Welcome to Editorial Manager for
International Journal of Digital Signal Processing and Artificial Intelligence for Automatic Learning              
 

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About the Journal
 

The journal Digital Signal Processing and Artificial Intelligence for Automatic Learning (AL) invites submissions of original contributions. AL usually plays an important role in transitions from data storage to decision support systems based on very large signal databases, such as the data obtained from sensor networks, internet services, or communication systems. These systems stimulate the development of both computational solutions and novel models. Signals from real-world systems, such as speech, music, biomedical, and multimedia systems, are usually quite complex, making digital signal processing very useful for the automation of analytic operations to retrieve information from data storage. 

 

Topics will range from foundations for real-world systems and speech processing to language analysis, biomedicine, convergence and complexity analysis, machine learning, social networks, sparse representations, visual analytics, and robust statistical methods.

 

This journal solicits papers on machine learning approaches for all aspects of digital signal processing and artificial intelligence with regard to AL, including (but not limited to) topics such as:

 
Topics covered include
 
Learning theory
Cognitive information processing
Neural networks
Classification and pattern recognition 
Nonlinear signal processing
Graphical models and kernel methods  
Genomic signals and sequences
Multichannel adaptive signal processing
Kernel methods and graphical models
Sparsity-aware learning
Subspace/manifold learning
Bayesian and distributed learning
Smart grids, games, and social networks
Computational intelligence
Data-driven adaptive systems
Measurement Analysis Techniques
Data-driven models
Multimodal data fusion
Multiset data analysis
Perceptual signal processing
Applications (biomedical signals, biometrics, bioinformatics)