Aims, Scope and Objective :
With the regeneration of artificial intelligence architectures and machine learning methodologies, the concept of deep learning has increasingly applied towards a wide variety of disciplines and proved highly successful acrossa massive class of applications. Deep learning is an interdisciplinary area, involves the combination of neural networks and artificial intelligence techniques. In the current scenario, deep learning with neural networks forms an active solution that helps to solve numerous challenges across natural language processing, computer vision, and artificial intelligence. It allows a model to work on its own to discover information, extract features, and perform classification most effectively. Most of the deep learning models work based onConvolutional Neural Networks with multiple layers. Bio-Inspired Computing for deep learning has gained significant consideration from researchers of various backgrounds due to the reason that bio-inspired analysis provides self–learning, adaptive, and most efficient solutions. These algorithms are computationally fast in nature with lesser sensitivity to input parameters. For the past few decades, numerous biologically inspired algorithms have been developed such as particle swarm optimization, ant colony optimization, metaheuristic algorithms, genetic algorithms, and many more. Appropriate use of these techniques across deep learning systems forms the basis of next-generation intelligence optimization algorithms.
In general, bio-inspired algorithms provide optimal solutions for specific problems. These algorithms solve different optimization problems in computer science using observations from naturally inspired insects and animals. The biological behavior of the animals is interpreted into mathematical modules and initial parameters through, which the algorithms are defined and tested. Depending upon the input and output parameters, the performance of the algorithm is evaluated. Many of these algorithms are built using biologically inspired actions such as food searching, natural collection, group movements, and several other natural activities acts as an effective alternative for traditional optimization techniques. On the other hand, the use of these algorithms with advanced techniques such as deep learning can provide excellent performance and optimization measures to practical real-world applications such as big data analysis, Internet of Things, Cyber-Physical Systems (CPS), Cyber Security, etc. However, solving complicated problems with high dimensions and increased uncertainty remains to be an open challenge for researchers and scholars who work in the field of Intelligence computing.
This special issue is specifically formulated with the intent to motivate researchers from various fields to present the novel and optimal solution for deep learning using bio-inspired analysis. The topics of the special issue include but not limited to the following:
- Significance of nature-inspired computing for deep learning
- Bio-inspired computing for neural information processing using deep learning
- Bio-inspired analysis with deep learning for brain informatics
- Deep learning for neuroimaging using bio-inspired computing
- DNA computing with bio-inspired analysis
- Bio-inspired computing in deep learning: architectural elements, methodologies, and future directions
- Performance optimization in real-time deep learning algorithms with bio-inspired computing algorithms
- Novel deep learning architectures using bio-inspired computing methodologies
- Combined effect of neural networks and bio-inspired computing for deep learning applications
- Bio-inspired deep learning algorithms for IoT applications (smart homes, smart buildings, smart transportation, etc.)
- Finding optimal solutions with bio-inspired computing and deep learning for real-time cyber-physical systems
- Bio-inspired analysis with deep learning for IoT data visualization and business intelligence
- Bio-inspired computing through artificial neural networks and deep learning algorithms
- Deep neural networks and bio-inspired computing
Dr. Carlos Enrique Montenegro Marin, Professor, District University Francisco José de Caldas, Bogotá, Colombia, email@example.com
Dr. Paulo Alonso Gaona Garcia, Professor, District University Francisco José de Caldas, Bogotá, Colombia, firstname.lastname@example.org
Dr. Edward Rolando Nuñez Valdez, Professor,University of Oviedo, Oviedo, Spain, email@example.com
Deadline for submissions: October 30, 2020
Deadline for review decisions: January 30, 2021
Deadline for revised version by authors : March 28, 2021
Deadline for 2nd review: May 7, 2021
Final decisions: July 7, 2021
Paper submissions for the special issue should follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines).
All the papers will be peer-reviewed following the NCA Journal reviewing procedures. Authors should select ‘SI: Bio-Inspired Computing for DLA’ during the submission step ‘Additional Information’.
The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended to be considered for this special issue.
Guest editors will make an initial determination of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance and contributions, as well as their suitability to the special issue. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review and the authors will be promptly informed in such cases.
A Peer Review procedure will follow in order to perform an objective and robust review of all the manuscripts. Every manuscript will be sent to at least 3 international reviewers, with recognized experience in the field.