Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.6
Journals
Technologies
Special Issues
Selected Papers from ICNC-FSKD 2023 Conference
share announcement

Selected Papers from ICNC-FSKD 2023 Conference

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 2393

Special Issue Editors

Dr. Lipo Wang

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering Nanyang Technological University Block S1, Nanyang Avenue, Singapore 639798, Singapore
Interests: machine learning; data mining; optimization; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liang Zhao

E-Mail Website
Guest Editor
CS Department, ICMC, University of Sao Paulo, Sao Carlos 05508-070, Brazil
Interests: artificial neural networks; nonlinear dynamical systems for information processing; complex networks; bioinformatics; pattern recognition and digital image processing

Special Issue Information

Dear Colleagues,

The 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2023) was successfully held from 29 to 31 July 2023 in Harbin, China. This is  an established conference on machine learning and its application in a variety of areas. This Special Issue comprises extended versions of a number of the best submissions to the conference. All papers will be re-reviewed.http://icnc2023.hrbust.edu.cn/  We warmly welcome the submission of papers addressing topics including, but not limited to, the following:

  • Neural Computation: deep learning; recurrent neural networks support vector machines and statistical neural network models; unsupervised and semi-supervised learning neural networks.
  • Evolutionary Computation: genetic and evolutionary algorithms; particle swarm optimization and ant colony optimization; multi-objective optimization; artificial life and artificial immune systems; intelligent agents; quantum computing; molecular, DNA, membrane, and cultural computing.
  • Fuzzy Theory and Algorithms: fuzzy theory and models; rough set; granular computation; uncertainty management.
  • Natural Computation Applications: data analysis; image, speech and signal processing; vision and multimedia; control and robotics, decision and support.
  • Knowledge Discovery Foundations: association rules; classification; clustering; privacy preserving data mining; statistical methods for data mining; parallel/distributed data mining; knowledge management; machine learning and artificial intelligence.
  • Knowledge Discovery in Specific Domains: big data, multimedia, text, web and the internet, graphic model discovery, software warehouse and software mining, bioinformatics, financial engineering.
  • Information Technology for Knowledge Discovery: data engineering; signal processing and multimedia; communications and networking; software engineering; automation, robotics, and control; distributed systems and computer hardware.

Dr. Lipo Wang
Prof. Dr. Liang Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neural computation
  • evolutionary computation
  • fuzzy theory and algorithms
  • natural computation applications
  • knowledge discovery foundations
  • knowledge discovery in specific domains
  • information technology for knowledge discovery

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 2838 KiB  
Article
Neonatal Hypoxic-Ischemic Encephalopathy Grading from Multi-Channel EEG Time-Series Data Using a Fully Convolutional Neural Network
by Shuwen Yu, William P. Marnane, Geraldine B. Boylan and Gordon Lightbody
Technologies 2023, 11(6), 151; https://doi.org/10.3390/technologies11060151 - 25 Oct 2023
Viewed by 2168
Abstract
A deep learning classifier is proposed for grading hypoxic-ischemic encephalopathy (HIE) in neonates. Rather than using handcrafted features, this architecture can be fed with raw EEG. Fully convolutional layers were adopted both in the feature extraction and classification blocks, which makes this architecture [...] Read more.
A deep learning classifier is proposed for grading hypoxic-ischemic encephalopathy (HIE) in neonates. Rather than using handcrafted features, this architecture can be fed with raw EEG. Fully convolutional layers were adopted both in the feature extraction and classification blocks, which makes this architecture simpler, and deeper, but with fewer parameters. Here, two large (335 h and 338 h, respectively) multi-center neonatal continuous EEG datasets were used for training and testing. The model was trained based on weak labels and channel independence. A majority vote method was used for the post-processing of the classifier results (across time and channels) to increase the robustness of the prediction. A dimension reduction tool, UMAP, was used to visualize the model classification effect. The proposed system achieved an accuracy of 86.09% (95% confidence interval: 82.41–89.78%), an MCC of 0.7691, and an AUC of 86.23% on the large unseen test set. Two convolutional neural network architectures which utilized time-frequency distribution features were selected as the baseline as they had been developed or tested on the same datasets. A relative improvement of 23.65% in test accuracy was obtained as compared with the best baseline. In addition, if only one channel was available, the test accuracy was only reduced by 2.63–5.91% compared with making decisions based on the eight channels. Full article
(This article belongs to the Special Issue Selected Papers from ICNC-FSKD 2023 Conference)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop