Network-Computation in Neural Systems最新文献_第10页

Optimization of data pre-processing methods for time-series classification of electroencephalography data. 用于脑电图数据时间序列分类的数据预处理方法的优化。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2023-02-01 Epub Date: 2023-11-09 DOI: 10.1080/0954898X.2023.2263083

Christoph Anders, Gabriel Curio, Bert Arnrich, Gunnar Waterstraat

{"title":"Optimization of data pre-processing methods for time-series classification of electroencephalography data.","authors":"Christoph Anders, Gabriel Curio, Bert Arnrich, Gunnar Waterstraat","doi":"10.1080/0954898X.2023.2263083","DOIUrl":"10.1080/0954898X.2023.2263083","url":null,"abstract":"The performance of time-series classification of electroencephalographic data varies strongly across experimental paradigms and study participants. Reasons are task-dependent differences in neuronal processing and seemingly random variations between subjects, amongst others. The effect of data pre-processing techniques to ameliorate these challenges is relatively little studied. Here, the influence of spatial filter optimization methods and non-linear data transformation on time-series classification performance is analyzed by the example of high-frequency somatosensory evoked responses. This is a model paradigm for the analysis of high-frequency electroencephalography data at a very low signal-to-noise ratio, which emphasizes the differences of the explored methods. For the utilized data, it was found that the individual signal-to-noise ratio explained up to 74% of the performance differences between subjects. While data pre-processing was shown to increase average time-series classification performance, it could not fully compensate the signal-to-noise ratio differences between the subjects. This study proposes an algorithm to prototype and benchmark pre-processing pipelines for a paradigm and data set at hand. Extreme learning machines, Random Forest, and Logistic Regression can be used quickly to compare a set of potentially suitable pipelines. For subsequent classification, however, machine learning models were shown to provide better accuracy.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71429258","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

Solving time delay fractional optimal control problems via a Gudermannian neural network and convergence results. 利用古德曼神经网络求解时滞分数阶最优控制问题并得到收敛结果。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2023-02-01 DOI: 10.1080/0954898X.2023.2173817

Farzaneh Kheyrinataj, Alireza Nazemi, Marziyeh Mortezaee

引用次数: 0

A novel approach for neural networks based diagnosis and grading of stroke in tumor-affected brain MRIs. 一种基于神经网络的肿瘤脑MRI脑卒中诊断和分级的新方法。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2023-02-01 Epub Date: 2023-06-23 DOI: 10.1080/0954898X.2023.2225601

Somasundaram Krishnamoorthy, Sivakumar Paulraj, Nagendra Prabhu Selvaraj, Balakumaresan Ragupathy, Selvapandian Arumugam

{"title":"A novel approach for neural networks based diagnosis and grading of stroke in tumor-affected brain MRIs.","authors":"Somasundaram Krishnamoorthy, Sivakumar Paulraj, Nagendra Prabhu Selvaraj, Balakumaresan Ragupathy, Selvapandian Arumugam","doi":"10.1080/0954898X.2023.2225601","DOIUrl":"10.1080/0954898X.2023.2225601","url":null,"abstract":"Recognition and diagnosis of stroke from magnetic resonance Image (MRIs) are significant for medical procedures in therapeutic standards. The primary goal of this scheme is the discovery of stroke in tumour locale in brain tissues influenced image. The probability of stroke is categorized on brain tumour influenced images into mild, moderate, or serious cases. The mild and moderate phases of stroke are recognized as \"Ahead of schedule\" findings and serious cases are distinguished as \"Advance\" determination. The proposed Glioblastoma brain tumour recognition strategy used the Multifaceted Brain Tumour Image Segmentation test open-access dataset for evaluating the presentation. The brain images are classified utilizing the Deep Neural Networks classification algorithm as normal and abnormal images. The tumour region is segmented from the identified set of abnormal images using the normalized graph cut algorithm. The stroke likelihood is identified using the Deep Neural Networks by analysing the proximity of tumour section in brain matters. The proposed stroke analysis framework accurately groups 10 images as \"Right on time\" stroke probability images and accomplishes 90% order rate. The proposed stroke prediction framework effectively characterizes images as \"Advance\" stroke probability images and accomplishes 90% characterization rate.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9858662","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

FMTM-feature-map-based transform model for brain image segmentation in tumor detection. 肿瘤检测中基于fmtm特征映射的脑图像分割变换模型。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2023-02-01 DOI: 10.1080/0954898X.2022.2110620

