Frontiers in Artificial Intelligence Research
Frontiers in Artificial Intelligence Research is a scholarly journal dedicated to advancing the field of artificial intelligence (AI) through high-quality research and interdisciplinary collaboration. The journal serves as a platform for AI experts, computer scientists, data analysts, and professionals from various domains to share the latest innovations, trends, and applications in AI technology.
RAG-Based AI Agents for Enterprise Software Development: Implementation Patterns and Production Deployment
The integration of artificial intelligence (AI) agents into enterprise software development has emerged as a transformative approach to enhance productivity and code quality. Retrieval-Augmented Generation (RAG) represents a paradigm shift in how AI systems access and utilize knowledge, combining the generative capabilities of large language models (LLMs) with dynamic information retrieval mechanisms. This review paper examines the current state of RAG-based AI agents in enterprise software development contexts, focusing on implementation patterns and production deployment strategies. We analyze the architectural foundations of RAG systems, including vector databases, embedding models, and retrieval mechanisms that enable AI agents to access up-to-date code repositories, documentation, and organizational knowledge bases. The paper explores various implementation patterns such as code completion agents, automated testing assistants, documentation generators, and intelligent code review systems. We investigate production deployment challenges including scalability, latency optimization, security considerations, and integration with existing development workflows. Through systematic analysis of recent research and industrial applications, we identify key success factors for deploying RAG-based AI agents in enterprise environments, including context window management, retrieval accuracy, and hallucination mitigation strategies. The review also addresses emerging trends such as multi-agent collaboration, fine-tuning strategies for domain-specific tasks, and hybrid retrieval approaches. Our findings suggest that while RAG-based AI agents demonstrate significant potential for transforming enterprise software development, successful deployment requires careful consideration of organizational context, data privacy requirements, and continuous model evaluation frameworks.
AI Agents; Enterprise Software Development
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Dynamic Workflow Partitioning via Reinforcement Learning for Edge-Cloud Heterogeneous Systems
The proliferation of Internet of Things devices and edge computing infrastructure has created unprecedented opportunities for distributed workflow execution across heterogeneous edge-cloud environments. However, optimal workflow partitioning in such dynamic systems remains a significant challenge due to the complexity of resource heterogeneity, network variability, and diverse application requirements. This paper proposes a novel Dynamic Workflow Partitioning framework leveraging Deep Reinforcement Learning to intelligently distribute workflow tasks between edge nodes and cloud data centers. The framework employs a Deep Q-Network architecture enhanced with a Graph Neural Network encoder to capture workflow dependencies and system state representations. Through comprehensive evaluation using real-world workflow applications including CyberShake, Epigenomics, Inspiral, Montage, and Sipht, our approach demonstrates superior performance in minimizing execution time, reducing network overhead, and maintaining quality of service guarantees compared to traditional heuristic-based methods. The experimental results show that the proposed approach achieves up to 32% reduction in average workflow completion time and 41% improvement in resource utilization efficiency across various heterogeneous edge-cloud configurations.
Mixed Signal Approach Using C++ and DSP Hardware for Low Latency and Secure Speech Systems
Real-time speech systems need to provide quick responses while remaining resistant to security risks. This study presents a mixed-signal design that links C++ modules with DSP hardware to speed up feature extraction and command parsing, while using sandboxing and adaptive checks for protection. Tests were carried out on mobile and smart home devices under quiet, office and street-noise conditions. The system reduced response latency by 34% compared with software-only and hybrid baselines and improved recognition accuracy by 2.5–4.6 percentage points. Latency stayed below 110 ms in all conditions, and replay and injection attack success rates were reduced by 40–60% with adaptive control. An ablation study confirmed that both sandboxing and adaptive checks are necessary to keep these gains. The findings show that the design is new in addressing latency and security together, with clear scientific value and potential for use in mobile, smart home, and healthcare devices, although scaling and energy use remain open challenges.
Copyright (c) 2025 Jonathan R. Clark, Matthew D. Harris, Charlotte L. Chan, Emily K. Wong, Olivia J. Taylor (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Improving Intrusion Detection Robustness Through Adversarial Training Methods
Network Intrusion Detection Systems (NIDS) leveraging deep learning architectures have demonstrated exceptional performance in identifying cyber threats through automated feature learning and pattern recognition. However, recent investigations reveal critical vulnerabilities when these systems encounter adversarial attacks, where malicious actors introduce carefully crafted perturbations to evade detection mechanisms. This paper presents a comprehensive study of adversarial training methodologies specifically designed to enhance the robustness of deep neural network-based NIDS against sophisticated evasion techniques. We systematically investigate multiple adversarial training approaches, integrating both Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attack generation with deep learning architectures including fully-connected Deep Neural Networks (DNN) and Recurrent Neural Networks (RNN). Through extensive experimentation on benchmark intrusion detection datasets, our adversarially-trained models achieve detection accuracy exceeding 94 percent even under strong adversarial perturbations, while maintaining competitive performance on clean network traffic. The research demonstrates that incorporating adversarial examples during training fundamentally reshapes decision boundaries, enabling intrusion detection systems to maintain operational effectiveness in adversarial environments.
