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Copyright terms are indicated in the Republic of Lithuania Law on Copyright and Related Rights, Articles 4-37.
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.
Copyright (c) 2025 Information Technology and Control
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.
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.
Efficient Screening-Based Optimization: A Greedy Approach for Large-Scale Sparse Learning
This paper proposes Efficient Screening-Based Optimization (ESO), a dual-threshold greedy screening framework for large-scale sparse learning. ESO integrates adaptive feature evaluation with dynamic parameter updates to address computational inefficiency in ultra-sparse scenarios. By employing a probabilistic screening mechanism and prox-based test functions, it achieves 50-70% faster computation than state-of-the-art methods when regularization parameters approach 105. Experiments on synthetic and real-world datasets demonstrate robustness across penalty functions (L1, SCAD, MCP) and data types (image, genomic). Theoretical analysis confirms solution consistency, while parameter sensitivity studies guide practical implementation. The method significantly enhances scalability for high-dimensional problems.
Forest Fire Recognition and Prediction Based on Fully Convolutional Network and Rothermel Model
This paper establish a combined fire recognition and prediction model to study the spread of forest fires in response to the frequent occurrence of forest hill fires and its difficult recognition and prediction pain point. Based on the traditional recognition neural network, this paper innovatively establishes Fully Convolutional Network (FCN) to improve the fire recognition accuracy. The Rothermel model is introduced for fire spread prediction of the Palisades Mountain Fire in Los Angeles, and it is found that the accuracy of the Rothermel model is as high as 87% and the stability is about 70.9%. Referring to the excellent model performance of the Rothermel model, this paper establishes a combined model for identification and prediction with the combination of FCN and Rothermel in order to improve the accuracy of fire identification and prediction, and provide double accuracy to ensure the reliability of the study. Based on the simulation of forest fires in a simulated wildland environment, the fire spreading stages are segmented into 4 parts, fire recognition is performed by FCN network, and Rothermel fire prediction is performed based on the recognition results. It is found that the combined model effectively reduces the errors of individual models, complements the advantages of individual models, and improves the fire identification and prediction accuracy. At last, this paper suggests a combination with the field of drones for smart fire prevention and reference.
The Application of Transformer Model in Building Information Modeling
Building information modeling leverages high-resolution satellite imagery and change detection for urban planning and disaster monitoring. This study enhances building information modeling accuracy by integrating coordinate attention with a Transformer hybrid architecture. Local feature extraction by convolutional neural network and global context modeling by Transformer are combined. Feature exchange techniques and a hollow space pyramid pooling module improve multi-scale change detection. Lightweight designs, including depthwise separable convolutions and Ghost modules, reduce computational costs. Experimental results show the model stabilizes after 80 iterations, achieving 95% accuracy and a 1.37% mIoU improvement. With a Kappa value of 0.795 and minimal parameters, the framework enables efficient synthetic aperture radar-based building change detection, suitable for real-time urban monitoring. The raised model can achieve the task of building information modeling, laying the foundation for large-scale automatic recognition and classification of building images.
Anormal Target Detection for Power Transmission Cable Drone Images Employing Improved YOLO10 and ESRGAN
The anomaly target detection accuracy and speed in transmission cable drone inspection batch images with low pixel size is not enough high to make operation decision. To address this problem by reconstruction of image enhancing pixel, a super-resolution transmission cable target detection method based on improved YOLO10 is proposed. First, the Best-Buddy loss function is used instead of the commonly used one-to-one MSE/MAE loss function, allowing low pixel images to dynamically find the most suitable supervised image, providing more reasonable image details and reducing training difficulty. Secondly, a region-aware adversarial learning strategy is introduced, focusing on training texture-rich image areas, enriching the texture structure of images, making them more realistic, reducing artifacts, and improving their visual effects. Finally, the feature-driven super-resolution algorithm is applied to the object detection of power transmission lines, using YOLO10's backbone as a feature extractor and fine-tuning the super-resolution model to generate more machine-readable feature maps to improve detection accuracy and speed. The experimental results show that the proposed model improves detection accuracy by 6.62% compared to using only the YOLO10 object detection model and 1.22% compared to using ESRGAN as image pre-processing before using YOLO7 for object detection, achieving a detection accuracy of 92.55%.
