Journal of Computer, Signal, and System Research
The Journal of Computer, Signal, and System Research(JCSSR) is a peer-reviewed journal focusing on cutting-edge research in computer science, signal processing, and systems engineering. It covers a wide range of topics, including algorithms, signal analysis, system design, and computational methods, aiming to promote innovation and interdisciplinary collaboration in these fields.
A Quality Control and Evaluation Framework for Generative AI in Personalized E-Commerce Product Descriptions
This research introduces a comprehensive quality control and evaluation framework tailored for generative AI applications in personalized e-commerce product descriptions. The framework addresses the critical need for ensuring the relevance, accuracy, fluency, and persuasiveness of AI-generated content, which directly impacts customer engagement and sales conversion rates. It incorporates multi-faceted metrics, including semantic similarity, content diversity, factual correctness, and user perception, to assess the quality of generated descriptions. Furthermore, the framework employs a feedback loop mechanism that continuously refines the AI models based on real-time performance data and user interactions. Through rigorous experimentation and comparative analysis with existing methods, we demonstrate the effectiveness of the proposed framework in producing high-quality, personalized product descriptions that enhance the e-commerce shopping experience. The study also explores the ethical considerations surrounding the use of AI in marketing and provides guidelines for responsible AI deployment.
Copyright (c) 2026 Xin Yuan (Author) https://creativecommons.org/licenses/by/4.0
Performance Analysis of YOLO for Object Detection under Complex Illumination Conditions
Object detection has become a crucial component in various computer vision applications, ranging from autonomous driving to surveillance systems. YOLO (You Only Look Once) has gained prominence due to its real-time performance and relatively high accuracy. However, its performance can be significantly affected by complex illumination conditions such as underexposure, overexposure, shadows, and varying light sources. This review paper provides a comprehensive analysis of YOLO's performance under these challenging illumination conditions. We begin with an overview of the YOLO architecture and its evolution, followed by a detailed exploration of how different illumination factors impact its detection accuracy and speed. We then delve into various techniques proposed to mitigate these issues, including image enhancement methods, adaptive thresholding approaches, and robust feature extraction strategies. Furthermore, we comparatively analyze the performance of different YOLO variants and other state-of-the-art object detectors under diverse illumination scenarios. The paper synthesizes the current research landscape, highlights the key challenges that remain, and discusses potential future directions for improving the robustness of object detection algorithms in adverse lighting conditions. This review aims to serve as a valuable resource for researchers and practitioners seeking to understand and address the limitations of YOLO in real-world applications with complex illumination.
Copyright (c) 2026 Tiantian Chen, Sheila M. Geronimo (Author) https://creativecommons.org/licenses/by/4.0
SmartMLOps Studio: Design of an LLM-Integrated IDE with Automated MLOps Pipelines for Model Development and Monitoring
The rapid expansion of artificial intelligence and machine learning (ML) applications has intensified the demand for integrated environments that unify model development, deployment, and monitoring. Traditional Integrated Development Environments (IDEs) focus primarily on code authoring, lacking intelligent support for the full ML lifecycle, while existing MLOps platforms remain detached from the coding workflow. To address this gap, this study proposes the design of an LLM-Integrated IDE with automated MLOps pipelines that enables continuous model development and monitoring within a single environment. The proposed system embeds a Large Language Model (LLM) assistant capable of code generation, debugging recommendation, and automatic pipeline configuration. The backend incorporates automated data validation, feature storage, drift detection, retraining triggers, and CI/CD deployment orchestration. This framework was implemented in a prototype named SmartMLOps Studio and evaluated using classification and forecasting tasks on the UCI Adult and M5 datasets. Experimental results demonstrate that SmartMLOps Studio reduces pipeline configuration time by 61%, improves experiment reproducibility by 45%, and increases drift detection accuracy by 14% compared to traditional workflows. By bridging intelligent code assistance and automated operational pipelines, this research establishes a novel paradigm for AI engineering-transforming the IDE from a static coding tool into a dynamic, lifecycle-aware intelligent platform for scalable and efficient model development.
Copyright (c) 2026 Jiawei Jin, Yingxin Su, Xiaotong Zhu (Author) https://creativecommons.org/licenses/by/4.0
Comparative Study of Machine Learning Models for U.S. Housing Price Prediction
This study compares machine learning models used to predict US house prices using a large-scale real estate dataset covering all US states, cities, and zip code regions. The goal is to evaluate prediction accuracy and identify effective preprocessing strategies for high-cardinality location features. After cleaning the data through missing value handling, deduplication, and outlier removal based on interquartile range (IQR), leakage-aware feature engineering was applied, including aggregating zip codes into ZIP3, grouping by the top K cities, and date decomposition, while excluding target derived variables. This study trains and evaluates three models-linear regression, random forest, and XGBoost-on a reserved test set using mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²). The results show that XGBoost achieves the best performance, outperforming linear regression and random forest, and feature importance indicates that location indicators play a dominant role in prediction gain. The findings demonstrate that the XGBoost model outperforms linear regression and random forest models in predicting US house prices.
