Network-Computation in Neural Systems最新文献

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Support vector machine-based stock market prediction using long short-term memory and convolutional neural network with aquila circle inspired optimization. 基于支持向量机的股票市场预测,使用长短期记忆和卷积神经网络,以及受奎拉圆圈启发的优化。
IF 1.6 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-06-10 DOI: 10.1080/0954898X.2024.2358957
J Karthick Myilvahanan, N Mohana Sundaram
{"title":"Support vector machine-based stock market prediction using long short-term memory and convolutional neural network with aquila circle inspired optimization.","authors":"J Karthick Myilvahanan, N Mohana Sundaram","doi":"10.1080/0954898X.2024.2358957","DOIUrl":"10.1080/0954898X.2024.2358957","url":null,"abstract":"<p><p>Predicting the stock market is one of the significant chores and has a successful prediction of stock rates, and it helps in making correct decisions. The prediction of the stock market is the main challenge due to blaring, chaotic data as well as non-stationary data. In this research, the support vector machine (SVM) is devised for performing an effective stock market prediction. At first, the input time series data is considered and the pre-processing of data is done by employing a standard scalar. Then, the time intrinsic features are extracted and the suitable features are selected in the feature selection stage by eliminating other features using recursive feature elimination. Afterwards, the Long Short-Term Memory (LSTM) based prediction is done, wherein LSTM is trained to employ Aquila circle-inspired optimization (ACIO) that is newly introduced by merging Aquila optimizer (AO) with circle-inspired optimization algorithm (CIOA). On the other hand, delay-based matrix formation is conducted by considering input time series data. After that, convolutional neural network (CNN)-based prediction is performed, where CNN is tuned by the same ACIO. Finally, stock market prediction is executed utilizing SVM by fusing the predicted outputs attained from LSTM-based prediction and CNN-based prediction. Furthermore, the SVM attains better outcomes of minimum mean absolute percentage error; (MAPE) and normalized root-mean-square error (RMSE) with values about 0.378 and 0.294.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1185-1220"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Statement of Retraction. 撤回。
IF 1.6 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-08-03 DOI: 10.1080/0954898X.2024.2385540
{"title":"Statement of Retraction.","authors":"","doi":"10.1080/0954898X.2024.2385540","DOIUrl":"10.1080/0954898X.2024.2385540","url":null,"abstract":"","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"iv"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network. 使用基于对称卷积的启发式辅助残差注意网络的智能无线信道损坏图像去噪框架。
IF 1.6 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-05-14 DOI: 10.1080/0954898X.2024.2350578
Sreedhar Mala, Aparna Kukunuri
{"title":"An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network.","authors":"Sreedhar Mala, Aparna Kukunuri","doi":"10.1080/0954898X.2024.2350578","DOIUrl":"10.1080/0954898X.2024.2350578","url":null,"abstract":"<p><p>Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the image quality. For efficient analysis, an effective denoising approach is needed to enhance the quality of the images. The main scope of this research paper is to correct errors and remove the effects of channel degradation. A corrupted image denoising approach is developed in wireless channels to eliminate the bugs. The required images are gathered from wireless channels at the receiver end. Initially, the collected images are decomposed into several regions using Adaptive Lifting Wavelet Transform (ALWT) and then the \"Symmetric Convolution-based Residual Attention Network (SC-RAN)\" is employed, where the residual images are obtained by separating the clean image from the noisy images. The parameters present are optimized using Hybrid Energy Golden Tortoise Beetle Optimizer (HEGTBO) to maximize efficiency. The image denoising is performed over the obtained residual images and noisy images to get the final denoised images. The numerical findings of the developed model attain 31.69% regarding PSNR metrics. Thus, the analysis of the developed model shows significant improvement.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"953-986"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140923502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach. 增强胸部X光图像中的多类肺病分类:混合蝠鲼觅食火山爆发算法增强多层感知器神经网络方法。
IF 1.6 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-05-16 DOI: 10.1080/0954898X.2024.2350579