Three-Dimensional UAV Path Planning in Urban Environments Based on an Improved Parrot Optimization Algorithm
With the rapid development of unmanned aerial vehicle (UAV) technology, its applications in military and civilian fields are becoming increasingly widespread. However, realizing autonomous navigation of UAVs in complex environments still faces numerous challenges, especially the three-dimensional path planning problem. This paper proposes an improved parrot optimization algorithm (IPO) by introducing SPM chaotic mapping, adaptive switching factor, and hybrid Cauchy and Gaussian mutation strategies to enhance the algorithm's global search capability and convergence speed. The improved IPO is combined with the MATLAB simulation platform to construct a complete three-dimensional path-planning solution framework. Extensive simulation experiments demonstrate that, compared with the standard parrot optimization and other optimization algorithms, this algorithm significantly improves optimization accuracy, convergence speed, and path smoothness, showing good potential for engineering applications. The experimental results indicate that under the complex three-dimensional environment modeling of the Beijing International Studies University (BISU) campus, the improved IPO algorithm can quickly and accurately find the optimal collision-free path with minimal flight cost, which demonstrates excellent prospects for engineering applications.
Task-guided Visual Information Extracting Network for Visual Question Answering
The most expected approach to Visual Question Answering tasks is to observe the scene and answer questions with human-like reasoning. Early multimodal fusion schemes focused more on the final result rather than the intermediate reasoning process. In contrast, the step-by-step reasoning method based on task decomposition is more capable of meeting the visual reasoning requirements in the task. Nevertheless, the real-world performance of most current models based on step-by-step reasoning is inadequate due to the absence of required reasoning information and the incapability to generate appropriate solution approaches when confronting real scenes and natural language questions presented by humans. A VQA model based on scene information extraction network (SIEN-VQA) is proposed to address the above issues. SIEN-VQA utilizes graph structured data and Task Decomposition Network to generate reasoning steps, extract relevant image scene information based on the reasoning steps for reasoning execution, and enhances the model's reasoning execution ability in natural language and real scenes. We conducted experimental validation on the CLEVR-Human and GQA datasets, and the validation results showed that our model is able to decompose and conquer problems according to human-like logic, and extract effective scene information that is relevant to the task, which improves the accuracy of answering questions compared to the comparative model.
Volleyball Pose Recognition System Based on DLSTM-GCN Algorithm
Aiming at the current problems of poor volleyball pose recognition and low recognition accuracy, this study proposes a volleyball pose recognition system based on dual long short term memory network graph convolutional network. A volleyball pose recognition system based on dual long short term memory network graph convolutional network is proposed. The new system firstly estimates and analyzes the volleyball sports data by multi-scale feature extraction and fusion network. Secondly, dual long short term memory network graph convolutional network is introduced to analyze the estimated video data. The outcomes revealed that among the different algorithmic model losses, multi-scale feature extraction and fusion network had the smallest loss value, which was reduced by 0.011 compared to the OpenPose algorithm. Meanwhile, the improved dual long short term memory network graph convolutional network was able to achieve the highest recognition accuracy of 93.68%, which was improved by 2.00% compared to the unimproved one. The running time of the improved dual long short term memory network graph convolutional network was shorter at only 2.1s, and the recall was also able to reach up to 92.68%. Meanwhile, in the actual running test of different algorithms, the improved dual long short term memory network graph convolutional network had more key point detection and more specific image action. In summary, the improved algorithm has better volleyball action recognition effect, which has a better application effect on volleyball sports pose recognition and volleyball action guidance research.