Cross-Modal Attention Mechanisms for Inventory Optimization: Fusing IoT Sensor Data, News Sentiment, and Geospatial Information
Modern supply chain management faces unprecedented challenges in maintaining optimal inventory levels amid volatile market conditions, disruption risks, and rapidly changing consumer demands. This paper proposes a novel cross-modal attention framework that integrates Internet of Things (IoT) sensor data, news sentiment analysis, and geospatial information to enhance inventory optimization decisions. The proposed architecture leverages a transformer-based encoder-decoder structure with multi-head attention mechanisms to dynamically weight and fuse heterogeneous data sources, enabling real-time adaptive inventory management. Through the implementation of global attention layers that compute context vectors across all input modalities, the framework captures complex inter-modal dependencies that traditional single-modality approaches fail to represent. Experimental validation demonstrates that the cross-modal attention approach achieves significant improvements in demand forecasting accuracy, reducing mean absolute percentage error by 18.7% compared to conventional methods, while simultaneously improving service levels from 92.3% to 96.8%. Visualization of learned attention alignment matrices reveals interpretable patterns where the model dynamically focuses on relevant information sources based on market conditions. The integration of news sentiment provides early warning signals for demand fluctuations, IoT sensors enable granular monitoring of inventory conditions, and geospatial analysis optimizes distribution network configurations. This work contributes to the emerging field of multimodal fusion in supply chain analytics by demonstrating how attention mechanisms can effectively integrate diverse data modalities for superior decision-making.
Reinforcement Learning Paradigms for Proactive Cybersecurity and Dynamic Risk Management
The escalating sophistication of cyber threats and the dynamic nature of modern network environments necessitate intelligent, adaptive security mechanisms capable of autonomous decision-making and continuous learning. Reinforcement learning (RL) has emerged as a promising paradigm for addressing these challenges by enabling security systems to learn optimal defense strategies through interaction with complex cyber environments. This review examines recent advances in RL applications for proactive cybersecurity and dynamic risk management, focusing on threat detection, intrusion prevention, malware analysis, and adaptive defense strategies. The paper synthesizes current research on various RL paradigms including deep Q-networks (DQN), policy gradient methods, actor-critic algorithms, and multi-agent reinforcement learning (MARL) in security contexts. We analyze how RL-based systems can autonomously discover vulnerabilities, respond to zero-day attacks, optimize security resource allocation, and adapt defense mechanisms in real-time. The review addresses critical challenges including reward function design, exploration-exploitation trade-offs in adversarial environments, sample efficiency, and the interpretability of learned security policies. Emerging trends such as adversarial RL, transfer learning for security applications, and the integration of RL with other artificial intelligence (AI) techniques are discussed. The findings indicate that while RL offers substantial potential for enhancing cybersecurity through autonomous learning and adaptation, practical deployment requires careful consideration of training stability, adversarial robustness, computational constraints, and the need for explainable decision-making in security-critical contexts. This comprehensive review provides researchers and practitioners with insights into the current state, open challenges, and future directions of RL-based cybersecurity systems.
AI Driven Systems for Improving Accounting Accuracy Fraud Detection and Financial Transparency
The use of artificial intelligence (AI) in accounting and finance is reshaping how organizations ensure accuracy, detect fraud, and maintain transparency in their financial operations. This paper reviews how AI-driven technologies-particularly machine learning (ML), deep learning (DL), and natural language processing (NLP)-are being applied to modern accounting systems. We discuss how these tools enhance financial accuracy by automating data processing, identifying anomalies in real time, and correcting errors intelligently. Advanced fraud detection systems based on supervised and unsupervised learning, neural networks, and ensemble methods are shown to recognize suspicious transactions and accounting irregularities with remarkable precision. The paper also explores how AI supports transparency through automated compliance checks, smart auditing systems, and blockchain-based solutions that build trust and accountability. In addition, we highlight recent developments in predictive analytics for financial forecasting, robotic process automation (RPA) in accounting workflows, and explainable AI (XAI) for regulatory compliance. Key implementation challenges are addressed, including data quality, algorithmic bias, model interpretability, and evolving regulatory frameworks. The review further considers how AI integrates with enterprise resource planning (ERP) systems, safeguards sensitive financial data, and raises new ethical questions around automation and human oversight. Finally, we identify emerging directions such as federated learning for cross-organization fraud detection, graph neural networks for analyzing complex transaction patterns, and hybrid human-AI collaboration models. These advancements point toward a future where continuous auditing, multimodal financial analysis, and AI-driven regulatory technologies transform the landscape of accounting and financial management.
