Journal of Computer Science and Frontier Technologies
Journal of Computer Science and Frontier Technologies ISSN Print: 3104-4204 ISSN Online: 3104-4212 CD ISSN: 3104-4220
Airport Bird Detection and Recognition Based on YOLOv11n
Bird strike is a typical sudden-onset disaster endangering civil aviation safety. Accurate and efficient bird target detection serves as a core prerequisite for airports to prevent bird strike incidents. To tackle the challenges of tiny bird targets in complex airport environments, including low pixel coverage, weak feature information and severe background interference, this paper presents AeroBird-YOLO, a lightweight improved YOLOv11n algorithm for airport bird detection.In the backbone network, lightweight spatial-to-depth convolution (L-SPDConv) is introduced in shallow layers, while a C3k2-DCNv4 module is constructed in deep layers to adapt to the non-rigid deformation of birds via the dynamic sampling mechanism of fourth-generation deformable convolution. Omni-dimensional dynamic convolution (ODConv) is embedded in the neck feature fusion stage to suppress background noise from airport runways and other facilities, and the Normalized Wasserstein Distance (NWD) loss function is adopted at the prediction head.Ablation experiments and multi-algorithm comparisons on the re-annotated and augmented AirBirds dataset demonstrate that AeroBird-YOLO achieves a mean average precision (mAP@50) of 91.71%, representing a 4.09 percentage point improvement over the baseline YOLOv11n model
Copyright (c) 2026 by author(s) and Erytis Publishing Limited https://creativecommons.org/licenses/by-sa/4.0
Multi-Target Behavior Monitoring Analysis System and Collaborative Optimization Method Based on AI Image Recognition
Aiming at the technical bottlenecks of existing behavior monitoring systems in complex scenarios, such as weak multi - target parallel processing capability, lack of time coordination mechanism.unreasonable computing resource allocation,and difficulty in balancing monitoring accuracy and real - time performance, this paper proposes a multi- target behavior monitoring analysis system and a full - process collaborative optimization method based on AI image recognition. Taking the standardized three - stage process of image collection–feature extraction–behavior analysis as the core, this method constructs an array - type multi - station parallel image acquisition hardware architecture.and realizes unified computing power scheduling and efficient data interaction through the central control module. A time - stage modeling strategy integrating scene complexity,target motion speed and recognition accuracy requirements is established to accurately quantify the duration of each monitoring link. A multi - target time - series collaborative scheduling rule is designed to avoid task execution phase conflicts. A full - cycle resource consumption model and a dynamic computing power balance mechanism are constructed to minimize system computing power fluctuation .Theoretical analysis and simulation verification show that the proposed system can achieve a monitoring coverage rate of 98. 7%in multi - target parallel monitoring cases lead to a 27. 3% improvement in utilization of computing resources and to an average accuracy of 95. 2% in behavior recognition. The system is more stable and more suitable for real - time use than previous approaches .From the technical standpoint .the work now offers full support—both practical and theoretical—for intelligent monitoring of people in public areas or in industry, in traffic or in parks.
Design and Prototype Validation of an STM32-Based Access-Control System with Four Alternative Authentication Methods
Access-control prototypes often depend on a single credential type, so a lost, forgotten, or temporarily unreadable credential can prevent legitimate entry. This paper presents an STM32F103C8T6-based prototype that provides four alternative authentication methods: fingerprint identification, RFID-card verification, keypad-password entry, and smartphone password entry through a JDY-31 Bluetooth serial link. The firmware routes all four inputs through a common decision and actuator path while retaining separate enrollment, revocation, and password-management procedures. A relay, LCD1602, and buzzer provide lock actuation and user feedback. We validated nominal enrollment, authentication, deletion, and rejection functions under laboratory conditions and then repeated each authentication method in 20 functional trials. Each method produced 20 observed successful operations; the reported mean response times were below 1 s for RFID, approximately 0.5 s for fingerprint identification, below 1 s for the keypad, and below 2 s for Bluetooth. These measurements establish function-level feasibility and response-time performance for the tested prototype, but they do not constitute a statistical estimate of biometric accuracy, long-term reliability, or resistance to attack. The resulting architecture is intended as a low-cost and extensible platform for residential, laboratory, and small-office access-control research.
The Applications and Challenges of AI in the Banking Sector
This study systematically explores the current state of artificial intelligence (AI) applications across three core scenarios in the banking industry: intelligent customer service, risk management, and wealth management. Based on empirical case studies, AI technology has significantly reduced labor costs in the banking sector, enhanced the efficiency of business processes such as credit approval, and enabled ordinary customers to access professional financial advice. However, AI also faces several limitations, including insufficient levels of intelligence, a lack of interpretability in model decision-making processes, risks of customer data leakage, and suboptimal system stability. In the banking industry—a sector where operational accuracy and security are paramount—the risks arising from these issues are particularly pronounced compared to those in other industries. In summary, the deep integration of AI in the banking sector has become an inevitable trend; the key challenge lies in how to achieve its deployment in a robust and controllable manner. Feasible approaches include: at the technical level, adopting methods that integrate multiple optimization algorithms to enhance the generalization capability and robustness of models; at the business process level, establishing a hybrid decision-making mechanism that integrates human and machine capabilities; and at the institutional and regulatory level, urgently formulating and refining specific regulations and standards tailored for AI-driven financial applications. Only through such multi-dimensional collaborative governance can the application value of AI in the banking sector be maximized, while its potential risks are confined within acceptable limits.
