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Copyright terms are indicated in the Republic of Lithuania Law on Copyright and Related Rights, Articles 4-37.
Knowledge Distillation and Distribution Calibration for Few-Shot Object Detection
Few-shot object detection (FSOD) aims to recognize and localize novel categories using only a limited number of annotated samples. Existing transfer learning–based approaches have attracted considerable attention for their structural simplicity and computational efficiency. However, merely fine-tuning the pretrained model parameters is insufficient to capture inter-class and intra-class relationships, thereby limiting the exploitation of transferable knowledge and further performance improvement. Therefore, we propose a prototype-guided semantic learning framework. By incorporating knowledge distillation, the method explicitly models transferable knowledge among classes for both classification and localization tasks. Specifically, a queue-based memory mechanism constructs dynamic class prototypes and distribution statistics in the feature space, enabling the modeling of class relationships. Classification knowledge transfer is achieved via Kullback-Leibler divergence, while localization knowledge transfer is guided through regression reweighting. Furthermore, to alleviate distribution bias from the scarcity of novel class samples, an adaptive distribution correction and augmentation strategy based on optimal transport is introduced to enhance novel class classification. Experimental results demonstrate that, compared with baseline methods, the proposed approach achieves 6% and 5% improvements in novel class mAP under the 1-shot setting on the VOC and COCO datasets, respectively.
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Unsupervised CycleGAN-Based Model for Optimizing Depth-of-Field Effects in Photographic Image
To address issues such as blurred foreground-background separation, low detail fidelity, and hazy background blur in traditional methods for optimizing depth-of-field effects in photographic images, this study proposes an improved unsupervised cyclic generative adversarial networks model. First, the study builds upon unsupervised cyclic generative adversarial networks, enhancing the generator through deep separable convolutions and coordinate attention modules. It replaces the dual-discriminator architecture with a class activation mapping module within the discriminator, while integrating the Retinex algorithm into post-processing to address background haze effects. Algorithm performance test results indicated that in the depth-of-field information processing scene, structural similarity reached 30.4. In the edge information processing scene, structural similarity reached 28.1. All scenes maintained optimal performance. In depth-of-field optimization tests for bow-and-net images, the model's output images achieved an average information entropy of 7.22 (compared to 7.12 for original images). It effectively mitigated typical depth-of-field defects such as background overexposure and motion blur. The model achieves a balance between depth-of-field effects, efficiency, and visual quality, generating high-resolution shallow depth-of-field images. It provides a solution for unsupervised depth-of-field optimization, applicable to post-production photography and industrial image analysis. Future performance enhancements can be achieved through multi-scene dataset expansion and end-to-end optimization.
Image Style Transfer for Visual Design Using StarGANv2 and Attention Mechanism
In visual design, high-quality facial attribute style transfer and diversified painting style transfer are key to meeting the diverse artistic creation needs. To improve the facial image generation effect in visual design, the study first designed a facial attribute style transfer method based on an improved star shaped generative adversarial network version 2. The generator introduced dense connection modules and random noise modules, and a spectral normalization network was added to the discriminator. On the basis of this model, a painting style transfer model was studied and constructed. Its generator adopted distribution shift convolution and attention mechanism, while its discriminator used spectral normalization and Markov dual discriminator. The findings demonstrated that the attribute generation accuracy of the facial attribute style transfer method was 98.56%, and its average time consumption in practical applications was 71.2ms, significantly better than the comparison model. The PSNR and maximum structural similarity index of the painting style transfer model are 38.68dB and 0.948. Both models had good performance and could provide efficient and high-quality style transfer solutions for visual design. This research holds immense importance in fostering the advancement of artistic creation in the field of visual design.
MP-Transformer: A Hybrid Model Integrating Multi-Period ARIMA and Dynamically Gated Attention for Time-Series Forecasting
Accurate time-series forecasting is challenging when multiple seasonalities interact with non-linear effects. We present MP-Transformer, a hybrid" decompose-then-refine" framework that couples an interpretable Multi-Period ARIMA baseline with a dynamically gated attention residual learner. The ARIMA component extracts dominant linear trends and multi-scale seasonality via seasonal phase templates with synchronous differencing, yielding an approximately stationary residual series and an interpretable baseline. A Transformer then models the remaining non-linear dynamics using a gated fusion of global attention (for long-range periodic dependencies) and content-driven Top-k local attention (for abrupt short-term variations). Period contributions are learned through non-negative, normalized gating weights. Across multiple real-world datasets, MP-Transformer consistently improves multi-horizon accuracy over statistical, deep, and hybrid baselines. The results demonstrate that combining explicit linear decomposition with implicit residual learning yields robust, data-efficient forecasting and enhanced interpretability.