Flow Intelligence: Cross-Domain Deep Learning from Environmental Risk to Networked Community Systems
This study explores a unifying perspective on artificial intelligence as flow intelligence—a learning paradigm that adapts to the continuity of time, structure, and uncertainty. Building upon five empirical foundations—ranging from LSTM-based hazard prediction in the Yellow River Basin to hybrid graph-based community detection and sociological analysis of mental health—this research identifies the shared structural principles underlying intelligent systems. Rather than introducing new experiments, this work synthesizes and generalizes findings from these studies to construct a theoretical model of intelligence that integrates memory, modularity, and adaptation. The analysis reveals that when intelligence is designed to flow with systems rather than resist them, it achieves higher coherence, interpretability, and transferability across domains.
Real-Time Cold Storage Temperature Control via CFD–ML Hybrid Modeling
Cold storage facilities play a critical role in maintaining food quality and safety throughout the supply chain, with temperature uniformity and stability being essential for preserving product integrity and minimizing spoilage. Traditional temperature control systems often rely on simplified thermal models that fail to capture the complex three-dimensional airflow patterns and heat transfer phenomena occurring within large-scale cold storage environments. This research presents a novel hybrid modeling approach that combines Computational Fluid Dynamics (CFD) simulations with Machine Learning (ML) algorithms to achieve real-time temperature control optimization in cold storage facilities. The proposed CFD-ML hybrid model integrates high-fidelity CFD simulations for spatial temperature prediction with ML-based predictive control algorithms that can adapt to varying operational conditions and product loads. Our methodology employs a two-stage approach: offline CFD simulations generate comprehensive training datasets capturing diverse operational scenarios, while online ML models provide real-time control decisions based on current sensor measurements and predicted thermal behavior. Experimental validation was conducted in a 2,400 cubic meter commercial cold storage facility over a six-month period, comparing the hybrid approach against conventional PID control systems. Results demonstrate significant improvements in temperature uniformity, with spatial temperature variations reduced by 47% (from ±2.1°C to ±1.1°C) and energy consumption decreased by 23% while maintaining target temperature ranges within ±0.5°C. The ML component achieved prediction accuracies of 95.3% for temperature forecasting up to 2 hours ahead, enabling proactive control adjustments that prevent temperature excursions. The hybrid system demonstrated robust performance across varying ambient conditions, product loading scenarios, and equipment configurations, with average response times of 3.2 minutes for temperature corrections compared to 8.7 minutes for traditional control systems. This research contributes to the advancement of intelligent cold storage management by providing a scalable framework for integrating physics-based modeling with data-driven control strategies.
Copyright (c) 2025 Nathan Baker , Hannah Evans (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cross-Modal Feature Synthesis for Detecting Packaging and Visual Contaminants in Food Safety
Food packaging systems play a crucial role in maintaining food safety, yet they can simultaneously serve as sources of contamination through migration of packaging components and degradation products. Traditional single-modal detection approaches struggle to comprehensively assess the complex interactions between packaging materials, food matrices, and environmental factors that influence contamination dynamics. This research presents an innovative cross-modal feature synthesis framework that integrates active packaging monitoring, advanced biosensor technologies, and multi-modal sensing systems to achieve comprehensive detection of packaging-related and visual contaminants in food safety applications. Our approach combines intelligent packaging systems with nanomaterial-based biosensors and biomimetic sensing technologies to create a unified contamination detection platform. The integrated system utilizes Ti3C2 MXene/MoS2@AuNPs nanocomposite biosensors for electrochemical detection, chitosan-based active packaging systems for controlled-release monitoring, and electronic nose-tongue technologies for volatile and taste compound analysis. Through systematic evaluation across diverse food-packaging combinations, our cross-modal synthesis method demonstrates superior performance with detection accuracies exceeding 96.8% for packaging migrants and 94.2% for visual contaminants. The integration of Principal Component Analysis (PCA) and machine learning algorithms enables real-time processing of multi-dimensional sensor data, facilitating rapid identification of contamination events before they compromise food safety. Key innovations include the development of biomimetic sensor arrays that replicate human sensory perception, nanomaterial-enhanced electrochemical detection systems with improved sensitivity and selectivity, and adaptive data fusion algorithms that optimize detection parameters based on packaging type and food matrix characteristics. The research contributes to advancing intelligent food packaging concepts by providing automated monitoring capabilities that can be seamlessly integrated into existing packaging systems without compromising their primary protective functions.
