[UAI] CFP: Intl Journal of Digital Signal Processing

ijwa Mon, 21 Dec 2020 11:37:20 -0800

International Journal of Digital Signal Processing and Artificial

Intelligence for Automatic Learning (DSPAIAL)

CALL FOR PAPERS

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.

Topics

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:

EDITOR IN CHIEF

Ricardo Rodriguez Jorge, (JEP University, Czech Republic)

MANAGING EDITOR

Pit Pichappan, (Digital Information Research Labs, India & UK)

DSPAIAL(Indexing/Abstracting)

"In process"

IMPORTANT DATES

Paper Submission Due: January 15, 2021

Notification to Authors: March 15, 2021

Camera-Ready Papers Due: May 15, 2021

Please visit: http://dspaial.org/ or contact ricardo.rodriguezjorge...@ieee.org

for Submission information and additional details

• Bayesian and distributed learning

• Smart grids, games, and social networks

• Computational intelligence

• Data-driven adaptive systems

• Data-driven models

• Multimodal data fusion

• Multiset data analysis

• Perceptual signal processing

• Applications (biomedical signals, biometrics,

bioinformatics)

• 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