European Journal of AI, Computing & Informatics
European Journal of AI, Computing & Informatics (EJACI) is an international, refereed, double-blind peer-reviewed, open-access journal dedicated to the advancement of research and development in the fields of artificial intelligence, computing, and informatics. The journal publishes original research articles, reviews, and technical papers that address the challenges, methodologies, and innovations in these rapidly evolving areas. It provides a platform for interdisciplinary collaboration and knowledge sharing among researchers, practitioners, and educators from diverse domains, contributing to the global scientific community's understanding of AI and its applications in computing and informatics.
Research on the Renovation Design Path for Enhancing the Efficiency of Mixed-Use Office and Retail Spaces
Urbanization and the evolving demands of modern commercial environments have increasingly highlighted the necessity for sustainable urban renewal, particularly concerning existing building stock. This article focuses on how to systematically enhance the operational and spatial efficiency of mixed-use office and retail spaces during the process of existing building renovation. By conducting a comprehensive analysis from the perspective of architectural space design, this study addresses the inherent complexities of integrating diverse functional requirements within a single structure. After clarifying the core principles of spatial organization for multi-functional environments and establishing a renovation framework based on benefit orientation, specific analyses are conducted on critical issues. These include functional layout optimization, pedestrian flow organization, dynamic space usage, and the provision of human-centric facilities. To resolve these challenges, multiple targeted strategies are proposed, such as reshaping functional relationships, optimizing circulation route design, implementing flexible space conversions, and integrating advanced building facilities and smart technologies. Furthermore, a rigorous quantitative indicator system and detailed case comparisons are utilized to empirically evaluate changes in space efficiency, flow intensity, and overall energy consumption. The analytical results demonstrate that, under the premise of optimizing both the spatial structure and underlying business strategies, the proposed design path can effectively improve the utilization rate of composite spaces and enhance management orderliness. Ultimately, this research provides a robust theoretical foundation and practical reference for future sustainable renovation projects in high-density urban contexts.
Copyright (c) 2026 Qiyuan Liang (Author) https://creativecommons.org/licenses/by/4.0
AI-Assisted Decision Support in Housing Policy Implementation: Distinguishing Deterministic Rules, Manual Review, and Heuristic Suggestion
U.S. housing policy directly shapes the housing options available to homeowners and families, but easing zoning rules and broadening legal permission do not, on their own, translate into completed homes. Implementation difficulties surface at two distinct stages: many eligible homeowners never formally apply once they encounter the cost, complexity, and uncertainty of pre-application analysis, and even among those who do apply, a substantial share of permitted projects never reach completion. The principal site of friction is the implementation phase, not the legalization phase—and addressing it requires mechanisms that translate policy rules into parcel-level feasibility judgments that property owners, designers, and local governments can rely on. This article examines how artificial intelligence and related computational methods should be applied to residential buildability assessment. The central question is where AI should sit—and where it should not—within decisions that involve regulatory compliance. The article proposes a three-layer architecture distinguishing rule sets, deterministic calculation, and heuristic suggestion. Matters involving regulatory boundaries—setbacks, lot coverage, building separation—should be handled through deterministic rule sets and calculation; AI may legitimately assist in building and maintaining such systems, but should not perform runtime compliance adjudication. Site conditions that cannot be reliably confirmed from public data should enter manual review. Heuristic methods are appropriate for ranking, placement suggestion, and scenario comparison within the feasible space defined by deterministic rules. Accessory dwelling units (ADUs) serve as the principal scenario for discussion and validation, but the architecture is not specific to ADUs—it applies more broadly to small-scale residential development, adaptive reuse, accessory-structure approval, and land-use change review. The article is presented as a normative contribution to the literatures on AI in public-rule systems, housing policy implementation, and digital governance of the built environment.
Copyright (c) 2026 Jingyuan Huang (Author) https://creativecommons.org/licenses/by/4.0
Research on Intelligent Automation Control Strategies for Reducing Building Energy Consumption and Carbon Emissions
This research investigates intelligent automation control strategies aimed at reducing building energy consumption and carbon emissions. By integrating advanced control algorithms, sensor technologies, and data-driven optimization techniques, the study explores innovative solutions to enhance energy efficiency in buildings. A comprehensive methodology is employed, encompassing system modeling, experimental validation, and performance analysis. Results demonstrate significant potential for intelligent automation to minimize energy usage and emissions while maintaining occupant comfort. The findings provide actionable insights for sustainable building management and contribute to global efforts in mitigating climate change.
