Network-Computation in Neural Systems最新文献

An optimized deep neural network with explainable artificial intelligence framework for brain tumour classification. 一个优化的深度神经网络，具有可解释的人工智能框架，用于脑肿瘤分类。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-04 DOI: 10.1080/0954898X.2025.2500046

Roohum Jegan, Bhakti Kaushal, Gajanan K Birajdar, Mukesh D Patil

{"title":"An optimized deep neural network with explainable artificial intelligence framework for brain tumour classification.","authors":"Roohum Jegan, Bhakti Kaushal, Gajanan K Birajdar, Mukesh D Patil","doi":"10.1080/0954898X.2025.2500046","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2500046","url":null,"abstract":"Brain tumour classification plays a significant role in improving patient care, treatment planning, and enhancing the overall healthcare system's effectiveness. This article presents a ResNet framework optimized using Henry gas solubility optimization (HGSO) for the classification of brain tumours, resulting in improved classification performance in magnetic resonance images (MRI). Two variants of the deep residual neural network, namely ResNet-18 and ResNet-50, are trained on the MRI training dataset. The four critical hyperparameters of the ResNet model: momentum, initial learning rate, maximum epochs, and validation frequency are tuned to obtain optimal values using HGSO algorithm. Subsequently, the optimized ResNet model is evaluated using two separate databases: Database1, comprising four tumour classes, and Database2, with three tumour classes. The performance is assessed using accuracy, sensitivity, specificity, precision, and F-score. The highest classification accuracy of 0.9825 is attained using the proposed optimized ResNet-50 framework on Database1. Moreover, the Gradient-weighted Class Activation Mapping (GRAD-CAM) algorithm is utilized to enhance the understanding of deep neural networks by highlighting the regions that are influential in making a particular classification decision. Grad-CAM heatmaps confirm the model focuses on relevant tumour features, not image artefacts. This research enhances MRI brain tumour classification via deep learning optimization strategies.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-35"},"PeriodicalIF":1.1,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040708","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

Correction. 修正。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-02 DOI: 10.1080/0954898X.2025.2501418

引用次数: 0

Heuristic multi-scale feature fusion with attention-based CNN for sentiment analysis. 启发式多尺度特征融合与基于注意力的CNN情感分析。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-02 DOI: 10.1080/0954898X.2025.2498735

Thogaru Maanasa, Prasath Raveendran, Praveen Joe Irudayaraj

{"title":"Heuristic multi-scale feature fusion with attention-based CNN for sentiment analysis.","authors":"Thogaru Maanasa, Prasath Raveendran, Praveen Joe Irudayaraj","doi":"10.1080/0954898X.2025.2498735","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2498735","url":null,"abstract":"The sentiment analysis is an essential component that enables automation of achieving insights from the information that is user generated. However, the difficulty of sentiment analysis is the lack of enough labelled data in the Natural Language Processing (NLP) sector. Thus, to evaluate these sentiments, multiple mechanisms have been utilized in the past decades. The deep learning-aided approaches are becoming very famous nowadays because of their better performances. To surmount such existing issues, an attention deep learning model is proposed using an improved heuristic approach. At first, the input text data is gathered from public resources. Further, it is followed by text pre-processing to prevent unrelated text data. Further, the obtained pre-processed text is fed into the Multiscale Feature Fusion-based Adaptive and Attention-based Convolution Neural Network (MFF-AACNet). In the developed system, the features are extracted from Bidirectional Encoder Representations from Transformers (BERT), Transformers, and word2vector. Furthermore, the resultant features are fused, and it is subjected to the MFF-AACNet, where the sentiment is analysed. The parameter tuning is done by an improved Fitness Opposition of Rat Swarm Optimizer (FORSO). Finally, the performance analysis was conducted for the implemented model. The proposed framework achieves higher accuracy compared to traditional methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058441","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

Energy and time-aware scheduling in diverse virtualized cloud computing environments using optimized self-attention progressive generative adversarial network. 利用优化的自关注渐进生成对抗网络，在多样化虚拟化云计算环境中实现能量和时间感知调度。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-09-25 DOI: 10.1080/0954898X.2024.2391401

G Senthilkumar, S Anandamurugan

{"title":"Energy and time-aware scheduling in diverse virtualized cloud computing environments using optimized self-attention progressive generative adversarial network.","authors":"G Senthilkumar, S Anandamurugan","doi":"10.1080/0954898X.2024.2391401","DOIUrl":"10.1080/0954898X.2024.2391401","url":null,"abstract":"The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"274-293"},"PeriodicalIF":1.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332541","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

