European Journal of AI, Computing & Informatics
type: channel
description: 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.
publisher: Pinnacle Press
language: en
publication name: European Journal of AI, Computing & Informatics
issn: 3079-871X
items: Drivers of Generative AI Adoption in Higher Education: A fsQCA Study on Student Motivations and Technology Perceptions
items: Democratizing Data Analytics: Lightweight AI Solutions to Improve Operational Efficiency in SMEs
items: Explainable AI Pipelines for Behavioral Fraud Modeling in Online Retail Environments
items: Study on Risk Assessment Methods and Multi-Dimensional Control Mechanisms in AI Systems
items: Research on Optimization of Neuromuscular Rehabilitation Program Based on Physiological Assessment
items: Leveraging Ensemble Machine Learning for Credit Risk Assessment in Underserved U.S. Small Businesses
items: ESE-Net: Edge-Shape Enhancement Network for Infrared Small Target Detection
items: Generative AI Test Generation and Intelligent Defect Attribution Method for Large Scale Distributed Systems
items: Multi-Armed Bandits and Robust Budget Allocation: Small and Medium-sized Enterprises Growth Decisions under Uncertainty in Monetization
items: Cross Modal Data Understanding Based on Visual Language Model
items: Use Generative Al and Natural Language Processing to Improve User Interaction Design
items: The Challenges and Opportunities of Leading an AI ML Team in a Startup
items: Innovative Application of AI in Medical Decision Support System and Implementation of Precision Medicine
items: Integrating Artificial Intelligence into Senior Healthcare: Innovation, Policy Alignment, and Impact of the ElderAI Wellness Hub
items: AI-Driven Cross-Cloud Operations Language Standardisation and Knowledge Sharing System
items: An Open-Source AI-Driven CRM Model: Balancing Performance, Cost, and Accessibility for Small Businesses
items: From Algorithm to System: Integrated Design of Compiler and Toolchain for Large Model Inference Optimization
items: Causal Attribution and Evaluation Based on Large-Scale Advertising Data
items: Application Status and Efficiency Improvement Path of AI-Assisted Tools in the UI Design Process
items: Design of a Generative AI-Driven Intelligent Investment Advisory System
items: Neurodevelopmental Screening for Autism: EEG-Driven Deep Learning in Early Childhood
items: Research on PID Control Parameter Tuning of Quadrotor UAV Based on Improved Intelligent Optimization Algorithm
items: Application of Network Security Vulnerability Detection and Repair Process Optimization in Software Development
items: Innovations in Machine Translation: The Role of Machine Learning in Enhancing Linguistic Accuracy and Efficiency
items: High-Performance Cloud-Based System Design and Performance Optimization Based on Microservice Architecture
items: Leveraging Large Language Models for Anomaly Event Early Warning in Financial Systems
items: Research on Cross-Modal Semantic Alignment Methods for Low-Resource Languages
items: Bridging the AI Adoption Gap: What Drives U.S. SME Owners' Willingness-to-Pay for Supply-Chain Risk Software?
items: Research on AI-Based Multilingual Natural Language Processing Technology and Intelligent Voice Interaction System
items: Data Consistency and Performance Scalability Design in High-Concurrency Payment Systems

Drivers of Generative AI Adoption in Higher Education: A fsQCA Study on Student Motivations and Technology Perceptions
type: item
description: 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.
subject: Article
  topic: section
subject: Generative Artificial Intelligence (GenAI)
  topic: keywords
subject: higher education
  topic: keywords
subject: motivations
  topic: keywords
subject: technology adoption
  topic: keywords
subject: fsQCA (Fuzzy-Set Qualitative Comparative Analysis)
  topic: keywords
creator: Rongjing Yuan
rights: Copyright (c) 2026 Rongjing Yuan (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-20
publication date: 2026-01-20
starting page: 67
ending page: 75
doi: 10.71222/w80kh941

Democratizing Data Analytics: Lightweight AI Solutions to Improve Operational Efficiency in SMEs
type: item
description: 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.
subject: Article
  topic: section
subject: lightweight AI
  topic: keywords
subject: small and medium-sized enterprises (SMEs)
  topic: keywords
subject: operational efficiency
  topic: keywords
subject: data-driven decision-making
  topic: keywords
subject: business process optimization
  topic: keywords
creator: Zhijun Liu
rights: Copyright (c) 2026 Zhijun Liu (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-17
publication date: 2026-01-17
starting page: 58
ending page: 66
doi: 10.71222/wsperd79

Explainable AI Pipelines for Behavioral Fraud Modeling in Online Retail Environments
type: item
description: 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.
subject: Article
  topic: section
subject: explainable AI
  topic: keywords
subject: behavioral fraud modeling
  topic: keywords
subject: logistic regression ensembles
  topic: keywords
subject: SHAP interpretability
  topic: keywords
subject: e-commerce cybersecurity
  topic: keywords
subject: false positive reduction
  topic: keywords
subject: predictive transaction scoring
  topic: keywords
subject: scalable ML deployment
  topic: keywords
creator: Siqi Chen
rights: Copyright (c) 2026 Siqi Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-17
publication date: 2026-01-17
starting page: 47
ending page: 57
doi: 10.71222/n9rch013

Study on Risk Assessment Methods and Multi-Dimensional Control Mechanisms in AI Systems
type: item
description: 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.
subject: Article
  topic: section
subject: AI security
  topic: keywords
subject: quantitative risk assessment
  topic: keywords
subject: adversarial machine learning
  topic: keywords
subject: Defense-in-Depth
  topic: keywords
subject: data poisoning
  topic: keywords
subject: human-in-the-loop
  topic: keywords
creator: Chong Lam Cheong
rights: Copyright (c) 2026 Chong Lam Cheong (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-15
publication date: 2026-01-15
starting page: 31
ending page: 46
doi: 10.71222/58dr7v22