Copyright (c) 2025 Matthew Young , Isabella Brooks (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Intelligent Task Planning for Reconfigurable Mechanisms Using Reinforcement Learning Based Structural Switching Strategy
Reconfigurable mechanisms can achieve multi-task execution through structural switching, but determining the optimal reconfiguration strategy in complex environments remains a major challenge. This study proposes an intelligent task planning method based on reinforcement learning. The structural switching process is modeled as a Markov decision process, where the action space corresponds to topology changes and the reward function jointly considers task completion rate and energy consumption. A deep Q-network is employed to train the optimal switching strategy. Experiments conducted in 15 task environments demonstrate that the proposed method achieves a 35% improvement in task completion rate and an 18% reduction in average energy consumption compared with baseline search algorithms. Moreover, after training, the decision-making speed on the simulation platform is approximately 10 times faster than that of traditional search methods. These results confirm that reinforcement learning can significantly enhance both efficiency and adaptability in reconfigurable mechanisms, providing an effective pathway for intelligent control of reconfigurable robots and adaptive mechanical systems.
Copyright (c) 2025 Daniel J. Carter, Xiaoling Wu, Emily R. Davies, Thomas H. Bennett, Olivia M. Clarke (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Adaptive Hierarchical RL for Dynamic Workload Management in Programmatic Advertising
Dynamic workload management in programmatic advertising environments presents complex multi-objective optimization challenges that conventional Reinforcement Learning (RL) approaches struggle to address effectively. The inherent difficulties include sparse reward signals, long-term credit assignment problems, and the need for hierarchical decision-making across multiple temporal scales. This study proposes an Adaptive Hierarchical Reinforcement Learning (AHRL) framework specifically designed for programmatic advertising workload management, drawing inspiration from the Manager-Worker paradigm established in hierarchical RL literature. The framework employs a dual-layer architecture where a high-level Manager module operates at reduced temporal resolution to establish strategic goals, while a low-level Worker module executes tactical actions at native system frequency. Through innovative goal embedding mechanisms and transition policy gradient algorithms, the AHRL system effectively decomposes complex advertising placement decisions into manageable hierarchical components. Experimental evaluation demonstrates significant improvements over baseline approaches, with performance gains of 18-25% in revenue optimization and 15-20% in cost efficiency, validating the effectiveness of hierarchical decomposition in programmatic advertising contexts.
Copyright (c) 2025 Vincent Rossi, Min-Jae Park (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Attention-Enhanced Cross-Modal Learning for Detecting Anomalies in System Software
System software anomaly detection has become increasingly critical as modern computing systems grow in complexity and scale. Traditional approaches typically analyze single data modalities in isolation, failing to capture the rich interplay between different system components that often signals anomalous behavior. This paper presents a novel attention-enhanced cross-modal learning framework specifically designed for detecting anomalies in system software environments. Our approach integrates multiple data modalities including system logs, performance metrics, network traffic patterns, and process execution traces through sophisticated convolutional neural network architectures and probabilistic graphical models. The framework employs deep learning-based feature extraction with specialized attention mechanisms that learn to focus on the most discriminative cross-modal relationships, combined with mixture-of-experts approaches that dynamically select the most reliable detection mechanisms based on system context. We introduce adaptive fusion techniques that leverage hierarchical Dirichlet process hidden Markov models for capturing temporal dependencies and switching dynamics in system behavior. Comprehensive evaluation on real-world datasets from enterprise environments demonstrates that our cross-modal approach achieves superior detection performance compared to single-modal baselines, with improvements of 23% in precision and 18% in recall for detecting zero-day attacks and system vulnerabilities. The probabilistic framework provides interpretable explanations for detected anomalies through confidence-based fusion mechanisms that enable security analysts to quickly understand the root causes and take appropriate remediation actions.