Rajendran Thavasimuthu, Sudheer Hanumanthakari, Sridhar Sekar, Sakthivel Kirubakaran
{"title":"Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach.","authors":"Rajendran Thavasimuthu, Sudheer Hanumanthakari, Sridhar Sekar, Sakthivel Kirubakaran","doi":"10.1080/0954898X.2024.2350579","DOIUrl":"10.1080/0954898X.2024.2350579","url":null,"abstract":"<p><p>One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-rays. However, issues of stability and class imbalance still need to be resolved. Hence in this manuscript, multi-class lung disease classification in chest x-ray images using a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach is proposed (MPNN-Hyb-MRF-VEA). Initially, the input chest X-ray images are taken from the Covid-Chest X-ray dataset. Anisotropic diffusion Kuwahara filtering (ADKF) is used to enhance the quality of these images and lower noise. To capture significant discriminative features, the Term frequency-inverse document frequency (TF-IDF) based feature extraction method is utilized in this case. The Multilayer Perceptron Neural Network (MPNN) serves as the classification model for multi-class lung disorders classification as COVID-19, pneumonia, tuberculosis (TB), and normal. A Hybrid Manta-Ray Foraging and Volcano Eruption Algorithm (Hyb-MRF-VEA) is introduced to further optimize and fine-tune the MPNN's parameters. The Python platform is used to accurately evaluate the proposed methodology. The performance of the proposed method provides 23.21%, 12.09%, and 5.66% higher accuracy compared with existing methods like NFM, SVM, and CNN respectively.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"987-1018"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
RETRACTED ARTICLE: A clustering approach for attack detection and data transmission in vehicular ad-hoc networks. 车辆自组织网络中攻击检测和数据传输的聚类方法。
IF 1.6 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2023-11-18 DOI: 10.1080/0954898X.2023.2279973
Atul Barve, Pushpinder Singh Patheja
{"title":"RETRACTED ARTICLE: A clustering approach for attack detection and data transmission in vehicular ad-hoc networks.","authors":"Atul Barve, Pushpinder Singh Patheja","doi":"10.1080/0954898X.2023.2279973","DOIUrl":"10.1080/0954898X.2023.2279973","url":null,"abstract":"","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"iii"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Plant leaf infected spot segmentation using robust encoder-decoder cascaded deep learning model. 基于鲁棒编码器-解码器级联深度学习模型的植物叶片侵染斑分割。
IF 1.6 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2023-11-21 DOI: 10.1080/0954898X.2023.2286002
David Femi, Manapakkam Anandan Mukunthan
{"title":"Plant leaf infected spot segmentation using robust encoder-decoder cascaded deep learning model.","authors":"David Femi, Manapakkam Anandan Mukunthan","doi":"10.1080/0954898X.2023.2286002","DOIUrl":"10.1080/0954898X.2023.2286002","url":null,"abstract":"<p><p>Leaf infection detection and diagnosis at an earlier stage can improve agricultural output and reduce monetary costs. An inaccurate segmentation may degrade the accuracy of disease classification due to some different and complex leaf diseases. Also, the disease's adhesion and dimension can overlap, causing partial under-segmentation. Therefore, a novel robust Deep Encoder-Decoder Cascaded Network (DEDCNet) model is proposed in this manuscript for leaf image segmentation that precisely segments the diseased leaf spots and differentiates similar diseases. This model is comprised of an Infected Spot Recognition Network and an Infected Spot Segmentation Network. Initially, ISRN is designed by integrating cascaded CNN with a Feature Pyramid Pooling layer to identify the infected leaf spot and avoid an impact of background details. After that, the ISSN developed using an encoder-decoder network, which uses a multi-scale dilated convolution kernel to precisely segment the infected leaf spot. Moreover, the resultant leaf segments are provided to the pre-learned CNN models to learn texture features followed by the SVM algorithm to categorize leaf disease classes. The ODEDCNet delivers exceptional performance on both the Betel Leaf Image and PlantVillage datasets. On the Betel Leaf Image dataset, it achieves an accuracy of 94.89%, with high precision (94.35%), recall (94.77%), and F-score (94.56%), while maintaining low under-segmentation (6.2%) and over-segmentation rates (2.8%). It also achieves a remarkable Dice coefficient of 0.9822, all in just 0.10 seconds. On the PlantVillage dataset, the ODEDCNet outperforms other existing models with an accuracy of 96.5%, demonstrating high precision (96.61%), recall (96.5%), and F-score (96.56%). It excels in reducing under-segmentation to just 3.12% and over-segmentation to 2.56%. Furthermore, it achieves a Dice coefficient of 0.9834 in a mere 0.09 seconds. It evident for the greater efficiency on both segmentation and categorization of leaf diseases contrasted with the existing models.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"407-425"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138464478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Neuromorphic computing spiking neural network edge detection model for content based image retrieval. 基于内容的图像检索的神经形态计算尖峰神经网络边缘检测模型。