Research on Optimization of Neuromuscular Rehabilitation Program Based on Physiological Assessment
type: item
description: 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.
subject: Article
  topic: section
subject: neuromuscular function
  topic: keywords
subject: physiological evaluation
  topic: keywords
subject: multimodal fusion
  topic: keywords
creator: Yihao Wang
rights: Copyright (c) 2026 Yihao Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-12
publication date: 2026-01-12
starting page: 21
ending page: 30
doi: 10.71222/b2hwa691

Leveraging Ensemble Machine Learning for Credit Risk Assessment in Underserved U.S. Small Businesses
type: item
description: This study investigates the role of ensemble machine learning techniques in improving credit risk assessment for underserved small businesses in the United States. Conventional credit evaluation systems rely heavily on formal financial documentation and collateral requirements, excluding a large proportion of early-stage, minority-owned, or cash-based enterprises. Drawing on the uploaded document's empirical modeling structure, the study integrates gradient boosting frameworks, random forest classifiers, and macro-behavioral feature engineering to construct a multidimensional risk assessment model. The methodology incorporates structured preprocessing, cross-validated ensemble training, and interpretability analysis using SHAP values. Results show that boosting-based models consistently outperform traditional approaches, achieving stronger precision, recall, and AUC scores while capturing nuanced behavioral and macroeconomic interactions. The findings highlight the pot ential of ensemble learning to expand access to credit, reduce misclassification bias, and support inclusive economic development. The study concludes with recommendations for lenders, policymakers, and researchers regarding the deployment of ensemble analytics and the future integration of fairness-aware modeling.
subject: Article
  topic: section
subject: ensemble learning
  topic: keywords
subject: credit risk
  topic: keywords
subject: underserved businesses
  topic: keywords
subject: boosting models
  topic: keywords
subject: financial inclusion
  topic: keywords
subject: machine learning interpretability
  topic: keywords
creator: Zaolin Zhang
rights: Copyright (c) 2026 Zaolin Zhang (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-12
publication date: 2026-01-12
starting page: 14
ending page: 20
doi: 10.71222/5tj1w207

ESE-Net: Edge-Shape Enhancement Network for Infrared Small Target Detection
type: item
description: Infrared small target detection (ISTD) aims to segment small targets from infrared images and is widely applied in military and industrial fields. Although recent deep learning-based methods have achieved remarkable performance, they often fail when targets are indistinguishable from complex backgrounds. This is mainly due to the limited use of spatial domain features, which cannot capture subtle boundary cues, making precise segmentation challenging. To address this, we propose an Edge-Shape Enhanced Network (ESE-Net), which reinforces edge feature representations to improve target discrimination in complex infrared scenes. First, we design a Multiscale Spatial Edge Attention (MSEA) module to strengthen target edges by perceiving directional gradient changes. To suppress background noise while highlighting target boundaries, we introduce an Edge Guidance Module (EGM) that extracts edge features in the frequency domain via a wavelet transform and performs reversible down sampling, discarding low-frequency components before fusing with spatial features. Furthermore, a Multiscale Group Convolution Module (MGCM) is integrated in deep layers to preserve target details and mitigate the risk of small target loss. Experiments on the NUAA-SIRST and IRSTD-1K datasets demonstrate the effectiveness of our method.
subject: Article
  topic: section
subject: infrared small target detection
  topic: keywords
subject: edge enhancement
  topic: keywords
subject: spatial-frequency fusion
  topic: keywords
subject: semantic segmentation
  topic: keywords
creator: Sen Yang
creator: Guanxun Cui
rights: Copyright (c) 2026 Sen Yang, Guanxun Cui (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-10
publication date: 2026-01-10
starting page: 1
ending page: 13
doi: 10.71222/ag4mh549

Generative AI Test Generation and Intelligent Defect Attribution Method for Large Scale Distributed Systems
type: item
description: To address the increasingly complex demands of large-scale distributed systems in test generation and defect localization, this study proposes a strategy grounded in a generative AI-driven test generation and intelligent attribution framework. The test environment is first constructed through the formal definition of system architecture, business semantics, and interaction constraints, ensuring that the generated tests accurately reflect real operational logic. Then, leveraging the reasoning and combinatorial capabilities of large-scale generative models, the test environment is expanded into a diversified set of execution paths, thereby enhancing the system's ability to cope with highly dynamic and uncertain runtime conditions and improving test coverage in complex operational states. Furthermore, multi-source observability data-including logs, traces, metrics, and dependency metadata-is integrated and modeled to produce a structured representation for abnormal correlation analysis. Based on this representation, causal reasoning is applied to derive dependency relationships and event propagation paths among system modules, enabling effective and efficient root cause diagnosis across distributed nodes. This approach significantly reduces the diagnostic search space and improves the interpretability of system anomalies. Experimental validation using a model test prototype demonstrates that the proposed method outperforms traditional testing and fault attribution approaches in terms of test diversity, fault excitability, and accuracy of causal determination. These results indicate that the framework offers robust support for the construction of AI-based assurance mechanisms in large-scale distributed systems, contributing to improved system reliability, fault tolerance, and automated quality assurance.
subject: Article
  topic: section
subject: generative AI
  topic: keywords
subject: test generation
  topic: keywords
subject: observability causal inference
  topic: keywords
subject: defect attribution
  topic: keywords
creator: Mingde Guo
rights: Copyright (c) 2025 Mingde Guo (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-16
publication date: 2025-12-16
starting page: 98
ending page: 105
doi: 10.71222/x16fdb68