DLSTM-GCN; MFEFNet; Volleyball; Pose estimation; Pose recognition
Towards Real-World Power Grid Scenarios: Video Action Detection with Cross-scale Selective Context Aggregation
In this study, we propose a single-stage model for video action detection and a real-world action detection dataset POWER collected from real power operation scenarios. While previous studies have made significant progress in overall classification and localization performance, they often struggle with the actions that have short duration, hindering the application of these approaches. To address this, we introduce the Cross-scale Selective Context Aggregation Network (CSCAN), which focuses on improving the detection of short actions. This network integrates three key components: 1) a cross-scale feature conduction structure combined with a tailored alignment mechanism; 2) a selective context aggregation module based on gating mechanism; and 3) an effective scale-invariant consistency training strategy to enable the model to learn scale-invariant action representation. We evaluated our method on the self-collected dataset POWER and on the most widely used action detection benchmarks THUMOS14 and ActivityNet v1.3. The extensive results show that our model outperforms other approaches, especially in detecting real-world short actions, demonstrating the effectiveness of our approach.
REWeather: A Unified Detection Framework for Automatic Driving Images Restoration and Enhancement in Adverse Weather
Recently, with the rapid development of autonomous driving technology, it prompts the vehicle detection technology to continuously improve its accuracy, stability and reliability to better meet the needs of self-driving. However, due to the interference of adverse factors in adverse weather, the decrease of detection accuracy of autonomous vehicle is led to the phenomenon of missing and wrong detection, which has a serious impact on the safety of autonomous vehicles. Therefore, we propose REWeather to solve such problems of autonomous vehicles in multiple adverse weather conditions. Firstly, to classify the types of adverse weather, distinguishing among foggy, rainy and snowy weather, Broad Learning System (BLS) which is simple and efficient is used in REWeather. Due to the impact of these adverse weathers on sensors, simple dark channel and guided filtering methods is used to preprocess foggy and rainy images, respectively. Then, we put the processed images into the Real-Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN) for further denoising and enhancing the details of detected objects, enabling the sensor to recognize other targets on the road faster and better in adverse weather. To ensure the best detection results, we also use latest Realtime Detection Transformer (RT-DETR) as the detector to validate our work and the final model is deployed on the edge device. Moreover, we use several public datasets and our own collected data to make a real world dataset containing a variety of adverse weathers to train and test our proposed framework, which makes it closer to the real situation. The results demonstrate that our framework achieves a 3.8% improvement in mAP, significantly enhancing the detection capability of autonomous vehicles under adverse weather conditions.
Super-Resolution Generative Adversarial Networks
Bi-Encoder Polyp Net: A Novel Architecture for Enhanced Polyp Segmentation in Endoscopic Images
Automatic polyp segmentation in endoscopic images holds critical clinical value for early colorectal cancer diagnosis. While existing segmentation models have achieved notable progress, two key challenges persist in algorithmic performance improvement. First, dynamic adjustments of colonoscope tip orientation during examinations induce viewpoint variations, which amplify polyp appearance diversity and hinder robust feature learning. Second, the inherent similarity between polyps and surrounding tissues leads to blurred boundaries. Although convolutional neural networks (CNNs) have demonstrated significant advancements, their limitations in modeling global dependencies and reliance on aggressive downsampling operations often cause redundant network structures and local detail loss. To address these bottlenecks, we propose Bi-Encoder Polyp Net – a novel parallel architecture integrating Pyramid Vision Transformer and ResNet. This dual-branch design effectively captures global contextual dependencies while preserving low-level spatial details. A feature alignment module bridges the semantic gap between dual-branch feature maps, and an iterative semantic embedding unit further injects high-level semantic information into aligned low-level features. Extensive experiments across five public polyp segmentation benchmarks validate the network’s effectiveness, demonstrating superior capability in processing real-world colonoscopy images.