Copyright (c) 2026 Wenguang Zhou, Wenjiao Zhou (Author) https://creativecommons.org/licenses/by/4.0
TRIZ-RAGNER: A Retrieval-Augmented Large Language Model for TRIZ-Aware Named Entity Recognition in Patent-Based Contradiction Mining
TRIZ-based contradiction mining is a fundamental task in patent analysis and systematic innovation, as it enables the identification of improving and worsening technical parameters that drive inventive problem solving. However, existing approaches largely rely on rule-based systems or traditional machine learning models, which struggle with semantic ambiguity, domain dependency, and limited generalization when processing complex patent language. Recently, large language models (LLMs) have shown strong semantic understanding capabilities, yet their direct application to TRIZ parameter extraction remains challenging due to hallucination and insufficient grounding in structured TRIZ knowledge. To address these limitations, this paper proposes TRIZ-RAGNER, a retrieval-augmented large language model framework for TRIZ-aware named entity recognition in patent-based contradiction mining. TRIZ-RAGNER reformulates contradiction mining as a semantic-level NER task and integrates dense retrieval over a TRIZ knowledge base, cross-encoder reranking for context refinement, and structured LLM prompting to extract improving and worsening parameters from patent sentences. By injecting domain-specific TRIZ knowledge into the LLM reasoning process, the proposed framework effectively reduces semantic noise and improves extraction consistency. Experiments on the PaTRIZ dataset demonstrate that TRIZ-RAGNER consistently outperforms traditional sequence labeling models and LLM-based baselines. The proposed framework achieves a precision of 85.6%, a recall of 82.9%, and an F1-score of 84.2% in TRIZ contradiction pair identification. Compared with the strongest baseline using prompt-enhanced GPT, TRIZ-RAGNER yields an absolute F1-score improvement of 7.3 percentage points, confirming the effectiveness of retrieval-augmented TRIZ knowledge grounding for robust and accurate patent-based contradiction mining.
Copyright (c) 2026 Zitong Xu, Yuqing Wu, Yue Zhao (Author) https://creativecommons.org/licenses/by/4.0
Multi-Task Temporal Fusion Transformer for Joint Sales and Inventory Forecasting in Amazon E-Commerce Supply Chain
Efficient inventory management and accurate sales forecasting are critical challenges in large-scale e-commerce platforms such as Amazon, where stockouts and overstocking can lead to substantial financial losses and operational inefficiencies. Traditional single-task forecasting models, which focus solely on sales or inventory, often fail to capture the complex temporal dependencies and cross-task interactions that characterize real-world supply chain dynamics. To address this limitation, this study proposes a Multi-Task Temporal Fusion Transformer (TFT-MTL) framework designed for joint sales and inventory forecasting within the Amazon e-commerce ecosystem. The model integrates heterogeneous data sources-including historical sales records, warehouse inventory levels, pricing, promotions, and event-driven factors such as holidays and Prime Day campaigns-through a unified deep learning architecture. A shared encoder captures long-term temporal patterns, while task-specific decoder heads predict sales volume, inventory turnover, and stockout probability simultaneously. Experiments on large-scale real-world datasets demonstrate that the proposed TFT-MTL model significantly outperforms baseline methods such as LSTM, GRU, and single-task TFT. Compared with the single-task TFT model, the proposed approach achieves a 6.2% reduction in Sales RMSE, a 12.7% decrease in Sales MAPE, a 6.4% reduction in Inventory RMSE, and a 12.4% decrease in Inventory MAPE. These results confirm the model's ability to effectively capture multi-dimensional dependencies across supply chain variables. The proposed framework provides an interpretable, data-driven decision support tool for optimizing Amazon's inventory scheduling and demand planning strategies.
Copyright (c) 2026 Zheqi Hu, Yiwen Hu, Hanwu Li (Author) https://creativecommons.org/licenses/by/4.0
Privacy-Preserving AI for Detecting and Mitigating Customer Price Discrimination in Big-Data Systems
With the rapid development of big data and artificial intelligence technologies, personalized pricing has become a common strategy for platforms to enhance profitability. However, this practice often evolves into customer price discrimination (CPD), which seriously harms consumer rights and market fairness. Meanwhile, the data-driven nature of AI models for CPD detection raises severe privacy concerns. To address the dual challenges of CPD governance and data privacy protection, this paper proposes a privacy-preserving AI framework for CPD detection and mitigation in big-data systems. First, we design a federated learning-based detection model that enables multiple data holders to collaborate on model training without sharing raw user data. Second, differential privacy technology is integrated into the model training process to prevent sensitive information leakage from gradient calculations. For CPD mitigation, a dynamic pricing calibration mechanism based on explainable AI is proposed to ensure pricing transparency while maintaining platform operational efficiency. Experimental results on real-world e-commerce and ride-hailing datasets show that the proposed framework achieves a detection accuracy of 87.6% for CPD behaviors, with a privacy budget consumption of only 1.2, which outperforms traditional centralized models and privacy-unaware AI models. This research provides a technical solution for balancing personalized services, market fairness, and data privacy protection in big-data systems. Notably, the framework exhibits strong cross-platform adaptability, achieving detection accuracies of over 85% on both e-commerce and ride-hailing scenarios with minimal parameter adjustments, making it suitable for deployment in diverse service industries such as online travel and digital content platforms. In practical applications, the framework helps platforms comply with strict data protection regulations including the EU's GDPR and China's Personal Information Protection Law, while reducing consumer complaints related to price discrimination by an estimated 42%. Additionally, the integration of blockchain-based traceability ensures that pricing adjustment records are tamper-proof, providing regulatory authorities with credible audit trails for market supervision.
