International Journal of Neural Systems最新文献_第10页

Dual-Modal Information Bottleneck Network for Seizure Detection. 缉获检测的双模态信息瓶颈网络。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-01-01 DOI: 10.1142/S0129065722500617

Jiale Wang, Xinting Ge, Yunfeng Shi, Mengxue Sun, Qingtao Gong, Haipeng Wang, Wenhui Huang

{"title":"Dual-Modal Information Bottleneck Network for Seizure Detection.","authors":"Jiale Wang, Xinting Ge, Yunfeng Shi, Mengxue Sun, Qingtao Gong, Haipeng Wang, Wenhui Huang","doi":"10.1142/S0129065722500617","DOIUrl":"https://doi.org/10.1142/S0129065722500617","url":null,"abstract":"In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimensional (1D) features of EEG signals into many segments and employ 1D-CNNs. Moreover, these investigations are further constrained by the absence of consideration for temporal links between time series segments or spectrogram images. Therefore, we propose a Dual-Modal Information Bottleneck (Dual-modal IB) network for EEG seizure detection. The network extracts EEG features from both time series and spectrogram dimensions, allowing information from different modalities to pass through the Dual-modal IB, requiring the model to gather and condense the most pertinent information in each modality and only share what is necessary. Specifically, we make full use of the information shared between the two modality representations to obtain key information for seizure detection and to remove irrelevant feature between the two modalities. In addition, to explore the intrinsic temporal dependencies, we further introduce a bidirectional long-short-term memory (BiLSTM) for Dual-modal IB model, which is used to model the temporal relationships between the information after each modality is extracted by convolutional neural network (CNN). For CHB-MIT dataset, the proposed framework can achieve an average segment-based sensitivity of 97.42%, specificity of 99.32%, accuracy of 98.29%, and an average event-based sensitivity of 96.02%, false detection rate (FDR) of 0.70/h. We release our code at https://github.com/LLLL1021/Dual-modal-IB.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9098298","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

Author Index Volume 32 (2022). 作者索引卷32(2022)。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-12-01 DOI: 10.1142/S0129065722990015

引用次数: 0

A Conditional Generative Adversarial Network and Transfer Learning-Oriented Anomaly Classification System for Electrospun Nanofibers. 基于条件生成对抗网络和迁移学习的电纺纳米纤维异常分类系统。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-12-01 Epub Date: 2022-10-13 DOI: 10.1142/S012906572250054X

Cosimo Ieracitano, Nadia Mammone, Annunziata Paviglianiti, Francesco Carlo Morabito

引用次数: 4

Convolutional Neural Networks Quantization with Double-Stage Squeeze-and-Threshold. 基于双阶段压缩和阈值的卷积神经网络量化。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-12-01 Epub Date: 2022-09-26 DOI: 10.1142/S0129065722500514

Binyi Wu, Bernd Waschneck, Christian Georg Mayr

{"title":"Convolutional Neural Networks Quantization with Double-Stage Squeeze-and-Threshold.","authors":"Binyi Wu, Bernd Waschneck, Christian Georg Mayr","doi":"10.1142/S0129065722500514","DOIUrl":"https://doi.org/10.1142/S0129065722500514","url":null,"abstract":"It has been proven that, compared to using 32-bit floating-point numbers in the training phase, Deep Convolutional Neural Networks (DCNNs) can operate with low-precision during inference, thereby saving memory footprint and power consumption. However, neural network quantization is always accompanied by accuracy degradation. Here, we propose a quantization method called double-stage Squeeze-and-Threshold (double-stage ST) to close the accuracy gap with full-precision models. While accurate colors in pictures can be pleasing to the viewer, they are not necessary for distinguishing objects. The era of black and white television proves this idea. As long as the limited colors are filled reasonably for different objects, the objects can be well identified and distinguished. Our method utilizes the attention mechanism to adjust the activations and learn the thresholds to distinguish objects (features). We then divide the numerically rich activations into intervals (a limited variety of numerical values) by the learned thresholds. The proposed method supports both binarization and multi-bit quantization. Our method achieves state-of-the-art results. In binarization, ReActNet [Z. Liu, Z. Shen, S. Li, K. Helwegen, D. Huang and K. Cheng, arXiv:abs/2106.11309] trained with our method outperforms the previous state-of-the-art result by 0.2 percentage points. Whereas in multi-bit quantization, the top-1 accuracy of the 3-bit ResNet-18 [K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, 2016 IEEE Conf. Computer Vision and Pattern Recognition, CVPR 2016, 27-30 June 2016, Las Vegas, NV, USA (IEEE Computer Society, 2016), pp. 770-778] model exceeds the top-1 accuracy of its full-precision baseline model by 0.4 percentage points. The double-stage ST activation quantization method is easy to apply by inserting it before the convolution. Besides, the double-stage ST is detachable after training and introducing no computational cost in inference.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40376589","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