Obstacle Avoidance Path Planning and Robust Tracking Control of Quadrotor UAVs under Uncertain Disturbances
In this paper, obstacle-avoidance path planning and robust trajectory tracking are addressed for quadrotor UAVs in the presence of model uncertainties and pulse disturbances. By using the Newton–Euler formulation, a 6-DOF nonlinear dynamic model is derived to construct a constrained 3D path-planning problem. To overcome the shortcomings of conventional PSO in terms of premature convergence and poor path smoothness, a hybrid adaptive multi-topology PSO with intelligent strategy learning and cubic B-spline smoothing, termed as HAMT-AISL-PSO, is proposed, which jointly enhances the global search ability and path executability. In the aspect of tracking control, a linear active disturbance rejection control (LADRC) scheme with a linear extended state observer is designed to estimate and compensate the total disturbances in real time. Simulation results on a cluttered 3D environment demonstrate that, compared with the standard PSO, the average cost function and average path length are reduced by 25.20% and 20.45%, respectively. Under a 3D spiral reference and pulse torque disturbances, LADRC achieves faster transient response and near-zero steady-state error with much smaller tracking deviations than PID, hence verifying the effectiveness of the proposed planning-and-control framework.
Linear active disturbance rejection control
CogniRAG: Integrating Causal Hyperedges and Counterfactual Reasoning for Knowledge-Intensive Tasks
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources. Yet, existing chunk- or graph-based frameworks remain confined to surface-level semantic correlations, lacking the capacity to model mechanistic causality, cross-document dependencies, and counterfactual reasoning. To address these limitations, this study introduces CogniRAG, a causal reasoning–centric RAG framework that systematically transforms retrieval into a structured process of causal inference. CogniRAG encodes four specialized causal hyperedge types—Linear Chain, Feedback Loop, Intervention Point, and System Stability—within a unified CogniGraphDB, enabling multi-entity causal tracing and intervention analysis. A dual retrieval strategy combining entity diffusion and relation expansion constructs causally enriched prompts that enhance inferential depth beyond conventional similarity-based retrieval. Experiments conducted across four knowledge-intensive domains (Medicine, Political Science, Computer Science, Finance) using an identical LLaMA-3.1-70B backbone and 480 systematically generated questions demonstrate consistent and statistically robust improvements. CogniRAG achieves an approximately five-percentage-point Overall gain over HyperRAG across all domains (for example, Medicine 78.67 vs 73.63), with the largest increases observed in Empowerment and Logical Coherence, corroborated by objective reliability metrics (+1.8 pp Faithfulness, +1.2 pp Factual Consistency, −0.7 pp Hallucination). Cross-backbone evaluation with Qwen-Plus and Mistral Large 2 confirms backbone-agnostic consistency (+3–5 pp), while efficiency analysis reveals moderate computational overheads (indexing +30–40%; latency +50%) proportional to added causal reasoning depth. Overall, CogniRAG advances RAG from semantic retrieval toward causal, counterfactual, and interpretable knowledge reasoning, with future work focusing on real-time causal-graph updating, multimodal evidence integration, reproducible benchmarking, and meta-reasoning for confidence calibration to strengthen scalability and external validity.
Improved Ant Colony Algorithm for Energy Storage Power Optimization in Multi-Power Distribution Networks
To address power imbalance, voltage fluctuations, and low new energy consumption rate caused by high penetration of distributed new energy in multi-power distribution networks, this study proposes an energy storage power control method based on an improved ant colony algorithm (ACO). First, A multi-objective optimization model was constructed, considering the reduction of network loss, improvement of new energy consumption, and stability of energy storage SOC. The entropy weight method was used to objectively determine the target weights. Second, to solve the poor accuracy of traditional ACO in continuous control, a "continuous domain discretization" strategy was introduced, and pheromone update rules were optimized to enhance algorithm convergence and precision. Finally, a case study of a 10kV industrial park distribution network is introduced. The research shows that compared with traditional ant colony algorithm and particle swarm algorithm, the improved algorithm reduces voltage fluctuations by 42%, lowers the daily network loss rate to 2.13% (44.2% lower than no energy storage), and increases the photovoltaic consumption rate on clear days to 96.2%. The results of the paper validate the effectiveness of the proposed approach in optimizing the operation of the distribution network and promoting the integration of new energy.
EGOOSE-LightGBM Model-Based Identity Recognition Using Cough Acoustic Signal Analysis
Speech signals-based identity recognition is an active research area, which has a wide range of important applications such as human–computer interaction, forensic investigation, sound surveillance, transaction authentication, and health monitoring. As a special speech event, cough plays a crucial supplementary role in identity recognition scenarios, especially when traditional biometric features are unavailable. However, compared to traditional speaker recognition, very few research efforts have been made by researchers to explore identity recognition using cough acoustic signal analysis. In this work, we propose an EGOOSE-LightGBM model for cougher recognition, in which the hyperparameters of LightGBM are optimized by EGOOSE algorithm. Chaotic mapping, Gaussian mutation, and crisscross optimization are employed in the proposed metaheuristic-type EGOOSE algorithm, which demonstrates superior performance in terms of convergence speed and accuracy compared to GOOSE algorithm. A static-dynamic feature fusion (SDFF) technique is used to fuse cough sound characteristics including Mel frequency cepstral coefficient (MFCC), fundamental frequency, formant, ∆MFCC, and jitter of formant to improve recognition effectiveness and noise robustness. Using the self-recorded cough sounds, we establish a dataset, the size of which is enlarged by data augmentation techniques. Performance improvement of 3.167% is obtained due to the exploitation of cough data augmentation. Experimental results show that EGOOSE-LightGBM outperforms other existing machine learning models such as SVM, XGBoost, RF-Adaboost, LSTM, and LightGBM, achieving remarkable recognition accuracy of 99.500%.
