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.
Generative AI Test Generation and Intelligent Defect Attribution Method for Large Scale Distributed Systems
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.
Copyright (c) 2025 Mingde Guo (Author) https://creativecommons.org/licenses/by/4.0
Multi-Armed Bandits and Robust Budget Allocation: Small and Medium-sized Enterprises Growth Decisions under Uncertainty in Monetization
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.
small and medium-sized enterprises (SMEs)
Copyright (c) 2025 Wenwen Liu (Author) https://creativecommons.org/licenses/by/4.0
Cross Modal Data Understanding Based on Visual Language Model
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.
Copyright (c) 2025 Bukun Ren (Author) https://creativecommons.org/licenses/by/4.0
Use Generative Al and Natural Language Processing to Improve User Interaction Design
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 development of future intelligent interactive applications.
Copyright (c) 2025 Xiao Liu (Author) https://creativecommons.org/licenses/by/4.0
The Challenges and Opportunities of Leading an AI ML Team in a Startup
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.
Copyright (c) 2025 Hongjun Wu (Author) https://creativecommons.org/licenses/by/4.0
Innovative Application of AI in Medical Decision Support System and Implementation of Precision Medicine
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.
Copyright (c) 2025 Danyating Shen (Author) https://creativecommons.org/licenses/by/4.0
Integrating Artificial Intelligence into Senior Healthcare: Innovation, Policy Alignment, and Impact of the ElderAI Wellness Hub
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.
Copyright (c) 2025 Jiyao Yang (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Cross-Cloud Operations Language Standardisation and Knowledge Sharing System
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.
Copyright (c) 2025 Zhengrui Lu (Author) https://creativecommons.org/licenses/by/4.0
An Open-Source AI-Driven CRM Model: Balancing Performance, Cost, and Accessibility for Small Businesses
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.
Copyright (c) 2025 Zongze Li (Author) https://creativecommons.org/licenses/by/4.0
From Algorithm to System: Integrated Design of Compiler and Toolchain for Large Model Inference Optimization
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.
Copyright (c) 2025 Shengyi Gao (Author) https://creativecommons.org/licenses/by/4.0
Causal Attribution and Evaluation Based on Large-Scale Advertising Data
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.
Copyright (c) 2025 Jing Xie (Author) https://creativecommons.org/licenses/by/4.0
Application Status and Efficiency Improvement Path of AI-Assisted Tools in the UI Design Process
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.
Copyright (c) 2025 Hao Cai (Author) https://creativecommons.org/licenses/by/4.0
Design of a Generative AI-Driven Intelligent Investment Advisory System
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.
Copyright (c) 2025 Mengxin Wu (Author) https://creativecommons.org/licenses/by/4.0
Neurodevelopmental Screening for Autism: EEG-Driven Deep Learning in Early Childhood
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.
Copyright (c) 2025 Hanwen Yuan (Author) https://creativecommons.org/licenses/by/4.0
Research on PID Control Parameter Tuning of Quadrotor UAV Based on Improved Intelligent Optimization Algorithm
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.
Copyright (c) 2025 Gong Cheng (Author) https://creativecommons.org/licenses/by/4.0
Application of Network Security Vulnerability Detection and Repair Process Optimization in Software Development
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.
Copyright (c) 2025 Shuang Yuan (Author) https://creativecommons.org/licenses/by/4.0
Innovations in Machine Translation: The Role of Machine Learning in Enhancing Linguistic Accuracy and Efficiency
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.
application of machine learning technology
Copyright (c) 2025 Kaiwen Xin, Bingchen Liu, Lihao Fan (Author) https://creativecommons.org/licenses/by/4.0
High-Performance Cloud-Based System Design and Performance Optimization Based on Microservice Architecture
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.
Copyright (c) 2025 Jin Li (Author) https://creativecommons.org/licenses/by/4.0
Leveraging Large Language Models for Anomaly Event Early Warning in Financial Systems
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.
Copyright (c) 2025 Luqing Ren (Author) https://creativecommons.org/licenses/by/4.0
Research on Cross-Modal Semantic Alignment Methods for Low-Resource Languages
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.
