reveal superior performance in terms of precision, recall, and explainability. The findings highlight the potential of privacy-aware causal inference to transform cloud security by enabling transparent, robust, and accountable threat detection in complex, distributed infrastructures.
AI-Driven Causal Graph Models for Cross-Cloud Anomaly and Threat Detection Using Obfuscated CloudTrail Logs
The rapid adoption of multi-cloud and cross-cloud architectures has fundamentally transformed enterprise computing, creating both opportunities and challenges for secure operations. CloudTrail logs are a primary source of operational and security-related data, yet their high dimensionality, noise, and obfuscation complicate anomaly detection and threat identification. Traditional statistical and machine learning techniques often fail to capture the causal dependencies between events across heterogeneous cloud environments. This paper proposes an AI-driven causal graph model to analyze obfuscated CloudTrail logs, leveraging causal inference principles to model dependencies between events and isolate abnormal behaviors. The methodology incorporates graph-based learning, temporal modeling, and domain-specific feature engineering to overcome data sparsity and obfuscation. Experimental evaluation demonstrates the model’s superiority in accuracy, precision, and interpretability compared to baseline anomaly detection techniques. The findings emphasize that causal graphs not only enhance cross-cloud anomaly detection but also improve forensic analysis by clarifying event chains that lead to potential threats.
Predictive Analytics for Loan Default risk using machine learning and real time financial streams
Credit risk assessment has moved beyond static scoring to real-time, data-driven analytics. By integrating diverse data (transactions, behavior, macroeconomic factors), modern ML models continuously evaluate borrower creditworthiness, enabling early warnings of default. Compared to legacy methods, this approach yields substantial predictive gains. We compare several classifiers—logistic regression, SVM, decision trees, random forest, and XGBoost—for loan default prediction. Ensemble tree-based methods consistently outperform others, delivering higher accuracy, precision, and F1 scores. In particular, Random Forest achieved the highest predictive performance, showing strong F1 and precision in identifying risky loans. Across metrics (accuracy, recall, AUC), this ensemble approach surpasses simpler models, as benchmarks confirm. Notably, one study found Random Forest anticipated ~12.7% of high-risk client transitions early and helped avert ~67.6% of potential losses. These findings suggest that a Random Forest–based model provides the most reliable credit-risk forecasts, enhancing decision-making and enabling cost savings in risk management. Crucially, these predictive improvements reinforce evidence-based lending: advanced ML models are noted to instill greater confidence in lenders by providing data-driven insights. By reducing misclassification of risky loans, they directly translate into cost savings and stronger overall financial stability. Ultimately, our results demonstrate that choosing the right ML model is key to robust credit risk management.
Bridging Theory and Practice: Student Perceptions of SDG 8 Pedagogy in Undergraduate Business Courses
The integration of the United Nations Sustainable Development Goals (SDGs) into higher education has emerged as a critical pathway for shaping the competencies of future business leaders. Among these goals, SDG 8—promoting sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all—holds particular importance for business schools that prepare students to navigate the complexities of global labor markets and corporate responsibility. This research paper examines student perceptions of pedagogical approaches aimed at embedding SDG 8 into business school education. Using a mixed-methods approach, the study evaluates the effectiveness of project-based learning, case study discussions, experiential learning, and blended teaching methods in fostering awareness, critical thinking, and applied knowledge. The findings reveal that while students generally appreciate the inclusion of sustainability-oriented topics, their learning experience and perceived value vary significantly depending on the instructional strategy employed. The results underscore the importance of aligning pedagogical techniques with learner expectations, professional aspirations, and practical outcomes, thereby advancing the broader agenda of sustainable and responsible management education.
The Green Algorithm: Optimizing Renewable Energy Integration and ESG Compliance through AI-Powered Market Analytics
Rapid growth in renewable energy capacity and rising investor demand for measurable ESG outcomes create a critical need for tools that align energy market operations with Environmental, Social, Governance objectives. This paper introduces the Green Algorithm, an AI-powered market analytics framework that integrates renewable generation forecasting, market-clearing optimization, anomaly detection, and explainable ESG scoring to support decision-making across utilities, traders, and financial institutions. The Green Algorithm aims to optimize renewable energy integration into electricity markets while improving ESG compliance and operational resilience. The goals are to (1) increase usable renewable supply, (2) reduce market inefficiencies and fraudulent transactions, and (3) produce transparent, audit-ready explanations of how market and investment decisions affect ESG outcomes. We combine multiple machine learning techniques tailored to specific subproblems: sequence models including LSTM and Transformer variants for high-resolution renewable generation forecasting; gradient-boosted decision trees for price impact and portfolio-level prediction; graph neural networks to model participant interactions and systemic risk in energy markets; and autoencoder-based and ensemble anomaly detectors for transaction-level fraud and manipulation detection. Explainability is provided by SHAP value decompositions and counterfactual generators to attribute ESG score changes to specific operational decisions. Models were trained and tested on an integrated dataset linking smart-meter generation traces, market-clearing prices, trading logs, and third-party ESG ratings. Performance was evaluated with standard predictive metrics and domain-specific operational and regulatory metrics: RMSE, MAE, MAPE for forecasts; precision, recall, F1-score, and AUC for classification tasks; and business-oriented measures including reduced curtailment, marginal cost savings, renewable utilization ratio, and changes in portfolio-level ESG scores. The Green Algorithm delivered consistent improvements across forecasting, market efficiency, fraud detection, and ESG alignment. Renewable generation forecast error decreased by 18% relative to established baselines, enabling a 12% reduction in curtailment and a 6% decrease in marginal balancing costs. Market anomaly detectors achieved a precision of 0.92 and a recall of 0.88 on validated incident sets, reducing settlement disputes and suspected fraud volume. When integrated into portfolio decision rules, the system increased aggregate ESG compliance scores by 9% while preserving or improving expected financial returns through optimized bidding and hedging strategies. Explainability outputs improved regulator and investor confidence, shown by reduced model explanation dispute rates and faster audit turnaround. The Green Algorithm demonstrates that thoughtfully combined AI methods can materially improve renewable energy integration and advance ESG compliance in energy-financial markets.