Algorithm for Safety Helmet Recognition and Parameter Optimization in Ship Engine Control Rooms
Ship engine rooms present difficult visual conditions, including low illumination, oil mist or steam, equipment occlusion, overhead viewpoints and densely distributed hazardous zones. To link safety-helmet recognition with hazard-zone warning under these conditions, this study proposes a YOLOv13s model-selection and electronic-fence fusion method. YOLOv5s, YOLOv8s, YOLOv11s, YOLOv13s and YOLOv13-EEAttne were compared on the same ship-engine-room helmet dataset under unified training settings. Scene subsets were further constructed from image brightness, target-box pixel area, inter-box overlap and target count, covering normal and low illumination, near-, mid- and far-range targets, and no, partial and severe occlusion. YOLOv13s was re-evaluated on these subsets, and its detection outputs were connected to foot-point electronic-fence rules and multi-frame confirmation. YOLOv13s achieved a final Precision of 0.9559, mAP@0.5 of 0.9201 and mAP@0.5:0.95 of 0.6795, outperforming the other compared models overall. The model contained approximately 9.55 million parameters, required 21.62 GFLOPs at an input size of 640 and reached 43.25 FPS on an RTX 4060 Laptop GPU. Scene-subset validation showed mAP@0.5:0.95 values of 0.672 and 0.597 under normal and low illumination, and 0.714, 0.673 and 0.470 for near-, mid- and far-range targets. These results indicate that distant small targets remain the main challenge and that YOLOv13s, combined with electronic-fence rules, provides a feasible detection basis for safety-helmet warning and hazardous-zone monitoring in ship engine rooms.
SA-LES-Dedup: Similarity-Aware Lightweight Encrypted Block Deduplication for Cloud–Edge Storage
Encrypted deduplication improves storage efficiency while preserving data confidentiality, but cloud-edge deployments still face low-entropy guessing, excessive cloud traffic, and costly edge-index lookups. This paper presents SA-LES-Dedup, which combines OPRF-assisted block-key derivation, edge-local/cloud-global deduplication, lightweight proof of ownership, and SimHash-based candidate selection. Clients derive deterministic block keys and exact tags from OPRF outputs. Edge nodes first search a bounded candidate set and then fall back to the complete local index before forwarding unresolved blocks to the cloud. Similarity tags only accelerate lookup, while exact tags and ownership proofs determine deduplication. We analyze leakage, correctness, and security under honest-but-curious edge and cloud servers. Model-based results show that cloud-bound ciphertext traffic decreases with the edge-local duplicate ratio, while candidate-first lookup reduces edge-index cost when similarity concentration is high. The scheme remains correct under similarity false positives and false negatives.
Constructing Purchase-Related Candidate Sets: Time-Ordered Prediction and Product Co-Occurrence
Clickstream logs capture observable product interactions rather than consumers’ latent consideration sets. Accordingly, previously interacted-with products are treated as a behavior-based candidate pool rather than as direct evidence of consideration. This study examines whether recent interactions can help rank products within this pool by their probability of purchase on the following day and whether a smaller candidate set can be effectively constructed. The analysis uses the 2019 JDATA competition dataset from JD company, comprising 37.2 million actions between 1 February and 15 April 2018. Without loss of generality, the largest anonymized category in the pre-April period (Cate 7) is selected for the primary analysis. The sample contains 1.52 million actions from 109,352 users involving 45,284 products. Using five-day histories, a Bayesian logistic model is estimated on target days 6–9 April, an F1-maximizing threshold is calibrated on 10 April, and performance is tested on 11–15 April. Across 1,873,710 test user–product–days, the model achieves an ROC-AUC of 0.7582 and a PR-AUC of 0.00559, compared with 0.7550 and 0.00473 for a views-recency benchmark. At the calibrated threshold, precision is 0.0124 and recall is 0.0735. Only 19.15% of test purchase pairs fall within the prior-interaction universe; among the 1,826 eligible user-days, Hit@5 reaches 0.776, compared with 0.741 for page views alone. The threshold retains 11,982 candidate records, from which a co-occurrence graph with 1,980 nodes and 7,148 edges is constructed. The results support the use of recent interactions as a narrow purchase-related filter, but not as a direct measure of cognitive consideration or product competition.