Copyright (c) 2025 Zhizhi Yu (Author) https://creativecommons.org/licenses/by/4.0
Bridging the AI Adoption Gap: What Drives U.S. SME Owners' Willingness-to-Pay for Supply-Chain Risk Software?
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.
Copyright (c) 2025 Xiangying Chen (Author) https://creativecommons.org/licenses/by/4.0
Research on AI-Based Multilingual Natural Language Processing Technology and Intelligent Voice Interaction System
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.
Copyright (c) 2025 Xiang Chen (Author) https://creativecommons.org/licenses/by/4.0
Data Consistency and Performance Scalability Design in High-Concurrency Payment Systems
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.
Copyright (c) 2025 Yue Qi (Author) https://creativecommons.org/licenses/by/4.0
The Influence of Financial Due Diligence in M&A on Investment Decision Based on Financial Data Analysis
With the gradual growth of enterprise mergers and acquisitions activities, the role of financial due diligence in investment decisions has become increasingly prominent. In the traditional financial due diligence process, due to the constraints of information granularity and analytical tools, potential financial risks cannot be fully revealed. Starting from the analysis of financial data, this article expounds its application value and practical effectiveness in the due diligence process. The research builds an assessment system based on four core dimensions: profitability, debt-paying ability, operational efficiency, and risk identification, and analyzes its role and function in valuation correction, risk early warning, and decision support. The research results show that the data-driven due diligence approach can improve the accuracy of investment decisions, is of great significance for merger and acquisition integration and performance prediction, and promotes the realization of high-quality merger and acquisition transactions.
Copyright (c) 2025 Wei Li (Author) https://creativecommons.org/licenses/by/4.0
Lightweight Strategic Path for Digital Transformation in Small and Medium Enterprises — A Solution Based on Management Consulting Practice
As global economic digitalization accelerates, small and medium-sized enterprises (SMEs) play a critical role in the U.S. economy. However, due to limited budgets, insufficient technical capabilities, and a lack of management experience, SMEs face significant challenges in digital transformation. This paper focuses on the digital pain points of SMEs and proposes a "lightweight" strategic approach, which emphasizes low-cost, easy-to-implement, and stepwise digital solutions to enhance operational efficiency. Drawing on management consulting practices, the study designs actionable digital transformation solutions encompassing tool selection, phased implementation, budget and resource allocation, and risk management. Case analyses demonstrate the effectiveness of the lightweight strategy in improving operational efficiency, reducing transformation risks, and optimizing resource utilization. Furthermore, the paper highlights the value of management consultants in facilitating SME digitalization. The findings provide practical guidance for SMEs in the U.S. and offer strategic insights for future digital development.
Copyright (c) 2025 Chongguang Zhu (Author) https://creativecommons.org/licenses/by/4.0
Designing Accessible AI Systems for SMEs: Compliance with ADA and Section 508 through Conversational Interfaces
With lawsuits related to the Americans with Disabilities Act (ADA) resulting in approximately $4 billion in annual economic losses for businesses, small and medium-sized enterprises (SMEs) face significant cost pressures when developing accessible applications. This paper proposes an accessibility-focused conversational system based on an open-source natural language processing (NLP) framework, aiming to reduce legal risks for SMEs while enhancing digital accessibility for users with disabilities. The system integrates text-to-speech (TTS) and speech-to-text (STT) modules with a simple user interface, leveraging technologies such as Azure Cognitive Services, Play Framework, and MongoDB. Through example applications in online retail and restaurant ordering systems, the paper demonstrates the system's usability, ADA and Section 508 compliance, and seamless integration with existing content management systems. Experimental results indicate that the proposed approach significantly reduces development costs and improves the user experience for individuals with disabilities, providing a viable path for SMEs to implement digital accessibility and laying the foundation for future integration with large language models (LLMs) to enable more natural human-computer interactions.