Complicated Scene Classification Using a Deep Active Learning Paradigm for Wetland Remote Sensing Analysis
Investigating the intricate semantics of diverse wetland landscapes is vital for the development of Intelli gent computing systems within remote sensing applications. This study introduces a cutting-edge approach that utilizes memristor-based architectures to integrate multi-channel perceptual visual features for classifying wetland remote sensing images, characterized by complex spatial and ecological structures. Our method leverages a deep hierarchical model designed to emulate human gaze dynamics through a memristor-enabled processing unit, employing the BING objectness metric to accurately detect key ecological features and details across multiple scales within wetland scenes. To enhance the human-like visual attention mechanism, we propose a Memristor-Enhanced Robust Deep Active Learning (MRDAL) strategy, which systematically generates gaze shifting paths (GSPs) and extracts their deep representations using memristor-based networks. A distinctive aspect of MRDAL is its resilience against label noise, achieved through a sparse penalty mechanism embedded within the memristor architecture, effectively filtering out irrelevant GSP features. We subsequently apply a manifold-regularized feature selector (MRFS) integrated with memristive components to extract high-quality deep GSP features, which are then utilized to train a linear Support Vector Machine (SVM) for the classification of wetland scenes. Empirical evaluations reveal the method’s superior performance over conventional models, demonstrating its exceptional capability in discerning complex patterns within a comprehensive dataset of large-scale wetland remote sensing images. This advancement highlights the potential of memristor-based intelligent computing technologies for ecological monitoring and environmental analysis.
ID-RTDETR: An Efficient Cross-Scale Fusion Algorithm for Insulator Detection
Insulator inspection is critical for power system maintenance, but existing methods have challenges in terms of efficiency and accuracy. We propose Insulator Detection Real-Time DEtection TRansformer (ID-RTDETR), an optimized RT-DETR framework with four key innovations for insulator detection. Firstly, Partial Reparameterized Block (PR Block) uses partial convolution with reparameterized in the backbone network, which reduces the computational cost and maintains the feature quality. Secondly, Focusing Diffusion Feature Pyramid Network (FDFPN) realizes the direct interaction of cross-scale features, which can effectively capture different defects. Thirdly, the Attention-based Intra-scale Feature Interaction efficient (AIFI-efficient) reduces the complexity of feature interaction through the additive attention mechanism. Fourth, the Generalized-Inner IoU (G-Inner IoU) loss function is proposed to combine global optimization with local refinement for accurate fault localization. Through the experiment, PR Block reduces parameters by 28.76% and computational cost by 24.78% while improving mAP50:95 by 1.62%. AIFI-Efficient enhances the inference speed of algorithms while maintaining accuracy. FDFPN enhances mAP50:95 by 3.10% through cross-scale feature interaction. G-Inner IoU increased by 2.74% in mAP50:95. The synergistic combination of all four modules achieves optimal performance. After experiments, compared with the baseline RT-DETR, ID-RTDETR improves the mAP50:95 by 1.8 percentage points, reduces the model size by 17.10%, and reaches 71.6 fps. ID-RTDETR provides an efficient and accurate solution for automatic insulator inspection.
Research on Underground Coal Mine Object Detection Based on Image Enhancement and YOLOv11
Underground coal-mine scenes are often affected by low lighting, dust, and motion blur., and monitoring images are often affected by low lighting, dust, and motion blur, which severely hinders the recognition performance of intelligent detection systems. To address the decline in object detection accuracy caused by poor image quality, this paper proposes an object detection method combining a detection-guided dual-branch image enhancement module with YOLOv11. The method includes two key innovations: first, a detection-guided image enhancement module is developed, which generates attention maps from low-confidence regions in YOLOv11’s detection output to guide the enhancement network in focusing on critical image areas, thereby improving detection accuracy. Second, a dual-branch structure combining brightness enhancement and detail reconstruction is designed, integrating the strengths of Zero-DCE and SRGAN to achieve global brightness correction and edge clarity enhancement. Experiments conducted on the publicly available underground coal mine image dataset DsLMF+ demonstrate that the proposed method outperforms traditional enhancement-plus-detection pipelines in multiple evaluation metrics. The enhancement module significantly improves image quality indicators, and achieves notable improvements in YOLOv11 detection accuracy, particularly in mean Average Precision (mAP). This study confirms the effectiveness of combining detection feedback and multi-scale enhancement in complex industrial environments, offering a promising solution for underground object detection tasks.