Copyright (c) 2026 Rongdong Chen (Author) https://creativecommons.org/licenses/by/4.0
Frontiers in Artificial Intelligence Algorithm Optimization: Fermatean Fuzzy Deep Neural Networks for Uncertainty-Aware Decision-Making
With the rapid development of Artificial Intelligence (AI) technology, contemporary AI decision-making systems face significant challenges when dealing with high levels of uncertainty, imprecise data, and complex decision-making environments. Traditional deep learning models often struggle to maintain performance under such ambiguous conditions. Fermatean Fuzzy Theory (FFT), which utilizes advanced fuzzy numbers to comprehensively describe uncertainty, provides AI systems with enhanced flexibility and robustness. This is especially critical in fields such as multi-criteria decision-making, compromise programming, and reinforcement learning. To further improve the capability of uncertainty modeling, this paper proposes a novel Fermatean Fuzzy Deep Neural Network (FF-DNN) framework by systematically integrating Fermatean fuzzy theory into deep learning architectures. This innovative framework enables the rigorous fuzzification of input data, network weights, and activation functions, thereby significantly enhancing the overall robustness, generalization, and adaptability of neural networks operating in highly uncertain environments. From the perspective of artificial intelligence algorithm optimization, this study deeply explores the synergistic integration of fuzzy theory and deep learning for uncertainty-aware decision-making. Furthermore, this paper comprehensively examines the practical application of Fermatean Fuzzy Theory in AI decision-making, particularly highlighting its distinct advantages and inherent challenges in handling uncertainty and fuzziness. Finally, the study validates the effectiveness and superiority of the proposed FF-DNN framework through rigorous theoretical analysis and extensive case study discussions, demonstrating its potential to revolutionize complex decision support systems.
Copyright (c) 2026 Jinghan Li (Author) https://creativecommons.org/licenses/by/4.0
Integrated Application of Liquid Lycra Technology and AI Bionic Structural Design in Optimizing Pressure Distribution in Shapewear Swimwear
BALNEAIRE partnered with the Lycra Group to establish the country's first collaborative laboratory. This research integrates Liquid Lycra Technology and AI-driven bionic structural design to optimize pressure distribution in shapewear swimwear. By combining advanced material science with computational design techniques, the study aims to enhance comfort, functionality, and aesthetic appeal. A systematic methodology was employed to analyze material properties, simulate pressure distribution, and validate results through experimental testing. The findings demonstrate significant improvements in pressure uniformity and wearer comfort, providing a transformative approach to shapewear swimwear design.
Copyright (c) 2026 Cun Zhang (Author) https://creativecommons.org/licenses/by/4.0
Research on the Application and Practice of AI Recruitment in Improving Talent Screening Efficiency
This research article investigates the application and practice of Artificial Intelligence (AI) in recruitment processes to enhance talent screening efficiency across contemporary organizational landscapes. Traditional recruitment methods often suffer from inherent human biases, procedural inconsistencies, and highly time-consuming administrative procedures, ultimately leading to suboptimal hiring decisions and increased operational costs. AI-driven recruitment tools, leveraging advanced machine learning algorithms, natural language processing, and predictive data analytics, offer robust potential solutions to effectively mitigate these persistent challenges. This comprehensive study explores the multifaceted effectiveness of AI integration across various critical stages of the recruitment lifecycle, specifically including targeted job advertisement, automated resume screening, dynamic candidate assessment, and intelligent interview automation. We systematically analyze the measurable impact of AI on essential key performance indicators, such as time-to-hire, cost-per-hire, and overall quality-of-hire metrics. Furthermore, we critically address the pressing ethical considerations and potential algorithmic biases embedded in AI systems, proposing actionable strategies for responsible, transparent, and legally compliant AI implementation in human resources. By examining diverse real-world case studies and extensive empirical data, this research provides profound insights into the current state and future trajectories of AI recruitment. It offers strategic recommendations for organizations actively seeking to optimize their talent acquisition frameworks while rigorously maintaining fairness, diversity, and equity. Ultimately, the findings significantly contribute to the evolving academic understanding of human-AI collaboration in human resources management and inform the continuous development of more effective, sustainable, and ethical AI-powered recruitment solutions.
Copyright (c) 2026 Tianran Yuan (Author) https://creativecommons.org/licenses/by/4.0
Decision-Making in Industry 4.0: A Layered Framework for Converting Cyber-Physical Information into Governed Action
Industry 4.0 has significantly expanded the amount, speed, and granularity of information available to modern industrial organizations, but richer information does not automatically produce better or more effective decisions. The fundamental practical problem is to systematically convert cyber-physical observations into accountable actions under conditions of uncertainty, strict operational constraints, and necessary human governance. To address this gap, this paper develops a comprehensive conceptual framework for Industry 4.0 decision-making that seamlessly connects sensing, state representation, prediction, optimization, authorization, execution, and continuous learning. The proposed framework is organized around three core claims. First, industrial decision-making should be treated as a dynamic, closed-loop process rather than a static, one-way analytics pipeline. Second, decision models must be carefully aligned with the specific time horizon, observability, uncertainty, and reversibility of the action being selected. Third, reliability-oriented applications—such as inspection, maintenance, inventory control, mission abort, and system reconfiguration—serve as highly useful integration cases because they clearly expose the critical connection between physical degradation, cyber representation, prescriptive optimization, and governed execution. Ultimately, the paper contributes a novel layered architecture, a horizon-based model alignment table, and a robust closed-loop decision cycle that can be utilized to organize future academic research and practical implementation. The resulting perspective shifts critical attention away from isolated technological solutions and toward the holistic quality of the complete decision loop, ensuring the system observes relevant states, represents uncertainty properly, recommends feasible actions, assigns authority clearly, and learns effectively from realized outcomes.
