New Generation Computing

  in Journal   Posted on August 27, 2017

Journal Ranking & Metrics

JCR Impact Factor: 0.657
SJR : 0.283
Scopus H-index : 24
Guide2Research Overall Ranking: 600

Journal Information

ISSN: 0288-3635
Publisher :
Periodicity : Quarterly
Journal & Submission Website:

Aims & Scope of the Journal

The journal is specially intended to support the development of new computational paradigms stemming from the cross-fertilization of various research fields. These fields include, but are not limited to, programming (logic, constraint, functional, object-oriented), distributed/parallel computing, knowledge-based systems and agent-oriented systems.

It also encourages theoretical and/or practical papers concerning all types of learning, knowledge discovery, evolutionary mechanisms, and emergent systems that can lead to key technologies enabling us to build more complex and intelligent systems.

The editorial board hopes that New Generation Computing will work as a catalyst among active researchers with broad interests by ensuring a smooth publication process.

Major Fields

Biocomputing: Molecular Robotics, Synthetic Biology, Artificial Cell, DNA Nano-engineering, Molecular Computing, Self-organizing Systems, Modeling and Simulation for Biocomputing, Image Processing and Visualization for Biocomputing, Control Theory and Systems for Biocomputing

Programming and Semantics: Foundations and Models of Computation, Computational Logic, Programming Systems, Declarative Programming, Concurrency and Parallelism, Quantum Computing

Social Computing: Social Media, Web Services, Web Mining, Social Studies, Semantic Web, Crowdsourcing, Social Systems

Cognitive Computing: Modeling Human Problem Solving and Learning, Modeling Human Communication, Interactive Systems, Data-intensive Approach to Cognitive Computing, Integration and Application of Cognitive Computing, Evaluation Methodology

Data Mining: Sequence and Stream Mining, Graph and Network Mining, Relational Data Mining, Data Mining Languages, Data Privacy

Learning: Foundations and Models of Learning, Computational Learning Theory, Inductive Logic Programming, Statistical Learning Methods, Bayesian Networks, Reinforcement Learning, Human Learning, Intelligent Tutoring Systems, Machine Learning with Humans

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