Human activity recognition utilizing optimized attention induced Multihead Convolutional Neural Network with Mobile Net V1 from Mobile health data. 利用优化的注意诱导多头卷积神经网络和移动网络V1从移动健康数据中识别人类活动。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-12-17 DOI: 10.1080/0954898X.2024.2438967

R Anandha Praba, L Suganthi

{"title":"Human activity recognition utilizing optimized attention induced Multihead Convolutional Neural Network with Mobile Net V1 from Mobile health data.","authors":"R Anandha Praba, L Suganthi","doi":"10.1080/0954898X.2024.2438967","DOIUrl":"10.1080/0954898X.2024.2438967","url":null,"abstract":"Human Activity Recognition (HAR) systems are designed to continuously monitor human behaviour, mainly in the areas of entertainment and surveillance in intelligent home environments. In this manuscript, Human Activity Recognition utilizing optimized Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 from Mobile Health Data (HAR-AMCNN-MNV1) is proposed. The input data is collected through MHEALTH and UCI HAR datasets. Neural Spectrospatial Filtering (NSF) is used for avoiding accurate labelling and reduces errors. Afterwards, Variational Density Peak Clustering Algorithm (VDPCA) is used for segmenting the data. Feature Extraction and Classification is done by Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 (AMCNN-MNV1). AMCNN is used for extracting Hand-crafted features. AMCNN-MNV1 effectively classifies the human activities as Sitting and relaxing (Sit), Climbing stairs (CS), Walking (Walk), Standing still (Std), Waist bends forward (WBF), Frontal elevation of arms (FEA), Jogging (Jog), Knees bending (crouching) (KB), Cycling (Cycl), Lying down (Lay), Jump front & back (JFB) and Running (Run). Siberian Tiger Optimization Algorithm (STOA) is proposed to optimize the weight parameter of AMCNN-MNV1 classifier. The proposed method attains 21.19%, 23.45%, and 21.76% higher accuracy, 31.15%, 24.65% and 22.72% higher precision; 21.15%, 20.18%, and 21.28% higher recall evaluated to the existing methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"294-321"},"PeriodicalIF":1.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840251","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

Omics data classification using constitutive artificial neural network optimized with single candidate optimizer. 使用单候选优化器优化的构成型人工神经网络进行 Omics 数据分类。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-05-12 DOI: 10.1080/0954898X.2024.2348726

Subramaniam Madhan, Anbarasan Kalaiselvan

{"title":"Omics data classification using constitutive artificial neural network optimized with single candidate optimizer.","authors":"Subramaniam Madhan, Anbarasan Kalaiselvan","doi":"10.1080/0954898X.2024.2348726","DOIUrl":"10.1080/0954898X.2024.2348726","url":null,"abstract":"Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial Neural Network Optimized with Single Candidate Optimizer (ODC-ZOA-CANN-SCO) is proposed in this manuscript. The input data is pre-processing by using Adaptive variational Bayesian filtering (AVBF) to replace missing values. The pre-processing data is fed to Zebra Optimization Algorithm (ZOA) for dimensionality reduction. Then, the Constitutive Artificial Neural Network (CANN) is employed to classify omics data. The weight parameter is optimized by Single Candidate Optimizer (SCO). The proposed ODC-ZOA-CANN-SCO method attains 25.36%, 21.04%, 22.18%, 26.90%, and 28.12% higher accuracy when analysed to the existing methods like multi-omics data integration utilizing adaptive graph learning and attention mode for patient categorization with biomarker identification (MOD-AGL-AM-PABI), deep learning method depending upon multi-omics data integration to create risk stratification prediction mode for skin cutaneous melanoma (DL-MODI-RSP-SCM), Deep belief network-base model for identifying Alzheimer's disease utilizing multi-omics data (DDN-DAD-MOD), hybrid cancer prediction depending upon multi-omics data and reinforcement learning state action reward state action (HCP-MOD-RL-SARSA), machine learning basis method under omics data including biological knowledge database for cancer clinical endpoint prediction (ML-ODBKD-CCEP) methods, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"343-367"},"PeriodicalIF":1.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913377","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

Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach. 智能植物病害网：自适应密集混合卷积网络与关注机制，通过改进的优化方法实现基于物联网的植物病害检测。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-02-24 DOI: 10.1080/0954898X.2024.2316080

N Ananthi, V Balaji, M Mohana, S Gnanapriya

{"title":"Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach.","authors":"N Ananthi, V Balaji, M Mohana, S Gnanapriya","doi":"10.1080/0954898X.2024.2316080","DOIUrl":"10.1080/0954898X.2024.2316080","url":null,"abstract":"Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"368-406"},"PeriodicalIF":1.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139944676","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 self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set. 深度自组织图神经网络改进了对不足的足底压力成像数据集的分割。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-10-14 DOI: 10.1080/0954898X.2024.2413849