Copyright (c) 2026 Suzhen Huang (Author) https://creativecommons.org/licenses/by/4.0
A Systematic Approach to Building Content Ecosystems: An End-to-End Perspective from Creation to Distribution
Emphasizing an end-to-end perspective from content creation to dispersion, this research article explores a taxonomical coming to building content ecosystem, and by analyzing current methodology and better recitation. This study fundamentally introduces a robust model that incorporate technical promotion and strategical preparation to optimise content ecosystems. Into segment insure the unveiling, literature review, method, answer, treatment, and end, the inquiry is structured, thereby the article include comprehensive data analysis, flowchart, and comparison tables that furnish abstruse brainwave into efficient ecosystem management. On big-scale content initiation, the focussing is, through logistic distribution networks, engage quantitative and qualitative method. Hence to raise content strategy. The determination add by providing actionable solutions for ecosystem development.
Copyright (c) 2026 Rui Li (Author) https://creativecommons.org/licenses/by/4.0
Design and Implementation of a Blockchain-Based Student Competency Modeling and Intelligent Employment Matching System
Addressing the persistent challenges and growing disconnect between higher education talent cultivation strategies and the dynamic employment needs of modern enterprises, this paper designs and implements an innovative blockchain-based student competency modeling and intelligent employment matching system. Currently, the recruitment landscape is plagued by unreliable student competency data, static and one-sided competency profiles, and notably low accuracy in employment matching. To overcome these critical limitations, the proposed system adopts a robust consortium blockchain architecture. This decentralized approach ensures the highly reliable storage, immutability, and mutual recognition of multi-source heterogeneous competency data across various educational and corporate stakeholders. Furthermore, the system constructs a comprehensive, multi-dimensional competency index framework that meticulously encompasses academic achievements, practical abilities, and essential professional qualities. To accurately reflect evolving student capabilities, we propose a dynamically weighted competency profile model that seamlessly integrates the analytic hierarchy process (AHP), the entropy weighting method, and a time decay function. Additionally, a multi-dimensional intelligent matching mechanism is introduced, effectively integrating attributes, dynamic weights, and trend similarity to optimize candidate selection. Comprehensive system implementation and rigorous evaluation results demonstrate that the proposed architecture performs exceptionally well in terms of functional applicability, performance efficiency, and data security. Most importantly, its matching accuracy and interpretability are significantly superior to those of traditional models. Ultimately, this research provides a highly effective new technical path and a practical, scalable solution for ensuring accurate talent supply and demand matching between universities and enterprises.
Copyright (c) 2026 Lianchao Zhang (Author) https://creativecommons.org/licenses/by/4.0
MCATSA: Multi-Strategy Collaborative Adaptive Tree-Seed Algorithm and Its Application in Dynamic Credit Risk Assessment
Credit risk assessment lies at the absolute core of maintaining global financial system stability and preventing systemic economic crises. However, traditional static evaluation models increasingly suffer from significant limitations when dealing with the complexities of multi-source heterogeneous data, intricate spatiotemporal risk contagion, and the urgent need for dynamic decision-making in modern financial markets. To comprehensively address these critical issues, this paper proposes a novel integrated risk control framework, denoted as IOA-DGNN-RL. This advanced architecture is fundamentally based on a newly improved Multi-Strategy Collaborative Adaptive Tree-Seed Algorithm (MCATSA), a Dynamic Graph Neural Network (DGNN), and Reinforcement Learning (RL) techniques. Firstly, the proposed MCATSA substantially improves the standard Tree-Seed Algorithm through the integration of five core mechanisms, which include heterogeneous chaotic initialization, multi-layer resource allocation, and nonlinear energy regulation. These enhancements achieve highly accurate dimensionality reduction of high-dimensional non-financial features, thereby optimizing computational efficiency. Secondly, a sophisticated DGNN model incorporating key macroeconomic indicators is constructed to precisely capture spatiotemporal risk propagation within the complex topological network among interconnected enterprises. Finally, a robust reinforcement learning decision module is designed to seamlessly transform dynamic risk predictions into actionable, optimal credit adjustment strategies. Comprehensive experiments demonstrate that the MCATSA performs excellently in standard benchmark optimization tasks. Furthermore, the complete integrated system achieves an impressive Area Under the Curve (AUC) of 0.901 on complex real-world credit datasets, significantly outperforming existing baseline methods and providing a highly reliable tool for modern financial risk management.
Copyright (c) 2026 Kaiyu Huang (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Manufacturing and Autonomous Driving Technologies Are Driving the Upgrading of the US New Energy Vehicle Industry
This paper focuses on analyzing the transformative advantages brought by intelligent manufacturing and unmanned driving technologies within the rapidly evolving United States new energy vehicle (NEV) industry. Currently, the global and domestic NEV sectors have fundamentally evolved from being primarily based on basic electrification to being heavily dominated by economies of scale, advanced artificial intelligence, and highly coordinated development across the entire global value chain. Within this context, intelligent manufacturing serves as a critical catalyst. It can significantly enhance overall manufacturing efficiency and bolster crisis response capabilities by meticulously optimizing complex production processes, improving rigorous quality control mechanisms, and enabling seamless, real-time information sharing throughout the supply chain network. Simultaneously, the integration of autonomous driving technologies can profoundly facilitate product intelligence, drive the platformization of mobility services, and lead to the fundamental reconfiguration of traditional automotive business models. Through a comprehensive analysis of current market trends and technological advancements, this study proposes that in the forthcoming stage of industrial development, strategic efforts must be prioritized. Specifically, stakeholders should focus on the robust construction of an integrated intelligent manufacturing system, the seamless coordination of key supply chain links, the widespread popularization of autonomous driving applications, and the strengthening of government-enterprise collaboration. Ultimately, these concerted initiatives will effectively promote the US new energy vehicle industry towards achieving higher operational levels, superior product quality, and a genuinely green, sustainable developmental trajectory.