Copyright (c) 2026 Wenwen Liu (Author) https://creativecommons.org/licenses/by/4.0
Privacy-Preserving AI in SMB Customer Service: Balancing Data Isolation, Compliance, and Automation Efficacy
This research investigates the application of Privacy-Preserving AI (PPAI) techniques in Small and Medium-sized Business (SMB) customer service environments, recognizing that SMBs constitute the backbone of the U.S. economy. It focuses on balancing the seemingly conflicting demands of data isolation, regulatory compliance (e.g., GDPR, CCPA), and the efficacy of AI-driven automation. The study explores various PPAI methodologies, including Federated Learning, Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation, assessing their suitability for different SMB customer service scenarios. We analyze the trade-offs between privacy guarantees, model accuracy, computational overhead, and implementation complexity. Real-world case studies and simulations are used to evaluate the performance of selected PPAI techniques across key customer service metrics such as response time, customer satisfaction, and issue resolution rate. Furthermore, the research addresses the practical challenges of deploying PPAI in resource-constrained SMBs, considering factors like data heterogeneity, limited technical expertise, and cost considerations. This study proposes a framework for SMBs to effectively adopt PPAI in their customer service operations, ensuring robust data protection, adherence to compliance regulations, and enhanced automation capabilities. By lowering the technical barrier for SMBs to adopt AI while strictly adhering to privacy laws, this work aims to support widespread economic modernization alongside robust consumer privacy protection. The findings provide valuable insights for SMBs, AI developers, and policymakers aiming to promote the responsible and ethical use of AI in customer service.
Copyright (c) 2026 Zhenyuan He (Author) https://creativecommons.org/licenses/by/4.0
Inequality Research on Digital Navigation LTSS Support Services
This research article examines the inequalities prevalent in accessing and utilizing Digital Navigation Long-Term Services and Supports (LTSS). While digital navigation tools promise to enhance the accessibility and efficiency of LTSS, disparities persist across various demographic groups, potentially exacerbating existing health and social inequities. Our study investigates these inequalities by analyzing data related to digital literacy, access to technology, awareness of digital navigation tools, and experiences with LTSS support services. We employ a mixed-methods approach, combining quantitative analysis of survey data with qualitative interviews to gain a comprehensive understanding of the barriers and facilitators influencing equitable access. Key findings highlight the significant impact of socioeconomic status, geographic location, and cultural background on digital navigation tool adoption and utilization. These findings are crucial for informing policy interventions and designing targeted strategies to bridge the digital divide in LTSS, ultimately promoting equitable access to support services for all individuals in need. We emphasize the importance of culturally tailored digital literacy programs and user-centered design approaches to ensure that digital navigation tools effectively address the needs of diverse populations.
Copyright (c) 2026 Jingyi (Jesy) Zhang (Author) https://creativecommons.org/licenses/by/4.0
Research on Q-Learning and Motion Control of Multi-Robot Systems Based on Community-Aware Networks
Collaborative perception and autonomous motion control in complex environments are key challenges for multi-robot systems. As system scale increases, traditional centralized control and conventional distributed learning methods suffer from high communication overhead, low learning efficiency, and reduced stability. To address these limitations, this paper introduces a community-aware network framework that partitions the communication topology of a multi-robot system into communities with strong internal interactions. Based on this structure, a community-aware Q-learning and motion control method is proposed. The communication relationships among robots are modeled from the perspective of complex networks, and a community-based state representation is designed to capture local cooperation while reducing state dimensionality. Community-level information is further incorporated into the Q-value update through an improved reward mechanism, enhancing learning efficiency and convergence stability. In addition, learning decisions are mapped to continuous control inputs using robot kinematic models, enabling coordinated motion within communities and effective obstacle avoidance between communities. Simulation results demonstrate that the proposed method outperforms traditional multi-robot Q-learning approaches in convergence speed, path efficiency, and overall cooperative performance.
Copyright (c) 2026 Kexuan Shen (Author) https://creativecommons.org/licenses/by/4.0
Application of an Improved Orb Algorithm in Image Matching
An improved ORB feature matching algorithm is proposed to solve the problem of high mismatching rate of non-uniform distribution of feature points extracted by traditional ORB algorithms. First of all, to extract feature points of image block, and then calculate each block information entropy, the entropy value dynamically setting the threshold of each block to extract the feature points, make to extract the feature points of distribution more uniform, improve the subsequent registration accuracy. Secondly, for the problem of high mismatch rate caused by insufficient use of local information by the rBRIEF descriptor in the ORB algorithm, on the basis of introducing gradient information to improve the LBP algorithm, an improved descriptor combining rBRIEF and LBP is proposed. The 128-bit descriptor generated by the improved LBP algorithm is used to replace the 128-bit descriptor with the smallest variance in the original rBRIEF to improve the stability of the feature point description. Finally, RANSAC was used to delete the mismatched points. Results Compared with the traditional ORB algorithm, the improved algorithm reduces the time overhead by 8% and the average mismatch rate by about 10%. The improved orb algorithm has lower mismatch rate and higher registration speed.
