Neural Computing & Applications最新文献_第7页

An enhanced PSO algorithm to configure a responsive-resilient supply chain network considering environmental issues: a case study of the oxygen concentrator device. 考虑环境问题配置响应弹性供应链网络的增强型粒子群算法:氧气浓缩器设备的案例研究。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07739-8

Soodeh Nasrollah, S Esmaeil Najafi, Hadi Bagherzadeh, Mohsen Rostamy-Malkhalifeh

{"title":"An enhanced PSO algorithm to configure a responsive-resilient supply chain network considering environmental issues: a case study of the oxygen concentrator device.","authors":"Soodeh Nasrollah, S Esmaeil Najafi, Hadi Bagherzadeh, Mohsen Rostamy-Malkhalifeh","doi":"10.1007/s00521-022-07739-8","DOIUrl":"https://doi.org/10.1007/s00521-022-07739-8","url":null,"abstract":"In recent years, the hyper-competitive marketplace has led to a drastic enhancement in the importance of the supply chain problem. Hence, the attention of managers and researchers has been attracted to one of the most crucial problems in the supply chain management area called the supply chain network design problem. In this regard, this research attempts to design an integrated forward and backward logistics network by proposing a multi-objective mathematical model. The suggested model aims at minimizing the environmental impacts and the costs while maximizing the resilience and responsiveness of the supply chain. Since uncertainty is a major issue in the supply chain problem, the present paper studies the research problem under the mixed uncertainty and utilizes the robust possibilistic stochastic method to cope with the uncertainty. On the other side, since configuring a supply chain is known as an NP-Hard problem, this research develops an enhanced particle swarm optimization algorithm to obtain optimal/near-optimal solutions in a reasonable time. Based on the achieved results, the developed algorithm can obtain high-quality solutions (i.e. solutions with zero or a very small gap from the optimal solution) in a reasonable amount of time. The achieved results demonstrate the negative impact of the enhancement of the demand on environmental damages and the total cost. Also, according to the outputs, by increasing the service level, the total cost and environmental impacts have increased by 41% and 10%, respectively. On the other hand, the results show that increasing the disrupted capacity parameters has led to a 17% increase in the total costs and a 7% increase in carbon emissions.Supplementary information: The online version contains supplementary material available at 10.1007/s00521-022-07739-8.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 3","pages":"2647-2678"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10538298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Exploring density rectification and domain adaption method for crowd counting. 探索人群计数的密度校正和域自适应方法。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07917-8

Sifan Peng, Baoqun Yin, Qianqian Yang, Qing He, Luyang Wang

{"title":"Exploring density rectification and domain adaption method for crowd counting.","authors":"Sifan Peng, Baoqun Yin, Qianqian Yang, Qing He, Luyang Wang","doi":"10.1007/s00521-022-07917-8","DOIUrl":"https://doi.org/10.1007/s00521-022-07917-8","url":null,"abstract":"Crowd counting has received increasing attention due to its important roles in multiple fields, such as social security, commercial applications, epidemic prevention and control. To this end, we explore two critical issues that seriously affect the performance of crowd counting including nonuniform crowd density distribution and cross-domain problems. Aiming at the nonuniform crowd density distribution issue, we propose a density rectifying network (DRNet) that consists of several dual-layer pyramid fusion modules (DPFM) and a density rectification map (DRmap) auxiliary learning module. The proposed DPFM is embedded into DRNet to integrate multi-scale crowd density features through dual-layer pyramid fusion. The devised DRmap auxiliary learning module further rectifies the incorrect crowd density estimation by adaptively weighting the initial crowd density maps. With respect to the cross-domain issue, we develop a domain adaptation method of randomly cutting mixed dual-domain images, which learns domain-invariance features and decreases the domain gap between the source domain and the target domain from global and local perspectives. Experimental results indicate that the devised DRNet achieves the best mean absolute error (MAE) and competitive mean squared error (MSE) compared with other excellent methods on four benchmark datasets. Additionally, a series of cross-domain experiments are conducted to demonstrate the effectiveness of the proposed domain adaption method. Significantly, when the A and B parts of the Shanghaitech dataset are the source domain and target domain respectively, the proposed domain adaption method decreases the MAE of DRNet by <math><mrow><mn>47.6</mn> <mo>%</mo></mrow> </math> .","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 4","pages":"3551-3569"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9568950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10638062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of machine learning techniques for stance detection and its applications. 对用于姿态检测的机器学习技术及其应用的系统回顾。

