Computational Intelligence and Neuroscience

Lightweight Deep Learning Models for Resource Constrained Devices


Publishing date
01 Dec 2022
Status
Closed
Submission deadline
15 Jul 2022

Lead Editor

1National Institute of Technology Hamirpur, Hamirpur, India

2Central South University, Changsha, China

3South Valley University, Qena, Egypt

This issue is now closed for submissions.

Lightweight Deep Learning Models for Resource Constrained Devices

This issue is now closed for submissions.

Description

With recent advancements in computational intelligence, deep learning has gained increased attention from many artificial intelligence (AI) researchers due to its applicability in various areas, including e-healthcare, autonomous cars, surveillance systems, or remote sensing, among others. Deep learning models have the ability to automatically extract the potential features of the given data and so do not require any kind of hand-crafted features for the model building process. However, deep learning models require high computational power and resources, therefore, these models are not well suited for lightweight devices such as mobiles and the Internet of Things (IoT). Additionally, these models require an efficient tuning of the hyper-parameters.

To overcome these problems, we must optimize the architecture and initial parameters of deep learning models in such a fashion that it can be implemented on light weight devices. However, the optimization of deep learning models is a challenging problem since it may compromise the performance. Therefore, light weight deep learning models should be developed in such a fashion that they take less resources for optimal architecture and at the same time improve performance. To achieve this, researchers have started utilizing metaheuristic techniques to efficiently select the initial parameters of deep learning models. Still, the optimization of deep learning architecture is necessary to further investigate the structural and functional properties of these models for lightweight devices.

This Special Issue will provide a platform for researchers to share cutting-edge solutions in the field and to promote research and development activities in light weight deep learning models for multimodal data by publishing high-quality original research and review articles in this rapidly growing interdisciplinary field.

Potential topics include but are not limited to the following:

  • Lightweight deep learning models
  • Metaheuristics-based deep learning models
  • Hardware for lightweight deep learning models
  • Explainable lightweight deep learning models
  • Lightweight deep reinforcement learning models
  • Lightweight deep generative adversarial models
  • Lightweight explainable machine learning models
  • Lightweight deep recurrent neural networks
  • Lightweight deep transfer learning models
  • Lightweight deep learning models for Internet of Things
  • Lightweight deep learning models for medical devices

Articles

  • Special Issue
  • - Volume 2023
  • - Article ID 9826567
  • - Retraction

Retracted: Application Based on Artificial Intelligence in Substation Operation and Maintenance Management

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9767493
  • - Retraction

Retracted: Film and Video Quality Optimization Using Attention Mechanism-Embedded Lightweight Neural Network Model

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9848931
  • - Retraction

Retracted: Tensor Multi-Clustering Parallel Intelligent Computing Method Based on Tensor Chain Decomposition

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9834646
  • - Retraction

Retracted: Analysis of Agriculture and Food Supply Chain through Blockchain and IoT with Light Weight Cluster Head

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9824891
  • - Retraction

Retracted: Deformation Analysis and Research of Building Envelope by Deep Learning Technology under the Reinforcement of the Diaphragm Wall

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9823840
  • - Retraction

Retracted: Evaluating College Students’ Comprehensive Quality by the AHP Algorithm

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9815829
  • - Retraction

Retracted: Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9845481
  • - Retraction

Retracted: Information Dissemination Model in Rural Live Broadcasting under Blockchain in the Era of Artificial Intelligence

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9806856
  • - Retraction

Retracted: The Use of Internet of Things and Cloud Computing Technology in the Performance Appraisal Management of Innovation Capability of University Scientific Research Team

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9797658
  • - Retraction

Retracted: Virtual Reality Technology of New Media Visual Simulation

Computational Intelligence and Neuroscience

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