Tobacco Plant Counting Based on Improved YOLOv8 and UAV Remote Sensing Images
The tobacco plant counting is an important aspect in tobacco production management, traditional manual methods are time-consuming, labor-intensive and inaccurate, failing to meet the efficiency demands of modern agriculture. To enhance the accuracy and efficiency of tobacco plant counting in the field environment, this study utilizes high-resolution remote sensing imagery collected by drones to construct a sample dataset and proposes an improved YOLOv8-based target detection model (YOLOv8-CSD). YOLOv8-CSD model, based on YOLOv8, incorporates a coordinate attention mechanism (CA) to improve the extraction ability of the model to tobacco plant features. It also optimizes the feature pyramid network (FPN) and adds a small target detection layer to enhance the detection ability for the small target tobacco plants. Additionally, the shape intersection over ratio (SIoU) loss function is used to accelerate model convergence, and the slice-assisted hyper inference (SAHI) strategy is introduced to improve the accuracy and inference efficiency of small target detection by slicing high-resolution images. The experimental results show that the YOLOv8-CSD model achieves a precision of 97.96%, a recall rate of 97.93%, and an average accurate mean (mAP0.5) of 99.32%, significantly outperforming the original YOLOv8 and other 5 commonly used target recognition models. In addition, the efficiency of YOLOv8-CSD model is only lower than YOlOv11, indicating good overall performance. The YOLOv8-CSD model has good adaptability and robustness in tobacco plant detection at different growth stages, with a low missed detection rate, and the YOLOv8-CSD model can effectively meet the requirements of tobacco plant counting in complex field scenarios.
Extraction of Film-Mulched Tobacco Fields and Estimation of Tobacco Planting Area Based on Deep Learning and High-Resolution Remote Sensing Images
In tobacco cultivation, the use of plastic film mulch in tobacco fields serves as a reliable indicator of farmers’ intent to transplant seedlings from greenhouses to the fields. An important research challenge is how to efficiently and accurately identify film-mulched tobacco fields over large areas using high-resolution satellite remote sensing imagery prior to the transplanting stage. Such identification can support the estimation of actual tobacco planting area, thereby assisting tobacco management authorities in evaluating the fulfillment of macro-level planting targets and formulating regulatory policies. To address the limitations of conventional manual and machine learning methods, such as low efficiency and insufficient accuracy in extracting tobacco field boundaries from high-resolution remote sensing images, this study proposes an approach based on the Skip_Segformer semantic segmentation model. Specifically, a SKIPAT module was integrated into the encoder of the traditional SegFormer model to reduce the number of training parameters and save computational resources. Additionally, the decoder was enhanced with a Multi-level Feature Fusion (MFF) mechanism to better integrate features across different scales, thereby significantly improving the accuracy of film-mulched field boundary extraction. The experiment was conducted in Siqian Township, Guangze County, Nanping City, using Jilin-1 satellite imagery with a spatial resolution of 0.5 meters, acquired on March 8, 2022. At both full-regional and local scales, the Skip_Segformer model was compared with four other networks (DeeplabV3+, Hrnet, Pspnet, and SegFormer) in extracting film-mulched tobacco field patches. The results were compared to identify the optimal model. Experimental results demonstrate that the Skip_Segformer achieved the highest extraction accuracy and generalization capability among the compared models. It attained an extraction accuracy of 97% across Siqian Township, with a relative error of only 1.6% in the estimated tobacco planting area, significantly outperforming the other four models. The proposed method shows strong feasibility and applicability for large-scale extraction of film-covered tobacco fields and estimation of planting area, effectively supporting tobacco administration departments in total planting area monitoring and providing a basis for local tobacco planting planning.
Enhancing Public Affairs Text Classification via BERT-CNN-BiLSTM Feature Fusion
Amid rapid advancement in digital governance and exponential growth of public appeal data, traditional manual text classification increasingly fails to meet governmental requirements for efficient, accurate, and timely service delivery. This study focuses on automatic classification of public affairs appeal texts through systematic investigation of deep learning models. To resolve data duplication, class imbalance, and textual noise, we implemented optimization strategies including deduplication and class resampling. Addressing the generalization and stability limitations of individual models — specifically Enhanced TextCNN, BiLSTM with attention, BERT, and ERNIE3.0 — we propose a deep neural network that integrates BERT’s contextual semantic embeddings, CNN’s local feature extraction, and BiLSTM’s temporal dependency modeling. This architecture employs feature concatenation and dropout mechanisms to effectively synthesize global semantics, local phrases, and sequential features. Experimental results demonstrate substantial superiority over conventional models, achieving 99.06% accuracy and 99.03% F1-score on the validation set, confirming exceptional classification performance and robustness. This approach offers an efficient solution for intelligent public appeal processing while advancing digital governance capabilities and governmental modernization. Furthermore, it establishes a valuable reference framework for complex Chinese text classification tasks.