Copyright (c) 2025 Rachel Stein (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cross-Domain Applications of Temporal Heterogeneous Graph Contrastive Learning: From Credit Cards to Insurance Claims
Temporal Heterogeneous Graph Contrastive Learning (THGCL) has emerged as a revolutionary approach for modeling complex dynamic relationships in multi-entity financial systems. This research presents a comprehensive cross-domain framework that demonstrates the transferability of THGCL methodologies between credit card fraud detection and insurance claim analysis. Through systematic investigation of heterogeneous graph structures containing multiple entity types including credit card numbers, merchant identifiers, and transaction records, we develop a unified architecture capable of capturing both structural relationships and temporal dynamics across diverse financial domains. Our methodology employs a sophisticated multi-module framework incorporating multi-dimensional gated update mechanisms, noise-aware adversarial modeling, and multi-layer embedding contrastive learning to address challenges including data sparsity, temporal heterogeneity, and class imbalance prevalent in financial fraud detection scenarios. Experimental results demonstrate that our THGCL-based approach achieves superior performance metrics with detection rates reaching 91.2% in credit card fraud detection and 87.8% in insurance claim analysis. The cross-domain adaptation framework exhibits remarkable knowledge transfer capabilities, reducing training time by 43% while maintaining detection accuracy above 89% across both domains. These findings establish THGCL as a robust foundation for developing scalable fraud detection systems capable of adapting to evolving fraudulent patterns across diverse financial sectors.
Copyright (c) 2025 Nathan Hall, Amelia Ward (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Spatiotemporal Prediction Model for Urban Road Freight Flow Based on Residual Graph Convolution and Attention Mechanism
To improve the foresight of urban logistics scheduling, this study proposes a freight flow prediction model that combines residual graph convolutional networks (ResGCN) with a multi-head spatiotemporal attention mechanism. The model constructs a traffic graph using the structure of the road network and integrates factors such as freight orders, road conditions, and holidays, aiming to capture non-Euclidean correlations between nodes and multi-dimensional temporal variations. Experimental results on a real-world freight platform dataset show that the proposed model achieves improvements of 14.5% and 11.8% in MAE and RMSE, respectively, compared with traditional LSTM and TCN methods.
Copyright (c) 2025 James R. Beckett, Olivia M. Chan, Thomas A. Ridley, Eleanor K. Shaw (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Application of Deep Learning in Music Genre Classification
Music genre classification is a fundamental task in Music Information Retrieval, yet achieving high accuracy remains challenging due to overlapping genre characteristics. This paper investigates deep learning approaches—specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs)—for automatic music genre classification using the GTZAN dataset. Audio tracks are transformed into time-frequency representations (spectrograms and Mel-frequency cepstral coefficients, MFCCs) to serve as input features for deep models. We design and evaluate a CNN model that treats spectrograms as images and a hybrid CNN–RNN architecture that captures both spectral patterns and temporal dynamics of music. The study details the data preprocessing (including audio segmentation and feature extraction), network architectures, training configuration, and evaluation metrics (accuracy, precision, recall, F1-score, and confusion matrix). We also explore model optimization strategies such as regularization (dropout) and hyperparameter tuning to improve generalization. Experimental results demonstrate that the proposed deep learning models achieve high classification performance on GTZAN, with the best model (a CNN with bidirectional gated recurrent unit) attaining an accuracy of approximately 89% on the test set. A detailed analysis of the results, including per-genre performance and confusion matrix, confirms that the deep learning approach outperforms traditional methods in capturing music genre characteristics.
Copyright (c) 2025 Minghao Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Reinforcement Learning-Based Framework for Autonomous Optimization in Artificial Intelligence Systems
Modern artificial intelligence (AI) systems often operate under dynamic conditions where static configurations lead to suboptimal performance. This paper proposes a novel reinforcement learning (RL)-based framework for autonomous, real-time optimization of AI systems. The framework employs a deep RL agent to continuously adjust computational resource allocation, algorithm configurations, and hyperparameters in response to changing workloads and performance feedback. We implement the framework using the OpenAI Gym toolkit to simulate an AI environment, focusing on minimizing latency and efficient resource utilization. The RL agent learns an adaptive policy (using Proximal Policy Optimization) that tunes system parameters to balance low processing delay with low resource cost. Experiments show that the proposed approach significantly reduces end-to-end latency while improving resource usage compared to static and heuristic baselines. The agent autonomously adapts to workload variations, achieving up to 40% latency reduction and higher resource efficiency. These results demonstrate the potential of reinforcement learning for self-optimizing AI systems, enabling real-time adaptive control and improved performance in complex, dynamic environments.
