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LLE-Fuse:Lightweight Infrared and Visible Light Image Fusion Based on Low-Light Image Enhancement

Computers, Materials & Continua 2025年第3期82卷 4069-4091页

作者： Song Qian Guzailinuer Yiming Ping Li Junfei Yang Yan Xue Shuping Zhang Faculty of Information Engineering Xinjiang Institute of TechnologyAksu843100China

Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive ***,in low-light scenarios,the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the *** this time,relying solely on the target saliency information provided by infrared images is far from *** address this challenge,this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement,named *** method is based on the improvement of the MobileOne Block,using the Edge-MobileOne Block embedded with the Sobel operator to perform feature extraction and downsampling on the source *** intermediate features at different scales obtained are then fused by a cross-modal attention fusion *** addition,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is used for image enhancement of both infrared and visible light images,guiding the network model to learn low-light enhancement capabilities through enhancement *** completion of network training,the Edge-MobileOne Block is optimized into a direct connection structure similar to MobileNetV1 through structural reparameterization,effectively reducing computational resource ***,after extensive experimental comparisons,our method achieved improvements of 4.6%,40.5%,156.9%,9.2%,and 98.6%in the evaluation metrics Standard Deviation(SD),Visual information Fidelity(VIF),Entropy(EN),and Spatial Frequency(SF),respectively,compared to the best results of the compared algorithms,while only being 1.5 ms/it slower in computation speed than the fastest method.

关键词： Infrared images image fusion low-light enhancement feature extraction computational resource optimization

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An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs

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Chinese Journal of Electronics 2025年第1期34卷 365-372页

作者： Yuanyuan Wang Xing Zhang Zhiguang Chu Wei Shi Xiang Li School of Electronics and Information Engineering Liaoning University of Technology Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Faculty of Information Technology Beijing University of Technology

As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a(k,t)-graph anonymity algorithm based on enhanced clustering. The algorithm uses k-means++ clustering for k-anonymity and t-closeness to improve k-anonymity. We evaluate the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

关键词： Data privacy Uncertainty Social networking (online) Publishing Heuristic algorithms Scalability Clustering algorithms Privacy breach Internet Protection

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Efficient Attention Transformer Network With Self-Similarity Feature Enhancement for Hyperspectral Image Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11469-11486页

作者： Wang, Yuyang Shu, Zhenqiu Yu, Zhengtao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China

Recently, Transformer has gained widespread application in hyperspectral image classification (HSIC) tasks due to its powerful global modeling ability. However, the inherent high-dimensional property of hyperspectral images (HSIs) leads to a sharp increase in the number of parameters and expensive computational costs. Moreover, self-attention operations in Transformer-based HSIC methods may introduce irrelevant spectral-spatial information, and thus may consequently impact the classification performance. To mitigate these issues, in this paper, we introduce an efficient deep network, named efficient attention Transformer network (EATN), for practice HSIC tasks. Specifically, we propose two self-similarity descriptors based on the original HSI patch to enhance spatial feature representations. The center self-similarity descriptor emphasizes pixels similar to the central pixel. In contrast, the neighborhood self-similarity descriptor explores the similarity relationship between each pixel and its neighboring pixels within the patch. Then we embed these two self-similarity descriptors into the original patch for subsequent feature extraction and classification. Furthermore, we design two efficient feature extraction modules based on the preprocessed patches, called spectral interactive Transformer (SIT) module and spatial conv-attention (SCA) module, to reduce the computational costs of the classification framework. Extensive experiments on four benchmark datasets show that our proposed EATN method outperforms other state-of-the-art HSI classification approaches. The source code of this work will be released later. © 2025 IEEE.