Copyright (c) 2025 Yuxin Liu (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Efficiency Improvement Path Scheme in Biomedical Industry Driven by AI
The biomedical industry faces persistent efficiency challenges, including prolonged R&D cycles, high development costs, complex clinical trials, and fragmented data management. Driven by advances in artificial intelligence (AI), novel solutions are emerging to address these bottlenecks across the drug discovery, clinical, manufacturing, and knowledge management domains. This review systematically analyzes AI-driven efficiency improvement pathways, highlighting accelerated drug discovery, optimized clinical trials, intelligent manufacturing and supply chain, and data-driven decision support. Key challenges, such as data quality, regulatory constraints, system integration, and talent gaps, are discussed, alongside potential future developments in self-supervised learning and generative models. The study emphasizes the transformative potential of AI to enhance productivity, reduce costs, and support informed decision-making, offering strategic insights for enterprises seeking sustainable innovation in the biomedical sector.
Copyright (c) 2025 Dajiang Guo (Author) https://creativecommons.org/licenses/by/4.0
Stock Price Prediction of Apple Inc. Based on LSTM Model: An Application of Artificial Intelligence in Individual Stock Analysis
This paper explores the potential of artificial intelligence in financial forecasting by applying a Long Short-Term Memory (LSTM) neural network to the task of predicting Apple Inc. (AAPL) stock prices. Using historical daily closing prices from August 2020 to August 2023, the model was trained and tested under a structured framework of data preprocessing, normalization, and sequential input construction. The forecasting performance was evaluated with widely used error measures, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Experimental results indicate that the LSTM model effectively captures sequential dependencies and nonlinear dynamics within the time series, generating predictions that closely align with observed prices. While the model demonstrates high accuracy in relatively stable market conditions, its reliance on univariate input limits adaptability to abrupt market fluctuations and external influences such as macroeconomic shifts. These findings suggest that LSTM-based models can serve as valuable tools for supporting individual stock analysis, but their effectiveness depends on stock-specific data availability and market characteristics. Future work may extend this framework by incorporating multi-source financial indicators and enhancing model interpretability to achieve broader practical relevance.
Copyright (c) 2025 Ruoyu Qi, Liao Hu (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Assisted Dual-Teacher Model Constructing Practices
This study explores the development and practical application of an innovative AI-assisted dual-teacher model for foreign language education. By introducing a dynamic framework that combines human educators with AI-driven teaching assistants, the research establishes a three-phase collaborative approach designed to meet the cognitive, linguistic, and cultural demands of language learners. The approach capitalizes on the complementary strengths of both human teachers and AI tools, offering a personalized and responsive learning experience. The findings of the study underscore the effectiveness of this model in leveraging AI's advanced capabilities, such as semantic generation and data analytics, to streamline and automate routine instructional tasks. This automation allows human instructors to focus their expertise on higher-level educational objectives, including fostering intercultural communication, nurturing emotional engagement, and cultivating critical thinking skills among students. As a result, the dual-teacher model significantly enhances the quality of foreign language instruction by diversifying teaching methods and creating opportunities for individualized learning trajectories. Furthermore, this approach promotes the development of digital literacy in both students and educators. The integration of AI technology empowers learners to navigate a variety of digital tools, while teachers are better equipped to manage and interpret the data generated by these systems. By blending the strengths of both human instruction and AI support, this model is positioned to transform traditional language education, providing a richer, more inclusive, and adaptive learning environment.
Copyright (c) 2025 Min Wang (Author) https://creativecommons.org/licenses/by/4.0
Innovative Exploration and Practice of Archives Collection Automation
As a fundamental part of archives management, the efficiency and quality of archives collection directly affect the utilization of archival resources. Currently, China's archives collection work is in a critical period of transition from traditional models to digitalization, facing multiple challenges such as technical bottlenecks, outdated concepts, and institutional obstacles. This paper systematically analyzes the current situation and existing problems of archives collection work from four dimensions: technical dilemmas in collection methods, backwardness in management concepts, innovative explorations in grassroots practice, and prospective development paths. It also proposes practical automation solutions for archives collection, providing references for promoting high-quality development of archival undertakings.
Copyright (c) 2025 Zhe Liu (Author) https://creativecommons.org/licenses/by/4.0