Evaluating Fairness in Machine Learning Models for Loan and Credit Risk Assessment
The integration of machine learning into loan and credit risk assessment has improved predictive accuracy and operational efficiency, yet it has simultaneously amplified concerns about fairness and bias in financial decision-making. Algorithms trained on historical financial and demographic data can inadvertently replicate or even intensify structural inequalities, leading to discriminatory lending practices that disadvantage vulnerable groups [1], [7], [13]. Addressing these challenges requires systematic evaluation of fairness, incorporating both technical and socio-economic perspectives. Recent studies have demonstrated that enforcing fairness constraints may reduce short-term accuracy or profitability but enhance long-term trust, regulatory compliance, and financial inclusion [2], [6], [9]. Approaches range from bias detection techniques, residual unfairness assessments, and counterfactual risk analysis [11], [12], to fairness-aware algorithms and toolkits such as Fairlearn, which enable transparent evaluation and mitigation of model disparities [5]. At the same time, context-conscious frameworks highlight that fairness is not a universal metric but must be adapted to specific legal, cultural, and market environments [7], [10]. This paper evaluates fairness in machine learning models for credit scoring and loan risk prediction by synthesizing advances in fairness metrics, model interpretability, and risk-adjusted performance analysis. Building on prior research in explainability, consumer lending, and regulatory perspectives [4], [10], the study argues that fairness must be treated as both a technical criterion and a socio-economic imperative. The findings emphasize that equitable credit assessment requires balancing predictive performance with ethical responsibility, ensuring not only accurate risk estimation but also inclusive access to financial resources.
Evaluating Hedging Techniques for Effective Risk Management in Global Payment Network
Global payment networks underpin the seamless flow of transactions across borders, facilitating international trade and commerce. However, they are inherently exposed to significant risks such as currency volatility, geopolitical uncertainties, and systemic financial disruptions. Effective hedging techniques are crucial to ensuring the operational stability and financial resilience of these networks. This paper evaluates the effectiveness of various hedging strategies, including forward contracts, options, swaps, and dynamic hedging. Through comprehensive analysis and experimental simulations, this study demonstrates that a hybrid approach combining multiple hedging techniques can significantly mitigate risk, optimize costs, and improve operational outcomes. The findings contribute to the understanding of risk management practices in global payment networks, offering insights for financial institutions and multinational corporations.
Real-Time Fraud Detection in Digital Wallet Systems Using Advanced Machine Learning Algorithms
With the rapid growth of digital payments and the increasing adoption of digital wallet systems, the risk of fraud has escalated significantly. Digital wallets have become a primary method for consumers to store and transfer funds, making them lucrative targets for cybercriminals. The complexity of transactions, coupled with the rise in digital fraud techniques, necessitates the development of robust systems capable of detecting fraudulent activities in real-time. This paper explores the application of advanced machine learning algorithms in real-time fraud detection within digital wallet systems. We delve into the types of fraud commonly encountered, the role of machine learning in identifying suspicious activities, and the challenges faced in building effective fraud detection models. Furthermore, we conduct experiments using various machine learning algorithms, evaluate their performance, and compare results to determine the most effective model for real-time fraud detection. Our findings indicate that machine learning, particularly techniques such as decision trees, neural networks, and ensemble learning, can significantly enhance the accuracy and speed of fraud detection in digital wallets, providing users with enhanced security and reducing financial losses.
The Synergy of Artificial Intelligence and Machine Learning in Reinforcing Fraud Prevention Mechanisms
The convergence of Artificial Intelligence (AI) and Machine Learning (ML) has given rise to powerful tools that have significantly impacted various industries. One of the critical areas benefiting from this synergy is fraud prevention. Fraudulent activities have grown in complexity, and traditional mechanisms often fail to address evolving threats. AI and ML techniques enhance fraud detection systems, providing an adaptive, dynamic approach to identifying fraudulent patterns and behaviors. This research investigates the role of AI and ML in strengthening fraud prevention strategies, focusing on their applications across financial sectors, e-commerce, and cybersecurity. By examining existing frameworks, methodologies, and experiments in fraud prevention, the paper explores the potential of AI and ML to deliver more efficient, accurate, and scalable solutions. The results show how AI-driven systems, leveraging ML algorithms, provide real-time detection and mitigation, thus transforming the way fraud is prevented. The paper concludes by discussing the future of AI and ML in fraud prevention and identifying challenges and opportunities for continued innovation.
