Computational Intelligence and Neuroscience

Modeling and Analysis of Data-Driven Systems through Computational Neuroscience


Publishing date
01 Mar 2021
Status
Closed
Submission deadline
16 Oct 2020

Lead Editor

1Texas Tech University, Lubbock, USA

2University of Manitoba, Manitoba, Canada

3Nanzan University, Nagoya, Japan

This issue is now closed for submissions.
More articles will be published in the near future.

Modeling and Analysis of Data-Driven Systems through Computational Neuroscience

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Artificial intelligence(AI)based application systems are becoming the mainstream in the software industry and application domains such as computational neuroscience, bioinformatics, and healthcare. Recent advances in big data generation and management have created an avenue for decision makers to utilize these huge volumes of data for different purposes and analyses. AI-based application developers have long utilized conventional machine learning techniques to design better user interfaces and vulnerability predictions. However, with the advancement of deep learning-based and neural-based networks and algorithms, researchers are able to explore and learn more about data and their exposed relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioral modeling. As such, computational neural networks algorithms can be refined to address problems in data-driven applications.

This Special Issue aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behavior of applications. Original research and review articles that consider how to model and build data-driven applications using computational neuroscience are encouraged. More specifically, it is of interest to learn how neuron networks can represent data-driven applications and their behaviors in order to extract key features. The Special Issue will enable researchers from academia and industry to share innovative applications and creative solutions to common problems when modeling neural-based applications in computational neuroscience and neuroengineering.

Potential topics include but are not limited to the following:

  • Neural programming and coding in data-driven systems using machine and deep learning
  • Attention and learning of data-driven systems using neural memory networks
  • Neural-based mathematical formulation of information and data using machine and deep learning
  • Human perception, cognition, and decision making through neural networks
  • Human activity recognition through deep neural modeling
  • Modeling and prediction of human behavior using computational neuroscience techniques
  • Modeling human-computer interactions through neural networks
  • Modeling humanoid intelligent bots and mimicking human behavior using computational neuroscience techniques

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 6680833
  • - Research Article

A Fast Spatial Pool Learning Algorithm of Hierarchical Temporal Memory Based on Minicolumn’s Self-Nomination

Lei Li | Tingting Zou | ... | Yuquan Zhu
  • Special Issue
  • - Volume 2021
  • - Article ID 6678355
  • - Research Article

Predicting Slurry Pressure Balance with a Long Short-Term Memory Recurrent Neural Network in Difficult Ground Condition

Qiang Wang | Xiongyao Xie | ... | Michael A Mooney
  • Special Issue
  • - Volume 2021
  • - Article ID 8820116
  • - Research Article

Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

Nhat-Duc Hoang
  • Special Issue
  • - Volume 2021
  • - Article ID 6699335
  • - Research Article

A Data-Driven and Biologically Inspired Preprocessing Scheme to Improve Visual Object Recognition

Zahra Sadat Shariatmadar | Karim Faez
  • Special Issue
  • - Volume 2020
  • - Article ID 8859407
  • - Research Article

Continuous Similarity Learning with Shared Neural Semantic Representation for Joint Event Detection and Evolution

Pengpeng Zhou | Yao Luo | ... | Bin Wu
  • Special Issue
  • - Volume 2020
  • - Article ID 8812370
  • - Research Article

Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research

Qian Gao | Pengcheng Ma
  • Special Issue
  • - Volume 2020
  • - Article ID 8859452
  • - Research Article

Mixed-Level Neural Machine Translation

Thien Nguyen | Huu Nguyen | Phuoc Tran
Computational Intelligence and Neuroscience
 Journal metrics
Acceptance rate28%
Submission to final decision79 days
Acceptance to publication37 days
CiteScore4.700
Impact Factor2.284
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