A Novel Employee Re-identification Method Based on Attention-free Capsule Network for Factory Surveillance Images
Traditional methods have low recognition rate and poor robustness when dealing with complex factory employee monitoring images, so a new employee re recognition method based on Attention-free Capsule Network for factory monitoring images is proposed. Least Squares Generative Adversarial Network (LSGAN) is used to restore the factory monitoring image to repair the missing or damaged image caused by lighting, occlusion, noise, etc. Wavelet Contourlet transform is used to improve the details and clarity of images and the accuracy of subsequent staff re recognition. The mixed Gaussian model (GMM) is used to accurately segment the employee foreground in the image, and the segmented employee foreground image is input into the Attention-free Capsule Network. The feature is extracted through multi-layer convolution and pooling operations, and the dynamic routing mechanism is used to extract and aggregate employee identity features. After training, the employee identity tags are output to achieve efficient employee re recognition of factory monitoring images. The experimental results show that compared with visual attention, KISS+, and center and scale prediction methods, the proposed method introduces a novel approach for employee re identification in factory monitoring images. The proposed method demonstrates strong anti-interference and adaptability. In the comparison of key indicators, the proposed method achieved a recognition accuracy of 95.8 on Rank-1, 98.4 on Rank-5, and 99.5 on Rank-10. This series of numerical comparison results fully demonstrates that the proposed method has strong image processing capabilities, as well as high recognition accuracy and robustness.
least square generation countermeasure network
Multi-strategy Hybrid Improved Intelligent Algorithm for Solving UAV-MTSP
Unmanned aerial vehicles (UAVs) have been increasingly used in fire monitoring and rescue operations, offering flexibility and efficiency. However, determining the shortest path for all UAVs to visit all regions is a crucial issue, known as the Multiple Traveling Salesman Problem (MTSP), which aims to save time and energy. This paper proposes a novel hybrid heuristic algorithm, MCPWOA, to solve MTSP with a focus on UAV path planning applications. The algorithm integrates the Whale Optimization Algorithm (WOA), Crested Porcupine Optimizer (CPO), Chaotic Mapping Strategy (CMS), Arcsine Control Strategy (ACS) and Reverse Learning Strategy (RLS) to diversify the initial population and achieve rapid exploration. The algorithm's performance is evaluated using the CEC2022 benchmark function set and TSPLIB dataset for function minimization and UAV-MTSP experimental solution finding. Results indicate that MCPWOA outperforms existing WOA, CPO, and other advanced algorithms on most tests, showing higher convergence accuracy. Moreover, MCPWOA's effectiveness is demonstrated in actual UAV fire monitoring and rescue path planning, enhancing fire response efficiency through optimized UAV configuration and task allocation.
Enhancing Open-Set Few-Shot Object Detection with Limited Visual Prompts
The text-prompt-based open-vocabulary object detection model effectively encapsulates the abstract concepts of common objects, thereby overcoming the limitations of pre-trained models, which are restricted to detecting a fixed, predefined set of categories. However, due to data scarcity and the constraints of textual descriptions, representing rare or complex objects solely through text remains challenging. In this study, we propose an open-set detection model that supports both visual and textual prompt queries (VTP-OD) to enhance few-shot object detection. A small number of visual prompts not only provide rich class-wise visual features, which enhance class textual representations, but also enable flexible extension to new classes for different downstream tasks. Specifically, we incorporate two adaptation modules based on cross-attention to adapt the pre-trained vision-language model, allowing it to support both text and visual queries. These modules facilitate (i) visual fusion between a limited number of visual prompts and query images and (ii) visual-language fusion between class-aware visual features and textual representations of the classes. Subsequently, the model undergoes prompt tuning using the available few-shot downstream data to adapt to target detection tasks. Experimental results demonstrate that our model outperforms the pre-trained model on the LVIS and COCO benchmarks. Furthermore, we validate its effectiveness on the real-world CoalMine dataset.