Multi-Armed Bandits and Robust Budget Allocation: Small and Medium-sized Enterprises Growth Decisions under Uncertainty in Monetization
type: item
description: Small and medium-sized enterprises (SMEs) often face significant uncertainty when allocating limited advertising budgets across multiple channels, as the return on investment (ROI) of each channel is typically unknown and volatile. This paper proposes a robust budget allocation framework based on the multi-armed bandit (MAB) model to address this challenge, specifically tailored to the advertising decisions of SMEs. By integrating robustness principles into traditional MAB algorithms, the framework balances "exploration" (testing new advertising channels) and "exploitation" (scaling effective channels) while mitigating the impact of ROI uncertainty. An empirical simulation is conducted using realistic advertising scenarios (including social media ads, search engine marketing, influencer collaborations, and offline promotions) to validate the model. Results show that the proposed robust MAB framework outperforms traditional budget allocation methods (e.g., equal distribution, heuristic allocation) in terms of cumulative ROI, budget efficiency, and risk resistance. This study provides SMEs with a practical, data-driven tool for advertising budget optimization under uncertainty, contributing to sustainable business growth. The findings also enrich the application of MAB models in the advertising domain, particularly for resource-constrained enterprises.
subject: Article
  topic: section
subject: multi-armed bandits
  topic: keywords
subject: robust budget allocation
  topic: keywords
subject: small and medium-sized enterprises (SMEs)
  topic: keywords
subject: monetization decision
  topic: keywords
subject: uncertainty
  topic: keywords
subject: business growth
  topic: keywords
creator: Wenwen Liu
rights: Copyright (c) 2025 Wenwen Liu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-14
publication date: 2025-12-14
starting page: 89
ending page: 97
doi: 10.71222/g1pdz259

Cross Modal Data Understanding Based on Visual Language Model
type: item
description: With the widespread adoption of multimodal data in artificial intelligence, visual language models that integrate cross-modal information have emerged as a prominent research hotspot. These models are capable of jointly processing and interpreting both image and text information, enabling a range of complex multimodal tasks such as image captioning, visual question answering, cross-modal retrieval, and content summarization. By effectively bridging visual and linguistic modalities, visual language models facilitate more intelligent and context-aware systems that enhance human-computer interaction and decision-making processes. This article provides a comprehensive introduction to visual language models, covering their definitions, fundamental operations, and core methodologies. Key techniques analyzed include visual-language joint embedding, attention mechanisms, graph convolutional networks, and generative adversarial networks, all of which play critical roles in enabling accurate cross-modal understanding and representation. The paper further examines the practical applications of these models in multiple domains, including product labeling and categorization on e-commerce platforms, intelligent home control systems, social media sentiment analysis, and personalized recommendation systems. Through this research, it is evident that the integration of cross-modal data understanding technologies can substantially improve the operational performance and intelligence of systems in complex, real-world scenarios. The ability to accurately interpret and fuse visual and textual information not only enhances system efficiency but also expands the potential for innovative applications. These findings underscore the promising application prospects of visual language models, highlighting their significance for future developments in AI-driven multimodal understanding and intelligent system design.
subject: Article
  topic: section
subject: visual language model
  topic: keywords
subject: cross modal data understanding
  topic: keywords
subject: image processing
  topic: keywords
creator: Bukun Ren
rights: Copyright (c) 2025 Bukun Ren (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-13
publication date: 2025-12-13
starting page: 81
ending page: 88
doi: 10.71222/90yzn263

Use Generative Al and Natural Language Processing to Improve User Interaction Design
type: item
description: Modern artificial intelligence technologies continue to mature, and the applications of generative AI and natural language processing (NLP) have become increasingly widespread across various intelligent systems. This study systematically examines the fundamental principles, technical characteristics, and internal connections between generative AI models and NLP mechanisms, forming the theoretical foundation for constructing a next-generation intelligent interaction system. On this basis, the paper designs and develops an integrated system architecture featuring semantic parsing, instant content generation, cross-modal adaptation, continuous self-adjustment, and closed-loop feedback. Through the combined application of intent recognition, generative reasoning, and multimodal information fusion strategies, the system enhances its capability to understand user needs, improves interaction fluency, and significantly strengthens the naturalness and stability of human-machine communication. The experimental component of this work focuses on three representative application scenarios: intelligent customer service agents, voice-based interactive terminals, and text generation and writing assistants. Leveraging extensive comparative experimental data, the study evaluates the performance of the proposed system in terms of response accuracy, generation quality, latency, user comfort, and interaction smoothness. The results demonstrate that the incorporation of generative AI and self-evolving NLP techniques can effectively enhance response speed, improve user experience, and elevate the overall effectiveness of interactive processes. Overall, the findings indicate that human-machine interaction systems built on generative AI and integrated NLP possess high degrees of intelligence, adaptability, and perceptive capability. These systems provide promising prospects for practical deployment and broader promotion across multiple industry sectors, offering valuable guidance for the de…
subject: Article
  topic: section
subject: generative AI
  topic: keywords
subject: natural language processing
  topic: keywords
subject: user interaction
  topic: keywords
subject: semantic recognition
  topic: keywords
subject: multi modal fusion
  topic: keywords
creator: Xiao Liu
rights: Copyright (c) 2025 Xiao Liu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-13
publication date: 2025-12-13
starting page: 74
ending page: 80
doi: 10.71222/dm2jms57