Personalized Classification Models for Service Systems: Algorithmic Commonality between Tourist Profiling and Patient Stratification
This study proposes a unified framework of personalized classification models for intelligent service systems by comparing tourist profiling in smart tourism with patient stratification in healthcare management. Rather than treating personalization as a domain-specific or medical innovation, the paper conceptualizes it as a service system and management science problem centered on classification-driven decision support. The analysis demonstrates that both tourism and healthcare rely on the same functional logic of personalization, including user segmentation, behavior prediction, recommendation, adaptive service design, and feedback-based learning. These shared functions reveal an algorithmic and governance equivalence between tourist personas and patient subtypes, as well as between itinerary recommendation and care pathway design. By shifting the evaluation of personalization from technical accuracy to service outcomes such as relevance, efficiency, transparency, and trust, the study highlights the managerial value of classification models in complex service environments. The findings confirm that personalization is a transferable service system methodology rather than a sector-specific technique. Tourism is identified as the methodological origin of many personalization practices, while healthcare serves as a high-stakes application context. This cross-domain perspective advances understanding of how AI-enabled classification supports intelligent services and institutional decision-making.
Building Machine-Learning-Based Decision Support Models for Complex Systems: A Multi-Omics Healthcare Case Study
This study frames cancer classification as a complex decision-support problem rather than a purely medical task. Drawing on theories of complex adaptive systems (CAS) and data-driven governance, we examine how machine-learning-based decision support systems (DSS) transform large-scale, multi-omics healthcare data into actionable risk stratification outputs. Public datasets from TCGA, CGGA, GEO, and the World Health Organization are used as a case context to evaluate algorithmic decision models, including Random Forest, Support Vector Machines, XGBoost, and SHAP-based explainability tools. Results show that these models achieve stable performance (accuracy 85-92%, AUC 0.88-0.95) while maintaining interpretability and decision reliability. By comparing healthcare analytics with tourism management and smart city governance, the study demonstrates that the same DSS logic applies across complex systems characterized by uncertainty, multi-source data, and nonlinear outcomes. The findings highlight the transferability of machine-learning-driven decision support methodologies to tourism destination management, urban governance, and other data-intensive policy domains.
AGV Assignment Strategy in Express Distribution Center Based on Closed Queueing Model
A rational assignment strategy can effectively reduce Automated Guided Vehicle (AGV) driving distance and improve resource utilisation. Firstly, we propose a partition assignment strategy considering the driving distance of AGV. On this basis, we propose task balanced partition assignment strategy by considering the utilisation balance of the pick-up platform. And we develop a closed queueing model to evaluate the performance of the AGV system. Secondly, we design experiments to verify the effectiveness of the theoretical model. Finally, we analyse and compare the performance of random assignment strategy, partition assignment strategy and task balanced partition assignment strategy. The results show as (1) The closed queueing model can effectively evaluate system performance. (2) Task balanced partition assignment strategy can obviously improve the system throughput. (3) The partition assignment strategy caused resource congestion when parcels arrive unevenly, while the task balanced partition assignment strategy can obviously balance the utilisation rate of the pick-up platform.
Design and Implementation of an Intelligent Learning Assistance System: A Full-Stack Web Application with React and FastAPI
This study presents the design and implementation of an Intelligent Learning Assistance System (ILAS), a full-stack web application that integrates machine learning services for student risk prediction, profiling, knowledge tracing, and personalized recommendations. The system adopts a four-tier architecture comprising a React 18 frontend with TypeScript, an Nginx gateway, a FastAPI backend with SQLAlchemy ORM, and a dedicated AI model service layer. The frontend implements 14 page routes with ECharts-based visualizations. The backend exposes 45+ RESTful API endpoints across six service modules, with JWT-based authentication and role-based access control. Key technical contributions include a Singleton Model Manager pattern for heterogeneous AI model lifecycle management, a three-strategy recommendation engine combining BKT-based weakness targeting (weight 0.5), BERT semantic matching (weight 0.3), and collaborative filtering (weight 0.2), and an asynchronous Celery-based LLM report generation pipeline. Performance evaluation demonstrates that database indexing improves query performance by 78% to 92%, and the system maintains an average response time of 312ms under 150 concurrent users with a 0.12% error rate.
Multimodal Data Fusion for Intelligent Early Warning System of Cucumber Diseases
To address the limitations of traditional cucumber disease recognition relying solely on image information with insufficient early warning accuracy, this paper proposes an intelligent early warning system for cucumber diseases based on multimodal data fusion. The system integrates leaf image data with environmental sensor data (temperature, humidity, light intensity, CO₂ concentration) and constructs an Attention Mechanism-based Convolutional Neural Network (AM-CNN). A multimodal dataset containing 12,000 samples across 7 disease categories was built by collecting cucumber leaf images and corresponding environmental parameters. Experimental results demonstrate that the AM-CNN model with multimodal fusion achieves an average recognition accuracy of 94.7%, which is 2.4 percentage points higher than the single-modal CNN model and 15.6-20.8 percentage points higher than traditional machine learning algorithms. The end-to-end response time is 1.6 seconds, meeting real-time monitoring requirements. This research provides a novel technical solution for precision disease warning in facility agriculture, with significant theoretical and practical value.