Copyright (c) 2026 Xiaofei Deng, Joan Lazaro (Author) https://creativecommons.org/licenses/by/4.0
PPE Recognition System Based on Improved YOLOv11-Safety Algorithm and Frontend-Backend Architecture Integration
With the expansion of global urbanization, the construction industry, a pillar of the national economy, remains a high-risk sector. Although the number of work safety accidents and fatalities in China decreased year-on-year in 2024, housing construction projects still have high accident and fatality rates. Personal Protective Equipment (PPE) is the last effective defense for workers, but PPE compliance management faces challenges such as limited manual supervision and complex working environments. Leveraging advancements in AI and computer vision, this research designs an AI-driven deep learning system centered on the YOLOv11-Safety algorithm to realize real-time and accurate identification of 9 categories of PPE-related targets on construction sites. The system integrates Spring Boot (backend), Vue.js (frontend), and Flask (AI middleware) to build a practical intelligent safety supervision solution. Through improvements in feature extraction, feature fusion, loss function, and inference optimization, the YOLOv11-Safety algorithm achieves higher detection accuracy, faster inference speed on edge devices, and stronger robustness. The system promotes the digital transformation of construction site safety management from passive response to active prevention, providing an effective intelligent solution for workplace safety supervision.
Copyright (c) 2026 Lu Bei, Joan Lazaro (Author) https://creativecommons.org/licenses/by/4.0
Research on the Analysis and Recognition System for Dangerous Driving Behaviors Based on Convolutional Neural Networks
To address the critical need for real-time monitoring of hazardous behaviors that compromise road safety, this study presents an intelligent detection system centered on the lightweight YOLOv11n model. The system integrates multiple functional modules, including object detection, multi-object tracking using ByteTrack and BoTSORT, and behavior recognition logic based on temporal windows, thereby forming a comprehensive technical workflow encompassing data acquisition, preprocessing, inference, tracking, behavior analysis, and early warning. It is capable of accurately detecting six common dangerous behaviors-yawning, eye closure, smoking, drinking, mobile phone usage, and in-vehicle conversations-while providing timely alerts to mitigate potential risks. Extensive experiments demonstrate that the system achieves a validation accuracy of 65.55%, a recall rate of 79.71%, and an mAP50 of 69.26%. The inference speed for individual images is maintained within 30-60 milliseconds, and video processing can consistently reach 20-30 frames per second, ensuring real-time performance suitable for practical deployment. The proposed framework not only enhances detection precision but also supports continuous monitoring and risk prevention in real-world driving environments, providing a technically feasible and operationally efficient solution for improving driver safety.
detection of dangerous driving behaviors
Copyright (c) 2026 Yunpeng Liu, Joan Lazaro (Author) https://creativecommons.org/licenses/by/4.0
SR-DTMA: A Digital Twin-Driven LLM Multi-Agent Framework for Systemic Risk Simulation and Coordinated Decision-Making in Supply Chains
Systemic risk in global supply chains has intensified with increasing interdependencies among firms, logistics infrastructure, and financial institutions, where localized disruptions such as port closures, financial crises, or policy shocks can propagate across networks and trigger large-scale cascading failures. Existing supply chain risk management and simulation approaches are limited in their ability to simultaneously capture decentralized agent behavior, dynamic system interactions, and the semantic complexity of real-world risk information expressed in natural language. To address these challenges, this paper proposes SR-DTMA (Systemic Risk-aware Digital Twin Multi-Agent framework), a digital twin-driven simulation and decision-making framework that integrates Large Language Model (LLM)-powered agents with decentralized multi-agent learning for systemic risk analysis in supply chains. In SR-DTMA, heterogeneous supply chain entities-including manufacturers, ports, and financial institutions-are modeled as autonomous agents that perceive local operational states, interpret unstructured risk events from policy announcements and news, and make adaptive decisions under systemic shocks. By embedding LLM-based risk cognition within a digital twin environment, the framework enables large-scale simulation of risk contagion, strategic interactions, and the conflict between locally rational decisions and global system stability. Extensive experiments under port closure and financial tightening scenarios show that conventional rule-based and RL-driven agents exhibit higher systemic loss and deeper risk propagation. In contrast, SR-DTMA, integrating LLM-driven agents with a lightweight coordination mechanism, reduces the cumulative systemic loss ratio from 0.327 to 0.163, decreases risk propagation depth from 4.8 to 2.6, and shortens recovery time from 21 to 9 steps. These results highlight the effectiveness of coordinated LLM-based decision-making in mitigating systemic supply chain risk and enhancing network resilience.