Copyright (c) 2026 Junchun Ding (Author) https://creativecommons.org/licenses/by/4.0
Research on Measuring User Behavior Response Differences Supported by Propensity Scoring Method
Measuring the differences in user behavior responses is increasingly crucial for identifying targeted intervention effects and improving overall digital platform operation in the era of big data. However, observational studies often face significant methodological challenges. To overcome the profound influence of user characteristic differences, inherent sample selection bias, and unobserved confounding factors, the propensity score method is systematically utilized to construct a robust measurement system. This study meticulously classifies users based on the specific type of behavior response and deeply explores the empirical possibility of applying this advanced statistical approach in complex groups that were not randomly sampled. A comprehensive and complete model is constructed around critical methodological issues, including the precise selection of dependent variables, the rigorous elimination of covariates, the accurate estimation of propensity scores, and the implementation of advanced sample matching and weighting techniques. Furthermore, analytical methods such as inter-group comparison, heterogeneity testing, and extensive robustness testing are adopted to significantly enhance the accuracy, reliability, and persuasiveness of the measurement results. Ultimately, this research provides vital technical support and actionable insights for platform administrators and marketers in identifying nuanced behavioral characteristics, rigorously evaluating marketing intervention effects, and strategically optimizing enterprise operations for sustainable growth and improved user engagement.
Copyright (c) 2026 Zhimeng Liu (Author) https://creativecommons.org/licenses/by/4.0
A Review of Integrated Artificial Intelligence and Big Data Analytics Models for Intelligent Decision-Making
This review paper examines the integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) models for intelligent decision-making. The study explores historical advancements, core methodologies, comparative analyses, challenges, and future perspectives. By dissecting the synergy between AI and BDA, the paper highlights their combined potential in optimizing decision-making processes across industries. Key themes include the evolution of AI-BDA frameworks, algorithmic innovations, and real-world applications. The paper also delves into challenges such as scalability, data privacy, and ethical considerations, while proposing future directions for research and development. The findings aim to provide a comprehensive understanding of the current state and future trajectory of integrated AI-BDA models.
Copyright (c) 2026 Zice Gao (Author) https://creativecommons.org/licenses/by/4.0
Causally Grounded LLM Attribution Agents for High-Dynamic Logistics Systems: Design and Experimental Validation
High-dynamic logistics systems frequently generate anomalies due to interacting operational mechanisms like demand surges, driver shortages, and exogenous shocks. While large language models (LLMs) can transform heterogeneous telemetry into natural-language explanations for operator diagnosis, unconstrained language reasoning remains unreliable for root-cause attribution in systems with structured dependencies. To address this, we propose a causally grounded attribution agent architecture integrating a streaming state-preparation layer, a structural causal graph (SCG) to constrain admissible cause-effect paths, a quantitative attribution core, and an LLM reasoning layer. This framework converts grounded evidence into reliable explanations and intervention suggestions. We validate the core components on a controlled synthetic benchmark. The SCG-aligned model achieves a superior macro F1 score of 0.753 on the in-distribution test set and demonstrates robust performance under distribution shifts, outperforming random forest and ungrounded heuristic baselines. Furthermore, a graph misspecification study confirms that the SCG provides critical structural information beyond mere regularization, as removing a single causal edge significantly reduces accuracy. Finally, an LLM evaluation across multiple grounding configurations reveals that full causal grounding improves attribution accuracy by 20 to 35 percentage points, with smaller models benefiting disproportionately. Ultimately, this study contributes a robust, causally grounded agent architecture and a replicable cross-tier evaluation framework for LLM-based causal reasoning, laying the groundwork for future validation on production telemetry and downstream operational impact assessments.
Copyright (c) 2026 Sixuan Li (Author) https://creativecommons.org/licenses/by/4.0
Learning Job Competency Models from Historical Recruitment Data Using Supervised Machine Learning
Based on the structured representation of job responsibilities, qualification requirements, and skill information within historical recruitment texts, this study investigates data-driven learning methods for job competency models. The research focuses on feature representation of recruitment texts, construction of job competency labels, and training mechanisms for supervised learning. It details the processes of identifying competency elements, learning structural patterns, and constructing hierarchical structures. Experimental validation using a multi-position recruitment dataset yielded an accuracy of 0.851 on test samples and a macro-average F1 score of 0.832. Technical R&D positions achieved 0.892 accuracy, while operational management positions reached a macro-average recall of 0.842. Results demonstrate that the supervised learning framework reliably reconstructs job competency combinations.