Comparing Chinese–English Translation Performance of Large Language Models: A Multidimensional Evaluation of Financial, Technological, and Political Texts
Large language models (LLMs) are increasingly used for machine translation; however, their reliability in professional Chinese–English translation, particularly for domain-specific texts, remains insufficiently examined. This challenge is especially evident in texts containing specialized terminology, numerical information, complex logical relations, and formal institutional phrasing. To address this issue, this study conducts a controlled comparison of six LLMs: ChatGPT-5.0, Claude Sonnet 4.5, DeepSeek-R1, GLM-4.6, Qwen3-MAX, and Grok 4.0. Three representative Chinese texts from the financial, technological, and political domains were selected and translated under three prompt conditions: zero-shot prompting, domain-enhanced prompting, and glossary-based prompting. Translation quality was assessed through a multidimensional evaluation framework. Standard reference-based metrics, including BLEU, ChrF++, TER, ROUGE, BERTScore, and COMET, were used to measure automatic translation quality. In addition, lexical richness and syntactic-discourse indicators were incorporated to examine stylistic and structural differences across model outputs. The results reveal a clear domain effect, indicating that financial, technological, and political texts present different levels and types of translation difficulty for LLMs. By contrast, prompt strategy did not produce significant main effects under the three tested conditions. At the model level, GLM-4.6 achieved the highest COMET-based ranking across all prompt settings, while DeepSeek-R1 showed comparatively stable second-place performance. Overall, the findings suggest that, within this controlled exploratory dataset, model type and text domain exert a stronger influence on translation performance than prompt modification alone. Rather than offering a definitive ranking of LLM translation capability, this study provides empirical evidence on how selected LLMs handle specialized Chinese–English translation under comparable experimental conditions. Future research should expand the dataset, incorporate human evaluation, and further investigate the role of terminology constraints in improving translation accuracy and stylistic appropriateness.
Flow Experience or Algorithm Trust? A Dual-Path Study of How AI Empowerment Drives Students’ Online Learning Stickiness
Now that artificial intelligence is available to all, it has changed how we learn online. Now many excellent systems that use artificial intelligence are available to provide personalised learning paths, intelligent tutoring, etc. Learning stickiness is the long-term intention of students to use a learning platform and stay engaged in learning over time, so it has become an important indicator of the sustainability of online education. Based on the experience of flow and algorithm trust, a two-way theoretical model is put forward in this paper to explore how the empowerment of artificial intelligence affects the stickiness of students' online learning in different ways. According to the theories of flow and trust, a total of 426 students were selected as the subjects of the questionnaire survey on the AI online learning platform in this paper. According to the results of the experiment, both the flow experience and trust in the algorithm were found to be moderators of the effect of AI empowerment on learning stickiness. The road of experience and flow is relatively more popular among adolescent students, and at the same time, the rational path of algorithm trust is used more frequently by adults. Personalisation of the platform, interactive feedback and data transparency are the three reasons for the appearance of the two mediating states. This paper studies the two motives behind AI-enabled online learning and offers some practical optimization ideas for the Design of learning platforms, Algorithm improvement, etc.
HMCO-AT: A Hierarchical Memory - Compute Orchestrated Adaptive Training Framework for Resource-Efficient and Adversarially Robust Large Models in Cloud–Edge Systems
Adversarial training has emerged as the most effective paradigm for improving the robustness of deep neural networks; however, it imposes substantial computational and memory demands, particularly during the adversarial example generation process. These challenges become more critical in cloud-edge collaborative learning scenarios, where edge devices have limited resources and communication bandwidth is highly variable. To address this problem, we propose HMCO-AT, a Hierarchical Memory-Compute Orchestrated Adaptive Training framework designed specifically for resource-efficient adversarial training of large-scale models. HMCO-AT explicitly models the memory footprint and computation peaks induced by adversarial sample generation and dynamically schedules model layers, training stages, and communication operations across cloud and edge nodes. Our framework integrates hierarchical parameter partitioning, memory-aware activation recomputation, quantized gradient transmission, and an RL-based adaptive scheduler that jointly optimizes memory consumption, computational load, and robustness performance. HMCO-AT is validated on large-scale language-modeling corpora including OpenWebText, WikiCorpus, and filtered C4 under realistic cloud–edge conditions. Experiments show that HMCO-AT reduces peak edge memory by 64.8% and lowers computation cost and latency by 35.8% and 26.7%, respectively. It also improves adversarial robustness, achieving 7.2% higher FGSM and 9.3% higher PGD-10 accuracy than strong baselines. These results confirm HMCO-AT as an efficient and robust framework for large-model cloud–edge training.