Dan Wang, Zairan Li, Nilanjan Dey, Adam Slowik, R Simon Sherratt, Fuqian Shi

{"title":"Deep self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set.","authors":"Dan Wang, Zairan Li, Nilanjan Dey, Adam Slowik, R Simon Sherratt, Fuqian Shi","doi":"10.1080/0954898X.2024.2413849","DOIUrl":"10.1080/0954898X.2024.2413849","url":null,"abstract":"This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"322-342"},"PeriodicalIF":1.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481197","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

Hybrid deep learning-based skin cancer classification with RPO-SegNet for skin lesion segmentation. 基于深度学习的皮肤癌分类与RPO-SegNet混合皮肤病变分割。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-01 Epub Date: 2024-12-03 DOI: 10.1080/0954898X.2024.2428705

Visu Pandurangan, Smitha Ponnayyan Sarojam, Pughazendi Narayanan, Murugananthan Velayutham

{"title":"Hybrid deep learning-based skin cancer classification with RPO-SegNet for skin lesion segmentation.","authors":"Visu Pandurangan, Smitha Ponnayyan Sarojam, Pughazendi Narayanan, Murugananthan Velayutham","doi":"10.1080/0954898X.2024.2428705","DOIUrl":"10.1080/0954898X.2024.2428705","url":null,"abstract":"Skin melanin lesions are typically identified as tiny patches on the skin, which are impacted by melanocyte cell overgrowth. The number of people with skin cancer is increasing worldwide. Accurate and timely skin cancer identification is critical to reduce the mortality rates. An incorrect diagnosis can be fatal to the patient. To tackle these issues, this article proposes the Recurrent Prototypical Object Segmentation Network (RPO-SegNet) for the segmentation of skin lesions and a hybrid Deep Learning (DL) - based skin cancer classification. The RPO-SegNet is formed by integrating the Recurrent Prototypical Networks (RP-Net), and Object Segmentation Networks (O-SegNet). At first, the input image is taken from a database and forwarded to image pre-processing. Then, the segmentation of skin lesions is accomplished using the proposed RPO-SegNet. After the segmentation, feature extraction is accomplished. Finally, skin cancer classification and detection are accomplished by employing the Fuzzy-based Shepard Convolutional Maxout Network (FSCMN) by combining the Deep Maxout Network (DMN), and Shepard Convolutional Neural Network (ShCNN). The established RPO-SegNet+FSCMN attained improved accuracy, True Negative Rate (TNR), True Positive Rate (TPR), dice coefficient, Jaccard coefficient, and segmentation analysis of 91.985%, 92.735%, 93.485%, 90.902%, 90.164%, and 91.734%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"221-248"},"PeriodicalIF":1.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774851","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

Quadratic discriminant feature selected broken stick regressive deep convolution neural learning classification for turmeric crop yield prediction. 二次判别特征选择断棒回归深度卷积神经学习分类姜黄产量预测。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-04-27 DOI: 10.1080/0954898X.2025.2488881

Raguvaran Krishnamoorthy, Rajasekaran Chinnappan, Jayanthi Krishnasamy Balasundaram

{"title":"Quadratic discriminant feature selected broken stick regressive deep convolution neural learning classification for turmeric crop yield prediction.","authors":"Raguvaran Krishnamoorthy, Rajasekaran Chinnappan, Jayanthi Krishnasamy Balasundaram","doi":"10.1080/0954898X.2025.2488881","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2488881","url":null,"abstract":"In this study, a novel technique termed Quadratic Discriminant Feature Selected Broken Stick Regressive Deep Convolution Neural Learning Classification (QDFSBSRDCNLC) Technique is proposed for disease classification and hence yields prediction of turmeric crop. Initially, we gathered the images of turmeric crops with and without diseases. The images are collected from the turmeric research field at Bhavanisagar. Quadratic Discriminant Analysis (QDA) is utilized to select relevant features from a dataset, reducing dimensionality. In this paper, four models, named FCN8, PSP Net, MobileNetV3 (small), and Deep Lab V3 are chosen for semantic segmentation of disease in turmeric crops. Turmeric crop production predicts is an important part of modern agriculture, allowing farmers to make sensible choices and optimize resources. We can predict turmeric crop yields accurately by using modern data analysis approaches. Predictive models take into consideration variables such as weather, soil quality, and farming techniques. The experimental results demonstrated that MobileNetV3 (small) performed better than other established ones with the accuracy of 97.99%, IoU of 96.82%, and Coefficient of 97.80% for 50 epochs. The proposed QDFSBSRDCNLC Technique effectively classifies diseases and predicts the yield of turmeric crops, with MobileNetV3 (small) showing superior performance among the tested models.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-38"},"PeriodicalIF":1.1,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060200","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