The Challenges and Opportunities of Leading an AI ML Team in a Startup
type: item
description: With the rapid development of artificial intelligence (AI) and machine learning (ML) technology, many startups have taken it as an important means of competition. But under the leadership of a startup, AI/ML teams face specific challenges and opportunities. This article focuses on the key concepts, challenges, and possible opportunities of leading an AI/ML team in a startup. First, starting from the basic description of artificial intelligence and machine learning technology, it analyzes the organizational structure of machine learning team of startup companies and the core capabilities that leaders must have. Second, it analyzes the challenges brought about by insufficient resources, insufficient skills and technological change. Third, take advantage of the opportunities presented by technological change, data-driven decision making and attracting investment.
subject: Article
  topic: section
subject: AI/ML team
  topic: keywords
subject: a start-up company
  topic: keywords
subject: technological innovation
  topic: keywords
creator: Hongjun Wu
rights: Copyright (c) 2025 Hongjun Wu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-13
publication date: 2025-12-13
starting page: 66
ending page: 73
doi: 10.71222/wt4sbt68

Innovative Application of AI in Medical Decision Support System and Implementation of Precision Medicine
type: item
description: With the deepening of the concept of precision medicine, the practicability of clinical decision support systems in hospitals has become increasingly significant. The powerful data processing and individualized prompts brought by AI applications have enabled it to transform from an auxiliary tool to an intelligent and precise platform. This article mainly focuses on the innovative application of AI in clinical decision support systems and explores the implementation process of its application in aspects such as the establishment of diagnostic models, treatment selection, and risk prediction. For the current obstacles existing in the process of precision medicine, such as low acceptance of usage, weak interpretability of the model, data heterogeneity, and insufficient generalization ability of the model, an optimization path is proposed, providing theoretical guidance and practical reference for high-quality precision medicine empowered by AI.
subject: Article
  topic: section
subject: AI
  topic: keywords
subject: innovation
  topic: keywords
subject: medical decision support system
  topic: keywords
subject: precision medicine
  topic: keywords
subject: diagnostic model
  topic: keywords
subject: treatment recommendation
  topic: keywords
creator: Danyating Shen
rights: Copyright (c) 2025 Danyating Shen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-13
publication date: 2025-12-13
starting page: 59
ending page: 65
doi: 10.71222/skc34r97

Integrating Artificial Intelligence into Senior Healthcare: Innovation, Policy Alignment, and Impact of the ElderAI Wellness Hub
type: item
description: The United States is undergoing a profound demographic transformation characterized by a rapid increase in its aging population, which presents significant challenges to the fiscal sustainability of Medicare and the broader national healthcare system. The integration of Artificial Intelligence (AI) with gerontechnology provides innovative opportunities to enhance preventive healthcare, streamline resource allocation, and empower older adults to actively manage their own well-being. This study offers a comprehensive analysis of the ElderAI Wellness Hub, an AI-driven, nonprofit digital health platform designed to combine predictive analytics, cost optimization, and caregiver support within a framework consistent with contemporary U.S. healthcare policies, including initiatives such as the One Big Beautiful Bill Act (OBBBA) and Healthy People 2030. Employing a mixed-method approach that merges quantitative modeling with qualitative policy assessment, the research evaluates the platform's impacts on health outcomes, economic efficiency, and social engagement among older populations. Findings demonstrate that ElderAI contributes to measurable improvements in patient health metrics, reductions in Medicare expenditures, and increased accessibility to technology-enabled care, while also promoting ethical AI governance and digital equity in geriatric services. By situating technological innovation within a policy-aligned context, this study highlights how AI-driven solutions can not only enhance the quality of care for aging individuals but also support the long-term sustainability of national healthcare systems. The results provide valuable insights for healthcare policymakers, technology developers, and social service providers seeking to design scalable, ethically responsible digital health interventions for older adults.
subject: Article
  topic: section
subject: aging population
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: gerontechnology
  topic: keywords
subject: Medicare optimization
  topic: keywords
subject: preventive health
  topic: keywords
subject: policy innovation
  topic: keywords
subject: public health equity
  topic: keywords
creator: Jiyao Yang
rights: Copyright (c) 2025 Jiyao Yang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-06
publication date: 2025-12-06
starting page: 51
ending page: 58
doi: 10.71222/0tmwbg17

AI-Driven Cross-Cloud Operations Language Standardisation and Knowledge Sharing System
type: item
description: With the widespread adoption of multi-cloud architectures, intelligent management across multiple clouds has also increased. However, due to the significant differences in interface design, syntax standards, and command rules among different cloud platforms, multi-cloud operation language structures are numerous, unstandardized, and dispersed, greatly affecting the reuse of knowledge and team collaboration efficiency. Therefore, this paper proposes a knowledge collaboration framework for the standardization of intelligent operational languages based on AI-driven cloud interoperability. This framework creates a universal standardized operational language and an intelligent command knowledge base in multi-cloud environments through unified language structure construction, AI semantic parsing, and command knowledge integration technologies. First, starting from the reasons behind cross-cloud language differences, the study explores that the root causes of these differences lie in semantic ambiguity and fragmented knowledge architecture. Then, by leveraging AI-based semantic interpretation models and semantic similarity evaluation methods, common operational language specification elements across different operating systems are constructed to form a single semantic architecture, resulting in a knowledge system that can be learned, transferred, and shared. Finally, relying on a knowledge-graph-based task recommendation strategy, intelligent sharing and reasoning at the semantic level are achieved, promoting multi-level association and automatic reuse of work knowledge.
subject: Article
  topic: section
subject: artificial intelligence
  topic: keywords
subject: cross-cloud operations
  topic: keywords
subject: language standardization
  topic: keywords
subject: knowledge graph
  topic: keywords
creator: Zhengrui Lu
rights: Copyright (c) 2025 Zhengrui Lu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-29
publication date: 2025-11-29
starting page: 43
ending page: 50
doi: 10.71222/jvn6gf47