A Private Web-Based Pipeline for Converting Images and PDF Pages into Editable PowerPoint Shapes
Rasterized slide images and PDF pages are easy to distribute but difficult to reuse because text, layout objects, borders, and background regions are flattened into pixels. This paper presents a private web-based pipeline that converts images and PDF pages into editable PowerPoint files. The proposed system emphasizes local deployment, backend-centered image analysis, optical character recognition, text-region removal, geometric shape reconstruction, an intermediate Shape JSON representation, and .pptx export. The method first normalizes input pages, detects text regions, estimates background colors, extracts primary layout shapes through color clustering and contour analysis, and then reconstructs editable PowerPoint shapes with fill colors, borders, layer order, and placeholder text fields. A lightweight browser interface supports upload, preview, basic shape correction, and re-export, while computation remains on the local server. The current prototype completes the core conversion loop from raster input to editable PowerPoint output and provides a foundation for improved text removal, manual correction, batch PDF processing, and reusable private templates. The study demonstrates a practical engineering approach for transforming static visual documents into reusable presentation assets without relying on public cloud processing.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
GIMeT: A Multimodal Transformer Framework for Automated Gastrointestinal Disease Diagnosis Using Endoscopic Images and Physiological Signals
Accurate and efficient diagnosis of gastrointestinal (GI) diseases is essential for timely clinical decision-making, yet traditional diagnostic workflows rely heavily on manual interpretation of endoscopic images and disconnected physiological measurements. These limitations often lead to variability in diagnosis and increased clinician workload. To address these challenges, we propose GIMeT (GastroIntestinal Multimodal Transformer), a novel deep learning framework that integrates endoscopic imaging, physiological time-series data, and optional clinical text information for automated GI disease classification and lesion segmentation. GIMeT consists of three modality-specific encoders—a CNN-based image encoder, a temporal encoder for physiological signals, and a BERT-based text encoder—followed by a Cross-Modal Transformer (CMT) to achieve fine-grained feature alignment and joint representation learning. A FiLM-conditioned U-Net decoder further enhances pixel-level lesion segmentation using multimodal cues. We evaluate GIMeT on a multi-center dataset comprising endoscopic images, electro-physiological measurements, and symptom descriptions. Experimental results show that GIMeT consistently surpasses all unimodal and multimodal baselines across both classification and segmentation tasks. The model achieves improvements of approximately 3-4% in accuracy and F1-score, and a 3% gain in lesion Dice coefficient. Ablation studies further validate the contribution of multimodal fusion and cross-modal attention, demonstrating GIMeT’s robustness and strong potential for computer-assisted gastrointestinal diagnosis.
Driver Fatigue Detection Method Based on Improved RT-DETR
To address the problem of poor robustness in driver fatigue detection under complex scenarios such as low illumination and motion blur, this paper proposes an optimization method based on hard sample augmentation. A hard sample dataset containing low illumination, motion blur, and their compound interference is constructed, and a progressive two-stage training strategy is designed to guide the model to adapt to various degradations step by step. Furthermore, a spatial-channel interleaved robustness module (SCIRM) is proposed to enhance the response of eye and mouth features, while the normalized Wasserstein distance (NWD) loss function is adopted to improve the localization accuracy of small targets. The improved RT-DETR model achieves mAP50 of 95.03%, 95.62%, and 94.56% under low illumination, motion blur, and compound interference scenarios, respectively, significantly outperforming the baseline model, while its performance on clear scenes remains almost unchanged. This method effectively enhances the robustness of fatigue detection under complex imaging conditions.
IoT Anomaly Traffic Detection Method Based on Transformer Encoder Architecture
The widespread deployment of IoT devices has made heterogeneous network traffic highly complex, making abnormal traffic detection vital for IoT security. This paper presents a Transformer-based IoT abnormal traffic detection method tailored to the long-range temporal correlation of IoT traffic and the drawbacks of existing approaches. It first builds traffic embeddings with learnable positional encoding to project raw traffic features into a high-dimensional space, then leverages Transformer multi-head self-attention to capture long-range dependencies by modeling temporal correlations in traffic sequences. Feature compression and residual enhancement modules are further adopted to lower model complexity and inference delay for resource-limited IoT terminals. Experiments verify that the scheme accurately identifies normal traffic and diverse anomalous attacks with outstanding detection accuracy and stability.
MUAD-Net: A Multi-scale URL Anomaly Detection Network for Malicious URL Identification
With the continuous evolution of cyber attack techniques, the propagation of malicious links has become a major threat in cybersecurity. Attackers induce users to visit target resources by constructing malicious URLs, leading to information theft and malware distribution. Effectively identifying malicious URLs in complex network environments has become an important research issue. To address this problem, this paper adopts an attack life-cycle perspective and proposes a deep learning-based malicious URL detection method. Considering the complex structure, short lifecycle, and fast generation speed of malicious URLs, the method first normalizes URL strings and encodes them as character sequences, then maps characters into vector representations through an embedding layer. A multi-scale convolutional structure is used to extract local pattern features from URL character sequences, combined with a sequence modeling mechanism to capture contextual dependencies, achieving automatic learning of malicious URL features. Experimental results show that the proposed method achieves high detection performance and good generalization ability. Through multi-scale feature extraction and model optimization, this paper effectively improves malicious URL detection performance, demonstrating good application value in real-world network scenarios.