Copyright (c) 2025 Xing Chang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cloud-Native Microservice Architecture for Inclusive Cross-Border Logistics: Real-Time Tracking and Automated Customs Clearance for SMEs
Small and medium-sized enterprises (SMEs) face high barriers to efficient cross-border logistics due to fragmented tracking, complex customs regulations, and costly legacy systems. This paper proposes a cloud-native microservice platform for cross-border logistics that integrates real-time shipment tracking and automated customs clearance to lower these barriers. The architecture employs containerized microservices orchestrated in the cloud, with a lightweight API gateway enabling multi-tenant operation and tenant-specific data isolation. Key components include a real-time tracking service (aggregating live shipment data from carriers and IoT devices) and a rule-driven customs clearance service (automating tariff calculation and document compliance checks). We leverage an open-source business rules engine for customs compliance, allowing rapid updates to regulatory rules without changing core code. Experiments on a prototype deployment demonstrate near-linear scalability – throughput increases from ~50 requests/s with 1 instance to over 5400 requests/s with 100 instances – and low latency under load. Automated customs processing on the platform reduces clearance times dramatically (e.g. from 2–3 days manually to <24 hours) through digital workflow automation. Compared to existing logistics platforms (e.g. project44 for enterprise visibility, or FlavorCloud for e-commerce shipping), our solution is distinguished by its open, multi-tenant architecture and focus on SME inclusivity. SME customers can adopt the platform with minimal upfront cost, benefiting from pay-per-use pricing and modular integration into their supply chain. The results demonstrate that our approach can significantly streamline global trade operations for SMEs, lowering costs and delays while improving supply chain transparency. We conclude that a cloud-native microservice architecture is a promising foundation for inclusive digital trade infrastructure supporting SME globalization.
Copyright (c) 2025 Zhiwen Fang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Autonomous CPU Resource Allocation in Cloud Environments Using Reinforcement Learning
Efficient CPU resource allocation is essential for optimizing performance and cost in cloud environments, where workloads are dynamic and multi-tenant applications demand real-time adaptability. Traditional allocation strategies rely on static heuristics or rule-based scheduling, which often fail to scale or generalize under rapidly changing conditions. This paper proposes an autonomous CPU resource allocation framework based on reinforcement learning (RL), which dynamically learns optimal allocation policies by interacting with the cloud environment. We present a model-free deep reinforcement learning (DRL) agent capable of adjusting CPU shares across virtual machines (VMs) and containers based on workload patterns, performance feedback, and system constraints. Experimental results on both simulated and real cloud workloads demonstrate that the proposed method significantly outperforms baseline strategies in terms of utilization efficiency, task latency, and SLA compliance. The framework introduces a scalable, adaptive, and fully automated solution for CPU resource management in cloud computing.
Copyright (c) 2025 Miguel Alvarez (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Computational and Data-Driven Integration for Sustainable Solar Thermal Collector Design
Solar thermal collectors represent a critical technology for sustainable energy harvesting, yet their optimal design remains challenging due to complex multi-physics interactions and numerous design parameters. This research presents a comprehensive computational framework that integrates Computational Fluid Dynamics (CFD) with data-driven optimization techniques to enhance solar thermal collector performance. The methodology combines advanced numerical modeling with machine learning algorithms to achieve both high prediction accuracy and computational efficiency. A validated three-dimensional CFD model was developed using ANSYS Fluent to simulate heat transfer phenomena within various collector geometries, including air-based systems with transverse triangular blocks and parabolic trough concentrators. The study generated 935 numerical cases across diverse operational parameters, which were subsequently used to train artificial neural networks (ANN), support vector regression (SVR), and linear regression models. The optimized ANN model achieved coefficient of determination values of 0.94, demonstrating superior predictive capabilities compared to traditional approaches. Entropy analysis identified thermal conductivity as the most influential parameter, contributing approximately 20% to overall thermal efficiency. The integrated approach successfully reduced computational time from over 1500 seconds for full CFD simulations to approximately 10 milliseconds for ANN predictions, while maintaining prediction accuracy within 6% of experimental data. Results indicate that collector designs incorporating heat transfer enhancement features such as triangular blocks and optimized geometric parameters achieve thermal efficiencies up to 68%, representing a 15% improvement over conventional configurations. Temperature distribution analysis revealed optimal operating ranges between 298K and 340K for maximum heat transfer effectiveness. The framework demonstrates significant potential for accelerating solar thermal system development while reducing computational costs and improving design optimization capabilities.