Revathi Sundarasekar, Ahilan Appathurai

{"title":"FMTM-feature-map-based transform model for brain image segmentation in tumor detection.","authors":"Revathi Sundarasekar, Ahilan Appathurai","doi":"10.1080/0954898X.2022.2110620","DOIUrl":"https://doi.org/10.1080/0954898X.2022.2110620","url":null,"abstract":"The segmentation of brain images is a leading quantitative measure for detecting physiological changes and for analysing structural functions. Based on trends and dimensions of brain, the images indicate heterogeneity. Accurate brain tumour segmentation remains a critical challenge despite the persistent efforts of researchers were owing to a variety of obstacles. This impacts the outcome of tumour detection, causing errors. For addressing this issue, a Feature-Map based Transform Model (FMTM) is introduced to focus on heterogeneous features of input picture to map differences and intensity based on transition Fourier. Unchecked machine learning is used for reliable characteristic map recognition in this mapping process. For the determination of severity and variability, the method of identification depends on symmetry and texture. Learning instances are taught to improve precision using predefined data sets, regardless of loss of labels. The process is recurring until the maximum precision of tumour detection is achieved in low convergence. In this research, FMTM has been applied to brain tumour segmentation to automatically extract feature representations and produce accurate and steady performance because of promising performance made by powerful transition Fourier methods. The suggested model's performance is shown by the metrics processing time, precision, accuracy, and F1-Score.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9335965","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

SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT. SCSLnO SqueezeNet:See Cosine Sea Lion Optimization使SqueeziNet能够在物联网中进行入侵检测。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2023-02-01 Epub Date: 2023-11-09 DOI: 10.1080/0954898X.2023.2261531

M Masthan, K Pazhanikumar, Meena Chavan, Jyothi Mandala, Sanjay Nakharu Prasad Kumar

引用次数: 0

A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques. 系统综述:基于人工智能技术的胸片图像(x线图像)分析与COVID-19分类诊断。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2023-02-01 DOI: 10.1080/0954898X.2022.2147231

Saravanan Suba, M Muthulakshmi

{"title":"A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques.","authors":"Saravanan Suba, M Muthulakshmi","doi":"10.1080/0954898X.2022.2147231","DOIUrl":"https://doi.org/10.1080/0954898X.2022.2147231","url":null,"abstract":"COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9335317","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

MDSR-NMF: Multiple deconstruction single reconstruction deep neural network model for non-negative matrix factorization. MDSR-NMF：用于非负矩阵分解的多重解构单重构深度神经网络模型。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2023-02-01 Epub Date: 2023-11-09 DOI: 10.1080/0954898X.2023.2257773

Prasun Dutta, Rajat K De

{"title":"MDSR-NMF: Multiple deconstruction single reconstruction deep neural network model for non-negative matrix factorization.","authors":"Prasun Dutta, Rajat K De","doi":"10.1080/0954898X.2023.2257773","DOIUrl":"10.1080/0954898X.2023.2257773","url":null,"abstract":"Dimension reduction is one of the most sought-after strategies to cope with high-dimensional ever-expanding datasets. To address this, a novel deep-learning architecture has been designed with multiple deconstruction and single reconstruction layers for non-negative matrix factorization aimed at low-rank approximation. This design ensures that the reconstructed input matrix has a unique pair of factor matrices. The two-stage approach, namely, pretraining and stacking, aids in the robustness of the architecture. The sigmoid function has been adjusted in such a way that fulfils the non-negativity criteria and also helps to alleviate the data-loss problem. Xavier initialization technique aids in the solution of the exploding or vanishing gradient problem. The objective function involves regularizer that ensures the best possible approximation of the input matrix. The superior performance of MDSR-NMF, over six well-known dimension reduction methods, has been demonstrated extensively using five datasets for classification and clustering. Computational complexity and convergence analysis have also been presented to establish the model.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220404","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

How somatosensory evoked potentials improve the diagnosis of the disturbance of consciousness: A retrospective analysis. 体感诱发电位如何改善意识障碍的诊断：回顾性分析。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2023-02-01 Epub Date: 2023-11-09 DOI: 10.1080/0954898X.2023.2269263

Xinwei Wang, Hongliang Gao, Jiulong Song, Peng Jing, Chao Wang, Nuanxin Yu, Shanshan Wu, Jianxiong Zhu, Zhiqiang Gao