Exploiting Textual Information for Fake News Detection. 利用文本信息进行假新闻检测。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-12-01 Epub Date: 2022-11-04 DOI: 10.1142/S0129065722500587

Dimitrios Panagiotis Kasseropoulos, Paraskevas Koukaras, Christos Tjortjis

{"title":"Exploiting Textual Information for Fake News Detection.","authors":"Dimitrios Panagiotis Kasseropoulos, Paraskevas Koukaras, Christos Tjortjis","doi":"10.1142/S0129065722500587","DOIUrl":"https://doi.org/10.1142/S0129065722500587","url":null,"abstract":"\"Fake news\" refers to the deliberate dissemination of news with the purpose to deceive and mislead the public. This paper assesses the accuracy of several Machine Learning (ML) algorithms, using a style-based technique that relies on textual information extracted from news, such as part of speech counts. To expand the already proposed styled-based techniques, a new method of enhancing a linguistic feature set is proposed. It combines Named Entity Recognition (NER) with the Frequent Pattern (FP) Growth association rule mining algorithm, aiming to provide better insight into the papers' sentence level structure. Recursive feature elimination was used to identify a subset of the highest performing linguistic characteristics, which turned out to align with the literature. Using pre-trained word embeddings, document embeddings and weighted document embeddings were constructed using each word's TF-IDF value as the weight factor. The document embeddings were mixed with the linguistic features providing a variety of training/test feature sets. For each model, the best performing feature set was identified and fine-tuned regarding its hyper parameters to improve accuracy. ML algorithms' results were compared with two Neural Networks: Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM). The results indicate that CNN outperformed all other methods in terms of accuracy, when companied with pre-trained word embeddings, yet SVM performs almost the same with a wider variety of input feature sets. Although style-based technique scores lower accuracy, it provides explainable results about the author's writing style decisions. Our work points out how new technologies and combinations of existing techniques can enhance the style-based approach capturing more information.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40465012","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

Personalized Watch-Based Fall Detection Using a Collaborative Edge-Cloud Framework. 使用协作边缘云框架的个性化基于手表的跌倒检测。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-12-01 Epub Date: 2022-08-15 DOI: 10.1142/S0129065722500484

Anne Hee Ngu, Vangelis Metsis, Shuan Coyne, Priyanka Srinivas, Tarek Salad, Uddin Mahmud, Kyong Hee Chee

{"title":"Personalized Watch-Based Fall Detection Using a Collaborative Edge-Cloud Framework.","authors":"Anne Hee Ngu, Vangelis Metsis, Shuan Coyne, Priyanka Srinivas, Tarek Salad, Uddin Mahmud, Kyong Hee Chee","doi":"10.1142/S0129065722500484","DOIUrl":"https://doi.org/10.1142/S0129065722500484","url":null,"abstract":"The majority of current smart health applications are deployed on a smartphone paired with a smartwatch. The phone is used as the computation platform or the gateway for connecting to the cloud while the watch is used mainly as the data sensing device. In the case of fall detection applications for older adults, this kind of setup is not very practical since it requires users to always keep their phones in proximity while doing the daily chores. When a person falls, in a moment of panic, it might be difficult to locate the phone in order to interact with the Fall Detection App for the purpose of indicating whether they are fine or need help. This paper demonstrates the feasibility of running a real-time personalized deep-learning-based fall detection system on a smartwatch device using a collaborative edge-cloud framework. In particular, we present the software architecture we used for the collaborative framework, demonstrate how we automate the fall detection pipeline, design an appropriate UI on the small screen of the watch, and implement strategies for the continuous data collection and automation of the personalization process with the limited computational and storage resources of a smartwatch. We also present the usability of such a system with nine real-world older adult participants.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40701026","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: 3