WNASNet: Wavelet-Guided Neural Architecture Search for Efficient Single-Image De-raining
On rainy days, the uncertainty of the shape and distribution of rain streaks can cause the images captured by RGB image-based measurement equipment to be blurred and distorted. The wavelet transform is extensively utilized in conventional image-enhancing techniques because of its capacity to deliver spatial and frequency domain information and its multidirectional and multiscale characteristics. In image de-raining, the distribution of rain streaks is intricately linked to both spatial domain characteristics and frequency domain spatial attributes. Nonetheless, deep learning-based rain removal models predominantly depend on the spatial characteristics of the image, and RGB data is sometimes insufficient to differentiate rain marks from image details, resulting in the loss of essential image information during the rain removal process. To overcome this limitation, we have created a lightweight single-image rain removal model named the wavelet-enhanced neural architecture search network (WNASNet). This technique isolates image features from rain-affected images and can more efficiently eliminate rain artifacts. The proposed WNASNet presents three notable contributions. Initially, it utilizes wavelet transform to extract multi-frequency feature components. It allocates a distinct feature search block (FSB) to each component, facilitating the identification of task-specific feature extraction networks to enhance deraining efficacy. Secondly, we present a straightforward yet efficient wavelet feature fusion technique (SFF) that selectively employs high- and low-frequency features during the inverse wavelet transformation. This method maintains deraining efficacy while substantially decreasing computational complexity relative to conventional frequency blending techniques. Comprehensive studies on four synthetic and two real-world datasets illustrate the better performance of WNASNet across many evaluation measures, including PSNR, SSIM, LPIPS, NIQE, and BRISQUE, thereby verifying its efficacy and robustness for single-image deraining tasks.
Yolov5-based Intelligent Detection Method for Retail Goods
In the current context, intelligent unmanned retail checkout systems offer the prospect of efficient and innovative development. This study proposes an enhanced lightweight YOLOv5 merchandise detection and recognition method. The method introduces SELayer and a multi-headed self-attentive module of Transformer in YOLOv5 to enable the network to focus more on essential factors such as commodities when performing retail merchandise detection, and improve the recognition performance of the model. Also, the Ghost module is introduced to reduce network parameters and computation, increase computation speed and reduce latency. We validated the performance of the approach on a public dataset. Compared with the existing YOLOv5 model, the model achieves a 0.9% improvement in detection accuracy and a 27.7% reduction in GFLOPs. With this study, we optimise the problem of small batch identification of retail goods, providing a basis for automated processing of intelligent retail supply and marketing systems with practical implications.
ORPTQ: An Improved Large Model Quantization Method Based on Optimal Quantization Range
Quantization reduces model storage by representing model in low bits. It can help to improve the application capability of transformer-based large models and make them possible to be deployed on resource-limited systems such as PCs and mobile devices. The best weight-only quantization method currently is to use second-order information to fine-tune the weight step by step during the quantization process, compensating for the quantization errors that have occurred. The method can minimize the functional loss of weight due to quantization by adjusting the remaining elements through algebraic transformations in each step. However, the performance of this quantization method will deteriorate rapidly when the adjustment for weight deviates too far from the starting point, especially in low-bit quantization (e.g. 4 bits or fewer). To meet the mathematical prerequisite of this method in the quantization, this paper introduces two parameters<span id="MathJax-Element-1-Frame" class="MathJax SCXW244387556 BCX0" style="-webkit-user-drag: none; -webkit-tap-highlight-color: transparent; margin: 0px; padding: 0px; user-select: text; display: inline-table; font-style: normal; font-weight: normal; line-height: normal; font-size: 14.6667px; font-size-adjust: none; text-indent: 0px; text-align: center; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: pre !important; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;" tabindex="0" role="presentation" data-mathml=" &#xA0; &#x3B1; , &#x3B2; "> α, β to adjust the quantization range based on the second-order method, and presents three approaches to seek their optimal values. The experimental results show that the performance of the proposed method significantly outperforms the original second-order method in low-bit quantization. The code of this paper is available on github.com/t-scen/ORPTQ.