Integrating Biometric Security into Digital Payment Solutions: Opportunities and Challenges
Biometric security has emerged as a transformative technology in the digital payment sector, offering enhanced security, convenience, and personalization. This research paper explores the integration of biometric systems into digital payment solutions, providing a comprehensive analysis of the opportunities and challenges involved. It examines the underlying technologies, the advantages of biometric authentication, and the critical obstacles, including privacy concerns, system vulnerabilities, and implementation complexities. Experimental studies are conducted to evaluate the efficacy of biometric methods such as fingerprint scanning, facial recognition, and voice recognition in secure transactions. Results indicate significant improvements in authentication accuracy and user experience but also highlight vulnerabilities to spoofing and data breaches. This paper concludes with a discussion on future directions, emphasizing the need for regulatory frameworks and technological advancements to address the challenges and maximize the potential of biometric security in digital payments.
The Evolution of Biometric Authentication in Payment Systems: Security and Usability Perspectives
Biometric authentication has revolutionized the payment system by providing an enhanced layer of security that aims to mitigate fraud, identity theft, and unauthorized transactions. As digital transactions have grown exponentially, the demand for secure and efficient authentication systems has increased significantly. Biometrics, such as fingerprint recognition, facial recognition, and voice recognition, has emerged as primary methods of authentication. This paper delves into the evolution of biometric authentication in payment systems, focusing on its development, security implications, usability, and challenges. We will present an analysis of current biometric technologies, evaluate their strengths and weaknesses, and explore the balance between security and usability. The study further includes experiments conducted on various biometric systems integrated into payment platforms to assess their effectiveness in real-world scenarios. Ultimately, the paper aims to provide a comprehensive understanding of the role of biometric authentication in transforming payment systems and its potential for future advancements.
Privacy-Preserving Machine Learning with Federated and Split Learning Architectures
The growing integration of artificial intelligence (AI) into sectors such as healthcare, finance, and industrial automation has elevated the urgency for privacy-preserving machine learning (PPML) techniques. Traditional machine learning models typically require centralized data collection, raising concerns about data breaches, misuse, and non-compliance with data protection laws. Federated Learning (FL) and Split Learning (SL) have emerged as potent alternatives that maintain user data locality while enabling collaborative training across distributed devices and institutions. This paper explores the core principles, comparative strengths, and architectural variants of FL and SL in privacy-sensitive contexts. Through detailed experimentation on healthcare and financial datasets, we investigate the efficiency, communication overhead, model performance, and privacy guarantees of these approaches. The results demonstrate that while FL offers scalability and control, SL provides better data privacy at the cost of computational load on central servers. This paper concludes that hybrid and adaptive integration of these models may offer the most robust route toward secure and decentralized AI systems.
Machine Learning for Predictive Maintenance in Industrial Systems
Predictive maintenance (PdM) has emerged as a transformative approach in industrial systems, aimed at minimizing equipment downtime, optimizing maintenance schedules, and improving operational efficiency. Traditional maintenance techniques often lead to either excessive maintenance or unexpected equipment failures. Machine learning (ML) offers a promising avenue to address these limitations by leveraging historical and real-time data to anticipate equipment failures before they occur. This research paper explores the integration of machine learning algorithms for predictive maintenance in industrial environments, discussing the various types of ML models used, data acquisition challenges, model evaluation techniques, and experimental validation. Through extensive experimentation using real-world datasets, we demonstrate that ML-based predictive models significantly outperform traditional methods in predicting faults and optimizing maintenance schedules. The results underscore the potential of ML to drive cost savings, enhance equipment reliability, and ensure the safety of industrial operations.
Stochastic Constraints and Strategic Selection: Exploring Tight Guarantees in Multi-Unit Online Allocation
This research explores the challenges of multi-unit online allocation in environments governed by both stochastic constraints and strategic agent behavior. In such settings, resources arrive sequentially and must be allocated in real time to agents who may act selfishly to maximize their individual gains. Traditional online allocation mechanisms often struggle to maintain efficiency and fairness when faced with uncertainty in item arrivals and the potential for misreported preferences. We propose new algorithmic frameworks that incorporate stochastic information into allocation decisions while preserving strategic robustness. Our mechanisms, designed to be truthful and fair, achieve tight competitive guarantees even in hybrid stochastic-adversarial models. We establish theoretical bounds that highlight the fundamental trade-offs between performance, truthfulness, and adaptivity, and support our analysis with simulation results demonstrating near-optimal behavior under realistic constraints. This work contributes to the broader understanding of how to design resilient and efficient online allocation systems in complex, uncertain, and strategic environments.