Copyright (c) 2026 Yunting Ling, Wenxuan Liu (Author) https://creativecommons.org/licenses/by/4.0
Generative Adversarial Network Enhanced Adaptive Control for Aerospace Launch Vehicles
A theoretical analysis of an on-line, autonomously intelligent, adaptive tracking controller for satellites-employing Generative Adversarial Networks (GANs)-is presented. The controller receives real-time sensory data and a scalar performance signal, autonomously refining thruster or attitude-control commands without requiring explicit foreknowledge of the satellite's internal dynamics. By leveraging an adversarial interplay between a generator and a discriminator module, the approach rapidly adapts to evolving orbital conditions and unanticipated disturbances, thus preserving robust tracking accuracy. The underlying on-line learning mechanism enables continuous policy adjustments, obviating the need for extensive offline tuning. Experimental evidence, derived from a representative satellite undergoing maneuvers in a low-Earth-orbit environment, demonstrates the algorithm's capacity to compensate for nonstationary aerodynamic drag and shifting mass distribution while maintaining precise trajectory regulation. This work underscores the viability of GAN-augmented adaptive methods for advanced aerospace applications, offering heightened resilience and efficiency in the face of dynamic mission constraints.
Copyright (c) 2026 Abdulrahman Fahad, Fatemeh Mohammadi, Amir Hussain, Mariam Eissa, Hassan El-Nur, Sara Al-Sabah, Krzysztof Zalewski (Author) https://creativecommons.org/licenses/by/4.0
From Data Pipelines to Platform Tooling: A Holistic Framework for Cloud-Native Backend Development
Cloud-native backend development has evolved rapidly, transitioning from monolithic architectures to microservices, serverless functions, and containerized applications managed by sophisticated orchestration platforms. This review provides a holistic framework for cloud-native backend development, tracing its historical evolution, examining key architectural patterns, and analyzing the tooling ecosystems that enable efficient development and deployment. We explore the transformation from data pipelines, which initially focused on data movement and transformation, towards comprehensive platform toolings that provide end-to-end development and deployment capabilities. Core themes covered include microservices architecture and serverless computing, highlighting their benefits and challenges in the cloud-native context. We also delve into containerization technologies like Docker and orchestration tools like Kubernetes, discussing their role in automating deployment and scaling applications. A critical comparison of different backend development approaches, focusing on trade-offs between cost, performance, scalability, and maintainability, is presented. This review also identifies key challenges faced by developers in adopting cloud-native practices, such as complexity in distributed systems, vendor lock-in, and security concerns. Finally, we discuss future perspectives, including the rise of low-code/no-code platforms and the increasing importance of observability and automated testing in ensuring the reliability and performance of cloud-native backends. This review serves as a comprehensive guide for developers and architects looking to navigate the complexities of cloud-native backend development and build scalable, resilient, and cost-effective solutions.
Copyright (c) 2026 Yingqiang Yuan (Author) https://creativecommons.org/licenses/by/4.0
Research on Feature Fusion and Multimodal Patent Text Based on Graph Attention Network
Aiming at the challenges of cross-modal feature fusion, low computational efficiency in long patent text modeling, and insufficient hierarchical semantic coherence in patent text semantic mining, this study proposes a novel deep learning framework termed HGM-Net. The framework integrates Hierarchical Comparative Learning (HCL), a Multi-modal Graph Attention Network (M-GAT), and Multi-Granularity Sparse Attention (MSA) to achieve robust, efficient, and semantically consistent patent representation learning. Specifically, HCL introduces dynamic masking, contrastive learning, and cross-structural similarity constraints across word-, sentence-, and paragraph-level hierarchies, enabling the model to jointly capture fine-grained local semantics and high-level thematic consistency. Contrastive and cross-structural similarity constraints are particularly enforced at the word and paragraph levels, effectively enhancing semantic discrimination and global coherence within complex patent documents. Furthermore, M-GAT models patent classification codes, citation relationships, and textual semantics as heterogeneous graph structures, and employs cross-modal gated attention mechanisms to dynamically fuse multi-source and multi-modal features, thereby improving representation completeness and robustness. To address the high computational cost of long-text processing, MSA adopts a hierarchical sparse attention strategy that selectively allocates attention across multiple granularities, including words, phrases, sentences, and paragraphs, significantly reducing computational overhead while preserving critical semantic information. Extensive experimental evaluations on patent classification and similarity matching tasks demonstrate that HGM-Net consistently outperforms existing state-of-the-art deep learning approaches. The results validate the effectiveness and generalization capability of the proposed framework, highlighting its theoretical innovation and practical value in improving patent examination efficiency and enabling large-scale technology relevance mining.