关键词： Pixels

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Classification of human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the spotted hyena algorithm

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Alexandria engineering Journal 2025年 126卷 116-130页

作者： Ragab, Mahmoud Ashary, Ehab Bahaudien Kateb, Faris Hakeem, Abeer Mosli, Rayan Albogami, Nasser N. Nooh, Sameer Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The rapid advancement of artificial intelligence (AI) in generating human-like text poses significant challenges in distinguishing between human-written and AI-generated content. Recent advancements in natural language generation have significantly enhanced the quality and variety of AI-generated text, making it almost indistinguishable from human-written content. ChatGPT, a popular AI model, belongs to the generative pre-trained transformer family. While human content is created with a clear intent to convey meaning, AI-generated text aims to replicate human-like language. Classifying human-written and AI-generated sentences is crucial for addressing issues like fake news, plagiarism, and spamming. AI text often follows repetitive patterns, while human writing is more creative and original, making detection significant for combating misinformation. Therefore, this study proposes to classify human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the Spotted Hyena Algorithm (CHWAIG-DLSHO) approach. The approach involves preprocessing text data through tokenization, lemmatization, and data splitting, followed by word embedding using Latent Dirichlet Allocation (LDA). A hybrid convolutional neural network (CNN) and gated recurrent unit (GRU) model is employed for sentence classification. The spotted hyena optimizer (SHO) model is utilized to fine-tune the hyperparameters of the CNN-GRU model, enhancing its performance. The analysis of the CHWAIG-DLSHO method takes place utilizing AI vs. human text dataset. The performance validation of the CHWAIG-DLSHO method portrayed a superior accuracy value of 99.17 % over existing techniques. © 2025 The Authors

关键词： Convolutional neural networks

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

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ECG-based transfer learning for cardiovascular disease: A scoping review

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International Journal of Cognitive Computing in engineering 2025年 6卷 280-297页

作者： Ismail, Sharifah Noor Masidayu Sayed Razak, Siti Fatimah Abdul Ab Aziz, Nor Azlina Faculty of Information Science & Technology Multimedia University Melaka 75450 Malaysia Faculty of Engineering & Technology Multimedia University Melaka 75450 Malaysia

An electrocardiogram (ECG) measures the electrical activity of the heart. It is valuable for the early detection of cardiovascular disease (CVD). Advancements in medical diagnosis using artificial intelligence have popularised transfer learning among researchers in machine learning and deep learning. This scoping review focuses on the application of ECG-based transfer learning for CVD identification, examining the most common types of CVD studied, the input formats used, frequently referenced databases, and the extent of transfer learning's application in diagnosing CVD through ECG analysis. We also conducted a bibliographic analysis to provide an overview of the current situation in the CVD research field. Our analysis of over 70 studies published in the last six years indicates that arrhythmias are the most extensively studied CVD, with the MIT-BIH arrhythmia dataset being the most commonly used in previous research. Pre-trained models such as ResNet, AlexNet, and VGG, which are trained on ImageNet, are often used with two-dimensional (2D) ECG data as network input, achieving accuracy rates exceeding 90 %. The bibliographic analysis indicates that China and India are the highest contributors in this field. Our study also identifies several issues requiring further investigation and recommends areas for future research, including hyperparameter tuning to enhance model performance. © 2025

关键词： Contrastive Learning

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Spatial-Frequency Complementary Transformer and Multi-Scale Attention Refinement for Image Inpainting

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Journal of Shanghai Jiaotong University (Science) 2025年 1-15页

作者： Ren, Shuangde Li, Huafeng Zhang, Yafei Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

The ability to capture long-range contextual information is crucial for image inpainting networks when dealing with complex semantic scenes and large areas of damage. Therefore, Transformers and frequency-based learning methods have gained wide attention due to their powerful global modeling capabilities. However, these methods only learn global features within a single domain, ignoring the complementarity of modeling global dependencies in both spatial and frequency domains. To address this issue, a spatial-frequency complementary Transformer is proposed, which combines the advantages of spatial global learning for global structure generation and frequency global learning for repetitive texture inpainting. This solution enhances the network’s performance in complex semantic scenes and large areas of damage. For spatial global learning, deformable convolution v4 (DCNv4) is introduced to image inpainting for the first time. By utilizing its sparse and efficient global modeling capabilities, a more lightweight inpainting is realized, and the occurrence of blurring and artifacts in the inpainting results is minimized. Additionally, a multi-scale attention refinement module is proposed to enhance the network’s ability to inpaint local detail. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms existing advanced inpainting models while maintaining lower parameter counts and a faster inference speed. © Shanghai Jiao Tong University 2025.