CloudPayGuard: Hardware-Aware Real-Time Fraud Detection for Cloud-Native Credit Systems with OoO CPU Microarchitecture Optimization
Real-time fraud detection in cloud-native credit payment systems is a critical challenge due to the increasing complexity of transaction networks, the rapid evolution of fraudulent behaviors, and the high computational demands of modern deep learning models. To address these challenges, we propose CloudPayGuard, a hardware-aware framework that integrates Temporal Heterogeneous Graph Neural Networks (TH-GNN) for dynamic transaction modeling, Large Language Models (LLM) for automated security policy generation, and out-of-order (OoO) CPU microarchitecture performance prediction for hardware-accelerated inference. CloudPayGuard constructs multi-modal transaction graphs incorporating user behavior sequences, device fingerprints, and geolocation information, enabling real-time identification of suspicious activities with millisecond-level latency. The framework dynamically generates and verifies risk policies through LLM-based reasoning and constraint checking, ensuring trustworthy and adaptive deployment in cloud-native environments. To optimize inference performance, a deep learning-based CPU microarchitecture predictor estimates IPC and identifies potential bottlenecks in ROB, IQ, and LSQ resources, allowing dynamic adjustment of CPU parameters and task scheduling. Experiments on a large-scale financial transaction dataset show that CloudPayGuard achieves an F1-score of 0.91 and an average inference latency of 6 milliseconds, outperforming baseline TH-GNN and other models. The OoO CPU microarchitecture optimization reduces latency by 34–40%, while LLM-driven policy generation and TH-GNN-based graph modeling ensure accurate fraud detection. These results demonstrate CloudPayGuard’s efficiency, scalability, and effectiveness for real-time fraud detection in cloud-native credit systems.
Temporal Heterogeneous Graph Neural Network
Transmission Dynamics Model Incorporating Risk Perception and Behavioral Fatigue
The classical SIR model provides a foundational framework for studying transmission dynamics, but its standard form often assumes a fixed transmission rate and therefore cannot directly represent adaptive changes in human behavior. In real transmission processes, individuals may reduce contact when perceived risk rises, while prolonged protection may also generate behavioral fatigue and weaken adherence. To describe this coupled mechanism, this paper extends the classical SIR model by introducing two bounded behavioral variables: risk perception and behavioral fatigue. The effective transmission rate is specified as an exponential function of these variables, which guarantees positivity and captures two opposite effects: risk perception suppresses transmission, whereas fatigue amplifies transmission. Ideal numerical simulations are conducted to compare the classical SIR model, a risk-perception-only model, and a full risk-perception-fatigue model. The results show that risk perception substantially lowers the infection peak and final epidemic size, while behavioral fatigue partially offsets the protective effect and delays the infection peak. Sensitivity analysis further indicates that increasing the risk-perception suppression coefficient reduces the epidemic scale, whereas increasing the fatigue amplification coefficient produces stronger rebound effects. The model offers a compact and interpretable framework for studying behavioral feedback in transmission dynamics.
Design and Implementation of a Robotic Dynamic Grasping System Based on Im-proved YOLO and Fuzzy PID Control
Industrial and service robots increasingly need to grasp objects that are moving on conveyors, sliding down chutes, or being handed over by humans, yet most production-grade pipelines still assume static targets. Two pain points dominate: detection networks that drop precision when the target is small, partially occluded, or motion-blurred, and joint-level controllers whose gains are tuned offline and therefore fail to compensate for the time-varying dynamics introduced by chasing a moving object. This paper proposes an end-to-end dynamic grasping system that couples an improved YOLO detector with a fuzzy PID joint controller. The detector embeds a Ghost-bottleneck CSPDarknet backbone, a coordinate-attention module that emphasizes motion-salient regions, a BiFPN neck for multi-scale fusion, and an SIoU + α-CIoU regression objective that converges faster on tightly packed parts. The controller treats tracking error and its derivative as fuzzy variables, online tunes ΔKp, ΔKi, and ΔKd through a 49-rule Mamdani inference base, and feeds the corrected gains to each joint in real time. The two modules are connected by a Kalman-smoothed pose stream and a quintic on-line trajectory re-planner. We trained the detector on a 12,800-image conveyor dataset and evaluated the integrated system in 300 dynamic trials on a 6-DOF UR5 with parallel jaws. Compared with a YOLOv5s + classical-PID baseline, the proposed pipeline raises mAP@0.5 from 74.3 % to 86.4 %, cuts joint-tracking overshoot from 18.6 % to 3.7 %, and increases dynamic-grasp success rate from 65.4 % to 91.2 % at object speeds up to 0.30 m/s, while keeping inference at 129 FPS on a single RTX 3060.