IF 1.6 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-05-06 DOI: 10.1080/0954898X.2024.2348018
Ambuj, Rajendra Machavaram
{"title":"Neuromorphic computing spiking neural network edge detection model for content based image retrieval.","authors":"Ambuj, Rajendra Machavaram","doi":"10.1080/0954898X.2024.2348018","DOIUrl":"10.1080/0954898X.2024.2348018","url":null,"abstract":"<p><p>In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial majority of CBIR studies continue to rely on linear methodologies such as gradient-based and derivative-based edge detection techniques. This research explores the integration of bioinspired Spiking Neural Network (SNN) based edge detection within CBIR. We introduce an innovative, computationally efficient SNN-based approach designed explicitly for CBIR applications, outperforming existing SNN models by reducing computational overhead by 2.5 times. The proposed SNN-based edge detection approach is seamlessly incorporated into three distinct CBIR techniques, each employing conventional edge detection methodologies including Sobel, Canny, and image derivatives. Rigorous experimentation and evaluations are carried out utilizing the Corel-10k dataset and crop weed dataset, a widely recognized and frequently adopted benchmark dataset in the realm of image analysis. Importantly, our findings underscore the enhanced performance of CBIR methodologies integrating the proposed SNN-based edge detection approach, with an average increase in mean precision values exceeding 3%. This study conclusively demonstrated the utility of our proposed methodology in optimizing feature extraction, thereby establishing its pivotal role in advancing edge centric CBIR approaches.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"892-922"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140866097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising. 基于深度去马赛克卷积神经网络和量子小波变换的图像去噪。
IF 1.6 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-07-11 DOI: 10.1080/0954898X.2024.2358950
Anitha Mary Chinnaiyan, Boyed Wesley Alfred Sylam
{"title":"Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising.","authors":"Anitha Mary Chinnaiyan, Boyed Wesley Alfred Sylam","doi":"10.1080/0954898X.2024.2358950","DOIUrl":"10.1080/0954898X.2024.2358950","url":null,"abstract":"<p><p>Demosaicking is a popular scientific area that is being explored by a vast number of scientists. Current digital imaging technologies capture colour images with a single monochrome sensor. In addition, the colour images were captured using a sensor coupled with a Colour Filter Array (CFA). Furthermore, the demosaicking procedure is required to obtain a full-colour image. Image denoising and image demosaicking are the two important image restoration techniques, which have increased popularity in recent years. Finding a suitable strategy for multiple image restoration is critical for researchers. Hence, a deep learning (DL) based image denoising and image demosaicking is developed in this research. Moreover, the Autoregressive Circle Wave Optimization (ACWO) based Demosaicking Convolutional Neural Network (DMCNN) is designed for image demosaicking. The Quantum Wavelet Transform (QWT) is used in the image denoising process. Similarly, Quantum Wavelet Transform (QWT) is used to analyse the abrupt changes in the input image with noise. The transformed image is then subjected to a thresholding technique, which determines an appropriate threshold range. Once the threshold range has been determined, soft thresholding is applied to the resulting wavelet coefficients. After that, the extraction and reconstruction of the original image is carried out using the Inverse Quantum Wavelet Transform (IQWT). Finally, the fused image is created by combining the results of both processes using a weighted average. The denoised and demosaicked images are combined using the weighted average technique. Furthermore, the proposed QWT+DMCNN-ACWO model provided the ideal values of Peak signal-to-noise ratio (PSNR), Second derivative like measure of enhancement (SDME), Structural Similarity Index (SSIM), Figure of Merit (FOM) of 0.890, and computational time of 49.549 dB, 59.53 dB, 0.963, 0.890, and 0.571, respectively.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1138-1162"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enhancing radiographic image interpretation: WARES-PRS model for knee bone tumour detection. 增强放射影像判读:用于膝关节骨肿瘤检测的 WARES-PRS 模型