An Open-Source AI-Driven CRM Model: Balancing Performance, Cost, and Accessibility for Small Businesses
type: item
description: An increasing number of small businesses utilize CRM platforms to optimize customer engagement, improve sales productivity, and facilitate informed decision-making. However, the high cost and complexity of proprietary AI-driven CRM platforms often prevent small and medium-sized enterprises (SMEs) from accessing advanced analytics tools. This study presents an open-source AI-driven CRM model designed to balance performance, cost-efficiency, and accessibility for SMEs. The proposed system integrates modular data management, AI analytics—including predictive modeling, customer segmentation, and natural language processing—and an intuitive user interface, all built on widely available open-source frameworks. A prototype implementation demonstrates that lightweight AI models can deliver accurate insights while maintaining low computational requirements, enabling deployment on standard hardware or affordable cloud services. Evaluation results show that the system achieves reliable performance, reduces total cost of ownership compared to commercial alternatives, and supports easy adoption by non-technical staff. Key challenges, such as data quality, model generalization, and organizational readiness, are discussed, along with potential improvements including automated data cleaning, adaptive AI models, and multilingual support. Overall, this study demonstrates that open-source AI-driven CRM solutions can democratize access to advanced business intelligence, empowering small businesses to compete effectively in a data-driven market while maintaining affordability, transparency, and usability.
subject: Article
  topic: section
subject: open-source CRM
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: small business
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: customer segmentation
  topic: keywords
subject: cost-efficiency
  topic: keywords
creator: Zongze Li
rights: Copyright (c) 2025 Zongze Li (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-29
publication date: 2025-11-29
starting page: 34
ending page: 42
doi: 10.71222/hsyp8v61

From Algorithm to System: Integrated Design of Compiler and Toolchain for Large Model Inference Optimization
type: item
description: With the rapid growth of deep learning model scales, especially large models such as Transformers and GPT, efficient inference has become a critical challenge due to increasing computational and memory demands. This paper proposes an integrated optimization framework that unifies algorithmic simplifications, compiler transformations, and system-level scheduling to enhance large model inference performance. By tightly coupling quantization, pruning, operator fusion, memory reuse, and automated heterogeneous hardware scheduling, the framework achieves significant improvements in computation reduction, memory efficiency, and parallel execution. Theoretical analysis and design considerations demonstrate the framework's potential for predictable performance gains and scalability across diverse hardware platforms. Future work will focus on extending hardware support, distributed inference, and adaptive optimization strategies. This integrated approach lays a foundation for efficient, scalable, and accurate large model deployment in practical AI applications.
subject: Article
  topic: section
subject: large model inference
  topic: keywords
subject: algorithm optimization
  topic: keywords
subject: compiler optimization
  topic: keywords
subject: toolchain scheduling
  topic: keywords
subject: heterogeneous hardware
  topic: keywords
creator: Shengyi Gao
rights: Copyright (c) 2025 Shengyi Gao (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 21
ending page: 33
doi: 10.71222/032q3y25

Causal Attribution and Evaluation Based on Large-Scale Advertising Data
type: item
description: As the reach of digital advertising expands and multi-touchpoint data volumes surge exponentially, traditional advertising effectiveness evaluation methods based on correlation analysis can no longer accurately and effectively reflect the impact level from advertising touchpoints to behavioural conversion. This study constructs a causal attribution model and advertising effectiveness evaluation system based on causal principles, suitable for large-scale advertising data. Through latent outcome frameworks and causal effect estimation techniques, the model identifies the trajectory of advertising touchpoint influence. It further constructs an indicator system for causal effectiveness, primarily comprising average causal effects, behavioural uplift rates, and causal return on investment, establishing a systematic framework from causal modelling to effectiveness evaluation. The research achieves precise quantitative interpretation of advertising effectiveness, providing scientific grounds for formulating rational advertising allocation methods and enabling intelligent decision-making.
subject: Article
  topic: section
subject: large-scale advertising data
  topic: keywords
subject: causal attribution
  topic: keywords
subject: effectiveness evaluation
  topic: keywords
subject: causal inference model
  topic: keywords
creator: Jing Xie
rights: Copyright (c) 2025 Jing Xie (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-21
publication date: 2025-11-21
starting page: 12
ending page: 20
doi: 10.71222/8xj7p454

Application Status and Efficiency Improvement Path of AI-Assisted Tools in the UI Design Process
type: item
description: Over the past decade, user interface (UI) design has become increasingly complex due to multi-platform ecosystems and diversified interaction channels. In response, AI-assisted tools-leveraging large language models, generative image models, and predictive interaction analytics-have begun transforming the UI design process by automating wireframe generation, visual variation exploration, layout optimization, and prototype code production. This review systematically examines the current application status of AI in UI design, highlighting measurable efficiency gains in time savings, design quality, and cross-functional collaboration. It also identifies persistent challenges, including creative limitations, model reliability, data privacy, and shifting designer roles. Finally, the paper proposes a structured efficiency improvement path, integrating technical, organizational, and ethical considerations, aiming to guide the responsible and effective deployment of AI in modern UI design workflows.
subject: Article
  topic: section
subject: AI-assisted UI design
  topic: keywords
subject: workflow efficiency
  topic: keywords
subject: generative models
  topic: keywords
subject: design automation
  topic: keywords
subject: usability prediction
  topic: keywords
subject: collaboration
  topic: keywords
creator: Hao Cai
rights: Copyright (c) 2025 Hao Cai (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-21
publication date: 2025-11-21
starting page: 1
ending page: 11
doi: 10.71222/kxngnz03