{"title":"How somatosensory evoked potentials improve the diagnosis of the disturbance of consciousness: A retrospective analysis.","authors":"Xinwei Wang, Hongliang Gao, Jiulong Song, Peng Jing, Chao Wang, Nuanxin Yu, Shanshan Wu, Jianxiong Zhu, Zhiqiang Gao","doi":"10.1080/0954898X.2023.2269263","DOIUrl":"10.1080/0954898X.2023.2269263","url":null,"abstract":"The interpeak latency is a crucial characteristic of upper limb somatosensory evoked potentials (USEPs). However, the existing research on the correlation between interpeak latency and consciousness disorders is currently limited. We aimed to investigate how USEPs can contribute to the diagnosis of consciousness disorders. A retrospective analysis was conducted on 10 patients who underwent repetitive transcranial magnetic stimulation (rTMS) for consciousness disorders. The interpeak latency N13-N20, Glasgow coma scale (GCS), and Chinese Nanjing persistent vegetative state scale (CNPVSS) were evaluated before and after rTMS treatment, and the linear correlation between N13-N20, GCS, and CNPVSS was analysed. The scores of CNPVSS and GCS significantly increased in the first, second, and third months after rTMS. The N13-N20 was shorter in the second and third months after rTMS compared to before treatment. rTMS was found to shorten the N13-N20 latency, and there was a negative correlation between N13-N20 and the score of consciousness disorders. N13-N20 can serve as an objective index for evaluating consciousness disorders. This research provides potential insights for doctors in diagnosing patients with consciousness disorders.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49685142","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

Shorter TR combined with finer atlas positively modulate topological organization of brain network: A resting state fMRI study. 较短的TR与较精细的图谱相结合正调节脑网络的拓扑组织：静息状态fMRI研究。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2023-02-01 Epub Date: 2023-05-22 DOI: 10.1080/0954898X.2023.2215860

Yan Zhang, Qili Hu, Jiali Liang, Zhenghui Hu, Tianyi Qian, Kuncheng Li, Xiaohu Zhao, Peipeng Liang

{"title":"Shorter TR combined with finer atlas positively modulate topological organization of brain network: A resting state fMRI study.","authors":"Yan Zhang, Qili Hu, Jiali Liang, Zhenghui Hu, Tianyi Qian, Kuncheng Li, Xiaohu Zhao, Peipeng Liang","doi":"10.1080/0954898X.2023.2215860","DOIUrl":"10.1080/0954898X.2023.2215860","url":null,"abstract":"Background: The use of shorter TR and finer atlases in rs-fMRI can provide greater detail on brain function and anatomy. However, there is limited understanding of the effect of this combination on brain network properties.Methods: A study was conducted with 20 healthy young volunteers who underwent rs-fMRI scans with both shorter (0.5s) and long (2s) TR. Two atlases with different degrees of granularity (90 vs 200 regions) were used to extract rs-fMRI signals. Several network metrics, including small-worldness, Cp, Lp, Eloc, and Eg, were calculated. Two-factor ANOVA and two-sample t-tests were conducted for both the single spectrum and five sub-frequency bands.Results: The network constructed using the combination of shorter TR and finer atlas showed significant enhancements in Cp, Eloc, and Eg, as well as reductions in Lp and γ in both the single spectrum and subspectrum (p < 0.05, Bonferroni correction). Network properties in the 0.082-0.1 Hz frequency range were weaker than those in the 0.01-0.082 Hz range.Conclusion: Our findings suggest that the use of shorter TR and finer atlas can positively affect the topological characteristics of brain networks. These insights can inform the development of brain network construction methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10237948","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

A study of neural artistic style transfer models and architectures for Indian art styles. 印度艺术风格的神经艺术风格迁移模型与架构研究。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2023-02-01 Epub Date: 2023-09-05 DOI: 10.1080/0954898X.2023.2252073

J Mercy Faustina, V Akash, Anmol Gupta, V Divya, Takasi Manoj, N Sadagopan, B Sivaselvan

{"title":"A study of neural artistic style transfer models and architectures for Indian art styles.","authors":"J Mercy Faustina, V Akash, Anmol Gupta, V Divya, Takasi Manoj, N Sadagopan, B Sivaselvan","doi":"10.1080/0954898X.2023.2252073","DOIUrl":"10.1080/0954898X.2023.2252073","url":null,"abstract":"Neural Style Transfer (NST) has been a widely researched topic as of late enabling new forms of image manipulation. Here we perform an extensive study on NST algorithms and extend the existing methods with custom modifications for application to Indian art styles. In this paper, we aim to provide a comprehensive analysis of various methods ranging from the seminal work of Gatys et al which demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style, to the state of the art image-to-image translation models which use Generative Adversarial Networks (GANs) to learn the mapping between two domain of images. We observe and infer based on the results produced by the models on which one could be a more suitable approach for Indian art styles, especially Tanjore paintings which are unique compared to the Western art styles. We then propose the method which is more suitable for the domain of Indian Art style along with custom architecture which includes an enhancement and evaluation module. We then present evaluation methods, both qualitative and quantitative which includes our proposed metric, to evaluate the results produced by the model.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":7.8,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10155805","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