IF 4.5 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2023-01-28 DOI: 10.1007/s00521-023-08285-7

Nora Alturayeif, Hamzah Luqman, Moataz Ahmed

{"title":"A systematic review of machine learning techniques for stance detection and its applications.","authors":"Nora Alturayeif, Hamzah Luqman, Moataz Ahmed","doi":"10.1007/s00521-023-08285-7","DOIUrl":"10.1007/s00521-023-08285-7","url":null,"abstract":"Stance detection is an evolving opinion mining research area motivated by the vast increase in the variety and volume of user-generated content. In this regard, considerable research has been recently carried out in the area of stance detection. In this study, we review the different techniques proposed in the literature for stance detection as well as other applications such as rumor veracity detection. Particularly, we conducted a systematic literature review of empirical research on the machine learning (ML) models for stance detection that were published from January 2015 to October 2022. We analyzed 96 primary studies, which spanned eight categories of ML techniques. In this paper, we categorize the analyzed studies according to a taxonomy of six dimensions: approaches, target dependency, applications, modeling, language, and resources. We further classify and analyze the corresponding techniques from each dimension's perspective and highlight their strengths and weaknesses. The analysis reveals that deep learning models that adopt a mechanism of self-attention have been used more frequently than the other approaches. It is worth noting that emerging ML techniques such as few-shot learning and multitask learning have been used extensively for stance detection. A major conclusion of our analysis is that despite that ML models have shown to be promising in this field, the application of these models in the real world is still limited. Our analysis lists challenges and gaps to be addressed in future research. Furthermore, the taxonomy presented can assist researchers in developing and positioning new techniques for stance detection-related applications.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 7","pages":"5113-5144"},"PeriodicalIF":4.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10642878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic detection of the mental state in responses towards relaxation. 对放松反应的心理状态的自动检测。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07435-7

Nagore Sagastibeltza, Asier Salazar-Ramirez, Raquel Martinez, Jose Luis Jodra, Javier Muguerza

{"title":"Automatic detection of the mental state in responses towards relaxation.","authors":"Nagore Sagastibeltza, Asier Salazar-Ramirez, Raquel Martinez, Jose Luis Jodra, Javier Muguerza","doi":"10.1007/s00521-022-07435-7","DOIUrl":"https://doi.org/10.1007/s00521-022-07435-7","url":null,"abstract":"Nowadays, considering society's highly demanding lifestyles, it is important to consider the usefulness of relaxation from the perspective of both psychology and clinical practice. The response towards relaxation (RResp) is a mind-body interaction that relaxes the organism or compensates for the physiological effects caused by stress. This work aims to automatically detect the different mental states (relaxation, rest and stress) in which RResps may occur so that complete feedback about the quality of the relaxation can be given to the subject itself, the psychologist or the doctor. To this end, an experiment was conducted to induce both states of stress and relaxation in a sample of 20 university students (average age of <math><mrow><mn>25.76</mn> <mo>±</mo> <mn>3.7</mn></mrow> </math> years old). The electrocardiographic and electrodermal activity signals collected from the participants produced a dataset with 1641 episodes or instances in which the previously mentioned mental states take place. This data was used to extract up to 50 features and train several supervised learning algorithms (rule-based, trees, probabilistic, ensemble classifiers, etc.) using and not using feature selection techniques. Besides, the authors synthesised the cardiac activity information into a single new feature and discretised it down to three levels. The experimentation revealed which features were most discriminating, reaching a classification average accuracy of up to <math><mrow><mn>94.01</mn> <mo>±</mo> <mn>1.73</mn></mrow> </math> % with the 6 most relevant features for the own-collected dataset. Finally, being restrictive, the same solution/subspace was tested with a dataset referenced in the bibliography (WESAD) and scored an average accuracy of <math><mrow><mn>90.36</mn> <mo>±</mo> <mn>1.62</mn></mrow> </math> %.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 8","pages":"5679-5696"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10868777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis. 基于特征选择的三角算子增强阿基米德优化算法。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07925-8

