International Journal of Neural Systems最新文献_第3页

Epileptic EEG Classification via Graph Transformer Network. 基于图变换网络的癫痫脑电分类。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-08-01 DOI: 10.1142/S0129065723500429

Jian Lian, Fangzhou Xu

{"title":"Epileptic EEG Classification via Graph Transformer Network.","authors":"Jian Lian, Fangzhou Xu","doi":"10.1142/S0129065723500429","DOIUrl":"https://doi.org/10.1142/S0129065723500429","url":null,"abstract":"Deep learning-based epileptic seizure recognition via electroencephalogram signals has shown considerable potential for clinical practice. Although deep learning algorithms can enhance epilepsy identification accuracy compared with classical machine learning techniques, classifying epileptic activities based on the association between multichannel signals in electroencephalogram recordings is still challenging in automated seizure classification from electroencephalogram signals. Furthermore, the performance of generalization is hardly maintained by the fact that existing deep learning models were constructed using just one architecture. This study focuses on addressing this challenge using a hybrid framework. Alternatively put, a hybrid deep learning model, which is based on the ground-breaking graph neural network and transformer architectures, was proposed. The proposed deep architecture consists of a graph model to discover the inner relationship between multichannel signals and a transformer to reveal the heterogeneous associations between the channels. To evaluate the performance of the proposed approach, the comparison experiments were conducted on a publicly available dataset between the state-of-the-art algorithms and ours. Experimental results demonstrate that the proposed method is a potentially valuable instrument for epoch-based epileptic EEG classification.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"33 8","pages":"2350042"},"PeriodicalIF":8.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9924079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 1

Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data. 利用多模态数据诊断帕金森病的非线性加权集成学习模型。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-08-01 DOI: 10.1142/S0129065723500417

D Castillo-Barnes, F J Martinez-Murcia, C Jimenez-Mesa, J E Arco, D Salas-Gonzalez, J Ramírez, J M Górriz

{"title":"Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data.","authors":"D Castillo-Barnes, F J Martinez-Murcia, C Jimenez-Mesa, J E Arco, D Salas-Gonzalez, J Ramírez, J M Górriz","doi":"10.1142/S0129065723500417","DOIUrl":"https://doi.org/10.1142/S0129065723500417","url":null,"abstract":"Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/ or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data. As shown by our results, the CAD proposal is able to detect PD with [Formula: see text] of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"33 8","pages":"2350041"},"PeriodicalIF":8.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9978491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Swarm-FHE: Fully Homomorphic Encryption-based Swarm Learning for Malicious Clients. Swarm- fhe:基于全同态加密的恶意客户端群学习。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-08-01 DOI: 10.1142/S0129065723500338

Hussain Ahmad Madni, Rao Muhammad Umer, Gian Luca Foresti

{"title":"Swarm-FHE: Fully Homomorphic Encryption-based Swarm Learning for Malicious Clients.","authors":"Hussain Ahmad Madni, Rao Muhammad Umer, Gian Luca Foresti","doi":"10.1142/S0129065723500338","DOIUrl":"https://doi.org/10.1142/S0129065723500338","url":null,"abstract":"Swarm Learning (SL) is a promising approach to perform the distributed and collaborative model training without any central server. However, data sensitivity is the main concern for privacy when collaborative training requires data sharing. A neural network, especially Generative Adversarial Network (GAN), is able to reproduce the original data from model parameters, i.e. gradient leakage problem. To solve this problem, SL provides a framework for secure aggregation using blockchain methods. In this paper, we consider the scenario of compromised and malicious participants in the SL environment, where a participant can manipulate the privacy of other participant in collaborative training. We propose a method, Swarm-FHE, Swarm Learning with Fully Homomorphic Encryption (FHE), to encrypt the model parameters before sharing with the participants which are registered and authenticated by blockchain technology. Each participant shares the encrypted parameters (i.e. ciphertexts) with other participants in SL training. We evaluate our method with training of the convolutional neural networks on the CIFAR-10 and MNIST datasets. On the basis of a considerable number of experiments and results with different hyperparameter settings, our method performs better as compared to other existing methods.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"33 8","pages":"2350033"},"PeriodicalIF":8.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9914554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 1

