仕訳帳情報
Neurocomputing
http://www.journals.elsevier.com/neurocomputing/
インパクト ・ ファクター:
4.072
出版社:
ELSEVIER
ISSN:
0925-2312
閲覧:
20452
追跡:
107

サプライズ
論文募集
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.

Neurocomputing welcomes theoretical contributions aimed at winning further understanding of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition.

Neurocomputing covers practical aspects with contributions on advances in hardware and software development environments for neurocomputing, including, but not restricted to, simulation software environments, emulation hardware architectures, models of concurrent computation, neurocomputers, and neurochips (digital, analog, optical, and biodevices).

Neurocomputing reports on applications in different fields, including, but not restricted to, signal processing, speech processing, image processing, computer vision, control, robotics, optimization, scheduling, resource allocation and financial forecasting.

Neurocomputing publishes reviews of literature about neurocomputing and affine fields.

Neurocomputing reports on meetings, including, but not restricted to, conferences, workshops and seminars.

Neurocomputing reports on functionality/availability of software, on comparative assessments, and on discussions of neurocomputing software issues.

Now also including: Neurocomputing Letters - for the rapid publication of special short communications.
最終更新 Dou Sun 2019-11-24
Special Issues
Special Issue on Deep Neural Networks for Precision Medicine
提出日: 2020-02-01

Complex diseases are often classified into many subtypes that may require different treatment regiments. The precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability of patients. This approach assists medical doctors and technicians in accurate prediction of treatment, prevention and prognosis strategies that would work best for a particular disease, a specific patient or a group of patients. This is in contrast to more traditional approaches where the treatment and prevention strategies are developed for large and other heterogeneous populations of patients, with little attention to the differences between individuals. Recent advances in the high throughput biotechnologies resulted in the creation of massive omics datasets (e.g., genomics, proteomics, transcriptomics, metabolomics), medical imaging datasets, clinical datasets, electronic medical records, and others. These data coming from patients having the same disease are often heterogeneous and provide unparalleled levels of insightful information that can be used to develop accurate methods for precision medicine. Moreover, integration of these multi-modal data is seen as a feasible approach to improve accuracy of these methods. However, the development of accurate methods for precision medicine is very challenging, as it requires design of novel and sophisticated computational tools. Recently deep neural networks have been showing promise as the tools that offer several advantages in this context. They are capable to extracting useful end-to-end data and knowledge representations, benefitting from the availability of the very large datasets. The deep neural networks integrate multiple network layers (e.g., convolution layer, maximum pooling layer, etc.) and/or network blocks (residual block, dense block, etc.) to provide accurate predictive performance when trained from big and multi-modal data. They were applied to develop numerous tools for precision medicine that span multiple application areas including processing of omics data, image analysis, and text classification. This special issue calls for high quality, state-of-the-art research results related to the analysis and prediction of the precision medicine-related data that relies on deep neural networks. The specific topics include, but are not limited to: Deep neural networks for next-generation sequencing data analysis We encourage submission of articles that present novel methodologies as well as review/survey/vision papers on the above topics. The editors will actively seek to invite expert authors to submit the latter types of articles.
最終更新 Dou Sun 2019-12-08
Special Issue on Deep Learning with Small Samples
提出日: 2020-04-15

