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.
Study of Generative AI in Automated Financial Report Generation
This paper provides a comprehensive review of the rapidly evolving field of Generative AI in automated financial report generation. Traditionally, financial report generation has been a labor-intensive process relying on manual data aggregation, analysis, and narrative composition. However, recent advancements in Generative AI, particularly Large Language Models (LLMs), have demonstrated the capacity to automate and significantly enhance this process. This paper describes the current state-of-the-art, tracing the historical development of AI techniques applied to financial reporting. The paper examines core themes such as the application of LLMs for narrative generation from structured financial data, and the use of Generative Adversarial Networks (GANs) for synthetic data generation and fraud detection. A critical comparison of different Generative AI models is presented, highlighting their strengths and weaknesses in the context of financial reporting, alongside a discussion of the inherent challenges, including data bias, regulatory compliance, and the need for explainable AI. Finally, the paper explores future research directions, such as the integration of multi-modal data, the development of more robust and transparent AI models, and the ethical considerations surrounding the widespread adoption of Generative AI in finance. This review aims to provide researchers, practitioners, and regulators with a thorough understanding of the opportunities and challenges presented by Generative AI in transforming the landscape of financial report generation.
Copyright (c) 2026 Jialong Li (Author) https://creativecommons.org/licenses/by/4.0
LPDM-YOLOv8: A Lightweight Low-Light Object Detection Method Based on Laplacian Pyramid
To address the performance degradation of YOLOv8 under low-light conditions, this paper proposes a lightweight low-light object detection method based on a Laplacian pyramid. A Laplacian Pyramid Dimming Module (LPDM) is integrated into YOLOv8 to construct an end-to-end detection framework with illumination-adaptive enhancement and multi-scale feature fusion. The proposed module performs brightness-aware adjustment and hierarchical detail reconstruction, effectively improving feature representation in dark scenes with negligible computational overhead. Experiments on the ExDark dataset show that LPDM-YOLOv8n achieves an mAP of 0.524, corresponding to an 8.0% relative improvement over the baseline YOLOv8n, while maintaining real-time performance at 32 FPS. Notably, only 16 additional parameters are introduced without increasing FLOPs. The results demonstrate that the proposed method significantly enhances detection robustness under low-light conditions while preserving efficiency, making it suitable for real-time applications.
Copyright (c) 2026 Yahao Guo, Dongxiang Fu (Author) https://creativecommons.org/licenses/by/4.0
Graph Neural Network-Based Governance of Fraudulent Traffic: Detecting and Suppressing Fake Impressions and Clicks in Digital Platforms
Digital advertising platforms rely heavily on impression and click metrics to quantify user engagement, but fraudulent traffic-including fake impressions generated by bot farms and click farming-causes over $68 billion in annual losses for global advertisers. Traditional anti-fraud methods, such as rule-based engines and isolated machine learning (ML) models, fail to capture the complex relational patterns among users, advertisements, and traffic sources, leading to high false positive rates and poor adaptability to evolving fraud tactics. To address these gaps, this study proposes a Dynamic Graph Neural Network-based Fraud Traffic Governance Framework (DGNN-FTG) that integrates real-time detection and adaptive suppression in a unified pipeline. First, we construct a User-Advertisement-Medium (UAM) dynamic interaction graph that encodes spatio-temporal behavioural features and relational dependencies, overcoming the limitations of static, single-node modelling. Second, we design a Spatio-Temporal Dynamic GNN (ST-DGNN) for fraud detection, which incorporates temporal attention to track behaviour evolution and spatial attention to identify anomalous relational clusters. Third, we develop an adaptive suppression module that adjusts traffic filtering thresholds based on detection confidence, combined with a false-positive compensation mechanism to minimize disruption to legitimate users. Validated on a real-world dataset from a leading e-commerce advertising platform, DGNN-FTG achieves a fraud detection F1-score of 92.8%, outperforming traditional XGBoost and static GNN models by 18.3% and 11.5%, respectively. The suppression module reduces fraudulent traffic by 89.2% while maintaining a false positive rate of only 2.1%, balancing anti-fraud efficacy and user experience. This framework provides a scalable, real-time solution for digital platforms to combat fraudulent traffic and safeguard the integrity of the advertising ecosystem. Notably, DGNN-FTG exhibits strong generalization across platform types, achieving an F1-score of over 90% when deployed on both e-commerce and social media advertising systems, with minimal retraining required for cross-platform adaptation. For small and medium-sized advertisers, the framework's low false positive rate translates to a 22.3% reduction in wasted ad expenditure, directly improving their return on investment (ROI) in digital marketing campaigns. Additionally, the dynamic graph update mechanism enables DGNN-FTG to detect emerging fraud tactics-such as AI-generated fake user behaviour-within 48 hours of their appearance, far faster than the 7-10 day response time of traditional anti-fraud systems.