An Attention-Aware Long Short-Term Memory-Like Spiking Neural Model for Sentiment Analysis. 用于情感分析的注意力感知型长短期记忆类尖峰神经模型

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-08-01 Epub Date: 2023-06-10 DOI: 10.1142/S0129065723500375

Qian Liu, Yanping Huang, Qian Yang, Hong Peng, Jun Wang

引用次数: 0

A Shared Hippocampal Network in Retrieving Science-related Semantic Memories. 共享海马网络在检索科学相关语义记忆中的作用。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-08-01 DOI: 10.1142/S012906572350034X

Hsiao-Ching She, Li-Yu Huang, Jeng-Ren Duann

{"title":"A Shared Hippocampal Network in Retrieving Science-related Semantic Memories.","authors":"Hsiao-Ching She, Li-Yu Huang, Jeng-Ren Duann","doi":"10.1142/S012906572350034X","DOIUrl":"https://doi.org/10.1142/S012906572350034X","url":null,"abstract":"In responding to the calls for revisiting the role that hippocampus (HIP) plays in semantic memory retrieval, this study used functional neuroimaging-based connectivity technique to elucidate the functional brain network involved in retrieving the correct and incorrect science-related semantic memories. Unlike episodic memory retrieval, the 40 scientific concepts learned during middle and high school were selected to assess 46 science majors' semantic memory retrieval and correctness monitoring, which requires neither the support of spatial information nor events to retrieve the memory. Our results demonstrated that HIP was significantly and robustly engaged in the semantic memory retrieval of correct scientific concepts than incorrect ones. Importantly, the Granger causality analysis indicated that effective connectivity of [Formula: see text] and [Formula: see text] was shared by the semantic memory retrieval of both correct and incorrect scientific concepts. On the other hand, the strengths of connectivity in the [Formula: see text] and [Formula: see text] brain networks appeared more pronounced during the processing of correct scientific concepts than of incorrect ones. The shared hippocampal networks highlight the role of the HIP as a hub to coordinate the INS, ACC, and MTG, in turn, support the semantic memory retrieval of scientific concepts.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"33 8","pages":"2350034"},"PeriodicalIF":8.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9923608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Few-Shot Pixel-Precise Document Layout Segmentation via Dynamic Instance Generation and Local Thresholding. 通过动态实例生成和局部阈值实现少镜头像素精确文档布局分割。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-07-28 DOI: 10.2139/ssrn.4333692

Axel De Nardin, Silvia Zottin, C. Piciarelli, E. Colombi, G. Foresti

{"title":"Few-Shot Pixel-Precise Document Layout Segmentation via Dynamic Instance Generation and Local Thresholding.","authors":"Axel De Nardin, Silvia Zottin, C. Piciarelli, E. Colombi, G. Foresti","doi":"10.2139/ssrn.4333692","DOIUrl":"https://doi.org/10.2139/ssrn.4333692","url":null,"abstract":"Over the years, the humanities community has increasingly requested the creation of artificial intelligence frameworks to help the study of cultural heritage. Document Layout segmentation, which aims at identifying the different structural components of a document page, is a particularly interesting task connected to this trend, specifically when it comes to handwritten texts. While there are many effective approaches to this problem, they all rely on large amounts of data for the training of the underlying models, which is rarely possible in a real-world scenario, as the process of producing the ground truth segmentation task with the required precision to the pixel level is a very time-consuming task and often requires a certain degree of domain knowledge regarding the documents at hand. For this reason, in this paper, we propose an effective few-shot learning framework for document layout segmentation relying on two novel components, namely a dynamic instance generation and a segmentation refinement module. This approach is able of achieving performances comparable to the current state of the art on the popular Diva-HisDB dataset, while relying on just a fraction of the available data.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"1 1","pages":"2350052"},"PeriodicalIF":8.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42274532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 1

Modeling Emerging Interpersonal Synchrony and its Related Adaptive Short-Term Affiliation and Long-Term Bonding: A Second-Order Multi-Adaptive Neural Agent Model. 新兴人际同步及其相关的自适应短期联系和长期联系的建模:一个二阶多自适应神经Agent模型。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-07-01 DOI: 10.1142/S0129065723500387