In machine learning and computer vision fields, due to the rapid development of deep learning, recent years have witnessed breakthroughs for large-sample classification tasks. However, it remains a persistent challenge to learn a deep neural network with good generalizability from only a small number of training samples. In fact, humans can easily learn the concept of a class from a small amount of data rather than from millions of data. Moreover, many types of real-world data are small in quantity and are expensive to collect and label. Motivated by this fact, research on deep learning with small samples becomes more and more prevalent in the communities of machine learning and computer vision, for example, researches focusing on one-shot classification, few-shot classification, as well as classification with small training samples. Recently, deep small-sample learning has achieved promising performance in certain small-sample problems, by transferring the "Knowledge" learned from other datasets containing rich labelled data or generating synthetic samples to approximate the distribution of real data. However, many challenging topics remain with small-sample deep leaning techniques, such as data augmentation, feature learning, prior construction, meta-learning, fine tuning, etc. Therefore, the goal of this special issue is to collect and publish the latest developments in various aspects of deep learning with small samples. The list of possible topics includes, but is not limited to: Survey/vision/review of deep learning with small samples Data augmentation methods for small-sample leaning Feature learning based methods small-sample leaning Regularization technology of deep model in small-sample leaning Ensemble learning based methods for small-sample learning Transfer learning methods for small-sample learning Semi-supervised learning methods for small-sample learning Prior based methods for few-shot learning Meta-leaning based methods for few-shot learning Fine-tuning based methods for small-sample learning Theoretical analysis for small-sample learning Applications of small-sample learning on person re-identification, object recognition, etc.
最終更新 Dou Sun 2019-09-14
関連仕訳帳
CCF完全な名前インパクト ・ ファクター出版社ISSN
cThe Journal of Supercomputing0.858Springer0920-8542
cNatural Language Engineering0.432Cambridge University Press1351-3249
bComputer Supported Cooperative Work1.305Springer0925-9724
bNeural Computation1.884MIT Press0899-7667
bIEEE Transactions on Services Computing4.418IEEE1939-1374
Central European Journal of Computer Science Springer1896-1533
International Journal on Soft Computing AIRCC2229-7103
cJournal of Grid Computing1.561Springer1570-7873
Neural Computing and Applications1.492Springer0941-0643
完全な名前インパクト ・ ファクター出版社
The Journal of Supercomputing0.858Springer
Natural Language Engineering0.432Cambridge University Press
Computer Supported Cooperative Work1.305Springer
Neural Computation1.884MIT Press
IEEE Transactions on Services Computing4.418IEEE
Central European Journal of Computer Science Springer
International Journal on Soft Computing AIRCC
Journal of Grid Computing1.561Springer
Neural Computing and Applications1.492Springer
関連会議
CCFCOREQUALIS省略名完全な名前提出日通知日会議日
bETAPSEuropean Joint Conferences on Theory and Practice of Software2019-10-242019-12-232020-04-25
cab1SCCInternational Conference on Service Computing2019-12-062020-03-012020-06-22
cb2Hot InterconnectsSymposium on High-Performance Interconnects2019-05-102019-05-312019-08-14
b1ICEC'International Conference on Electronic Commerce2013-03-162013-04-242013-08-13
ICNMEInternational Conference on Nanomaterials and Materials Engineering2019-09-292019-10-102019-10-25
ba2ICACInternational Conference on Autonomic Computing2019-02-222019-04-082019-06-16
BEPInternational Conference on Biological Engineering and Pharmacy2016-11-18 2016-12-09
CSITYInternational Conference on Computer Science, Engineering and Information Technology2018-07-022018-07-102018-07-28
MicroservicesInternational Conference on Microservices2018-11-302019-01-182019-02-19
ACMSEACM Southeast Conference2013-12-022014-01-242014-03-28
省略名完全な名前提出日会議日
ETAPSEuropean Joint Conferences on Theory and Practice of Software2019-10-242020-04-25
SCCInternational Conference on Service Computing2019-12-062020-06-22
Hot InterconnectsSymposium on High-Performance Interconnects2019-05-102019-08-14
ICEC'International Conference on Electronic Commerce2013-03-162013-08-13
ICNMEInternational Conference on Nanomaterials and Materials Engineering2019-09-292019-10-25
ICACInternational Conference on Autonomic Computing2019-02-222019-06-16
BEPInternational Conference on Biological Engineering and Pharmacy2016-11-182016-12-09
CSITYInternational Conference on Computer Science, Engineering and Information Technology2018-07-022018-07-28
MicroservicesInternational Conference on Microservices2018-11-302019-02-19
ACMSEACM Southeast Conference2013-12-022014-03-28
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