Copyright (c) 2026 Wenwen Liu (Author) https://creativecommons.org/licenses/by/4.0
Low-Cost Predictive Maintenance Modeling for SMB Fleets Using Operational Data
This research explores the application of low-cost predictive maintenance (PdM) models for small and medium-sized business (SMB) fleets, leveraging readily available operational data. SMB fleets often lack the resources for sophisticated PdM systems. This study investigates the feasibility of using easily accessible telematics data, such as mileage, fuel consumption, and basic engine diagnostics, to predict component failures and optimize maintenance schedules. We compare the performance of several machine learning algorithms, including logistic regression, support vector machines (SVM), and random forests, in predicting failures of critical fleet components. The models are trained and validated using a real-world dataset from a diverse SMB fleet. The results demonstrate that even with limited data and computational resources, effective PdM models can be developed to reduce downtime, lower maintenance costs, and improve the overall operational efficiency of SMB fleets. Furthermore, the study provides a framework for SMBs to implement these models using open-source tools and cloud-based platforms, thus minimizing upfront investment. The implications of this research are significant for SMBs looking to enhance their fleet management strategies through data-driven decision-making.
Copyright (c) 2026 Ziru Wang (Author) https://creativecommons.org/licenses/by/4.0
Evaluating the Impact of Lightweight AI Architectures on SMB Customer Retention: A Case Study of High-Performance, Low-Cost Systems
This research article investigates the impact of lightweight Artificial Intelligence (AI) architectures on Small and Medium-sized Businesses’ (SMB) customer retention. The study focuses on high-performance, low-cost AI systems and their effectiveness in enhancing customer engagement and reducing churn. Given that SMBs play a critical role in the U.S. economy, democratizing access to high-performance AI is essential for sustaining this sector, preventing SMB bankruptcy, and protecting jobs. We analyze various lightweight AI models, including optimized deep learning networks and efficient machine learning algorithms, implemented on resource-constrained infrastructure. The research employs a case study approach, examining several SMBs across different sectors that have adopted these AI solutions. Key performance indicators (KPIs) related to customer retention, such as churn rate, customer lifetime value, and customer satisfaction scores, are evaluated. Furthermore, the study explores the trade-offs between AI model complexity, computational cost, and customer retention benefits. The findings provide practical insights for SMBs seeking to leverage AI for improved customer relationship management without incurring significant financial or operational overhead. The study also provides theoretical contributions in the field of efficient AI deployment in resource-constrained environments.
Copyright (c) 2026 Zhenyuan He (Author) https://creativecommons.org/licenses/by/4.0
Research on AI-Based Voice-Based Real-Time Assessment of Psychological Stress
This research investigates the application of artificial intelligence (AI) to assess psychological stress in real-time through voice analysis. Addressing the limitations of traditional stress assessment methods, which are often subjective and retrospective, this study explores the potential of AI to provide objective and immediate stress evaluations. The methodology involves collecting voice samples from participants under varying stress conditions, extracting relevant acoustic features, and training machine learning models to classify stress levels. Key acoustic features include pitch, speech rate, intensity, and spectral characteristics. We compare the performance of different AI models, including Support Vector Machines (SVMs), Random Forests, and Deep Neural Networks (DNNs), in accurately detecting stress. The experimental results demonstrate the feasibility and reliability of using AI-based voice analysis for real-time stress assessment. The proposed system offers significant advantages in various applications, such as mental health monitoring, workplace stress management, and emergency response scenarios. The findings highlight the potential of AI technology to transform the way stress is measured and managed, paving the way for more proactive and personalized interventions. Future work will focus on improving the robustness and generalizability of the models across diverse populations and environments.
Copyright (c) 2026 Jingyi (Jesy) Zhang (Author) https://creativecommons.org/licenses/by/4.0
Drivers of Generative AI Adoption in Higher Education: A fsQCA Study on Student Motivations and Technology Perceptions
Generative Artificial Intelligence (GenAI) is reshaping higher education, yet the drivers of students' continuance intention in academic contexts remain underexplored. Building on the Rich Intrinsic Motivation (RIM) framework and technology adoption theories, this study investigates the configurational effects of intrinsic motivations (accomplishment, knowledge, stimulation), extrinsic motivation (perceived usefulness), and technology characteristics (ease of use, novelty) on GenAI adoption. Using Fuzzy-Set Qualitative Comparative Analysis (fsQCA) on data from 238 university students, the study reveals that no single factor is necessary for adoption. Instead, four distinct sufficient configurations drive high continuance intention: (1) "Happy Achievers" (Hedonic-Mastery), (2) "Curious Explorers" (Hedonic-Knowledge), (3) "Conquerors" (Pure Mastery), and (4) "Determined Strivers" (Utilitarian-Striving). These findings highlight the complex interplay between motivational and technological factors, offering tailored insights for educators to foster sustainable GenAI integration in learning.