关键词： Contrastive Learning

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DeepCancer: deep learning for brain tumor detection-based application system

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Neural Computing and Applications 2025年第7期37卷 5577-5596页

作者： AlShowarah, Suleyman A. Department of Software Engineering Faculty of Information Technology Mutah University Al-Karak Jordan

A brain tumor is the abnormal cells that growth in the brain, and it is considered as one of the most dangerous diseases that lead to the cause of death. Diagnosis at early is important for increasing the survival rate from the brain tumors. Specialists can identify the tumors manually, but it is very time and effort consuming, and are subject to human error, especially when dealing with large amounts of images. The automatic identification algorithms-based applications can facilitate the process. This study aimed to investigate the possibility of detecting brain cancer based on images using Deep Learning (DL) techniques and statistical operations. The features were extracted using two models of Convolutional Neural Network (CNN), (VGG-19 and AlexNet), then they were used to generate new datasets for statistical operations. CNN is used to extract features with distinct details from brain MRI images. The data were trained in three different training–testing data splitting ratios. Then, the features were classified based on the KNN, RF, and SVM to find the best accuracy of brain MRI image. At the end, the obtained classification accuracy was in favor of statistical operations especially for Large-Value, and Merge between features using KNN (99.1) and SVM (99.1). The features that extracted used in this study can provide high influence on the classification accuracy. The results across all three training–testing data splitting ratios were almost similar, and this approves that the brain cancer can be identified with high accuracy even if the training dataset sizes were minimal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Numerical design of transverse gradient coil with transformed magnetic gradient field over an effective imaging area

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Magnetic Resonance Letters 2025年第1期5卷 40-51页

作者： Chaoqun Niu Hongyi Qu College of Information and Communication Engineering Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Institute of Electrical Engineering Chinese Academy of SciencesBeijing100190China

Gradient coil is an essential component of a magnetic resonance imaging(MRI)*** achieve high spatial resolution and imaging speed,a high-efficiency gradient coil with high slew rate is *** consideration of the safety and comfort of the patient,the mechanical stability,acoustic noise and peripheral nerve stimulation(PNS)are also need to be concerned for practical *** our previous work,a high-efficiency whole-body gradient coil set with a hybrid cylindrical-planar structure has been presented,which offers significantly improved coil *** this work,we propose to design this transverse gradient coil system with transformed magnetic gradient *** shifting up the zero point of gradient fields,the designed new Y-gradient coil could provide enhanced electromagnetic *** more uniform coil winding arrangement,the net torque of the new coil is significantly reduced and the generated sound pressure level(SPL)is lower at most tested frequency *** the other hand,the new transverse gradient coil designed with rotated magnetic gradient fields produces considerably reduced electric field in the human body,which is important for the use of rapid MR ***'s demonstrated that a safer and patient-friendly design could be obtained by using transformed magnetic gradient fields,which is critical for practical use.

关键词： Magnetic resonance imaging(MRI) Gradient coil Transformed magnetic gradient field Acoustic noise Induced electric field

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JLOS: a Cooperative UAV-Based Optical Wireless Communication With Multi-Agent Reinforcement Learning

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1345-1356页

作者： Liu, Jiangang Luo, Hanjiang Tao, Hang Liu, Jiahong Zhou, Jiehan Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland

In maritime Internet of Things (IoT) systems, leveraging a swarm of Unmanned Aerial Vehicles (UAVs) and optical communication can achieve a variety of potential maritime missions. However, due to the high directionality of the optical beam and interference from the marine environment, the optical link via UAVs as relays is prone to interruption. To address this challenge, we propose a Joint Link Optimization Scheme (JLOS) that includes Wind Disturbance Resistance (WDR) and Adaptive Beamwidth Adjustment (ABA). In WDR, we first model the problem as a Partially Observed Markov Decision Process (POMDP), and then design a collaborative Multi-Agent Reinforcement Learning (MARL) approach to control a swarm of UAVs in windy conditions, to maintain mechanical stability and prevent link interruption. Furthermore, in ABA, to reduce uncertainties from control activities and environmental factors like sunlight and fog, we design an adaptive algorithm using distributed MARL. It adjusts beamwidth based on historical UAV locations and link Bit Error Ratio (BER) to improve communication reliability. Numerical simulations confirm its effectiveness in enhancing robust data transmission. © 2004-2012 IEEE.

关键词： Adaptive algorithms

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