Convolutional Neural Networks-Based Framework for Early Identification of Dementia Using MRI of Brain Asymmetry. 基于卷积神经网络的脑不对称MRI早期识别痴呆框架。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-12-01 Epub Date: 2022-09-15 DOI: 10.1142/S0129065722500538

Nitsa J Herzog, George D Magoulas

{"title":"Convolutional Neural Networks-Based Framework for Early Identification of Dementia Using MRI of Brain Asymmetry.","authors":"Nitsa J Herzog, George D Magoulas","doi":"10.1142/S0129065722500538","DOIUrl":"https://doi.org/10.1142/S0129065722500538","url":null,"abstract":"Computer-aided diagnosis of health problems and pathological conditions has become a substantial part of medical, biomedical, and computer science research. This paper focuses on the diagnosis of early and progressive dementia, building on the potential of deep learning (DL) models. The proposed computational framework exploits a magnetic resonance imaging (MRI) brain asymmetry biomarker, which has been associated with early dementia, and employs DL architectures for MRI image classification. Identification of early dementia is accomplished by an eight-layered convolutional neural network (CNN) as well as transfer learning of pretrained CNNs from ImageNet. Different instantiations of the proposed CNN architecture are tested. These are equipped with Softmax, support vector machine (SVM), linear discriminant (LD), or [Formula: see text] -nearest neighbor (KNN) classification layers, assembled as a separate classification module, which are attached to the core CNN architecture. The initial imaging data were obtained from the MRI directory of the Alzheimer's disease neuroimaging initiative 3 (ADNI3) database. The independent testing dataset was created using image preprocessing and segmentation algorithms applied to unseen patients' imaging data. The proposed approach demonstrates a 90.12% accuracy in distinguishing patients who are cognitively normal subjects from those who have Alzheimer's disease (AD), and an 86.40% accuracy in detecting early mild cognitive impairment (EMCI).","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40359825","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

Reward-Penalty Weighted Ensemble for Emotion State Classification from Multi-Modal Data Streams. 基于奖罚加权集成的多模态数据流情绪状态分类。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-12-01 Epub Date: 2022-09-21 DOI: 10.1142/S0129065722500496

Arijit Nandi, Fatos Xhafa, Laia Subirats, Santi Fort

{"title":"Reward-Penalty Weighted Ensemble for Emotion State Classification from Multi-Modal Data Streams.","authors":"Arijit Nandi, Fatos Xhafa, Laia Subirats, Santi Fort","doi":"10.1142/S0129065722500496","DOIUrl":"https://doi.org/10.1142/S0129065722500496","url":null,"abstract":"Researchers have shown the limitations of using the single-modal data stream for emotion classification. Multi-modal data streams are therefore deemed necessary to improve the accuracy and performance of online emotion classifiers. An online decision ensemble is a widely used approach to classify emotions in real-time using multi-modal data streams. There is a plethora of online ensemble approaches; these approaches use a fixed parameter ([Formula: see text]) to adjust the weights of each classifier (called penalty) in case of wrong classification and no reward for a good performing classifier. Also, the performance of the ensemble depends on the [Formula: see text], which is set using trial and error. This paper presents a new Reward-Penalty-based Weighted Ensemble (RPWE) for real-time multi-modal emotion classification using multi-modal physiological data streams. The proposed RPWE is thoroughly tested using two prevalent benchmark data sets, DEAP and AMIGOS. The first experiment confirms the impact of the base stream classifier with RPWE for emotion classification in real-time. The RPWE is compared with different popular and widely used online ensemble approaches using multi-modal data streams in the second experiment. The average balanced accuracy, F1-score results showed the usefulness and robustness of RPWE in emotion classification in real-time from the multi-modal data stream.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40373938","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: 3