Copyright (c) 2026 Yuerong Yan (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Digitalization and Network Technology on the Inheritance and Innovation of Traditional Culture
This review paper explores the intersection between digitalization, network technology, and the inheritance and innovation of traditional culture, with a particular focus on the role of computing technologies in these processes. Digitalization, which involves converting analog information into digital formats, alongside network technologies enabling global communication and data exchange, has significantly transformed how traditional cultural practices are preserved, adapted, and disseminated. This paper examines the evolution of these technologies, emphasizing how advancements such as artificial intelligence, machine learning, blockchain, and virtual reality have reshaped cultural preservation and innovation. Key themes include the application of computational techniques to cultural heritage, digital artistic expression, and the globalization of cultural exchange. The review also addresses the challenges of digital appropriation, homogenization, and the digital divide. By synthesizing existing literature, the paper provides a comprehensive overview of the multifaceted impact of digitalization and network technology, with a focus on computing-driven innovations, offering insights into future research directions and policy considerations.
Copyright (c) 2026 Anshi Yin, Joan P. Lazaro, Ronnel P. Agulto, Qiang He (Author) https://creativecommons.org/licenses/by/4.0
Research on Lightweight LLM Recommendation Algorithm in Few-Shot Cold-Start Scenarios
To address the persistent challenges of feature sparsity, weak generalization ability, and high computational cost faced by traditional recommendation systems in few-shot cold-start scenarios, this paper proposes a novel, lightweight large language model (LLM)-based recommendation algorithm named LLM-RecLite. As digital platforms increasingly rely on personalized content delivery, mitigating the cold-start problem remains critical for user retention. The proposed LLM-RecLite algorithm first performs rigorous domain adaptation on lightweight LLMs using parameter-efficient fine-tuning techniques, specifically QLoRA. This step effectively bridges the semantic gap between general-purpose linguistic representations and specific recommendation tasks without incurring prohibitive training costs. Secondly, the methodology incorporates a meticulously designed hierarchical prompt template that seamlessly integrates historical user-item interactions with rich content features, enabling robust semantic reasoning under strictly few-shot conditions. Finally, the framework introduces an advanced knowledge distillation mechanism to transfer the complex reasoning capabilities of the larger model to a significantly more lightweight inference model. This ensures the system meets the stringent low-latency performance requirements of real-time recommendation environments. Comprehensive experimental results conducted on two widely recognized public datasets, MovieLens-1M and Amazon Beauty, demonstrate the superior efficacy of the proposed approach. Compared with traditional cold-start algorithms and mainstream LLM-based recommendation frameworks, LLM-RecLite significantly improves the NDCG@10 metric by 18.3% and 9.7%, respectively, while simultaneously increasing inference speed by 4.2 times. Ultimately, this research effectively balances recommendation accuracy and computational efficiency, providing a highly feasible and scalable solution for few-shot cold-start recommendations in resource-constrained industrial applications.
Copyright (c) 2026 Zhenyu Ni (Author) https://creativecommons.org/licenses/by/4.0
URP-Based Shell Volumetric Fur Rendering with Multi-Layer Transparency and Ambient Occlusion: Optimization and Style Adaptation for Real-Time Game Graphics
This research article explores the optimization and style adaptation of URP-based shell volumetric fur rendering, enhanced with multi-layer transparency and ambient occlusion, for real-time game graphics. We investigate the performance implications of various parameters within the Universal Render Pipeline (URP) when rendering complex fur structures using a shell-based volumetric approach. The study focuses on implementing and optimizing multi-layer transparency to simulate the depth and density of fur, as well as integrating ambient occlusion to enhance the realism of light interaction within the fur volume. Furthermore, we explore methods for adapting the fur’s appearance to different artistic styles, considering aspects like color palettes, density variations, and geometric shaping. Our methodology involves a combination of shader development, performance profiling, and subjective visual evaluation. We analyze the impact of shell count, texture resolution, and transparency levels on rendering time, frame rates, and overall visual quality. Through iterative refinement and optimization, we aim to provide practical guidelines for achieving high-quality fur rendering in real-time game environments while maintaining artistic flexibility. The findings contribute to the advancement of efficient and visually appealing fur rendering techniques in game development, offering a balance between performance and aesthetic control. The developed techniques are evaluated in a test scene using Unity’s URP, demonstrating significant performance improvements and stylistic adaptability compared to naive implementations. This research provides insights into the challenges and opportunities of fur rendering in real-time applications, paving the way for more immersive and visually compelling game experiences.
Copyright (c) 2026 Yimier Yilihamujiang (Author) https://creativecommons.org/licenses/by/4.0
Task-Constrained Manipulation Planning in Robot Joint Space Using Long Short-Term Memory Networks
This study delves into comprehensive randomized trajectory planning within the robot joint-space framework, specifically tailored for articulated mechanisms constrained by task-space requirements. A novel representation of constrained motion is formulated to facilitate joint-space planning, leveraging Long Short-Term Memory (LSTM) networks as a cornerstone methodology. These networks adeptly encapsulate temporal dependencies and non-linear dynamics, enabling robust trajectory prediction and constraint adherence. The framework introduces two pioneering approaches for sampling joint configurations: tangent-space sampling (TS) and first-order retraction (FR), both designed to enhance global sampling efficiency for linear task-space transformations. Theoretical analysis substantiates the efficacy of FR in ensuring convergence to globally optimal solutions. This methodology addresses real-world applications encompassing workspace-coordinated tasks, such as precise rotational movements, guided linear translations, or maintaining stability under transport-induced perturbations. Furthermore, the joint-space approach effectively exploits redundant degrees of freedom (DOFs), ensuring obstacle avoidance and auxiliary goal satisfaction during task execution. Comparative evaluations reveal that the proposed methods, underpinned by LSTM networks, exhibit superior adaptability and reduced sensitivity to parametric variations relative to existing paradigms.
