Computational Intelligence and Neuroscience

Recent Developments in Deep Learning for Engineering Applications


Status
Published

1National Technical University of Athens, Athens, Greece

2University of Nevada, Reno, USA

3Imperial College London, London, UK


Recent Developments in Deep Learning for Engineering Applications

Description

Deep learning allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction mimicking how the brain perceives and understands multimodal information, thus implicitly capturing intricate structures of large‐scale data. Deep learning is a rich family of methods, encompassing neural networks, hierarchical probabilistic models, and a variety of unsupervised and supervised feature learning algorithms. The recent surge of interest in deep learning methods is due to the fact that they have been shown to outperform previous state-of-the-art techniques in several tasks, as well as the abundance of complex data from different sources (e.g., visual, audio, medical, social, sensor, etc.).

At the same time, recent advances in machine learning have brought about tremendous development to many areas of interest to the engineering community. Data-driven or domain-oriented engineering applications can significantly benefit or even promote the development of algorithms, optimization approaches, fusion techniques, novel hardware, and network architectures. Great strides have been made, but significant challenges remain, and further insights must be gained into the strengths and limitations of deep learning methods.

This special issue will provide a forum to present new research on deep learning for engineering applications, focusing on state-of-the-art methods, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), deep Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), Long Short-Term Memory (LSTM), autoencoders, and their graphical model, sparse coding, and kernel machine based variants. The special issue aims at original research papers covering new theories, algorithms, systems, and new implementations and applications incorporating deep learning techniques. Review articles works on performance evaluation and benchmark datasets are also solicited.

Potential topics include but are not limited to the following:

  • Computer vision and multimedia analysis: object detection and tracking, activity recognition, anomaly detection, multimedia annotation, classification, and retrieval
  • Robotics and automation: robotic grasping and manipulation, robotic vision, autonomous vehicles navigation, and neurorobotics
  • Remote sensing and civil and geospatial engineering: hyperspectral image classification, mining and construction optimization, seismic data processing, and urban planning
  • Biomedical and neural engineering: medical imaging, neuroimaging (MRI, PET), brain computer interfaces, and bio-, sensor-, and neuroinformatics
  • Online social media and big data analysis: social media analytics, ranking and recommendation, event detection and sentiment analysis, and social cognitive neuroscience applications

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 8141259
  • - Editorial

Recent Developments in Deep Learning for Engineering Applications

Athanasios Voulodimos | Nikolaos Doulamis | ... | Tania Stathaki
  • Special Issue
  • - Volume 2018
  • - Article ID 7068349
  • - Review Article

Deep Learning for Computer Vision: A Brief Review

Athanasios Voulodimos | Nikolaos Doulamis | ... | Eftychios Protopapadakis
  • Special Issue
  • - Volume 2017
  • - Article ID 7186120
  • - Research Article

Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees

Xiaohui Zhao | Yicheng Jiang | Tania Stathaki
  • Special Issue
  • - Volume 2017
  • - Article ID 8348671
  • - Research Article

High Performance Implementation of 3D Convolutional Neural Networks on a GPU

Qiang Lan | Zelong Wang | ... | Yijie Wang
  • Special Issue
  • - Volume 2017
  • - Article ID 8163949
  • - Research Article

Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces

Eduardo Carabez | Miho Sugi | ... | Yasuhiro Wada
  • Special Issue
  • - Volume 2017
  • - Article ID 1583847
  • - Research Article

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT

Xiaohua Nie | Wei Wang | Haoyao Nie
  • Special Issue
  • - Volume 2017
  • - Article ID 4216281
  • - Research Article

Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

Jihyun Kim | Thi-Thu-Huong Le | Howon Kim
  • Special Issue
  • - Volume 2017
  • - Article ID 7643065
  • - Research Article

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

Yuntian Feng | Hongjun Zhang | ... | Gang Chen
  • Special Issue
  • - Volume 2017
  • - Article ID 2917536
  • - Research Article

Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning

Guan Wang | Yu Sun | Jianxin Wang
  • Special Issue
  • - Volume 2017
  • - Article ID 5169675
  • - Research Article

Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification

Jiangfan Feng | Yuanyuan Liu | Lin Wu
Computational Intelligence and Neuroscience
 Journal metrics
Acceptance rate27%
Submission to final decision77 days
Acceptance to publication38 days
CiteScore2.270
Impact Factor2.154
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