Design of a Generative AI-Driven Intelligent Investment Advisory System
type: item
description: To address the need for dynamic strategy generation and semantic adaptation in intelligent investment advisory systems, this study proposes a generative architecture that integrates multi-source knowledge while supporting semantic reasoning, interpretability, and real-time user interaction. The system comprises modular components, including task scheduling, multi-source data fusion, a generation engine, semantic understanding, and strategy explanation, all enhanced by context-aware mechanisms and multi-dimensional security protection. The architecture leverages a multi-layered Transformer-based model and a tensor-level knowledge fusion framework, enabling real-time asset allocation and policy explanation. Empirical validation using heterogeneous financial datasets demonstrates the system's superiority in generative quality and robustness. Evaluation metrics indicate a BLEU-4 score of 44.89, a BERTScore of 91.31, semantic consistency of 0.89, strategy accuracy of 93.4%, and a recognition success rate exceeding 94.7% under adversarial perturbations. As shown in comparative experiments, the proposed system outperforms existing models such as GPT-2 and FinBERT in interpretability and interaction latency. The results confirm that the proposed system achieves high-quality generation, strong semantic alignment, and user trustworthiness in complex financial advisory scenarios.
subject: Article
  topic: section
subject: intelligent investment advisor
  topic: keywords
subject: generation mechanism
  topic: keywords
subject: knowledge fusion
  topic: keywords
subject: semantic modeling
  topic: keywords
subject: system security
  topic: keywords
creator: Mengxin Wu
rights: Copyright (c) 2025 Mengxin Wu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-19
publication date: 2025-11-19
starting page: 120
ending page: 129
doi: 10.71222/mh7h8y73

Neurodevelopmental Screening for Autism: EEG-Driven Deep Learning in Early Childhood
type: item
description: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by atypical patterns of communication, social interaction, and behavior. The prevalence of ASD has risen sharply in recent decades, with recent estimates indicating that approximately one in every thirty-six children in the United States is diagnosed. Early diagnosis is crucial because interventions initiated during early childhood-when neural plasticity is greatest-can substantially improve behavioral, cognitive, and linguistic outcomes. However, since current diagnostic practices largely rely on behavioral assessments, symptoms often remain undetected until around three years of age. This underscores the urgent need for objective biological markers capable of identifying ASD risk earlier, ideally during infancy or toddlerhood. Previous research has demonstrated that ASD is associated with atypical event-related potential (ERP) responses across both early and late stages of cognitive processing. Nevertheless, few studies have explored whether such ERP features can support reliable individual-level predictions using modern machine learning (ML) approaches, and even fewer have applied systematic validation procedures. Addressing this gap, the present study employs multiple ML frameworks-particularly a multilayer perceptron (MLP)-to classify ERP features derived from a visual oddball paradigm. Model performance was rigorously evaluated using cross-validation at the single-subject level. The MLP achieved an overall accuracy of 0.746, with a precision of 0.72, recall of 0.736, and an F1 score of 0.734, demonstrating that the model effectively distinguished ERP feature patterns between ASD and typically developing participants.
subject: Article
  topic: section
subject: Autism Spectrum Disorder
  topic: keywords
subject: electroencephalography
  topic: keywords
subject: event-related potentials
  topic: keywords
subject: P100
  topic: keywords
subject: N100
  topic: keywords
subject: P300
  topic: keywords
subject: visual oddball paradigm
  topic: keywords
subject: machine learning
  topic: keywords
creator: Hanwen Yuan
rights: Copyright (c) 2025 Hanwen Yuan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-12
publication date: 2025-11-12
starting page: 112
ending page: 119
doi: 10.71222/fz2hyy28

Research on PID Control Parameter Tuning of Quadrotor UAV Based on Improved Intelligent Optimization Algorithm
type: item
description: Quadrotor unmanned aerial vehicles (UAVs) are nonlinear, strongly coupled, and underactuated systems, making precise attitude control crucial for stable flight and mission efficiency. Traditional PID parameter tuning methods rely heavily on manual experience, often resulting in suboptimal performance. This paper proposes a PID parameter tuning approach based on an improved particle swarm optimization (PSO) algorithm, integrating adaptive inertia weight, dynamic learning factors, and Latin hypercube sampling to enhance optimization efficiency and convergence. A cascade PID control structure is designed, with an outer loop for attitude control and an inner loop for angular velocity control. Simulation and experimental results demonstrate that the proposed method effectively improves settling time and reduces overshoot, ensuring robust and stable UAV attitude control in various flight conditions. The study provides a practical solution for efficient and reliable PID tuning in quadrotor UAV systems.
subject: Article
  topic: section
subject: quadrotor UAV
  topic: keywords
subject: PID control
  topic: keywords
subject: particle swarm optimization
  topic: keywords
subject: cascade control
  topic: keywords
subject: attitude stabilization
  topic: keywords
subject: parameter tuning
  topic: keywords
creator: Gong Cheng
rights: Copyright (c) 2025 Gong Cheng (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-08
publication date: 2025-11-08
starting page: 102
ending page: 111
doi: 10.71222/4eamzx24