Copyright (c) 2026 Ali Rezaei, Ahmed Khan, Omar Hamdi, Juan Carlos López, Diego Paredes, Krzysztof Zalewski (Author) https://creativecommons.org/licenses/by/4.0
Study of Generative AI in Automated Financial Report Generation
This paper provides a comprehensive review of the rapidly evolving field of Generative AI in automated financial report generation. Traditionally, financial report generation has been a labor-intensive process relying on manual data aggregation, analysis, and narrative composition. However, recent advancements in Generative AI, particularly Large Language Models (LLMs), have demonstrated the capacity to automate and significantly enhance this process. This paper describes the current state-of-the-art, tracing the historical development of AI techniques applied to financial reporting. The paper examines core themes such as the application of LLMs for narrative generation from structured financial data, and the use of Generative Adversarial Networks (GANs) for synthetic data generation and fraud detection. A critical comparison of different Generative AI models is presented, highlighting their strengths and weaknesses in the context of financial reporting, alongside a discussion of the inherent challenges, including data bias, regulatory compliance, and the need for explainable AI. Finally, the paper explores future research directions, such as the integration of multi-modal data, the development of more robust and transparent AI models, and the ethical considerations surrounding the widespread adoption of Generative AI in finance. This review aims to provide researchers, practitioners, and regulators with a thorough understanding of the opportunities and challenges presented by Generative AI in transforming the landscape of financial report generation.
Copyright (c) 2026 Jialong Li (Author) https://creativecommons.org/licenses/by/4.0
LPDM-YOLOv8: A Lightweight Low-Light Object Detection Method Based on Laplacian Pyramid
To address the performance degradation of YOLOv8 under low-light conditions, this paper proposes a lightweight low-light object detection method based on a Laplacian pyramid. A Laplacian Pyramid Dimming Module (LPDM) is integrated into YOLOv8 to construct an end-to-end detection framework with illumination-adaptive enhancement and multi-scale feature fusion. The proposed module performs brightness-aware adjustment and hierarchical detail reconstruction, effectively improving feature representation in dark scenes with negligible computational overhead. Experiments on the ExDark dataset show that LPDM-YOLOv8n achieves an mAP of 0.524, corresponding to an 8.0% relative improvement over the baseline YOLOv8n, while maintaining real-time performance at 32 FPS. Notably, only 16 additional parameters are introduced without increasing FLOPs. The results demonstrate that the proposed method significantly enhances detection robustness under low-light conditions while preserving efficiency, making it suitable for real-time applications.
Copyright (c) 2026 Yahao Guo, Dongxiang Fu (Author) https://creativecommons.org/licenses/by/4.0
Graph Neural Network-Based Governance of Fraudulent Traffic: Detecting and Suppressing Fake Impressions and Clicks in Digital Platforms
Digital advertising platforms rely heavily on impression and click metrics to quantify user engagement, but fraudulent traffic-including fake impressions generated by bot farms and click farming-causes over $68 billion in annual losses for global advertisers. Traditional anti-fraud methods, such as rule-based engines and isolated machine learning (ML) models, fail to capture the complex relational patterns among users, advertisements, and traffic sources, leading to high false positive rates and poor adaptability to evolving fraud tactics. To address these gaps, this study proposes a Dynamic Graph Neural Network-based Fraud Traffic Governance Framework (DGNN-FTG) that integrates real-time detection and adaptive suppression in a unified pipeline. First, we construct a User-Advertisement-Medium (UAM) dynamic interaction graph that encodes spatio-temporal behavioural features and relational dependencies, overcoming the limitations of static, single-node modelling. Second, we design a Spatio-Temporal Dynamic GNN (ST-DGNN) for fraud detection, which incorporates temporal attention to track behaviour evolution and spatial attention to identify anomalous relational clusters. Third, we develop an adaptive suppression module that adjusts traffic filtering thresholds based on detection confidence, combined with a false-positive compensation mechanism to minimize disruption to legitimate users. Validated on a real-world dataset from a leading e-commerce advertising platform, DGNN-FTG achieves a fraud detection F1-score of 92.8%, outperforming traditional XGBoost and static GNN models by 18.3% and 11.5%, respectively. The suppression module reduces fraudulent traffic by 89.2% while maintaining a false positive rate of only 2.1%, balancing anti-fraud efficacy and user experience. This framework provides a scalable, real-time solution for digital platforms to combat fraudulent traffic and safeguard the integrity of the advertising ecosystem. Notably, DGNN-FTG exhibits strong generalization across platform types, achieving an F1-score of over 90% when deployed on both e-commerce and social media advertising systems, with minimal retraining required for cross-platform adaptation. For small and medium-sized advertisers, the framework's low false positive rate translates to a 22.3% reduction in wasted ad expenditure, directly improving their return on investment (ROI) in digital marketing campaigns. Additionally, the dynamic graph update mechanism enables DGNN-FTG to detect emerging fraud tactics-such as AI-generated fake user behaviour-within 48 hours of their appearance, far faster than the 7-10 day response time of traditional anti-fraud systems.