Generative Artificial Intelligence (GenAI)
fsQCA (Fuzzy-Set Qualitative Comparative Analysis)
Copyright (c) 2026 Rongjing Yuan (Author) https://creativecommons.org/licenses/by/4.0
Democratizing Data Analytics: Lightweight AI Solutions to Improve Operational Efficiency in SMEs
Small and medium-sized enterprises (SMEs) generate significant amounts of operational data, yet often lack the resources and technical expertise to transform this data into actionable insights. Traditional data analytics solutions are frequently too complex, costly, or resource-intensive for SMEs, limiting their ability to leverage data effectively. This paper explores the role of lightweight artificial intelligence (AI) in democratizing data analytics and enhancing operational efficiency within SMEs. Lightweight AI solutions are characterized by ease of deployment, low technical barriers, and a focus on practical, high-impact applications. They enable SMEs to optimize business processes, gain insights into customer behavior, and improve resource allocation without requiring substantial infrastructure or specialized personnel. Implementation considerations include clearly defining business objectives, adopting incremental pilot projects, engaging employees, and maintaining transparency and human oversight in interpreting analytical outputs. While limitations such as data quality, resource constraints, and the potential for overreliance on automated recommendations exist, SMEs can mitigate these challenges through careful planning, iterative adoption, and flexible decision-making. Looking forward, lightweight AI has the potential to integrate progressively into SME operations, fostering a culture of data-driven decision-making and enabling organizations to respond more effectively to dynamic business environments. By leveraging accessible AI tools, SMEs can enhance efficiency, improve operational resilience, and build sustainable competitive advantages.
small and medium-sized enterprises (SMEs)
Copyright (c) 2026 Zhijun Liu (Author) https://creativecommons.org/licenses/by/4.0
Explainable AI Pipelines for Behavioral Fraud Modeling in Online Retail Environments
With online retail fraud posing growing threat to consumers, explainable AI (XAI) has become increasingly important for transparent and actionable risk assessment. This paper presents an XAI-integrated pipeline for behavioral fraud modeling, fusing supervised ensembles of logistic regression and random forests with unsupervised isolation forests to detect both known and emerging behavioral anomalies, including irregular cart sequences and geolocation inconsistencies. SHAP-based attributions are incorporated to deliver instance-level explanations that enhance auditability and support compliance requirements (e.g., PCI DSS). Using a heterogeneous dataset of 150,000 transaction records, the proposed system achieves an F1-score of 0.93 and reduces false positives and manual interventions by 82% relative to an industry-standard rule-based baseline. The architecture supports offline batch analysis and scalable serverless deployment. Pilot studies indicate potential operational cost reductions driven by decreased review workloads and improved detection efficiency. The open-source implementation fosters iterative community refinements, advocating XAI's role in fortifying e-commerce resilience against evolving threats like synthetic identities.
Copyright (c) 2026 Siqi Chen (Author) https://creativecommons.org/licenses/by/4.0
Study on Risk Assessment Methods and Multi-Dimensional Control Mechanisms in AI Systems
As Artificial Intelligence (AI) rapidly transitions from experimental prototypes to critical infrastructure, the historical "Performance-First" paradigm has left systems inherently vulnerable to adversarial attacks and data manipulation. This dissertation addresses the critical lack of standardized, quantitative methods for managing these risks by introducing the Risk Assessment Model for AI (RAM-AI). Utilizing a dual-domain simulation approach across Computer Vision and Financial datasets, the study empirically quantifies the "robustness boundary" of deep learning models. The findings reveal that single-layer defenses are inadequate; specifically, models exhibit "Data Hypersensitivity," suffering non-linear performance collapse under data poisoning rates as low as 3%. Furthermore, standard accuracy metrics fail to detect high-confidence evasion attacks. To mitigate these vulnerabilities, the research validates a Multi-dimensional Control Framework that integrates technical safeguards—such as adversarial training and input sanitization—with procedural governance, including Human-in-the-Loop (HITL) protocols. The results demonstrate that this Defense-in-Depth architecture significantly recovers system integrity, reducing critical error rates by 88% in high-stakes scenarios, and offers a strategic playbook for Enterprise Risk Management in the era of emerging AI regulations.
Copyright (c) 2026 Chong Lam Cheong (Author) https://creativecommons.org/licenses/by/4.0
Research on Optimization of Neuromuscular Rehabilitation Program Based on Physiological Assessment
This study focuses on the rehabilitation of neuromuscular dysfunction and explores the key role of physiological assessment in the design of rehabilitation plans. And analyzed the current problems of fragmented evaluation methods, weak functional correlation of indicators, and unclear results transformation pathways from the characteristics of physiological parameters such as surface electromyography (sEMG), near-infrared spectroscopy (NIRS), and heart rate variability (HRV). At present, it is necessary to establish a multimodal fusion evaluation system, strengthen functional oriented modeling thinking, and establish a standard mapping mechanism between physiological indicators and intervention prescriptions. In order to achieve personalized rehabilitation pathways for physiological data, promote the scientific and accurate nature of neuromuscular rehabilitation, and provide theoretical and practical references for sports rehabilitation and physical therapy.
Copyright (c) 2026 Yihao Wang (Author) https://creativecommons.org/licenses/by/4.0
Leveraging Ensemble Machine Learning for Credit Risk Assessment in Underserved U.S. Small Businesses
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.
Copyright (c) 2026 Zaolin Zhang (Author) https://creativecommons.org/licenses/by/4.0
ESE-Net: Edge-Shape Enhancement Network for Infrared Small Target Detection
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.
Copyright (c) 2026 Sen Yang, Guanxun Cui (Author) https://creativecommons.org/licenses/by/4.0
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.
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