Application of Network Security Vulnerability Detection and Repair Process Optimization in Software Development
type: item
description: Ensuring system safety and maintaining high-quality software development critically depend on the timely identification and rapid remediation of network security vulnerabilities. In this study, an efficient operating framework is established based on the optimization of the vulnerability management process. From the dual perspectives of vulnerability detection and repair, a comprehensive process improvement is implemented, forming a technical framework that integrates both detection and repair optimization modules. This framework emphasizes security robustness while offering flexible scalability, enabling it to adapt to varying software environments and evolving threat landscapes. During the software development lifecycle, the framework leverages the coordinated application of multiple tools, including automated repair technologies, intelligent detection mechanisms, and cross-department collaboration strategies. Such integration significantly enhances both the efficiency and accuracy of handling security vulnerabilities. Furthermore, the proposed approach supports continuous monitoring and iterative refinement, ensuring that potential security risks are proactively addressed, and that the overall reliability and stability of software systems are improved. By systematically combining detection, repair, and collaborative optimization, the framework provides a practical and scalable solution for strengthening software security and supporting sustainable software development.
subject: Article
  topic: section
subject: network security
  topic: keywords
subject: vulnerability detection
  topic: keywords
subject: bug repair
  topic: keywords
subject: repair process
  topic: keywords
subject: software development
  topic: keywords
creator: Shuang Yuan
rights: Copyright (c) 2025 Shuang Yuan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-31
publication date: 2025-10-31
starting page: 93
ending page: 101
doi: 10.71222/wn1zdt93

Innovations in Machine Translation: The Role of Machine Learning in Enhancing Linguistic Accuracy and Efficiency
type: item
description: This essay explores Instance Induction, Analogy Induction, and Machine Learning, with a particular focus on the application of analogy-based machine learning in machine translation. It examines techniques such as full instance translation, case pattern translation, and analogical reasoning. The study investigates the underlying principles, advantages, and potential limitations of these methods to provide a theoretical foundation for further optimization of machine translation (MT). Furthermore, an in-depth analysis of Machine Learning Theory, especially through the paradigms of Analogy Induction and Instance Induction, is conducted to uncover latent patterns and features that are pivotal for the technological advancement of this field. The efficacy of these methodologies in enhancing machine translation performance is critically evaluated and discussed.
subject: Article
  topic: section
subject: machine translation
  topic: keywords
subject: application of machine learning technology
  topic: keywords
subject: artificial intelligence in Linguistics
  topic: keywords
creator: Kaiwen Xin
creator: Bingchen Liu
creator: Lihao Fan
rights: Copyright (c) 2025 Kaiwen Xin, Bingchen Liu, Lihao Fan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-27
publication date: 2025-10-27
starting page: 85
ending page: 92
doi: 10.71222/2396v567

High-Performance Cloud-Based System Design and Performance Optimization Based on Microservice Architecture
type: item
description: With the rapid advancement of cloud computing and microservice architecture, designing cloud systems based on microservices has demonstrated significant potential for delivering efficient, flexible, and highly scalable services. As cloud systems expand in scale and service complexity increases, however, microservice architectures face substantial challenges in maintaining optimal performance. This study explores strategies for constructing high-performance cloud systems within a microservice framework, focusing on three critical aspects: module partitioning, inter-service communication mechanisms, and distributed data management. Specifically, it examines methods to optimize the granularity of microservice modules to balance system maintainability with performance efficiency, and investigates communication patterns that minimize latency and reduce inter-service overhead. The study further addresses strategies for distributed data handling, ensuring consistency, fault tolerance, and scalability across heterogeneous service nodes. To enhance overall system performance, measures such as efficient service interaction protocols, automated scaling and load balancing, and comprehensive performance monitoring coupled with proactive fault detection are proposed. Collectively, these strategies provide a structured approach to designing microservice-based cloud systems that are resilient, adaptive, and capable of sustaining high throughput under dynamic workloads.
subject: Article
  topic: section
subject: microservice architecture
  topic: keywords
subject: cloud-based system
  topic: keywords
subject: performance optimization
  topic: keywords
subject: service communication
  topic: keywords
subject: automated expansion
  topic: keywords
creator: Jin Li
rights: Copyright (c) 2025 Jin Li (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-27
publication date: 2025-10-27
starting page: 77
ending page: 84
doi: 10.71222/a0y6gj89

Leveraging Large Language Models for Anomaly Event Early Warning in Financial Systems
type: item
description: Given the strong semantic concealment, complex data structures, and high responsiveness requirements of anomaly events in financial systems, this study investigates advanced anomaly early warning methods that integrate large language models (LLMs) to enhance detection and response capabilities. The research systematically outlines semantic processing approaches for multi-source heterogeneous data, including structured transaction records, unstructured textual reports, and real-time market indicators, to ensure comprehensive analysis of potential anomalies. It further details the construction path for risk identification models, emphasizing feature extraction, multi-dimensional correlation analysis, and adaptive learning mechanisms that allow the system to dynamically adjust to evolving financial patterns. In addition, the study presents the generation and push mechanism for early warning information, ensuring that alerts are delivered promptly to relevant stakeholders while maintaining high interpretability and actionable insight. The system's response efficiency and recognition performance are rigorously evaluated in real trading environments, demonstrating the framework's robustness under high-frequency and high-volume transaction conditions. Comparative experiments with various baseline models show that the proposed approach exhibits strong adaptability and practical value, achieving superior performance in both recognition accuracy and response timeliness. These results indicate the potential of LLM-enhanced anomaly early warning systems to support more reliable and intelligent risk management in complex financial environments.
subject: Article
  topic: section
subject: financial anomaly events
  topic: keywords
subject: large language models
  topic: keywords
subject: multi-source data fusion
  topic: keywords
subject: early warning systems
  topic: keywords
creator: Luqing Ren
rights: Copyright (c) 2025 Luqing Ren (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-16
publication date: 2025-10-16
starting page: 69
ending page: 76
doi: 10.71222/ke7nmh09