Copyright (c) 2026 Wenwen Liu (Author) https://creativecommons.org/licenses/by/4.0
Low-Cost Predictive Maintenance Modeling for SMB Fleets Using Operational Data
This research explores the application of low-cost predictive maintenance (PdM) models for small and medium-sized business (SMB) fleets, leveraging readily available operational data. SMB fleets often lack the resources for sophisticated PdM systems. This study investigates the feasibility of using easily accessible telematics data, such as mileage, fuel consumption, and basic engine diagnostics, to predict component failures and optimize maintenance schedules. We compare the performance of several machine learning algorithms, including logistic regression, support vector machines (SVM), and random forests, in predicting failures of critical fleet components. The models are trained and validated using a real-world dataset from a diverse SMB fleet. The results demonstrate that even with limited data and computational resources, effective PdM models can be developed to reduce downtime, lower maintenance costs, and improve the overall operational efficiency of SMB fleets. Furthermore, the study provides a framework for SMBs to implement these models using open-source tools and cloud-based platforms, thus minimizing upfront investment. The implications of this research are significant for SMBs looking to enhance their fleet management strategies through data-driven decision-making.
Copyright (c) 2026 Ziru Wang (Author) https://creativecommons.org/licenses/by/4.0
Evaluating the Impact of Lightweight AI Architectures on SMB Customer Retention: A Case Study of High-Performance, Low-Cost Systems
This research article investigates the impact of lightweight Artificial Intelligence (AI) architectures on Small and Medium-sized Businesses’ (SMB) customer retention. The study focuses on high-performance, low-cost AI systems and their effectiveness in enhancing customer engagement and reducing churn. Given that SMBs play a critical role in the U.S. economy, democratizing access to high-performance AI is essential for sustaining this sector, preventing SMB bankruptcy, and protecting jobs. We analyze various lightweight AI models, including optimized deep learning networks and efficient machine learning algorithms, implemented on resource-constrained infrastructure. The research employs a case study approach, examining several SMBs across different sectors that have adopted these AI solutions. Key performance indicators (KPIs) related to customer retention, such as churn rate, customer lifetime value, and customer satisfaction scores, are evaluated. Furthermore, the study explores the trade-offs between AI model complexity, computational cost, and customer retention benefits. The findings provide practical insights for SMBs seeking to leverage AI for improved customer relationship management without incurring significant financial or operational overhead. The study also provides theoretical contributions in the field of efficient AI deployment in resource-constrained environments.
Copyright (c) 2026 Zhenyuan He (Author) https://creativecommons.org/licenses/by/4.0
Research on AI-Based Voice-Based Real-Time Assessment of Psychological Stress
This research investigates the application of artificial intelligence (AI) to assess psychological stress in real-time through voice analysis. Addressing the limitations of traditional stress assessment methods, which are often subjective and retrospective, this study explores the potential of AI to provide objective and immediate stress evaluations. The methodology involves collecting voice samples from participants under varying stress conditions, extracting relevant acoustic features, and training machine learning models to classify stress levels. Key acoustic features include pitch, speech rate, intensity, and spectral characteristics. We compare the performance of different AI models, including Support Vector Machines (SVMs), Random Forests, and Deep Neural Networks (DNNs), in accurately detecting stress. The experimental results demonstrate the feasibility and reliability of using AI-based voice analysis for real-time stress assessment. The proposed system offers significant advantages in various applications, such as mental health monitoring, workplace stress management, and emergency response scenarios. The findings highlight the potential of AI technology to transform the way stress is measured and managed, paving the way for more proactive and personalized interventions. Future work will focus on improving the robustness and generalizability of the models across diverse populations and environments.
Copyright (c) 2026 Jingyi (Jesy) Zhang (Author) https://creativecommons.org/licenses/by/4.0
Drivers of Generative AI Adoption in Higher Education: A fsQCA Study on Student Motivations and Technology Perceptions
Generative Artificial Intelligence (GenAI) is reshaping higher education, yet the drivers of students' continuance intention in academic contexts remain underexplored. Building on the Rich Intrinsic Motivation (RIM) framework and technology adoption theories, this study investigates the configurational effects of intrinsic motivations (accomplishment, knowledge, stimulation), extrinsic motivation (perceived usefulness), and technology characteristics (ease of use, novelty) on GenAI adoption. Using Fuzzy-Set Qualitative Comparative Analysis (fsQCA) on data from 238 university students, the study reveals that no single factor is necessary for adoption. Instead, four distinct sufficient configurations drive high continuance intention: (1) "Happy Achievers" (Hedonic-Mastery), (2) "Curious Explorers" (Hedonic-Knowledge), (3) "Conquerors" (Pure Mastery), and (4) "Determined Strivers" (Utilitarian-Striving). These findings highlight the complex interplay between motivational and technological factors, offering tailored insights for educators to foster sustainable GenAI integration in learning.