Copyright (c) 2026 Zhenzhen Song, Ziwei Liu, Hongji Li (Author) https://creativecommons.org/licenses/by/4.0
ACE-Sync: An Adaptive Cloud-Edge Synchronization Framework for Communication-Efficient Large-Scale Distributed Model Training
Large-scale deep learning models impose substantial communication overhead in distributed training, particularly in bandwidth-constrained or heterogeneous cloud-edge environments. Conventional synchronous or fixed-compression techniques often struggle to balance communication cost, convergence stability, and model accuracy. To address these challenges, we propose ACE-Sync, an Adaptive Cloud-Edge Synchronization Framework that integrates (1) an attention-based gradient importance predictor, (2) a differentiated parameter compression strategy, and (3) a hierarchical cloud-edge coordination mechanism. ACE-Sync dynamically selects which parameter groups to synchronize and determines appropriate compression levels under per-device bandwidth budgets. A knapsack-based optimization strategy is adopted to maximize important gradient preservation while reducing redundant communication. Furthermore, residual-based error compensation and device clustering ensure long-term convergence and cross-device personalization. Experiments show that ACE-Sync substantially reduces communication overhead while maintaining competitive accuracy. Compared with FullSync, ACE-Sync lowers communication cost from 112.5 GB to 44.7 GB (a 60% reduction) and shortens convergence from 41 to 39 epochs. Despite aggressive communication reduction, ACE-Sync preserves high model quality, achieving 82.1% Top-1 accuracy-only 0.3% below the full-synchronization baseline-demonstrating its efficiency and scalability for large-scale distributed training. These results indicate that ACE-Sync provides a scalable, communication-efficient, and accuracy-preserving solution for large-scale cloud-edge distributed model training.
Copyright (c) 2026 Yi Yang, Ziyu Lin, Liesheng Wei (Author) https://creativecommons.org/licenses/by/4.0
Reverse Incentive Generation Mechanism for Agent Calibration and Stability
This paper focuses on the reverse incentive generation mechanism for agent calibration and stability. Firstly, it clarifies the background and core significance of researching this mechanism, then conducts an in-depth analysis of various factors affecting agent calibration and stability. Finally, aiming at these issues, it discusses in detail the operational principles and specific methods of the reverse incentive generation mechanism in agent calibration and stability improvement. By establishing a reasonable reverse incentive model combined with practical cases, the paper analyzes its application effects, aiming to provide theoretical support and practical guidance for enhancing agent performance, strengthening its reliability and stability in practical applications, and promoting the development and application of agent technology in various fields.
Copyright (c) 2026 Juyi Yang (Author) https://creativecommons.org/licenses/by/4.0
Customer Retention Optimization for SMEs Using Predictive Machine Learning Models
Customer retention is a critical determinant of sustainable performance for small and medium-sized enterprises (SMEs), yet many SMEs continue to rely on reactive and experience-based approaches to manage customer churn. The increasing availability of customer data creates new opportunities for more proactive and data-driven retention strategies. This study develops a conceptual framework for optimizing customer retention in SMEs through the application of predictive machine learning models. The framework integrates customer data, predictive analytics, and risk-based decision-making to support early identification of churn risk and targeted retention actions. By emphasizing model interpretability, practical implementation, and resource efficiency, the study highlights how predictive insights can be effectively embedded into SME operational processes. The analysis demonstrates the potential of predictive machine learning to transform customer retention management from a reactive function into a proactive and strategic capability under SME-specific constraints.
Copyright (c) 2026 Zhijun Liu (Author) https://creativecommons.org/licenses/by/4.0
Cross-Region Few-Shot Remote Sensing Image Captioning via Adaptive Vision-Language Feature Fusion
Remote Sensing Image Captioning (RSIC) enables automated interpretation of aerial imagery by converting complex visual scenes into coherent natural language descriptions. A key challenge in RSIC is the scarcity of annotated data and the significant domain shifts across geographic regions. Models trained on specific regional features often degrade in performance when applied to visually distinct landscapes such as agricultural or coastal areas. To address this, we propose the Adaptive Vision-Language Feature Fusion (AVLF) network, a few-shot learning framework designed to achieve robust cross-region transfer with minimal data. The AVLF framework bridges the semantic gap between visual and linguistic representations through an adaptive gating mechanism that dynamically balances visual and language features during caption generation. Extensive experiments on cross-region splits of multiple remote sensing datasets demonstrate that AVLF achieves state-of-the-art performance, maintains high captioning quality with limited support sets, generalizes effectively to unseen semantic categories, and incurs minimal computational overhead. Feature space visualizations show well-separated class distributions, while attention maps confirm that the model focuses on semantically relevant geographic objects. Ablation studies further highlight the importance of the adaptive fusion strategy in overcoming domain discrepancies and enhancing few-shot learning capability.
Copyright (c) 2026 Qikun Zuo (Author) https://creativecommons.org/licenses/by/4.0
UniGuard-Cascade: A Unified LLM-Driven Safety Scoring and Multi-Stage Audit Framework for Cross-Platform User Comments
User-generated comments across social media, e-commerce platforms, and online forums pose increasing challenges for automated content safety management. Existing moderation systems typically target a single platform, rely on inconsistent labeling standards, and struggle to balance accuracy with operational cost. To address these limitations, we propose UniGuard-Cascade, a unified safety scoring and cascading moderation framework that integrates large language models (LLMs), lightweight classifiers, and retrieval-augmented verification. UniGuard-Cascade introduces a platform-agnostic unified safety taxonomy covering toxicity, hate speech, spam, sexual/NSFW content, and misinformation, generated through LLM-based label alignment and normalization. The system operates in a three-stage cascade: (1) Fast Path Screening using a DistilBERT-based multi-label classifier for low-cost filtering; (2) Slow Path LLM Examination that provides refined labels and natural-language explanations; and (3) RAG-Enhanced Misinformation Verification, retrieving external evidence to validate factuality. Experiments conducted on four real-world datasets-Twitter, Amazon Reviews, Reddit, and YouTube Comments-show that UniGuard-Cascade consistently outperforms existing moderation baselines, achieving a Micro-F1 of 0.91 and a ROC-AUC of 0.97. The framework further reduces LLM usage by 87 percent compared with single-stage GPT-4 moderation, yielding a 5.7×reduction in overall inference cost while maintaining state-of-the-art multi-platform safety performance.