Imène Neggaz, Nabil Neggaz, Hadria Fizazi

{"title":"Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis.","authors":"Imène Neggaz, Nabil Neggaz, Hadria Fizazi","doi":"10.1007/s00521-022-07925-8","DOIUrl":"https://doi.org/10.1007/s00521-022-07925-8","url":null,"abstract":"Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO).","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 5","pages":"3903-3923"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9569187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10622872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A cyber warfare perspective on risks related to health IoT devices and contact tracing. 关于健康物联网设备和接触者追踪相关风险的网络战视角。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2022-01-20 DOI: 10.1007/s00521-021-06720-1

Andrea Bobbio, Lelio Campanile, Marco Gribaudo, Mauro Iacono, Fiammetta Marulli, Michele Mastroianni

引用次数: 3

Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework. 使用基于深度学习的智能计算框架识别和分类肺炎疾病。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2021-05-20 DOI: 10.1007/s00521-021-06102-7

Rong Yi, Lanying Tang, Yuqiu Tian, Jie Liu, Zhihui Wu

{"title":"Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework.","authors":"Rong Yi, Lanying Tang, Yuqiu Tian, Jie Liu, Zhihui Wu","doi":"10.1007/s00521-021-06102-7","DOIUrl":"10.1007/s00521-021-06102-7","url":null,"abstract":"Pneumonia is one of the hazardous diseases that lead to life insecurity. It needs to be diagnosed at the initial stages to prevent a person from more damage and help them save their lives. Various techniques are used to identify pneumonia, including chest X-ray, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Chest X-ray is the most widely used method to diagnose pneumonia and is considered one of the most reliable approaches. To analyse chest X-ray images accurately, an expert radiologist needs expertise and experience in the desired domain. However, human-assisted approaches have some drawbacks: expert availability, treatment cost, availability of diagnostic tools, etc. Hence, the need for an intelligent and automated system comes into place that operates on chest X-ray images and diagnoses pneumonia. The primary purpose of technology is to develop algorithms and tools that assist humans and make their lives easier. This study proposes a scalable and interpretable deep convolutional neural network (DCNN) to identify pneumonia using chest X-ray images. The proposed modified DCNN model first extracts useful features from the images and then classifies them into normal and pneumonia classes. The proposed system has been trained and tested on chest X-ray images dataset. Various performance metrics have been utilized to inspect the stability and efficacy of the proposed model. The experimental result shows that the proposed model's performance is greater compared to the other state-of-the-art methodologies used to identify pneumonia.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 20","pages":"14473-14486"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s00521-021-06102-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9565919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches. 在预防、检测和服务提供方法中抗击新冠肺炎的计算机辅助方法。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2023-05-05 DOI: 10.1007/s00521-023-08612-y