IF 1.6 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-06-26 DOI: 10.1080/0954898X.2024.2357660
Rahamathunnisa Usuff, Sudhakar Kothandapani, Rajesh Rangan, Saravanan Dhatchnamurthy
{"title":"Enhancing radiographic image interpretation: WARES-PRS model for knee bone tumour detection.","authors":"Rahamathunnisa Usuff, Sudhakar Kothandapani, Rajesh Rangan, Saravanan Dhatchnamurthy","doi":"10.1080/0954898X.2024.2357660","DOIUrl":"10.1080/0954898X.2024.2357660","url":null,"abstract":"<p><p>The early diagnosis of tumour is significant in biomedical research field to lower the severity level and restrict the process extension from cancer. Moreover, the detection of early sign of cancer is undertaken with extensive research efforts that dedicated to the disclosure and recognition of tumours. However, the limited data size as well as diverse appearance of images lowered the detection performance and failed to detect complex stage of tumour. So to solve these issues, a Weighted Adaptive Random Ensemble Support Vector-based Partial Reinforcement Search (WARES-PRS) algorithm is proposed that detected bone lesions accurately and also predicted the severity level stage efficiently. Further, the detection is performed with varied stages to diminish the presence of noise and undertaken effective classification. The performance is validated with CNUH dataset that enhanced image pre-processing tasks. Despite the proposed method uncover the mutual relationships between each pixel's local texture and the overall image's global context. The detection and classification efficiency is validated with various measures and the experimental results revealed that the detection accuracy is enhanced for the proposed approach by 98.5%. The outcomes of our study have exhibited a substantial contribution to assisting physicians in the detection of knee bone tumours.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1107-1137"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141460767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Neuro connect: Integrating data-driven and BiGRU classification for enhanced autism prediction from fMRI data. 神经连接:整合数据驱动和 BiGRU 分类,从 fMRI 数据中增强自闭症预测。
IF 1.6 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-10-13 DOI: 10.1080/0954898X.2024.2412679
Pavithra Rajaram, Mohanapriya Marimuthu
{"title":"Neuro connect: Integrating data-driven and BiGRU classification for enhanced autism prediction from fMRI data.","authors":"Pavithra Rajaram, Mohanapriya Marimuthu","doi":"10.1080/0954898X.2024.2412679","DOIUrl":"10.1080/0954898X.2024.2412679","url":null,"abstract":"<p><p>Autism Spectrum Disorder (ASD) poses a significant challenge in early diagnosis and intervention due to its multifaceted clinical presentation and lack of objective biomarkers. This research presents a novel approach, termed Neuro Connect, which integrates data-driven techniques with Bidirectional Gated Recurrent Unit (BiGRU) classification to enhance the prediction of ASD using functional Magnetic Resonance Imaging (fMRI) data. This study uses both structural and functional neuroimaging data to investigate the complex brain underpinnings of autism spectrum disorder (ASD). They use an Auto-Encoder (AE) to efficiently reduce dimensionality while retaining critical information by learning and compressing important characteristics from high-dimensional data. We treat the feature-extracted data using a BiGRU model for the classification task of predicting ASD. They provide a new optimization strategy, the Horse Herd Algorithm (HHA), and show that it outperforms other established optimizers, such SGD and Adam, in order to improve classification accuracy. The model's performance is greatly enhanced by the HHA's novel optimization technique, which more precisely refines weight modifications made during training. The proposed ASD and EEG dataset accuracy value is 99.5%, and 99.3 compared to the existing method the proposed has a high accuracy value.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1221-1252"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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