Journal Information
Impact Factor:
Call For Papers
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.
Last updated by Dou Sun in 2021-03-07
Special Issues
Special Issue on Deep Neural Networks with Cloud Computing
Submission Date: 2022-03-15

To perform high performance computing in terms of speed and memory, training and executing DNNs are increasingly performed using cloud platforms. Training with reasonable time can be performed in cloud machine learning platforms such as AWS Deep Learning and Google Colab, which have huge computing and memory resources. Executions at high speeds can also be performed by implementing the DNNs in the cloud with lower latency networks. Edge computing is necessary, as we need fast DNN execution time on the edge to have a quick responses from the DNNs. Integration of deep learning and cloud computing is a growing and popular area currently. A cloud based DNN can be developed if the cloud limitations are overcome and advantages of cloud resources are effectively used to train or execute the DNNs. This special issue aims to solicit original papers describing innovative techniques that develop DNNs based on cloud computing. Researchers are welcome to submit research, technical, review, survey, or vision articles which contribute on the algorithmic development, implementations, or real applications of integrations of DNNs and cloud computing. Topics include, but are not limited to: ● Deep learning approaches using cloud computing services ● Low-power DNN engines in the cloud ● Energy efficiency of implementing DNNs in the cloud ● Implementation of DNNs using mobile cloud computing ● Parallel computing to train DNNs using cloud services ● Optimization of computational resources for training DNNs in the cloud ● Low-cost and powerful GPUs in cloud for training DNNs ● Memory allocations in cloud for deep learning ● Cloud data streaming for deep learning ● High resolution video processing using DNNs in the cloud ● Real-time metrics for measuring DNN performance, accuracy, and complexity in the cloud ● Training data privacy such as medical data, finance data in the cloud ● Tools and libraries in the cloud for deep learning ● Optimization of DNN configurations and architectures in the cloud ● Implementation of hybrid DNNs/compressed DNNs in the cloud ● Evolutionary computation for DNNs using the cloud ● DNNs for Knowledge Graph Embeddings using the cloud ● Implementing deep learning in the cloud for big data analysis such as business intelligence, e-commences, bioinformatics, weather forecasting, smart surveillance, traffic flow forecasting, autonomous driving, climate change forecasting, etc.
Last updated by Dou Sun in 2021-12-20
Related Journals
CCFFull NameImpact FactorPublisherISSN
cThe Journal of Supercomputing0.858Springer0920-8542
cNatural Language Engineering0.432Cambridge University Press1351-3249
bComputer Supported Cooperative Work1.305Springer0925-9724
International Journal of ComputingResearch Institute of Intelligent Computer Systems1727-6209
bIEEE Transactions on Services Computing5.823IEEE1939-1374
bNeural Computation1.884MIT Press0899-7667
Central European Journal of Computer Science Springer1896-1533
Memetic Computing2.674Springer1865-9284
cJournal of Grid Computing1.561Springer1570-7873
Full NameImpact FactorPublisher
The Journal of Supercomputing0.858Springer
Natural Language Engineering0.432Cambridge University Press
Computer Supported Cooperative Work1.305Springer
International Journal of ComputingResearch Institute of Intelligent Computer Systems
IEEE Transactions on Services Computing5.823IEEE
Neural Computation1.884MIT Press
Central European Journal of Computer Science Springer
Memetic Computing2.674Springer
Journal of Grid Computing1.561Springer
Related Conferences
CCFCOREQUALISShortFull NameSubmissionNotificationConference
cb3CGIVInternational Conference on Computer Graphics, Imaging and Visualization0000-00-002013-05-222013-08-06
COITInternational Conference on Computing and Information Technology2022-01-232022-02-102022-02-19
cab1SCCInternational Conference on Services Computing2022-03-012022-04-152022-07-10
CIMSimInternational Conference on Computational Intelligence, Modelling and Simulation2018-08-15 2018-09-18
Feedback ComputingInternational Workshop on Feedback Computing 2013-04-262013-06-25
ba2ICACInternational Conference on Autonomic Computing2019-02-222019-04-082019-06-16
cb2Hot InterconnectsSymposium on High-Performance Interconnects2021-05-212021-06-212021-08-18
ICCBNInternational Conference on Communications and Broadband Networking2021-09-012021-09-252022-02-25
GMEEInternational Conference on Green Materials and Environmental Engineering2020-12-28 2021-02-02
PPSIAM Conference on Parallel Processing for Scientific Computing2021-10-01 2022-02-23