Integration of Explainable AI with Deep Learning for Breast Cancer Prediction and Interpretability
The present paper proposes an integrated breast cancer diagnosis that includes ML, DL, and Explanatory AI methods using the Breast Cancer Wisconsin (Diagnostic) Data Set. We compare standard machine learning approaches, namely Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), with more intricate techniques based on deep learning. Although ML models help understand the problem, a DL model may be more appropriate when the data’s dimensionality and complexity are huge. Addressing these limitations, we present a new Hybrid Explainable Attention Mechanism (HEAM) for DL models that utilise attention performance. This method is used in CNNS with saliency maps and Grad-CAM methods to provide clinical users with attention on parts of the input that the model is based upon in its predictions, such as characteristics of cell nuclei in images. Using the Breast Cancer Wisconsin dataset, the novel deep learning model with HEAM enhancement is tested against traditional ML models concerning breast cancer classification. The findings of this investigation provide evidence that HEAM not only boosts the prediction accuracy by 99.5% but also enhances the model by allowing for the provision of sound and visual attention that explicates the prediction made, thereby improving the clinical relevance of the model.
An Early Warning Model for Industrial Network Security Issues: A Crafted Strategy for High Accuracy Based on Machine Learning Approach
An industrial network has become an important infrastructure. As industrial networks develop, their cybersecurity problems become more and more prominent. The attacks currently realized to networks turn out to be advancing quicker than ever, and their destructive force also continuously gets bigger. Thus, the available early warning technology for industrial network security issues requires more accuracy and timeliness since a serious amount of delays occurs in real cases. The article proposes a strategy with high accuracy based on a machine-learning algorithm. Nonlinear high-dimensional data with different feature characteristics in cyber-attacks and low training efficiency of conventional early warning models to predict attacks are underlined as a significant part of the problem to deal with. Thus, the manuscript suggests a feature selection method based on the Tuna Swarm Optimization (TSO) algorithm to filter out redundant features and reduce the data’s dimensionality. Then, the Extreme Learning Machine (ELM) and Auto-Encoder (AE) are combined to construct the model called Extreme Learning Machine-Auto Encoder (ELM-AE) to be implemented as the basis of the early warning model for industrial network security. Afterward, the improved Whale Optimization Algorithm (I-WOA) is used to optimize the parameters of the ELM, to construct the obtained optimization model. Finally, the obtained optimization model is applied to detect attacks on industrial cyber security systems as an early warning method. Eventually, the proposed model is tested by constructing an evaluation index system on how effective the early warning system functions. The experimental results show that the proposed warning model for industrial network security issues has high warning accuracy and efficiency concurrently, which provides an advanced early warning model for network attacks. The proposed model with 92.64% precision and 51.84 s average execution time excels over other methods.
Complex Texture Segmentation of Digital Painting Image Based on Widow-HoffLMS Adaptive Algorithm
When digital painting images are constructed to ensure a better visual effect of an image, it is necessary to carry out targeted processing on different areas of an image. So, image segmentation is a basis to ensure this processing effect. Therefore, this study proposes a method, called Widow-HoffLMS, for conducting complex texture segmentation for digital painting images. Firstly, the proposed algorithm is used to filter and process digital painting images, and the processing capability of the Hammerstein spline optimization algorithm is introduced to avoid damaging texture details of images and ensure detailed integrity of images. Then, filtered digital painting images are input into the adaptive network based on ghost convolution. A Ghost Module functions as an image block for convolutional neural networks that aims to generate more attributes by utilizing fewer parameters. After texture features in images are extracted through the ghost convolution layer of the network, the feature map is fused and extracted through the U-Net segmentation layer. Finally, the decoder is implemented to restore the spatial resolution of images and produce the result of complex texture segmentations of digital painting images. The test results show that the filtering effect of the proposed method is good, and the contrast of the filtered digital painting image is above 0.932. Moreover, the proposed method can complete the texture feature extraction of images with different sizes and different pixel scores, and the results of the inverse variance are all below 0.018. Furthermore, the complex texture segmentation of animated digital images can be completed according to the preset needs. The integrity state of the complex texture segmentation is also high.
widow-HoffLMS adaptive algorithm, digital painting image, complex texture, segmentation method, ghost convolution, feature extraction.
