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A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

Computers, Materials & Continua 2024年第5期79卷 2277-2293页

作者： Zhigao Zeng Aoting Tang Shengqiu Yi Xinpan Yuan Yanhui Zhu School of Computer Hunan University of TechnologyZhuzhou412007China Hunan Key Laboratory of Intelligent Information Perception and Processing Technology Zhuzhou412007China

Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for *** has received great attention due to its huge application prospects in recent *** can know that the number of features selected by the existing radiomics feature selectionmethods is basically about *** this paper,a heuristic feature selection method based on frequency iteration and multiple supervised training mode is *** on the combination between features,it decomposes all features layer by layer to select the optimal features for each layer,then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination *** with the currentmethod with the best prediction performance in the three data sets,thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification *** proposed method has better interpretability and generalization ability,which gives it great potential in the feature selection of radiomics.

关键词： Radiomics feature selection machine learning metaheuristic

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Micro-Expression Recognition Based on Spatio-Temporal Feature Extraction of key Regions

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Computers, Materials & Continua 2023年第10期77卷 1373-1392页

作者： Wenqiu Zhu Yongsheng Li Qiang Liu Zhigao Zeng College of Computer Science Hunan University of TechnologyZhuzhou412007China Intelligent Information Perception and Processing Technology Hunan Province Key LaboratoryZhuzhou412007China

Aiming at the problems of short duration,low intensity,and difficult detection of micro-expressions(MEs),the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature *** on traditional convolution neural network(CNN)and long short-term memory(LSTM),a recognition method combining global identification attention network(GIA),block identification attention network(BIA)and bi-directional long short-term memory(Bi-LSTM)is *** the BIA,the ME video frame will be cropped,and the training will be carried out by cropping into 24 identification blocks(IBs),10 IBs and uncropped *** alleviate the overfitting problem in training,we first extract the basic features of the preprocessed sequence through the transfer learning layer,and then extract the global and local spatial features of the output data through the GIA layer and the BIA layer,*** the BIA layer,the input data will be cropped into local feature vectors with attention weights to extract the local features of the ME frames;in the GIA layer,the global features of the ME frames will be ***,after fusing the global and local feature vectors,the ME time-series information is extracted by *** experimental results show that using IBs can significantly improve the model’s ability to extract subtle facial features,and the model works best when 10 IBs are used.

关键词： Micro-expression recognition attention mechanism long and short-term memory network transfer learning identification block

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HRAM-VITON: High-Resolution Virtual Try-On with Attention Mechanism

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Computers, Materials & Continua 2025年第2期82卷 2753-2768页

作者： Yue Chen Xiaoman Liang Mugang Lin Fachao Zhang Huihuang Zhao College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.

关键词： Virtual try-on attention mechanism high-resolution image generation

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Deep semantic network for hyperspectral image classification using enhanced bifurcation and light self-Gaussian attention mechanism

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Wan, Xiaoqing Chen, Feng He, Yupeng Hengyang Normal University College of Computer Science and Technology Hengyang China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China

Convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI) classification due to their strong feature extraction capabilities. Nevertheless, CNN-based classification methods face challenges in capturing deep semantic features effectively, and as the number of layers increases, computational expenses escalate significantly. In addition, spatial locations and spectral bands in HSI data have varying discriminative power. To address these challenges, we propose a novel approach, DSNet, which integrates a multiway attention mechanism with a CNN-based deep semantic network to improve HSI classification. First, we extract shallow abstract features using a module that combines 3D and 2D convolution layers, followed by transformation through a Gaussian-weighted feature tokenizer. Second, we introduce an enhanced bifurcation attention mechanism with dynamically adjustable perceptual ranges to capture spatial semantic information from both horizontal and vertical perspectives. Third, a convolutional multilayer perceptron module is employed to extract high-level feature representations in both spatial and spectral dimensions, which are then refined by a light self-Gaussian attention module. Finally, a linear layer is used to determine the sample labels. Experiments conducted on three publicly available hyperspectral datasets illustrate that the proposed DSNet achieves superior performance compared with several contemporary state-of-the-art methods, even when trained with limited samples. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Convolutional neural networks

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A Novel Stacked Network Method for Enhancing the Performance of Side-Channel Attacks

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Computers, Materials & Continua 2025年第4期83卷 1001-1022页

作者： Zhicheng Yin Lang Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangNormal UniversityHengyang421002China

The adoption of deep learning-based side-channel analysis(DL-SCA)is crucial for leak detection in secure *** previous studies have applied this method to break targets protected with *** the increasing number of studies,the problem of model *** research mainly focuses on exploring hyperparameters and network architectures,while offering limited insights into the effects of external factors on side-channel attacks,such as the number and type of *** paper proposes a Side-channel Analysis method based on a Stacking ensemble,called *** our method,multiple models are deeply *** the extended application of base models and the meta-model,Stacking-SCA effectively improves the output class probabilities of the model,leading to better ***,this method shows that the attack performance is sensitive to changes in the number of ***,five independent subsets are extracted from the original ASCAD database as multi-segment datasets,which are mutually *** method shows how these subsets are used as inputs for Stacking-SCA to enhance its attack *** experimental results show that Stacking-SCA outperforms the current state-of-the-art results on several considered datasets,significantly reducing the number of attack traces required to achieve a guessing entropy of ***,different hyperparameter sizes are adjusted to further validate the robustness of the method.