ACT-YOLO: An Efficient Multi-Module Fusion Object Detection Algorithm for Steel Surface Defect Detection
With the development of intelligent manufacturing, higher requirements for real-time performance and accuracy have been placed on the inspection of surface quality in industrial products. As a core basic material in manufacturing, steel has various complex surface defects, such as cracks, scratches, and scale, which are characterised by small size, diverse shapes, and strong background interference, posing significant challenges for automated inspection. To address the issues of insufficient detection accuracy, limited feature fusion capabilities, and coupling of classification and localisation tasks in existing YOLO models when processing fine-grained defects on steel surfaces, this paper proposes a high-performance object detection algorithm based on an improved YOLOv8m: ACT-YOLO (Adaptive Content-guided Task-aligned YOLO). This algorithm integrates three key modules: the AFMA module to enhance multi-scale perception capabilities for small objects; the CGAF module to achieve content-guided multi-attention feature fusion; and the TADD module to optimise the dynamic alignment between classification and regression tasks in the detection head. Evaluations on the NEU-DET steel surface defect benchmark dataset demonstrate that ACT-YOLO achieves an mAP@0.5 of 86.4% and a detection speed of 115 FPS. Compared to non-YOLO methods such as SSD (mAP@0.5: 61.0%, FPS: 41), RetinaNet (mAP@0.5: 69.5%, FPS: 15), and RT-DETR-r101 (mAP@0.5: 78.8%, FPS: 108), as well as other YOLO series models, ACT-YOLO exhibits significant advantages in both detection accuracy and real-time performance. Generalisation experiments on the GC10-DET dataset also validate its cross-scenario adaptability. ACT-YOLO balances detection accuracy, speed, and model lightweighting, making it suitable for the demand for efficient, real-time defect detection systems in actual industrial environments, with broad engineering application prospects and research value.
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DIST-C-SEGAN: A Novel Distributed Dimension Reduction Neural Framework for Visual Data
This paper proposes a distributed unsupervised dimensionality reduction network framework for visual data, referred to as DIST-C-SEGAN. It aims to achieve low-bandwidth, high-fidelity, scalable, and privacy-friendly unsupervised dimensionality reduction and compression of large-scale image data, laying a solid foundation for downstream tasks in resource-constrained distributed or edge computing environments. This method captures local spatial information through a lightweight convolutional network and explores deep feature information using a graph neural propagation structure that combines compression and activation techniques with multi-head self-attention mechanisms. At the same time, it introduces the Proximal Alignment Algorithm to generate low-dimensional embedding features, achieving end-to-end "near-lossless" preservation of local neighborhood structure and global manifold continuity. Experimental results show that DIST-C-SEGAN achieves an original variance retention rate of over 98% in all data tasks, demonstrating high reliability and strong continuity. Its structural evaluation metrics are significantly superior to those of classic dimensionality reduction techniques, such as Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Autoencoder (AE), Variational Autoencoder (VAE), UMAP, and t-SNE. Sensitivity analysis determines the optimal range of key hyperparameters and confirms their robustness to random parameter initialization. Ablation experiments verify the necessity of multi-head self-attention mechanisms and geometric alignment, while downstream tasks further confirm their effectiveness. Moreover, DIST-C-SEGAN theoretically reduces communication costs and provides new solutions for high-fidelity and communication-efficient compression and recovery in distributed environments.
A Novel Method of Object Detection Based on YOLOV8 and PixelAttention in Transmission Lines
In recent years, a large amount of debris has appeared on power transmission lines, affecting circuit power supply and endangering human safety. At present, deep learning based object detection algorithms have made continuous progress, but they still cannot meet the practical application requirements of real-time performance. It is necessary to further reduce model complexity and improve detection speed. Therefore, a transmission line debris recognition based on fusion point attention improved YOLOV8 is proposed. The model uses the original YOLOV8 model as the base model and adds PixelAttention in detection to enhance the model's receptive field and multi-scale object perception, thereby improving the model detection accuracy. The experimental results show that the average accuracy of the original model is only 92.3%, while the average accuracy of the improved model has increased to 94%.
Miscellaneous items on transmission lines
Real-time Binocular 3D Reconstruction for Enhanced Depth Perception and Virtual Reality
Motivated by the escalating demand for increasingly immersive and realistic virtual reality experiences, the enhancement of depth perception via precise 3D reconstruction has emerged as a fundamental requirement. In response to existing challenges such as depth ambiguity, calibration inaccuracies, and occlusion handling, this paper introduces a novel real-time binocular 3D reconstruction framework based on the integration of feature-based and learning-based process. Specifically, the proposed approach features a hybrid neural network improving depth accuracy in textureless areas and a deep learning model incorporating local geometric priors to refine disparity maps, thereby enhancing depth prediction in occluded regions of the inputs. Experimental results demonstrate significant improvements in the accuracy and efficiency of real-time binocular 3D reconstructions, with the system capable of producing high-quality, detailed models in real time. This work not only addresses some key limitations of current technologies but also significantly advances the potential for more sophisticated and immersive VR applications.
