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Electronics
Special Issues
AI-Driven Bioinformatics: Emerging Trends and Technologies
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AI-Driven Bioinformatics: Emerging Trends and Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (15 March 2025) | Viewed by 2065

Special Issue Editor

Dr. Abdur Rasool

E-Mail Website
Guest Editor
Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Interests: AI in healthcare; bioinformatics; autism detection; DNA data storage; deep learning; machine learning; data mining; and NLP

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is pivotal in advancing bioinformatics by providing innovative solutions for the analysis and interpretation of complex biological data. This Special Issue focuses on the emerging trends and technologies in AI-driven bioinformatics, highlighting the latest research and developments in this interdisciplinary field, where AI techniques, including machine learning, deep learning, natural language processing, and evolutionary algorithms, are currently being utilized to address various challenges.

We welcome contributions focusing on, but not limited to, the following areas:

  • AI and Computer Vision in Autism Spectrum Disorder (ASD): Exploring the use of AI and computer vision techniques to analyze behavioural data, identify patterns, and improve diagnosis and intervention strategies for ASD.
  • Biomedical Text Mining: AI-driven approaches for extracting insights from the biomedical literature, including sentiment analysis and semantic annotation.
  • AI in DNA Data Storage: Innovative AI techniques for encoding, decoding, and optimizing DNA-based data storage systems.
  • AI in Medical Imaging and Video Segmentation: AI-based methods for medical image analysis and video segmentation.
  • Evolutionary Algorithms in Bioinformatics: Application of evolutionary algorithms for optimizing bioinformatic processes, including the design of robust codes for DNA data storage.

We look forward to receiving your contributions.

Dr. Abdur Rasool
Guest Editor

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • artificial intelligence
  • bioinformatics
  • machine learning
  • deep learning
  • DNA data storage
  • medical imaging
  • evolutionary algorithms

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

15 pages, 3573 KiB  
Article
Electrocardiogram-Based Driver Authentication Using Autocorrelation and Convolutional Neural Network Techniques
by Giwon Ku, Choeljun Choi, Chulseung Yang, Jiseong Jeong, Pilkyo Kim, Sangyong Park, Taekeon Jung and Jinsul Kim
Electronics 2024, 13(24), 4974; https://doi.org/10.3390/electronics13244974 - 17 Dec 2024
Viewed by 721
Abstract
This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the [...] Read more.
This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the unique characteristics and forgery resistance of ECG signals. The proposed system is designed using autocorrelation profiles (ACPs) and a convolutional neural network and is optimized for real-time processing even in constrained hardware environments. Additionally, advanced signal processing algorithms were applied to refine the ECG data and minimize noise in driving environments. The system’s performance was evaluated using a public dataset of 154 participants and a real-world dataset of 10 participants, achieving F1-Scores of 96.8% and 96.02%, respectively. Furthermore, an ablation study was conducted to analyze the importance of components such as ACPs, normalization, and filtering. When all components were removed, the F1-Score decreased to 60.1%, demonstrating the critical role of each component. These findings highlight the potential of the proposed system to deliver high accuracy and efficiency not only in vehicle environments but also in various security applications. Full article
(This article belongs to the Special Issue AI-Driven Bioinformatics: Emerging Trends and Technologies)
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