Generative Artificial Intelligence (GenAI)
fsQCA (Fuzzy-Set Qualitative Comparative Analysis)
Copyright (c) 2026 Rongjing Yuan (Author) https://creativecommons.org/licenses/by/4.0
Democratizing Data Analytics: Lightweight AI Solutions to Improve Operational Efficiency in SMEs
Small and medium-sized enterprises (SMEs) generate significant amounts of operational data, yet often lack the resources and technical expertise to transform this data into actionable insights. Traditional data analytics solutions are frequently too complex, costly, or resource-intensive for SMEs, limiting their ability to leverage data effectively. This paper explores the role of lightweight artificial intelligence (AI) in democratizing data analytics and enhancing operational efficiency within SMEs. Lightweight AI solutions are characterized by ease of deployment, low technical barriers, and a focus on practical, high-impact applications. They enable SMEs to optimize business processes, gain insights into customer behavior, and improve resource allocation without requiring substantial infrastructure or specialized personnel. Implementation considerations include clearly defining business objectives, adopting incremental pilot projects, engaging employees, and maintaining transparency and human oversight in interpreting analytical outputs. While limitations such as data quality, resource constraints, and the potential for overreliance on automated recommendations exist, SMEs can mitigate these challenges through careful planning, iterative adoption, and flexible decision-making. Looking forward, lightweight AI has the potential to integrate progressively into SME operations, fostering a culture of data-driven decision-making and enabling organizations to respond more effectively to dynamic business environments. By leveraging accessible AI tools, SMEs can enhance efficiency, improve operational resilience, and build sustainable competitive advantages.
small and medium-sized enterprises (SMEs)
Copyright (c) 2026 Zhijun Liu (Author) https://creativecommons.org/licenses/by/4.0
Explainable AI Pipelines for Behavioral Fraud Modeling in Online Retail Environments
With online retail fraud posing growing threat to consumers, explainable AI (XAI) has become increasingly important for transparent and actionable risk assessment. This paper presents an XAI-integrated pipeline for behavioral fraud modeling, fusing supervised ensembles of logistic regression and random forests with unsupervised isolation forests to detect both known and emerging behavioral anomalies, including irregular cart sequences and geolocation inconsistencies. SHAP-based attributions are incorporated to deliver instance-level explanations that enhance auditability and support compliance requirements (e.g., PCI DSS). Using a heterogeneous dataset of 150,000 transaction records, the proposed system achieves an F1-score of 0.93 and reduces false positives and manual interventions by 82% relative to an industry-standard rule-based baseline. The architecture supports offline batch analysis and scalable serverless deployment. Pilot studies indicate potential operational cost reductions driven by decreased review workloads and improved detection efficiency. The open-source implementation fosters iterative community refinements, advocating XAI's role in fortifying e-commerce resilience against evolving threats like synthetic identities.
Copyright (c) 2026 Siqi Chen (Author) https://creativecommons.org/licenses/by/4.0
Study on Risk Assessment Methods and Multi-Dimensional Control Mechanisms in AI Systems
As Artificial Intelligence (AI) rapidly transitions from experimental prototypes to critical infrastructure, the historical "Performance-First" paradigm has left systems inherently vulnerable to adversarial attacks and data manipulation. This dissertation addresses the critical lack of standardized, quantitative methods for managing these risks by introducing the Risk Assessment Model for AI (RAM-AI). Utilizing a dual-domain simulation approach across Computer Vision and Financial datasets, the study empirically quantifies the "robustness boundary" of deep learning models. The findings reveal that single-layer defenses are inadequate; specifically, models exhibit "Data Hypersensitivity," suffering non-linear performance collapse under data poisoning rates as low as 3%. Furthermore, standard accuracy metrics fail to detect high-confidence evasion attacks. To mitigate these vulnerabilities, the research validates a Multi-dimensional Control Framework that integrates technical safeguards—such as adversarial training and input sanitization—with procedural governance, including Human-in-the-Loop (HITL) protocols. The results demonstrate that this Defense-in-Depth architecture significantly recovers system integrity, reducing critical error rates by 88% in high-stakes scenarios, and offers a strategic playbook for Enterprise Risk Management in the era of emerging AI regulations.
Copyright (c) 2026 Chong Lam Cheong (Author) https://creativecommons.org/licenses/by/4.0
Research on Optimization of Neuromuscular Rehabilitation Program Based on Physiological Assessment
This study focuses on the rehabilitation of neuromuscular dysfunction and explores the key role of physiological assessment in the design of rehabilitation plans. And analyzed the current problems of fragmented evaluation methods, weak functional correlation of indicators, and unclear results transformation pathways from the characteristics of physiological parameters such as surface electromyography (sEMG), near-infrared spectroscopy (NIRS), and heart rate variability (HRV). At present, it is necessary to establish a multimodal fusion evaluation system, strengthen functional oriented modeling thinking, and establish a standard mapping mechanism between physiological indicators and intervention prescriptions. In order to achieve personalized rehabilitation pathways for physiological data, promote the scientific and accurate nature of neuromuscular rehabilitation, and provide theoretical and practical references for sports rehabilitation and physical therapy.
Copyright (c) 2026 Yihao Wang (Author) https://creativecommons.org/licenses/by/4.0