Enhanced Brain Tumor Image Segmentation Using U²-Net with Dual Attention Mechanisms
Recent advancements in the field of deep learning have fundamentally transformed the landscape of medical image segmentation, particularly in the critical area of brain tumor diagnostics. This study introduces DAU²-Net, a novel dual attention-enhanced U²-Net architecture that integrates spatial and channel attention mechanisms with MobileViT blocks to prioritize tumor-specific features and model global contextual relationships. The primary objective of this integration is to significantly enhance the segmentation accuracy, thereby providing more reliable and precise diagnostic tools for medical professionals. Evaluated on BraTS 2019, DAU²-Net achieves a state-of-the-art Dice coefficient of 0.92. This remarkable result not only surpasses the performance of the traditional U-Net model, which recorded a Dice coefficient of 0.88 (+4%), but also outperforms the Fully Convolutional Network (FCN) with a Dice coefficient of 0.85 (+7%). And it also achieves 94.2% sensitivity and 97.5% specificity. These outstanding results highlight the significant effectiveness of the dual attention mechanisms utilized in DAU²-Net, which excel at capturing multi-scale contextual information. This capability is crucial for achieving precise tumor delineation, ultimately contributing to more accurate diagnoses and improved patient outcomes in the field of brain tumor diagnostics.
An Efficient Algorithm for Distinguishing Triple Negative Breast Cancer with Ultrasound Image and High-dimensional Radiomics Data
Triple negative breast cancer develops rapidly with high mortality and limited treatment. Therefore, it is important to detect and diagnose breast cancer in a timely manner. However, breast tumors in ultrasound images are characterized by irregular shapes, large-scale changes, and blurry boundaries, which bring great challenges to the segmentation and classification of breast ultrasound images. In this paper, we propose an approach for ultrasound image segmentation and classification of Triple Negative Breast Cancer (TNBC) and Non-Triple Negative Breast Cancer (NTNBC) based on deep learning and radiomics. Regarding the deep learning framework, we propose a benign and malignant segmentation model based on the U-Net model for Channel and Spatial Efficient U-Net (CSE-U-Net) breast ultrasounds. First, during the skip connection stage, to obtain more spatial and contextual semantic information, we implemented a channel attention module (CM) and spatial awareness module (SM). Then, Efficient Channel Attention (ECA) is added to the feature extraction part of the U-Net backbone network to ensure model robustness. Regarding the radiomics, we extract image features using histogram, texture, and filter features, and compare them using six machine learning classification algorithms. Experimental results indicate that CSE-U-Net is more effective and accurate in many evaluation indexes; among the six Machine Learning (ML) algorithms evaluated, the extreme gradient boosting algorithm was able to classify breast cancer more accurately into TNBC and NTNBC than the other five algorithms evaluated. The prediction results show that the CSE-U-Net and ML algorithms are efficient and can be used for the segmentation and classification of breast cancer into TNBC and NTNBC.
Relationship-Enhanced Session-based Recommendation with Graph Neural Networks
The session-based recommendation systems analyze anonymous users' recent behavior data to infer their preferences and provide accurate recommendations. However, existing methods often fail to adequate­ly leverage the click order and frequency of items within a session, thus lacking the ability to fully cap­ture complex dependencies among items. To address these limitations, we propose a novel model named RESR-GNN. The key innovations of our work include: (1) a relation-enhanced session graph that incorpo­rates click information to differentiate the importance of items; and (2) an integrated soft attention and multi-layer self-attention mechanism to comprehensively model pairwise item preferences within the entire session. Extensive experiments on two public datasets demonstrate that RESR-GNN consistently outperforms state-of-the-art baseline models. On the Diginetica and Yoochoose1/64 datasets, the evalu­ation metrics P@20 and MRR@20 were respectively improved by 3.15%, 5.51%, 1.53% and 2.36%, which proved the effectiveness of the proposed model.
Robust Industrial Equipment Fault Detection via Bayesian Federated Learning with Channel Importance
In the era of industrial automation and smart manufacturing, the reliability of industrial equipment is of paramount importance as equipment failures can trigger severe production disruptions and safety hazards. While Bayesian Learning effectively handles uncertainties and enhances fault detection interpretability, it faces challenges in data privacy and centralized processing, whereas Federated Learning safeguards data privacy. Bayesian Federated Learning (BFL) integrates their strengths for privacy-preserving probabilistic modeling with superior generalization. This paper innovatively proposes BFL-CI (Bayesian Federated Learning with Channel Importance), presenting a comprehensive framework and detection process that analyzes channel parameter distributions and transmits selected channel distributions as prior distributions for subsequent clients. Experiments on bearing and gearbox datasets simulating multi-sensor scenarios verify BFL-CI’s effectiveness in improving fault detection accuracy and robustness. This paper fills the gap in applying BFL to industrial equipment fault detection, offering a cutting-edge privacy-preserving solution for reliable intelligent industrial monitoring.
