Journal Information
Neurocomputing
http://www.journals.elsevier.com/neurocomputing/
Impact Factor:
3.317
Publisher:
ELSEVIER
ISSN:
0925-2312
Viewed:
8702
Tracked:
27

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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 Xin Yao in 2017-11-12
Special Issues
Special Issue on New Trends in Soft Computing for Industrial and Environmental Application
Submission Date: 2017-11-20

Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. This special issue is mainly focus on its industrial and environmental applications. They provide us with the opportunity to use both, our knowledge, and raw data to solve complex problems in a more interesting and promising way. This multidisciplinary research field is in continuous expansion in the artificial intelligence research community. NEURCOMPUTING- SOCO 2017 special issue provides an interesting opportunity to present and discuss the latest theoretical advances and real world applications related to industrial and environmental fields. The list of possible topics includes, but is not limited to, the use of Soft computing techniques and their combination, including always topics under the umbrella of neurocomputing (https://www.elsevier.com/journals/neurocomputing/0925-2312?generatepdf=true), this is neurocomputing theory, practice and applications (i.e. Neural Networks and Learning Systems (and related areas), where up-to-date deep neural networks and machine learning results are particularly welcome) and some others of the following topics: - Evolutionary Computing - Neuro Computing - Probabilistic Computing - Immunological Computing - Hybrid Methods - Causal Models - Case-based Reasoning - Chaos Theory Fuzzy Computing - Intelligent Agents and Agent Theory - Interactive Computational Models The application fields of interest cover, but are not limited to: - Decision Support - Process and System Control - System Identification and Modelling - Optimization - Signal or Image Processing - Vision or Pattern Recognition - Condition Monitoring - Fault Diagnosis - Systems Integration - Internet Tools - Human Machine Interface - Time Series Prediction - Robotics - Motion Control & Power Electronics - Biomedical Engineering - Virtual Reality - Reactive Distributed AI - Telecommunications - Consumer Electronics - Industrial Electronics - Manufacturing Systems - Power and Energy - Data Mining - Data Visualization - Intelligent Information Retrieval - Bio-inspired Systems - Autonomous Reasoning - Intelligent Agents - Applications of novel algorithms in industrial and environmental applications.
Last updated by Dou Sun in 2017-06-14
Special Issue on Advances in Data Representation and Learning for Pattern Analysis
Submission Date: 2017-12-01

With the availability of millions or even billions of social media to people, a lot of new research opportunities and challenges arise for massive data analytics, such as knowledge mining from social media, deep neural network modeling for pattern analysis, transfer learning for heterogeneous media analysis, etc. This special issue will target the most recent advances in data representation and learning algorithms which are important to the research and applications of pattern analysis. It is not difficult to enumerate a large number of successful examples in this research area, e.g., semi-supervised manifold learning has been successfully applied to large scale multimedia retrieval; multiview learning and ensemble algorithms provide attractive solutions for heterogeneous media mining; metric learning, kernel learning and causality reasoning investigate the relationship (spatial or temporal) among different patterns; and deep neural networks produces promising results in many applications. The editors expect to collect a set of recent advances in the related topics, to provide a platform for researchers to exchange their innovative ideas on data representation and learning solutions for pattern analysis, and to bring in interesting utilizations of learning algorithms for particular pattern analysis applications. To summarize, this special issue welcomes a broad range of submissions developing and using data representation and learning techniques for pattern analysis. We are especially interested in 1) theoretical advances as well as algorithm developments in data representation and learning for specific pattern analysis problems, 2) reports of practical applications and system innovations in pattern analysis, and 3) novel data sets as test-beds for new developments, preferably with implemented standard benchmarks. The following list contains topics of interest (but not limited to): - Advances in neural networks and learning systems - Novel deep/broad learning architectures for pattern analysis - Training techniques for deep learning - Neural network modeling and design for pattern analysis - Optimization for deep/broad representation learning - Advances in supervised, semi-supervised and supervised learning with deep/broad architectures - Novel sparse representation and coding for pattern analysis - Deep reinforcement learning algorithms - Deep transfer learning algorithms - Applications of data representation and learning
Last updated by Dou Sun in 2017-09-30
Special Issue on Advances in Parallelism in Artificial Intelligence
Submission Date: 2017-12-30

Artificial intelligence (AI) is a comprehensive area of study consisting of numerous subjects including intelligent search, machine learning, knowledge management, pattern recognition, uncertain management, neural networks, and so forth. With the development of big data and deep learning, AI has become a subject of board and current interest; recent key breakthroughs in information technology especially in computation ability are often related to AI, and becomes a key factor to advance the development of AI. Traditional AI technologies have challenges in processing massive data, large-scale communication as well as collaboration, and collaborative computing of various algorithms. To meet these challenges, parallel computing has been introduced. This Special Issue focuses on all forms of advances in parallel computing in artificial intelligence. The topics relevant to the Special Issue include (but are not limited to) the following topics. - Architecture, Algorithm for Intelligent Computing - Soft Computing, Fuzzy Logic and Artificial Neural Networks - Machine Learning and Artificial Intelligence - New Parallel Methods and Hardware for Natural Computing - Distributed Parallel Systems and Computer Hardware for AI - Fuzzy Theory and Models - Parallel/Distributed Data Mining - Knowledge Management - Databases and Applications - Semi-Structured/Unstructured Data Mining - Uncertainty Data Management
Last updated by Dou Sun in 2017-09-30
Special Issue on Deep Learning Neural Networks: Methods, Systems, and Applications
Submission Date: 2018-03-31

Neural networks (NNs) and deep learning (DL) currently provide the best solutions to many problems in image recognition, speech recognition, natural language processing, control and precision health. NN and DL make the artificial intelligence (AI) much closer to human thinking modes. However, there are many open problems related to DL in NN, e.g.: convergence, learning efficiency, optimality, multi-dimensional learning, on-line adaptation. This requires to create new algorithms and analysis methods. Practical applications both require and stimulate this development. The aim of this special issue of Neurocomputing is to showcase state-of-the-art work in the field of deep learning neural networks including their methods, systems, and applications. Original papers related are welcome. The list of possible topics includes, but is not limited to: l New deep learning algorithms l New neural network architectures for deep learning l Hierarchical deep learning l Multi-dimensional deep learning l Deep learning of spatio-temporal data l On-line deep learning neural networks l Neuromorphic deep learning architectures l Better combinations of existing algorithms and techniques for deep learning l Combining policy learning, value learning, and model-based search l Data-driven deep learning and control l Optimization by deep neural networks l Optimization and optimal decision in games by deep learning l Mathematical analysis of deep learning (regarding convergence, optimality, stability, robustness, adaptability and so on) l Applications of deep learning algorithms, architectures, and systems to robotics, control, data analysis, prediction and forecast, modeling and simulation, precision health, and other.
Last updated by Dou Sun in 2017-11-02
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