Copyright (c) 2026 Hao Tan, Ziming Chen (Author) https://creativecommons.org/licenses/by/4.0
AI Driven Payment System Security Improvement and User Privacy Protection Mechanism
With the continuous expansion of electronic payment systems and the rapid evolution of sophisticated cyber-attack methodologies, traditional security measures are increasingly finding it difficult to address the multifaceted risk challenges of the modern era. Leveraging the high-dimensional feature extraction and adaptive learning characteristics of artificial intelligence, this paper establishes a comprehensive AI-driven payment security and privacy protection framework. In terms of system security, the proposed architecture utilizes a residual attention mechanism for precise anomaly detection, while incorporating graph neural networks to analyze and cluster complex account relationship topologies. Furthermore, reinforcement learning is integrated to dynamically adjust risk control strategies in real-time, facilitating the construction of a collaborative defense system through the fusion of multi-source information. Regarding data privacy and integrity, the system adopts homomorphic encryption to enable complex model operations within an encrypted state, which is further combined with blockchain technology to ensure the rigorous traceability and immutability of the entire data flow. The implementation of this integrated technological architecture significantly enhances the intelligent defense capabilities of payment systems, providing a robust and scalable solution for safeguarding digital transactions and sensitive information in high-risk environments. This research not only offers a theoretical advancement in payment security but also provides a practical implementation roadmap for developing next-generation resilient financial information systems.
Copyright (c) 2026 Yue Qi (Author) https://creativecommons.org/licenses/by/4.0
Research on the Design and Optimization of Automated Data Collection and Visual Dashboard in the Medical Industry
With the ongoing advancement of medical informatization, automated data collection and visual dashboards have become critical for patient management, operational efficiency, and intelligent decision-making. Standardized data collection systems and display platforms improve the timeliness and accuracy of information gathering. To support diverse devices and complex use cases, system architecture, data transmission, display logic, and multi-terminal adaptation must be optimized to enhance data interoperability, boost efficiency, and maximize the value of intelligent healthcare systems.
Copyright (c) 2026 Xiangtian Hui (Author) https://creativecommons.org/licenses/by/4.0
A Dynamic Intrusion Detection System Integrating Concept Drift Detection and Incremental Learning
The dynamic, evolving, and inherently non-stationary characteristics of modern network environments present a fundamental challenge to traditional intrusion detection systems that rely on static learning paradigms. As network traffic patterns, system usage behaviors, and threat manifestations continuously change over time, the statistical properties underlying detection data are prone to both explicit and implicit variations, commonly described as concept drift. Such drift leads to a gradual mismatch between previously learned models and current data distributions, resulting in performance degradation, delayed responses, and reduced practical effectiveness of fixed detection mechanisms. To address these limitations and support the construction of an intelligent defense system with long-term adaptability, this study conducts a systematic theoretical investigation into dynamic intrusion detection from the perspective of learning evolution. On this basis, a unified framework is proposed that tightly integrates concept drift detection mechanisms with incremental learning strategies, enabling models to identify distributional changes in a timely manner and update their knowledge without retraining from scratch. The framework emphasizes continuity, stability, and adaptability, aiming to balance detection accuracy with computational efficiency under continuously changing conditions. By clarifying the internal relationship between drift detection and incremental model updating, this work provides a structured theoretical foundation for the development of adaptive intrusion detection systems capable of maintaining robust performance in complex and evolving network scenarios.
Copyright (c) 2026 Zhuoqi Liao (Author) https://creativecommons.org/licenses/by/4.0
DeepSeqCaus: A Deep Sequential Causal Inference Framework for User Churn Prediction and Optimal Retention Intervention Generation
In digital platforms, understanding and mitigating user churn is crucial for sustaining long-term engagement and revenue. Traditional machine learning approaches often rely on correlation-based predictive models without explicitly accounting for causal relationships underlying user behavior. This study proposes DeepSeqCaus, a unified deep sequential causal inference framework that integrates sequence modeling and treatment effect estimation to enable accurate churn prediction and optimal retention intervention policy generation. DeepSeqCaus consists of a dual-branch architecture: (1) a Temporal Feature Encoder using gated convolution and bidirectional gated recurrent networks to extract multi-granular temporal representations from behavioral sequences; and (2) a Causal Effect Estimator based on counterfactual representation learning to estimate heterogeneous treatment effects (HTEs) for candidate interventions such as personalized notifications, discount offers, or content recommendations. Using large-scale user interaction logs from an online service, we conduct extensive experiments comparing DeepSeqCaus with conventional predictive models and causal inference baselines. The results showed that DeepSeqCaus outperformed the baseline model in all cases. The proposed framework provides actionable insights for targeted retention and demonstrates strong potential for deployment in intelligent customer management systems.