Hierarchical Deep Learning with Nature-Inspired Optimization for Robust Network Intrusion Detection
This study proposes a Swarm-Based Multi-Layer Intrusion Detection System (SML-IDS) that combines hierarchical deep learning and swarm intelligence-based feature optimization to enhance network security. Our framework integrates Convolutional Neural Networks (CNNs) for packet-level anomaly detection, Long Short-Term Memory (LSTM) networks for session-level behavior analysis, and fuzzy logic-based context analyzers to minimize false alarms. Feature selection is optimized through bio-inspired Elephant Herding Optimization (EHO), improving classification accuracy while reducing computational overhead. Evaluation on CIC-IDS2017 dataset shows that SML-IDS outperforms conventional IDS models, achieving superior detection accuracy, false positive rate reduction, and real-time feasibility.
A CLIP-Based Cross-Modal Matching Model for Image-Text Retrieval
In recent years, the demand for multimodal data retrieval has been growing rapidly. As two major modalities for information transmission, images and texts exhibit significant differences in feature distribution. To address challenges in image-text retrieval—such as balancing efficiency with performance and enhancing semantic modelling—this paper proposes an efficient cross-modal feature matching model based on the CLIP framework, including two parts: feature extraction and contrastive learning. During feature extraction, pre-trained VIT and BERT models are used to capture deep semantic features of images and texts, which achieve significant improvements in Feature Entropy (text: 4.27 vs. 3.62; image: 4.13 vs. 3.47) and Mutual Information (28.3% for text, 31.5% for image) compared with the baseline, indicating stronger semantic expressiveness and alignment. Through contrastive learning with the cosine-based loss function and Adam optimization, the model ensures stable convergence. Furthermore, preprocessing innovations such as removing redundant text tokens and Base64 image encoding boost training efficiency. Experiments on a dataset of 50,000 image-text pairs demonstrate that our model achieves high and stable retrieval performance with R@1, R@5, and R@10 scores ranging from 80% to 90%. Compared to the classic DeViSE model, our approach yields improvements of 12.9%, 10.0%, and 9.0% across the three metrics, confirming the model’s superior accuracy and generalization in large-scale retrieval scenarios. Finally, the model is evaluated on image-text retrieval tasks, where it consistently demonstrates strong cross-modal matching capabilities and accurately captures the semantic associations between images and texts.
MSPF-LMFF: Category-Level 6D Object Pose Estimation via Multi-Scale Prior Point Cloud Fusion and Lightweight Multi-Feature Fusion
Object pose estimation is a critical task in the ﬁeld of machine vision. Existing pose estimation methods often suﬀer from challenges such as large parameter sizes, complex architectures, and high computational costs, which limit their applicability in real-world scenarios. To address these issues, we propose a novel category-level object pose estimation model, named MSPF-LMFF. This model eliminates the reliance on attention mechanisms or precise 3D models, signiﬁcantly reduces computational complexity, and enhances pose estimation accuracy, demonstrating superior performance on both real and synthetic datasets. Specifically, the MSPF module enriches the features of point clouds by integrating multi-scale image texture features with prior point cloud features, making them closer to the target object point cloud. Subsequently, the LMFF module combines geometric features of fused point cloud, depth image features, and geometric features of the target object point cloud to enhance the robustness of the model. At the same time, this module fuses adaptive point cloud features with the target object’s geometric features to improve the reliability of shape information, thereby enhancing the model’s generalization capability across diﬀerent instances of the same category. Following this, a multi-layer perceptron (MLP) generates deformation and mapping matrices to reconstruct the target object’s normalized object coordinate space (NOCS) model. Finally, based on the NOCS model, the point cloud registration module computes the target object’s 6D pose and 3D dimensions. Experimental results demonstrate that MSPF-LMFF outperforms existing methods on the NOCS-REAL and NOCS-CAMERA datasets while signiﬁcantly reducing parameter sizes and training time. Moreover, the proposed model exhibits exceptional generalization capabilities on the Wild 6D dataset, further validating its eﬀectiveness.
特征融合、融合点云、轻量级模型、多尺度特征融合、轻量级多特征融合。
TransGNN-DTA: A Framework for Drug-Target Affinity Prediction Based on a Chunked Transformer-GNN
Accurate prediction of drug-target binding affinity (DTA) is a core challenge in computer-aided drug design. In this study, we propose the TransGNN-DTA framework to construct a multi-scale feature representation and dynamic fusion mechanism by hierarchically integrating Transformer and graph neural network (GNN): 1) byte-pair encoding (BPE) based unified semantic characterization of drug SMILES and protein sequences, which preserves the atomic-level chemical structure and residue-level functional motifs; 2) hierarchical Transformer-GNN encoding architecture to capture sequence global dependencies (e.g., protein functional domain interactions) and molecular local structures (e.g., drug-functional group interactions); 3) chunked adaptive training strategy, hybrid accuracy, and the combination of an optimizer and scheduler to effectively reduce the hardware resource requirements for training. On the DAVIS and KIBA datasets, the model improves the CI metrics by an average of 3.23% over the existing optimal methods (DGraphDTA, GraphDTA). The computational efficiency is significantly optimized by systematic chunking, providing a scalable end-to-end solution for large-scale DTA prediction. The source code and datasets are publicly accessible at https://github.com/Quietpeng/TransGNN_DTA.