Research on Real Time Prediction Method of Kiln Flame Temperature Based on 5G Communication and CA-ResNet50 Fusion Network
As intermittent kilns, shuttle kilns are often used in the production of daily-use ceramics. The temperature has a significant impact on the products inside the kiln, and currently, most shuttle kilns still rely on human observation of the flame to adjust the temperature, which has uncertainties and limitations. This paper proposes a real-time prediction method for kiln flame temperature based on 5G communication and CA-ResNet50 fusion network, which utilizes the low latency and high bandwidth characteristics of 5G networks to collect real-time data and ensure the correspondence between flame images and temperature. And combine the CA (Coordinate attention) mechanism with the ResNet50 network to improve the network's attention to flame image features, thereby enhancing prediction accuracy. The experimental results show that the proposed method can improve the accuracy of temperature prediction based on flame images, providing new ideas for temperature control in shuttle kilns.
Single-Pulse Detection Method of Radar Weak Target Based on a Two-Stage Deep Neural Network
With the increasing prevalence of drones in low-altitude airspace, the radar detection of weak targets with a low signal-to-noise ratio (SNR) still poses a crucial challenge. Traditional constant false alarm rate (CFAR) methods encounter issues of high false alarms and low accuracy when the SNR is below-15dB. This paper puts forward a two-stage deep neural network to improve weak target detection by emulating human visual perception. In the first stage (coarse detection), potential targets are rapidly localized through grid-based regression. In the second stage (fine detection), depth-wise separable convolution (DSC) and residual connections are utilized for accurate classification. Experimental results show that, at an SNR of -20dB, the detection rate of the proposed method is 20% higher than that of CFAR methods, and the inference speed is 3.66 times faster than that of single-stage networks. Ablation studies confirm the efficiency improvements brought by the coarse detection network. This approach offers a robust solution for real-time drone surveillance in complex and cluttered environments.
SAEDF: A Synthetic Anomaly-Enhanced Detection Framework for Detection of Unknown Network Attacks
Detecting unknown cyber-attacks (i.e., zero-day) is difficult because network environments change frequently and there are few labeled examples of anomalies. Traditional methods for detecting anomalies often struggle to handle unknown attack types and work effectively with complex, high-dimensional data. To overcome these problems, we propose a new approach called the synthetic attack-enhanced detection framework (SAEDF). SAEDF combines synthetic anomaly generation, flexible feature extraction, and unsupervised anomaly detection. The framework employs a model known as the adaptive and dynamic generative variational autoencoder (ADGVAE). This model generates realistic synthetic attacks and adapts its structure to work effectively with datasets of varying complexity. This helps the model work well with a wide range of attack patterns while still being efficient. Tests on benchmark datasets show that SAEDF performs better than other methods. It achieves higher scores for F1, Recall, and has a much lower rate of false positives. These results show that SAEDF is effective in finding unknown attacks, improving detection accuracy, and handling complex and changing network traffic.