关键词： Side-channel analysis deep learning stacking ensemble learning model generalization

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Side-Channel Leakage Analysis of Inner Product Masking

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Computers, Materials & Continua 2024年第4期79卷 1245-1262页

作者： Yuyuan Li Lang Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

The Inner Product Masking(IPM)scheme has been shown to provide higher theoretical security guarantees than the BooleanMasking(BM).This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of *** previous work unfolds when certain(adversarial and implementation)conditions are met,and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected *** this paper,we investigate the security characteristics of IPM under different *** adversarial condition,the security properties of first-order IPMs obtained through parametric characterization are preserved in the face of univariate and bivariate *** implementation condition,we construct two new polynomial leakage functions to observe the nonlinear leakage of the IPM and connect the security order amplification to the nonlinear *** observe that the security of IPMis affected by the degree and the linear component in the leakage *** addition,the comparison experiments from the coefficients,signal-to-noise ratio(SNR)and the public parameter show that the security properties of the IPM are highly implementation-dependent.

关键词： Side-channel analysis inner product masking mutual information nonlinear leakage

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A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

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Computers, Materials & Continua 2024年第1期78卷 197-225页

作者： Xiaozhong Yu Jinhua Zheng College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

In rice production,the prevention and management of pests and diseases have always received special *** methods require human experts,which is costly and *** to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is ***,deep learning technology has been employed to detect and identify rice diseases and *** paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing ***,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and *** study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

关键词： Deep learning rice diseases and pests image recognition object detection

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SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

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Computers, Materials & Continua 2024年第6期79卷 4641-4657页

作者： Jiahui Liu Lang Li Di Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys ***,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within *** challenges significantly hinder key recovery *** paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these ***-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained ***,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each ***,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct ***,an intelligent search method is proposed to identify optimal parameters,further improving attack *** were conducted on both simulated environments and real power traces collected from the SAKURA-G *** the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

关键词： Side-channel analysis correlation power analysis genetic algorithm crossover mutation

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SepFE:Separable Fusion Enhanced Network for Retinal Vessel Segmentation

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2465-2485页

作者： Yun Wu Ge Jiao Jiahao Liu College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The accurate and automatic segmentation of retinal vessels fromfundus images is critical for the early diagnosis and prevention ofmany eye diseases,such as diabetic retinopathy(DR).Existing retinal vessel segmentation approaches based on convolutional neural networks(CNNs)have achieved remarkable ***,we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net,which is one of the most popular *** view of the excellent work of depth-wise separable convolution,we introduce it to replace the standard convolutional *** complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for *** ensure performance while lowering redundant parameters,we integrate the pre-trained MobileNet V2 into the ***,a feature fusion residual module(FFRM)is designed to facilitate complementary strengths by enhancing the effective fusion between adjacent levels,which alleviates extraneous clutter introduced by direct ***,we provide detailed comparisons between the proposed SepFE and U-Net in three retinal image mainstream datasets(DRIVE,STARE,and CHASEDB1).The results show that the number of SepFE parameters is only 3%of U-Net,the Flops are only 8%of U-Net,and better segmentation performance is *** superiority of SepFE is further demonstrated through comparisons with other advanced methods.

关键词： Retinal vessel segmentation U-Net depth-wise separable convolution feature fusion

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DBST:a lightweight block cipher based on dynamic S-box

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Frontiers of Computer Science 2023年第3期17卷 177-185页

作者： Liuyan YAN Lang LI Ying GUO College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China

IoT devices have been widely used with the advent of *** devices contain a large amount of private data during *** is primely important for ensuring their ***,we proposed a lightweight block cipher based on dynamic S-box named *** is introduced for devices with limited hardware resources and high throughput *** is a 128-bit block cipher supporting 64-bit key,which is based on a new generalized Feistel variant *** retains the consistency and significantly boosts the diffusion of the traditional Feistel *** SubColumns of round function is implemented by combining bit-slice technology with *** S-box is dynamically associated with the *** has been demonstrated that DBST has a good avalanche effect,low hardware area,and high *** S-box has been proven to have fewer differential features than RECTANGLE *** security analysis of DBST reveals that it can against impossible differential attack,differential attack,linear attack,and other types of attacks.

关键词： internet of things 5G dynamic S-box bit-slice technology lightweight block cipher

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