Research on Cross-Modal Semantic Alignment Methods for Low-Resource Languages
type: item
description: This study addresses the challenge of cross-modal semantic alignment in low-resource languages, a critical problem for enabling inclusive and equitable AI-driven multimodal applications. We propose a novel framework that synergistically integrates multi-level textual embeddings, visual Transformer modeling, and the construction of a unified cross-modal projection space. To enhance alignment quality, the approach incorporates advanced mechanisms including contrastive learning, distributed semantic constraints, and fine-grained local alignment strategies. Furthermore, to mitigate data scarcity inherent in low-resource settings, we leverage transfer enhancement techniques such as cross-lingual knowledge distillation, pseudo-pair augmentation, and multi-task training. Comprehensive experiments on the FLORES-200 dataset demonstrate that our method consistently surpasses state-of-the-art models such as CLIP and ALIGN across multiple metrics. Specifically, significant gains are observed in Recall@1 and Mean Rank for languages including Swahili and Sinhala, underscoring the method's effectiveness, robustness, and generalizability in low-resource scenarios. These findings highlight the potential of the proposed approach for advancing cross-lingual multimodal understanding and bridging the performance gap for underrepresented languages.
subject: Article
  topic: section
subject: low-resource languages
  topic: keywords
subject: cross-modal semantic alignment
  topic: keywords
subject: contrastive learning
  topic: keywords
subject: transfer enhancement
  topic: keywords
creator: Zhizhi Yu
rights: Copyright (c) 2025 Zhizhi Yu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-09
publication date: 2025-10-09
starting page: 60
ending page: 68
doi: 10.71222/tvftmg24

Bridging the AI Adoption Gap: What Drives U.S. SME Owners' Willingness-to-Pay for Supply-Chain Risk Software?
type: item
description: The application of artificial intelligence (AI) in supply chain risk management offers small and medium-sized enterprises (SMEs) opportunities to enhance early warning capabilities, improve compliance, and strengthen operational resilience. However, SMEs often face resource constraints and cognitive differences during technology adoption, and their willingness-to-pay (WTP) remains unclear. This study employs an online discrete choice experiment (N = 512) conducted in August–September 2024 to examine SME owners' decision-making regarding AI-enabled risk management software. The experiment incorporates attributes such as monthly fee, early-warning lead time, compliance module, and data localization option. A mixed logit model and latent class analysis are applied, followed by Bayesian post-estimation to derive optimal price ranges. Results indicate that risk sensitivity, technology adoption willingness, compliance awareness, and understanding of supply chain complexity are significant drivers of WTP. Distinct customer segments are identified: price-sensitive firms focus on subscription cost, whereas function-oriented firms value early-warning and compliance features. Analysis further shows that lower-revenue SMEs exhibit lower maximum acceptable prices compared to larger firms, yet their subscription appeal can be increased through tailored feature bundles. Based on these findings, the study proposes a tiered pricing strategy: an entry-level plan emphasizing core early-warning, a mid-tier plan adding compliance functions, and a premium plan including data localization. The study contributes by highlighting the role of behavioral and cognitive factors in SME pricing decisions and provides empirical guidance for the design and pricing of AI-based supply chain risk management software.
subject: Article
  topic: section
subject: willingness-to-pay (WTP)
  topic: keywords
subject: small and medium-sized enterprises (SMEs)
  topic: keywords
subject: supply chain risk management
  topic: keywords
subject: artificial intelligence (AI) adoption
  topic: keywords
subject: discrete choice experiment
  topic: keywords
creator: Xiangying Chen
rights: Copyright (c) 2025 Xiangying Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-05
publication date: 2025-10-05
starting page: 54
ending page: 59
doi: 10.71222/9n1cbn63

Research on AI-Based Multilingual Natural Language Processing Technology and Intelligent Voice Interaction System
type: item
description: It has shown broad development prospects in intelligent application fields such as cross-lingual voice interaction, machine translation, and voice assistants. Faced with challenges such as complex speech features, diverse semantic structures, and limited terminal deployment, technical systems need to achieve effective collaboration between recognition accuracy, semantic consistency, and operational efficiency. The application of training language models, context-aware mechanisms, and end-cloud collaborative structures provides a new path for optimizing system performance. This article focuses on key aspects such as speech recognition, semantic understanding, and deployment mechanisms, exploring technical bottlenecks and feasible improvement solutions in multilingual environments, with the aim of providing a theoretical basis and practical guidance for cross-language applications of intelligent speech systems.
subject: Article
  topic: section
subject: artificial intelligence
  topic: keywords
subject: speech recognition
  topic: keywords
subject: semantic modeling
  topic: keywords
subject: multilingual system
  topic: keywords
creator: Xiang Chen
rights: Copyright (c) 2025 Xiang Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-05
publication date: 2025-10-05
starting page: 47
ending page: 53
doi: 10.71222/hpr7bc38

Data Consistency and Performance Scalability Design in High-Concurrency Payment Systems
type: item
description: In the blowout development of mobile payment and Internet financial payment systems, highly concurrent payment systems face dual challenges of data consistency assurance and system performance expansion. To address these issues, design strategies including hierarchical consistency control, data distribution optimization, and consistency scalability collaborative regulation are proposed to achieve a system architecture that balances transaction accuracy and throughput. The analysis of three typical cases-aggregated payment, high-speed sales system, and financial remittance channel-demonstrates that this strategy can be applied to complex operation scenarios, enhancing efficiency and providing a theoretical foundation and practical guidelines for future development of high-reliability payment systems.
subject: Article
  topic: section
subject: high concurrency payment system
  topic: keywords
subject: data consistency
  topic: keywords
subject: performance scalability
  topic: keywords
subject: distributed architecture
  topic: keywords
creator: Yue Qi
rights: Copyright (c) 2025 Yue Qi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-05
publication date: 2025-10-05
starting page: 39
ending page: 46
doi: 10.71222/qmaq2c51