Dental-DPMNet: A Latent Disease Progression Modeling Network for Predicting Pulpitis and Periapical Disease Evolution Using Longitudinal Dental Imaging and Clinical Indicators
Pulpal and periapical diseases exhibit a continuous and progressive pathological process driven by lesion evolution and inflammatory deterioration, yet most existing artificial intelligence approaches focus on static diagnosis and fail to model disease dynamics over time. In pharmaceutical research, Disease Progression Models (DPMs) have been widely adopted to characterize disease natural history using longitudinal biomarkers, enabling individualized prediction of disease trajectories. Inspired by this paradigm, this study proposes Dental-DPMNet, a latent disease progression modeling network for predicting the evolution of pulpitis and periapical diseases using longitudinal dental imaging and clinical indicators. Dental-DPMNet integrates cone-beam computed tomography and periapical radiographs with clinical examination records, including pain intensity, thermal test responses, and percussion sensitivity, to construct multimodal lesion evolution representations. A continuous latent disease state is introduced to capture the underlying severity of pulpal inflammation, and its temporal dynamics are explicitly modeled via a neural ordinary differential equation, allowing flexible handling of irregular follow-up intervals and patient-specific progression patterns. Multi-task decoders are employed to jointly reconstruct multimodal observations and estimate disease stage transition probabilities and future progression risks. Experiments on a retrospective longitudinal dental dataset demonstrate that Dental-DPMNet achieves an accuracy of 87.6% and an AUC of 0.91 in multi-stage prediction of pulpal and periapical disease progression. Compared with CNN-based, LSTM-based, and ODE-based baselines, the proposed model consistently shows superior performance. These results confirm the effectiveness of latent disease progression modeling for capturing lesion-driven evolution and supporting personalized endodontic decision-making.
FRAdRec: A Federated Real-Time Advertising Recommendation Framework Based on User Behavior Modeling and Cloud-Native Data Infrastructure
Advertising recommendation systems have become a core component of modern e-commerce and digital marketing platforms, where accurately capturing dynamic user preferences and supporting large-scale real-time recommendation services are critical for improving advertising effectiveness and platform revenue. However, traditional centralized recommendation frameworks often suffer from limited scalability, delayed interest modeling, insufficient optimization of business-oriented objectives, and increasing privacy risks caused by centralized user data collection. To address these challenges, this paper proposes FRAdRec, a federated real-time advertising recommendation framework based on user behavior modeling and cloud-native data infrastructure. The proposed framework integrates a Transformer-based sequential learning module with a Deep Interest Network (DIN) attention mechanism to capture both long-term and short-term user interests from click and purchase behavior sequences. Meanwhile, a federated learning strategy is introduced to enable decentralized model training without transmitting raw user data, thereby improving privacy preservation and reducing data leakage risks. Furthermore, a multi-task optimization mechanism jointly predicts click-through rate (CTR), conversion rate (CVR), and return on investment (ROI) to enhance advertising profitability. To support industrial-scale deployment, a cloud-native streaming architecture based on Kafka, Flink, Kubernetes, and Redis is designed for low-latency recommendation and online feature updating. Experimental results on the Criteo and Alibaba Taobao datasets demonstrate that FRAdRec achieves an AUC of 0.857 and reduces recommendation latency to 41 ms, outperforming several state-of-the-art recommendation models in both recommendation accuracy and real-time serving efficiency.
An sEMG-Controlled RHex-T3 Hexapod Mobile Grasping Robot for As-sistive Object Retrieval
Assistive robots have strong potential in object retrieval, delivery, and remote interaction, but their practical usability depends on natural and low-burden human-machine interfaces. This study aims to develop an sEMG-controlled RHex-T3 hexapod mobile grasping robot system for assistive object retrieval. The proposed system integrates a multimodal hexapod chassis, robotic arm, camera-based wireless visual transmission module, sEMG acquisition and recognition module, and host-computer control terminal. Existing sEMG gesture recognition methods are deployed as a task-level robotic control interface, and a gesture-to-action mapping mechanism with hierarchical mode switching is designed to convert discrete gesture labels into chassis locomotion, mode-switching, and robotic-arm commands. Experiments are designed for online recognition, chassis control, grasping execution, and composite task completion. The results indicate that the proposed system can complete basic mobility, approach, grasping, and delivery tasks, verifying the preliminary feasibility of using sEMG to control a hexapod mobile manipulator for assistive object retrieval.
A Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System for Advertisement Retrieval and Personalization
In modern digital marketing, the growing complexity of advertisement data demands intelligent systems capable of understanding semantic relationships among products, audiences, and advertising content. To address this challenge, this paper proposes a Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System (KGSR-ADS) for advertisement retrieval and personalization. The proposed framework integrates a heterogeneous Ad-Knowledge Graph (Ad-KG) that captures multi-relational semantics, a Semantic Embedding Layer that leverages large language models (LLMs) such as GPT and LLaMA to generate context-aware vector representations, a GNN + Attention Model that infers cross-entity dependencies, and a Database Optimization & Retrieval Layer based on vector indexing (FAISS/Milvus) for efficient semantic search. This layered architecture enables both accurate semantic matching and scalable retrieval, allowing personalized ad recommendations under large-scale heterogeneous workloads. Experiments were conducted on a large-scale real-world advertisement dataset containing approximately 1.2 million user profiles, 250,000 advertisements, and 20 million user-ad interactions. Experimental results demonstrate that the proposed KGSR-ADS model outperforms state-of-the-art baselines across all evaluation metrics. Compared with the strongest baseline GraphRec, KGSR-ADS achieves a 5.7% improvement in Precision@10, a 5.5% increase in Recall@10, a 6.3% gain in NDCG@10, and a 5.0% enhancement in MRR, while reducing average response latency by 23.9%. These results confirm the effectiveness and efficiency of integrating knowledge graph reasoning with semantic representation learning for personalized advertisement recommendation. These results demonstrate the system’s effectiveness and potential for next-generation semantic advertising platforms that integrate knowledge reasoning and deep learning for adaptive, real-time personalization.