Automatic Seizure Identification from EEG Signals Based on Brain Connectivity Learning. 基于脑连通性学习的脑电信号癫痫发作自动识别。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-11-01 Epub Date: 2022-08-26 DOI: 10.1142/S0129065722500502

Yanna Zhao, Mingrui Xue, Changxu Dong, Jiatong He, Dengyu Chu, Gaobo Zhang, Fangzhou Xu, Xinting Ge, Yuanjie Zheng

{"title":"Automatic Seizure Identification from EEG Signals Based on Brain Connectivity Learning.","authors":"Yanna Zhao, Mingrui Xue, Changxu Dong, Jiatong He, Dengyu Chu, Gaobo Zhang, Fangzhou Xu, Xinting Ge, Yuanjie Zheng","doi":"10.1142/S0129065722500502","DOIUrl":"https://doi.org/10.1142/S0129065722500502","url":null,"abstract":"Epilepsy is a neurological disorder caused by brain dysfunction, which could cause uncontrolled behavior, loss of consciousness and other hazards. Electroencephalography (EEG) is an indispensable auxiliary tool for clinical diagnosis. Great progress has been made by current seizure identification methods. However, the performance of the methods on different patients varies a lot. In order to deal with this problem, we propose an automatic seizure identification method based on brain connectivity learning. The connectivity of different brain regions is modeled by a graph. Different from the manually defined graph structure, our method can extract the optimal graph structure and EEG features in an end-to-end manner. Combined with the popular graph attention neural network (GAT), this method achieves high performance and stability on different patients from the CHB-MIT dataset. The average values of accuracy, sensitivity, specificity, F1-score and AUC of the proposed model are 98.90%, 98.33%, 98.48%, 97.72% and 98.54%, respectively. The standard deviations of the above five indicators are 0.0049, 0.0125, 0.0116 and 0.0094, respectively. Compared with the existing seizure identification methods, the stability of the proposed model is improved by 78-95%.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33439791","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

Diagnosis of Autism Disorder Based on Deep Network Trained by Augmented EEG Signals. 基于增强脑电信号训练的深度网络诊断自闭症。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-11-01 Epub Date: 2022-08-22 DOI: 10.1142/S0129065722500460

Habib Adabi Ardakani, Maryam Taghizadeh, Farzaneh Shayegh

{"title":"Diagnosis of Autism Disorder Based on Deep Network Trained by Augmented EEG Signals.","authors":"Habib Adabi Ardakani, Maryam Taghizadeh, Farzaneh Shayegh","doi":"10.1142/S0129065722500460","DOIUrl":"https://doi.org/10.1142/S0129065722500460","url":null,"abstract":"Autism spectrum disorder is a neurodevelopmental disorder typically characterized by abnormalities in social interaction and stereotyped and repetitive behaviors. Diagnosis of autism is mainly based on behavioral tests and interviews. In recent years, studies involving the diagnosis of autism based on analysis of EEG signals have increased. In this paper, recorded signals from people suffering from autism and healthy individuals are divided to without overlap windows considered as images and these images are classified using a two-dimensional Deep Convolution Neural Network (2D-DCNN). Deep learning models require a lot of data to extract the appropriate features and automate data classification. But, in most neurological studies, preparing a large number of measurements is difficult (a few 1000s as compared to million natural images), due to the cost, time, and difficulty of recording these signals. Therefore, to make the appropriate number of data, in our proposed method, some of the data augmentation methods are used. These data augmentation methods are mainly introduced for image databases and should be generalized for EEG-as-an-image database. In this paper, one of the nonlinear image mixing methods is used that mixes the rows of two images. According to the fact that any row in our image is one channel of EEG signal, this method is named channel combination. The result is that in the best case, i.e., augmentation according to channel combination, the average accuracy of 88.29% is achieved in the classification of short signals of healthy people and ASD ones and 100% for ASD and epilepsy ones, using 2D-DCNN. After the decision on joined windows related to each subject, we could achieve 100% accuracy in detecting ASD subjects, according to long EEG signals.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40719479","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