A Neural Adaptive Assessment Framework for Real-Time Knowledge Estimation
Real-time knowledge estimation in educational assessment systems requires sophisticated frameworks that can continuously adapt to evolving student knowledge states while providing accurate and reliable measurements across diverse learning contexts. Traditional assessment approaches rely on static models that cannot effectively capture the dynamic nature of learning processes or adapt quickly to changing student performance patterns. The challenge lies in developing systems that can provide immediate, accurate knowledge state estimates while maintaining computational efficiency and educational validity in real-time assessment scenarios. This study proposes a Neural Adaptive Assessment Framework (NAAF) that integrates deep neural networks with adaptive algorithms to enable real-time knowledge estimation in educational environments. The framework employs recurrent neural architectures to model temporal learning patterns while utilizing adaptive mechanisms to continuously update knowledge state estimates based on real-time student interactions. The system incorporates uncertainty quantification techniques to provide confidence measures for knowledge estimates and employs reinforcement learning principles to optimize assessment strategies dynamically. Experimental evaluation using comprehensive educational datasets demonstrates that the proposed framework achieves 43% improvement in knowledge estimation accuracy compared to traditional adaptive testing methods. The neural adaptive approach results in 38% faster convergence to accurate knowledge states and 35% better prediction of future student performance. The framework successfully maintains real-time responsiveness with average processing latency under 100 milliseconds while providing interpretable knowledge state assessments that support educational decision-making and personalized learning interventions.
Multi-Objective Reinforcement Learning for Anticipatory Data Placement across Diverse Storage Technologies
Modern data centers deploy diverse storage technologies including solid-state drives, persistent memory, optical storage, and tape systems to optimize cost-performance trade-offs across varying workload requirements. Traditional data placement strategies fail to effectively leverage the heterogeneous characteristics of diverse storage technologies, resulting in suboptimal resource utilization and missed opportunities for performance optimization. The challenge lies in anticipating future data access patterns while simultaneously optimizing multiple conflicting objectives including access latency, storage costs, energy consumption, and data durability across heterogeneous storage infrastructures. This study proposes a Multi-Objective Reinforcement Learning (MORL) framework for anticipatory data placement across diverse storage technologies. The framework employs Pareto-based optimization techniques combined with Deep Deterministic Policy Gradient (DDPG) algorithms to learn optimal placement policies that balance competing objectives. Predictive models forecast data access patterns and technology-specific performance characteristics, enabling proactive placement decisions that anticipate future system requirements. Experimental evaluation using real-world datacenter workloads demonstrates that the proposed framework achieves 47% reduction in average access latency while decreasing overall storage costs by 38% compared to traditional placement methods. The anticipatory approach reduces data migration overhead by 34% through proactive placement decisions, while the multi-objective optimization ensures balanced performance across all optimization criteria including energy efficiency and data durability requirements..
A Hybrid Transformer and GNN Framework for Interpretable Fair Value Classification in Accounting
Fair value classification in accounting requires accurate assessment of financial instruments across three hierarchical levels, where Level 1 represents quoted market prices, Level 2 involves observable market inputs, and Level 3 relies on unobservable inputs with significant estimation uncertainty. Traditional classification methods struggle with complex financial instruments that exhibit ambiguous characteristics across multiple levels, leading to inconsistent valuations and regulatory compliance issues. This study proposes a hybrid framework combining Transformer architecture with Graph Neural Networks (GNNs) for interpretable fair value classification. The Transformer component captures sequential patterns in financial instrument characteristics and market conditions, while the GNN component models relationships between related financial entities and market participants. An interpretability module provides transparent classification reasoning, enabling auditors and regulators to understand the decision-making process. Experiments on real-world financial datasets demonstrate that the proposed framework achieves superior classification accuracy compared to traditional methods while maintaining high interpretability. The integration of attention mechanisms and graph-based learning enables comprehensive analysis of financial instrument complexity, market liquidity, and valuation uncertainty, resulting in more reliable fair value classifications that align with accounting standards and regulatory requirements
A Motion Synthesis Framework Without Motion Capture, Integrating Language and Visual Priors
To address the problem that human motion synthesis heavily depends on motion capture data, this paper introduces a motion synthesis framework without motion capture, which integrates language and visual priors. This method builds a multimodal representation module to map natural language and image prompts into a shared latent space. A skeleton structure constraint network is also designed to improve the physical plausibility and continuity of motion generation. Experiments are carried out on HumanML3D, UMLS-Motion and a self-constructed multimodal instruction set. The results show that the proposed method improves motion quality (Frechet Gesture Distance) and coherence by 9.4% and 7.8%, respectively, compared to existing methods. Transfer tests show that the method performs well in both cross-domain and zero-shot prompts. The results confirm that combining visual and language-based multimodal prompts can effectively enhance the diversity and controllability of motion generation.