EduRL-GPT: A Reinforcement Learning Optimized Generative AI Framework for Intelligent Teaching Content Generation and Personalized Feedback
The rapid advancement of generative artificial intelligence (Generative AI), particularly large language models such as GPT, Claude, and Gemini, has significantly reshaped the design of intelligent tutoring systems by enabling automatic generation of instructional content, adaptive assessments, and personalized learning feedback. However, most existing generative AI-based educational systems rely on static prompting strategies and lack mechanisms to continuously optimize feedback quality according to students’ evolving learning states, which limits their effectiveness in personalized education scenarios. To address this limitation, this paper proposes EduRL-GPT, a reinforcement learning optimized generative AI framework for intelligent teaching content generation and personalized feedback. EduRL-GPT integrates a state-aware generative teaching engine with a reinforcement learning-based feedback optimization module, allowing the system to dynamically generate explanations, quizzes, and learning suggestions tailored to individual students’ knowledge mastery, learning behaviors, and preferences. Specifically, a Proximal Policy Optimization (PPO) algorithm is employed to adaptively select pedagogically appropriate feedback strategies based on observed learning gains and engagement signals, forming a closed-loop intelligent tutoring process. The proposed framework is evaluated on an online learning dataset with real student interaction logs. Experimental results show that EduRL-GPT significantly outperforms strong baselines. Compared with an LLM-based one-shot tutoring system, EduRL-GPT achieves a 12.8% improvement in learning gain and approximately a 13.0% relative increase in student engagement, while also yielding higher feedback satisfaction and accuracy improvement. These results demonstrate that reinforcement learning-optimized generative AI can deliver more effective, stable, and personalized instructional feedback for intelligent tutoring and adaptive learning systems.
Quantitative Characterization of Pore–Fracture Structures in Deep Coal Reservoirs Based on the QSGS Method
This study aims to improve the characterization of multiscale pore structures in coal by constructing a more representative digital core model. Mercury intrusion porosimetry, micro-CT scanning, Avizo-based image processing, and the quartet structure generation set (QSGS) method were integrated to reconstruct the pore–fracture networks of four coal samples. Mercury intrusion data were used to determine porosity and pore-size distribution, while micro-CT images were applied to establish three-dimensional pore network models. To overcome the resolution limitation of CT imaging, the QSGS method was further used to supplement unresolved micropores. The results indicate that the proposed multiscale digital core can more accurately represent the pore structure of coal samples. Connectivity analysis based on Euler number, tortuosity, and coordination number shows clear differences in pore connectivity among the samples. This approach provides an effective method for pore structure characterization and offers a basis for permeability prediction and coalbed methane development.
CLASNet: A Cognitive Load–Aware CNN-LSTM-Attention Framework for Supply Chain Demand Forecasting and Adaptive Human–Computer Interaction
This paper proposes CLASNet, a Cognitive Load–Aware CNN-LSTM-Attention framework for supply chain demand forecasting and adaptive human–computer interaction. Unlike conventional deep learning forecasting approaches that primarily emphasize predictive accuracy while neglecting human cognitive constraints, CLASNet integrates demand prediction and cognitive load modeling within a unified end-to-end architecture to improve both forecasting performance and decision-support efficiency. The proposed framework combines a CNN-LSTM demand feature encoder for capturing local temporal patterns and long-term dependencies, a Cognitive Load Encoder (CLE) for modeling user interaction behaviors, a Cognitive-Aware Attention Fusion (CAAF) mechanism that dynamically adjusts temporal attention weights according to users’ cognitive states, and an Adaptive Interaction Decision Layer (AIDL) that optimizes visualization complexity and information density. Experiments were conducted on a real-world retail supply chain dataset containing three years of demand records and dashboard interaction logs collected from intelligent decision-support systems. Comparative results against ARIMA, LSTM, CNN-LSTM, and CNN-LSTM-Attention baselines demonstrate that CLASNet achieves superior forecasting accuracy, obtaining an RMSE of 14.55, MAE of 9.22, and MAPE of 10.9%, corresponding to an approximately 9.6% RMSE reduction compared with the strongest baseline. Ablation studies further verify the effectiveness of the cognitive load encoder, cognitive-aware attention mechanism, and adaptive interaction module. In addition, user-centered evaluations indicate that the proposed cognitive-aware adaptive interface significantly reduces task completion time and perceived cognitive load during demand analysis tasks. Overall, the results demonstrate that integrating cognitive awareness into deep learning–based supply chain forecasting systems can substantially enhance both predictive performance and human–AI collaborative decision-making efficiency.