Cross-View Image Geo-localization Based on Attention Weight Masks
Cross-view Image Geo-localization is the process of determining the geographic location of a ground-view query image by matching it with geotagged satellite or unmanned aerial vehicle (UAV) captured images. In the context of ground images characterized by a constrained field of view (FoV), the query image exhibits a reduced coverage area, limited scene content, and an unknown imaging direction. Furthermore, reference satellite images from the same location may contain significant feature redundancy. These issues lead to low localization accuracy when existing methods are applied to ground images with a limited FoV. We propose a cross-view image geo-localization method based on attention weight mask alignment. The Coordinate Attention (CA) mechanism, embedded in a lightweight ResNet18 network, generates weight masks to enable precise alignment of limited FoV ground images with satellite image feature maps. This process eliminates redundant areas in satellite images, thereby enhancing localization accuracy. Since feature maps at various levels capture images at different granularities, we introduce a multi-scale feature fusion strategy. It generates more representative image descriptors by combining features from different convolutional layers. Experimental results on the CVUSA and CVACT_val benchmark datasets demonstrate that when the FoV of ground images to be located is 70° and 90° with a random imaging direction, the proposed method significantly improves location accuracy.
A TCGAN-Based Real-Time Personalized Motion Guidance System to Reduce Compensatory Movements in Post-Stroke Rehabilitation
Stroke rehabilitation is essential for motor function recovery, yet traditional methods require therapist supervision, which can be costly and inaccessible. Home-based rehabilitation offers an alternative, but without real-time guidance, patients may develop compensatory movements, hindering progress. Existing approaches provide feedback only after exercises are completed, limiting their effectiveness. To address this, we propose a Temporal Conditional Generative Adversarial Network (TCGAN)-based motion generation system that provides real-time skeletal guidance tailored to each patient’s body structure and positioning. By detecting key anatomical landmarks and generating adaptive motion sequences, the system ensures precise movement execution, reducing errors and improving rehabilitation outcomes. Both qualitative and quantitative evaluations confirm the effectiveness of the generated exercises, benefiting from the proposed architecture, improved loss function, optimized training process, and TCGAN hyperparameter tuning. Experimental results show a high degree of similarity between generated and real movements, with a Fréchet Inception Distance (FID) score of 0.87, demonstrating the system’s realism and reliability. This approach enhances patient autonomy and recovery efficiency, offering a more interactive and adaptive rehabilitation experience.
BioDiag-Net: Dynamic Multisite Graph Convolutional Network for Computer-Aided ASD Diagnosis with Biomarker Visualization
Autism spectrum disorder (ASD), characterized by social communication deficits and restricted/repetitive behaviors, faces limitations in conventional behavioral diagnostics. We propose BioDiag-Net, a deep learning-based dynamic graph convolutional network that integrates spatial brain topology with resting-state fMRI (rs-fMRI) functional connectivity. The architecture adaptively fuses whole-brain features with default mode network (DMN) dynamics through mutual information-driven selection, effectively suppressing noise while preserving neurobiologically relevant patterns. Evaluated on the ABIDE I dataset (1,035 multisite rs-fMRI scans), BioDiag-Net achieves 83.48% accuracy in ASD classification, capturing both global network reorganization and localized DMN aberrations. Biomarker visualization enables interpretable neuroimaging diagnosis, revealing neurophysiological substrates, providing interpretable diagnostic evidence while demonstrating transfer potential for cross-disorder neuroimaging analysis. This work bridges computational neuroscience with clinical practice, advancing personalized psychiatric diagnostics.
Dense-Attention CNN with Spatial-Attention Fusion for Robust Facial Expression Recognition
Currently, facial expression recognition technology has been gradually applied in fields such as intelligent healthcare, online education, and assisted driving. However, traditional Convolutional Neural Network (CNN) lack attention to facial local regions related to emotions, and classic loss functions cannot handle intra-class variability in facial expressions. This paper establishes a facial expression recognition model combining deep learning and attention mechanisms for both static and dynamic facial expressions. By extracting image features, it obtains rich multi-scale information flow and controls the number of model parameters. It constructs a spatial attention unit to focus on information with significant emotional intensity, and combines an intra-class distance penalty term and classification loss to supervise the network learning process. This approach addresses the issue of CNN paying insufficient attention to regions of interest while reducing the variability among facial expressions of the same class. Experimental results show that the accuracy of this model has increased by 1.1% and 2.7% on the CK+ and FER2013 public datasets, respectively.