Copyright (c) 2025 Emily Carter, Jason M. Bennett, Sofia Almeida, Thomas R. Walker (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Responsible AI in Tax Filing: Legal and Ethical Challenges of LLM-Based Assistants
The integration of Large Language Models (LLMs) into tax preparation workflows introduces both transformative potential and significant legal and ethical challenges. While these AI-driven assistants can enhance efficiency, accessibility, and accuracy in tax filing, they also raise concerns regarding data privacy, compliance, liability, bias, and explainability. This paper explores the current landscape of LLM-based tax filing assistants, analyzing their capabilities and limitations within legal frameworks such as GDPR and IRS regulations. It also highlights the ethical dilemmas arising from automation in decision-making, including the risks of misinformation and reduced human accountability. The study concludes by proposing a responsible AI framework that emphasizes transparency, user control, and regulatory alignment to ensure safe and effective deployment of LLMs in tax services.
Graph-Based Deep Learning for Fault Localization in Service Dependency Networks
As microservice architectures scale in complexity, identifying the root causes of failures in distributed service ecosystems becomes increasingly challenging. Traditional fault localization approaches often fall short in capturing the intricate dependency relationships and dynamic behaviors of services. This paper presents a graph-based deep learning framework designed to perform fault localization in service dependency networks with high precision and explainability. By modeling service-to-service interactions as directed graphs and employing Graph Neural Networks (GNNs) to capture structural and temporal patterns, the proposed method outperforms conventional statistical and rule-based techniques. Experimental evaluation on real-world microservice datasets shows that our model can detect and localize faults with significant improvements in accuracy, latency, and robustness. This work lays the foundation for autonomous monitoring and recovery in cloud-native environments.
Towards Adaptive API Traffic Management: A Systematic Architecture for High Availability and Performance
As modern applications increasingly rely on distributed microservices, managing API traffic has become central to ensuring system reliability and responsiveness. High volumes of client requests, coupled with variable network conditions and fluctuating backend load, challenge the scalability and resilience of traditional API gateways. This paper presents a systematic architecture for adaptive API traffic management, integrating intelligent load balancing, request prioritization, and real-time feedback control to optimize performance and availability. We design a layered framework that dynamically adjusts routing decisions based on service health, response latency, and resource utilization. Through simulation and real-world deployment scenarios, our approach demonstrates significant improvements in throughput, failure recovery, and response time consistency when compared to static policies. The proposed architecture offers a scalable and resilient foundation for cloud-native systems in high-demand environments
Multi-Task Learning for Sentiment and Topic Classification in Social Media Texts
Social media platforms have emerged as rich sources of textual data, offering insights into public opinion, consumer preferences, and emerging topics. However, extracting meaningful information from such unstructured and noisy content presents considerable challenges. This study proposes a multi-task learning (MTL) framework to simultaneously perform sentiment classification and topic classification on social media texts. By sharing representations across tasks, the model leverages interrelated patterns and dependencies between sentiment and topical content. Experimental results demonstrate that the MTL approach outperforms single-task baselines in both accuracy and generalization, especially in scenarios with limited labeled data. The integration of attention mechanisms and transformer-based encoders further enhances model interpretability and robustness, offering a scalable solution for real-time social media analytics.
Hadoop-based Online Shopping Behavior Analysis: Design and Implementation
This research conducts big data analysis based on open-source Taobao user behavior data. Leveraging the Hadoop big data analytics platform, we performed multi-dimensional user behavior analysis on the publicly available Alibaba Tianchi dataset to provide actionable insights for e-commerce sales decisions. The study utilizes open-source Taobao user behavior data, where each row represents an individual user action. The dataset was first uploaded to Hadoop's HDFS storage. Subsequently, we configured Hadoop's Flume component to automate data ingestion, loading the data into a Hive database for comprehensive analysis. Key e-commerce metrics—including PV (Page Views), UV (Unique Visitors), bounce rate, and repurchase rate—were statistically analyzed. A multi-dimensional perspective was applied to examine user behavior patterns and activity levels across time dimensions. Additionally, we conducted statistical analyses on top-selling item IDs, popular product categories, and user geographic distribution. The resulting analytical tables were stored in Hive. Using Sqoop, these result tables were automatically exported to a relational MySQL database for efficient storage and analytical presentation. For visualization, Python's PyEcharts library was employed to create front-end interactive displays. By querying datasets from MySQL, we generated multi-dimensional visualizations to enhance data interpretability. Finally, PyEcharts' `Page` method facilitated the design of an interactive dashboard, while static HTML deployment enabled a dynamic large-screen visualization interface. These visually rich presentations empower decision-makers to rapidly derive strategic insights.