Data-Fusion Based On Transfer Learning For Plant Disease Recognition
In this paper, the research focused on wild and introduced cultivated flowers with multiple diseases such as Stephanitis, Sooty Mould, Xanthosis, and Leaf Blight, utilizing transfer learning and and data fusion technology to construct a plant disease detection model employing Faster R-CNN.The self-built data set collected during the flower growth cycle was trained and identified.To solve the problem of disease category imbalance in the actual collected data samples, the data of small category samples is enhanced from the perspective of category balance and label balance, and FocalLoss is used to improve the original classification loss function. Based on this self-built data set, the constructed IFRCNN disease detection model was compared with the SSD (Single Shot multibox Detector), ResNet18 and Yolov3 models. The results showed that for several common plant diseases in the dataset, the mAP of IFRCNN disease detection model was significantly higher than that of the other three models. It can effectively locate plant leaf disease areas, realize the detection of multiple diseases, and provide reference for accurate disease prevention and control.
MEA-IFE: An Improved Multi-modal Fusion Framework Based on DCNN-BERT-BiLSTM and Its Application in Sentiment Analysis
In the real world, emotional data often comes from multiple heterogeneous sources, making it difficult for unimodal approaches to capture emotional information fully. Existing sentiment analysis models struggle with accuracy when handling complex emotional expressions. Accordingly, this paper proposes a multi-modal sentiment analysis framework, MEA-IFE, which is characterized by effective feature extraction and high predictive accuracy. To mitigate potential information loss and expression limitations in BERT-BiLSTM during text feature extraction, MEA-IFE introduces a parallel structure of SK-Net and BiLSTM, enhancing the ability to extract multi-dimensional text features. Additionally, it integrates the ECA mechanism to improve the capture of essential information in text. For image-related challenges, MEA-IFE incorporates Vision Transformer better to capture both global and detailed features of images, combining CNN and Transformer architectures. During the feature fusion phase, MEA-IFE employs a multi-head attention mechanism to dynamically integrate text and image features, exploring the interactive potential between different modalities. Experiments performed using the Kaggle text dataset and the FER2013 image dataset demonstrate an impressive accuracy of up to 98.00%, validating its effectiveness. When compared with models like AM-MF, AMSAER, HAN-CA-SA, and TBGAV, MEA-IFE shows outstanding performance across accuracy, precision, recall, and F1 score, with respective improvements of 0.40%, 0.20%, 0.75%, and 0.52%. The model also excels in the AUC metric, further confirming its advantages. The proposed MEA-IFE model possesses high predictive accuracy and strong feature integration capabilities, meeting the precision demands of complex multi-modal sentiment tasks.
A Lightweight Multi-Party Key Authentication Management Protocol Based on Cyber-Physical Systems
In the era of digital healthcare, secure information interaction among users, gateways, and multiple devices in a cyber-physical system (CPS) is very important, but also very challenging. However, existing authentication schemes can only accomplish authentication between gateways and smart devices, and do not consider the authentication needs of gateways, users and multiple devices. In addition, users need to initiate multiple key authentication requests to complete multi-device authentication, which greatly increases the communication overhead and security risks. In response, this paper proposes a lightweight multi-party key authentication protocol based on cyber-physical system. On the basis of meeting the user, gateway and multi-device authentication requirements, the key authentication process is effectively simplified by the CPS architecture, and the user only needs to initiate a request to complete the three-party multi-device authentication, which greatly reduces the communication overhead, reduces the security risks, and improves the scheme's adaptability and generalization ability in largescale device scenarios. Finally, the mathematical analysis confirms the reliability of the proposed scheme and points out that the scheme reduces the computational and communication requirements compared with similar methods, which is crucial for CPSs with limited resources.
Driver Fatigue Detection Based on Multiple Physiological Signals and an Improved Deep Belief Network
In order to accurately discriminate the driver fatigue, multiple physiological signals of 10 drivers were collected by a wireless body area network in actual driving, including neck electromyography (EMG) and electroencephalography (EEG). Then, the noises of signals were removed by several denoising methods, and 22 features were extracted, including energy entropy, multiscale entropy, and other relevant features. Subsequently, a deep belief network (DBN) was used to further extract multi-domain features. And then, a grey wolf optimization algorithm was used to optimize the performance of the DBN. The results showed that the accuracy of the model built in the present work was up to 96% in discriminating the fatigue states.