Sophie C F Hendrikse, Jan Treur, Sander L Koole

{"title":"Modeling Emerging Interpersonal Synchrony and its Related Adaptive Short-Term Affiliation and Long-Term Bonding: A Second-Order Multi-Adaptive Neural Agent Model.","authors":"Sophie C F Hendrikse, Jan Treur, Sander L Koole","doi":"10.1142/S0129065723500387","DOIUrl":"https://doi.org/10.1142/S0129065723500387","url":null,"abstract":"When people interact, their behavior tends to become synchronized, a mutual coordination process that fosters short-term adaptations, like increased affiliation, and long-term adaptations, like increased bonding. This paper addresses for the first time how such short-term and long-term adaptivity induced by synchronization can be modeled computationally by a second-order multi-adaptive neural agent model. It addresses movement, affect and verbal modalities and both intrapersonal synchrony and interpersonal synchrony. The behavior of the introduced neural agent model was evaluated in a simulation paradigm with different stimuli and communication-enabling conditions. Moreover, in this paper, mathematical analysis is also addressed for adaptive network models and their positioning within the landscape of adaptive dynamical systems. The first type of analysis addressed shows that any smooth adaptive dynamical system has a canonical representation by a self-modeling network. This implies theoretically that the self-modeling network format is widely applicable, which also has been found in many practical applications using this approach. Furthermore, stationary point and equilibrium analysis was addressed and applied to the introduced self-modeling network model. It was used to obtain verification of the model providing evidence that the implemented model is correct with respect to its design specifications.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"33 7","pages":"2350038"},"PeriodicalIF":8.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9833214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 2

Evolving a Pipeline Approach for Abstract Meaning Representation Parsing Towards Dynamic Neural Networks. 面向动态神经网络的抽象意义表示分析的管道方法。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-07-01 DOI: 10.1142/S0129065723500405

Florin Macicasan, Alexandru Frasie, Nicoleta-Teodora Vezan, Camelia Lemnaru, Rodica Potolea

引用次数: 0

Introduction. 介绍。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-07-01 DOI: 10.1142/S0129065723030028

Lazaros Iliadis

引用次数: 0

Transformer-Based Approach Via Contrastive Learning for Zero-Shot Detection. 基于变压器对比学习的零弹检测方法。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-07-01 DOI: 10.1142/S0129065723500351

Wei Liu, Hui Chen, Yongqiang Ma, Jianji Wang, Nanning Zheng

{"title":"Transformer-Based Approach Via Contrastive Learning for Zero-Shot Detection.","authors":"Wei Liu, Hui Chen, Yongqiang Ma, Jianji Wang, Nanning Zheng","doi":"10.1142/S0129065723500351","DOIUrl":"https://doi.org/10.1142/S0129065723500351","url":null,"abstract":"Zero-shot detection (ZSD) aims to locate and classify unseen objects in pictures or videos by semantic auxiliary information without additional training examples. Most of the existing ZSD methods are based on two-stage models, which achieve the detection of unseen classes by aligning object region proposals with semantic embeddings. However, these methods have several limitations, including poor region proposals for unseen classes, lack of consideration of semantic representations of unseen classes or their inter-class correlations, and domain bias towards seen classes, which can degrade overall performance. To address these issues, the Trans-ZSD framework is proposed, which is a transformer-based multi-scale contextual detection framework that explicitly exploits inter-class correlations between seen and unseen classes and optimizes feature distribution to learn discriminative features. Trans-ZSD is a single-stage approach that skips proposal generation and performs detection directly, allowing the encoding of long-term dependencies at multiple scales to learn contextual features while requiring fewer inductive biases. Trans-ZSD also introduces a foreground-background separation branch to alleviate the confusion of unseen classes and backgrounds, contrastive learning to learn inter-class uniqueness and reduce misclassification between similar classes, and explicit inter-class commonality learning to facilitate generalization between related classes. Trans-ZSD addresses the domain bias problem in end-to-end generalized zero-shot detection (GZSD) models by using balance loss to maximize response consistency between seen and unseen predictions, ensuring that the model does not bias towards seen classes. The Trans-ZSD framework is evaluated on the PASCAL VOC and MS COCO datasets, demonstrating significant improvements over existing ZSD models.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"33 7","pages":"2350035"},"PeriodicalIF":8.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10188479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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