Bahareh Rezazadeh, Parvaneh Asghari, Amir Masoud Rahmani

{"title":"Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches.","authors":"Bahareh Rezazadeh, Parvaneh Asghari, Amir Masoud Rahmani","doi":"10.1007/s00521-023-08612-y","DOIUrl":"10.1007/s00521-023-08612-y","url":null,"abstract":"The infectious disease Covid-19 has been causing severe social, economic, and human suffering across the globe since 2019. The countries have utilized different strategies in the last few years to combat Covid-19 based on their capabilities, technological infrastructure, and investments. A massive epidemic like this cannot be controlled without an intelligent and automatic health care system. The first reaction to the disease outbreak was lockdown, and researchers focused more on developing methods to diagnose the disease and recognize its behavior. However, as the new lifestyle becomes more normalized, research has shifted to utilizing computer-aided methods to monitor, track, detect, and treat individuals and provide services to citizens. Thus, the Internet of things, based on fog-cloud computing, using artificial intelligence approaches such as machine learning, and deep learning are practical concepts. This article aims to survey computer-based approaches to combat Covid-19 based on prevention, detection, and service provision. Technically and statistically, this article analyzes current methods, categorizes them, presents a technical taxonomy, and explores future and open issues.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 20","pages":"14739-14778"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9576950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Toward reliable machine learning with Congruity: a quality measure based on formal concept analysis. 迈向具有一致性的可靠机器学习:基于形式概念分析的质量度量。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07853-7

Carmen De Maio, Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia, Claudio Stanzione

{"title":"Toward reliable machine learning with Congruity: a quality measure based on formal concept analysis.","authors":"Carmen De Maio, Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia, Claudio Stanzione","doi":"10.1007/s00521-022-07853-7","DOIUrl":"https://doi.org/10.1007/s00521-022-07853-7","url":null,"abstract":"The spreading of machine learning (ML) and deep learning (DL) methods in different and critical application domains, like medicine and healthcare, introduces many opportunities but raises risks and opens ethical issues, mainly attaining to the lack of transparency. This contribution deals with the lack of transparency of ML and DL models focusing on the lack of trust in predictions and decisions generated. In this sense, this paper establishes a measure, namely Congruity, to provide information about the reliability of ML/DL model results. Congruity is defined by the lattice extracted through the formal concept analysis built on the training data. It measures how much the incoming data items are close to the ones used at the training stage of the ML and DL models. The general idea is that the reliability of trained model results is highly correlated with the similarity of input data and the training set. The objective of the paper is to demonstrate the correlation between the Congruity and the well-known Accuracy of the whole ML/DL model. Experimental results reveal that the value of correlation between Congruity and Accuracy of ML model is greater than 80% by varying ML models.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 2","pages":"1899-1913"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9540094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10510179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Novel transfer learning schemes based on Siamese networks and synthetic data. 基于Siamese网络和合成数据的迁移学习新方案。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-08115-2

Philip Kenneweg, Dominik Stallmann, Barbara Hammer

{"title":"Novel transfer learning schemes based on Siamese networks and synthetic data.","authors":"Philip Kenneweg, Dominik Stallmann, Barbara Hammer","doi":"10.1007/s00521-022-08115-2","DOIUrl":"https://doi.org/10.1007/s00521-022-08115-2","url":null,"abstract":"Transfer learning schemes based on deep networks which have been trained on huge image corpora offer state-of-the-art technologies in computer vision. Here, supervised and semi-supervised approaches constitute efficient technologies which work well with comparably small data sets. Yet, such applications are currently restricted to application domains where suitable deep network models are readily available. In this contribution, we address an important application area in the domain of biotechnology, the automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation, where data characteristics are very dissimilar to existing domains and trained deep networks cannot easily be adapted by classical transfer learning. We propose a novel transfer learning scheme which expands a recently introduced Twin-VAE architecture, which is trained on realistic and synthetic data, and we modify its specialized training procedure to the transfer learning domain. In the specific domain, often only few to no labels exist and annotations are costly. We investigate a novel transfer learning strategy, which incorporates a simultaneous retraining on natural and synthetic data using an invariant shared representation as well as suitable target variables, while it learns to handle unseen data from a different microscopy technology. We show the superiority of the variation of our Twin-VAE architecture over the state-of-the-art transfer learning methodology in image processing as well as classical image processing technologies, which persists, even with strongly shortened training times and leads to satisfactory results in this domain. The source code is available at https://github.com/dstallmann/transfer_learning_twinvae, works cross-platform, is open-source and free (MIT licensed) software. We make the data sets available at https://pub.uni-bielefeld.de/record/2960030.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 11","pages":"8423-8436"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9757634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9156452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1