Copyright (c) 2026 Tangtang Wang, Wen Ding (Author) https://creativecommons.org/licenses/by/4.0
A Secure Federated Learning Algorithm for Emotion Recognition Towards Multimodal Speaker Signals on the Client Side
With the widespread application of multimodal data in dialog emotion recognition, effectively integrating text, audio, and visual information while addressing data heterogeneity across multiple clients and ensuring user privacy has become a key research challenge. This paper integrates a Transformer self-distillation model with attention scores and federated learning algorithms to propose a multimodal emotion recognition framework. The framework employs intra-modal and inter-modal Transformers to capture multimodal interactions, enhances modality representations through attention weights, and incorporates a federated learning structure to safeguard data privacy. A global model distance-weighted aggregation strategy is introduced to mitigate model bias caused by heterogeneous data. Experimental results on the IEMOCAP dataset demonstrate that the proposed framework achieves superior emotion recognition accuracy and exhibits more stable model convergence compared to existing baseline models.
Copyright (c) 2026 Xin Wang, Longlong Qiao, Guangxin Dai, Quanping Chen, Yan Zhang, Wensong Li (Author) https://creativecommons.org/licenses/by/4.0
AI-Empowered Technical Knowledge Modelling and Transformation Mechanism in Cross-Cloud Environments
For technical knowledge in multi-cloud and cross-cloud environments, there exist characteristics such as structural diversity, meaning differences, and dynamic continuity. The existing knowledge management methods cannot adapt to this form of cooperation. This study focuses on proposing and answering how to establish and transform technical knowledge in a cross-cloud environment. It has established an AI modeling system of "extraction - matching - integration - update", proposed quantitative expressions suitable for cross-cloud knowledge, and developed strategies for automatic model extraction, semantic matching, and dynamic knowledge network construction. In terms of knowledge transformation, a cross-cloud technology knowledge transformation model centered on AI has been established, and a reasoning and decision-making mechanism has been introduced to accelerate the transformation efficiency. The effectiveness of the proposed method has been proved through typical cases, providing technical path support for cross-cloud knowledge organization and intelligent management.
Copyright (c) 2025 Zhengrui Lu (Author) https://creativecommons.org/licenses/by/4.0
Research on AI Security Strategies and Practical Approaches for Risk Management
The rapid integration of Artificial Intelligence (AI) into critical infrastructures has exposed significant security vulnerabilities that traditional cybersecurity paradigms fail to address. Unlike deterministic IT systems, AI models are stochastic and data-dependent, making them susceptible to unique threats such as data poisoning, adversarial evasion, and model inversion. This dissertation investigates the comprehensive landscape of AI security, aiming to bridge the gap between isolated technical defenses and holistic organizational risk management. Through a systematic review and taxonomic analysis, this research categorizes AI risks across three distinct layers: data integrity, model robustness, and system deployment. The study evaluates current defense strategies, demonstrating that technical measures like adversarial training and differential privacy are necessary but insufficient when applied in isolation. Consequently, a practical AI Risk Management Framework is proposed, structured around a continuous lifecycle of mapping, measuring, managing, and monitoring risks. The findings suggest that effective AI security requires a "Defense-in-Depth" strategy that integrates robust MLOps infrastructure with rigorous governance policies. The dissertation concludes that shifting from static security controls to dynamic, lifecycle-based assurance is essential for deploying trustworthy AI systems in high-stakes environments.
Copyright (c) 2025 Chong Lam Cheong (Author) https://creativecommons.org/licenses/by/4.0
Research on Privacy-Preserving Sharing and Collaborative Computing of Transaction Data for Intelligent Risk Control
This paper conducts a systematic study of the challenges in privacy-preserving sharing of transaction data faced by the financial intelligent risk control sector. It first analyzes how data silos lead to specific operational bottlenecks, including fraud detection models that "cannot see and cannot track", credit assessment that "cannot clearly perceive and cannot accurately evaluate", and group-wide risk management that "cannot fully observe and cannot effectively prevent". It also reveals the dual dilemma of traditional plaintext data-sharing models in terms of legal compliance and commercial trust. On this basis, the paper proposes an "One Core, Two Wings, Three Drivers" application framework for privacy-preserving collaborative computing designed to resolve these conflicts. This framework takes business value and joint risk prevention as its core objective, supported by two wings-technical integration and governance collaboration-and driven by three key elements: scenarios, compliance, and performance. The paper provides a systematic and actionable theoretical reference and practical solution for financial institutions to achieve secure data value circulation and collaborative intelligent analytics across institutions and scenarios, while strictly adhering to data security and privacy compliance requirements.
Copyright (c) 2025 Zhijian Liu (Author) https://creativecommons.org/licenses/by/4.0