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Data
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Signal Processing for Data Mining
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Signal Processing for Data Mining

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (22 November 2023) | Viewed by 5683

Special Issue Editors

Dr. Kele Xu

E-Mail Website
Guest Editor
National Key Laboratory of Parallel and Distributed Processing (PDL), Changsha, China
Interests: audio signal processing: audio classification, sound event detection
Dr. Lingbin Zeng

E-Mail Website
Guest Editor
School of Computer, National University of Defense Technology, Changsha, China
Interests: fMRI brain connectivity; cognitive neuroscience; functional connectivity

Special Issue Information

Dear Colleagues,

In the era of data explosion, a large amount of heterogeneous data is rapidly accumulating and the potential value hidden in data is getting higher and higher. Correspondingly, data mining for different data is becoming more difficult. In the past decade, data analysis and mining methods based on deep neural networks have effectively promoted the progress of speech signal processing, image recognition and understanding, multimodal cognitive decision-making and other fields. However, deep neural networks also encounter various challenges in the specific application of signal processing and data mining, such as noise in signals and data annotations, heterogeneous data sources, limitations of computing resources, etc.

This Special Issue entitled "Signal Processing for Data Mining " mainly focuses on the intersectional research of signal processing methods and data mining and explores the use of signal processing techniques to achieve effective data analysis and mining. Specifically, it includes the application of new signal analysis theory, algorithms, performance analysis, and techniques aimed at improving the processing, understanding, learning, retrieval, mining, and extraction of information in signals. We seek research articles with novel results or comprehensive reviews, which will provide a comprehensive update of the field. We encourage the submission of comprehensive studies, reviews, corresponding applications and highly rated manuscripts covering the following topics. Topics of interest in this Special Issue include, but are not limited to:

  • Theoretical research on signal processing
  • Data Mining Algorithms
  • Multimodal signal processing
  • Applications of signal processing
  • Biologically inspired signal processing methods
  • Medical signal processing and mining
  • Sound signal processing and mining
  • Medical image analysis and interpretation
  • Wireless sensor network analysis

Dr. Kele Xu
Dr. Lingbin Zeng
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. Data 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.

Published Papers (2 papers)

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Research

27 pages, 604 KiB  
Article
Deep Learning-Based Black Spot Identification on Greek Road Networks
by Ioannis Karamanlis, Alexandros Kokkalis, Vassilios Profillidis, George Botzoris, Chairi Kiourt, Vasileios Sevetlidis and George Pavlidis
Data 2023, 8(6), 110; https://doi.org/10.3390/data8060110 - 16 Jun 2023
Cited by 1 | Viewed by 3384
Abstract
Black spot identification, a spatiotemporal phenomenon, involves analysing the geographical location and time-based occurrence of road accidents. Typically, this analysis examines specific locations on road networks during set time periods to pinpoint areas with a higher concentration of accidents, known as black spots. [...] Read more.
Black spot identification, a spatiotemporal phenomenon, involves analysing the geographical location and time-based occurrence of road accidents. Typically, this analysis examines specific locations on road networks during set time periods to pinpoint areas with a higher concentration of accidents, known as black spots. By evaluating these problem areas, researchers can uncover the underlying causes and reasons for increased collision rates, such as road design, traffic volume, driver behaviour, weather, and infrastructure. However, challenges in identifying black spots include limited data availability, data quality, and assessing contributing factors. Additionally, evolving road design, infrastructure, and vehicle safety technology can affect black spot analysis and determination. This study focused on traffic accidents in Greek road networks to recognize black spots, utilizing data from police and government-issued car crash reports. The study produced a publicly available dataset called Black Spots of North Greece (BSNG) and a highly accurate identification method. Full article
(This article belongs to the Special Issue Signal Processing for Data Mining)
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13 pages, 1462 KiB  
Article
A Fast Deep Learning ECG Sex Identifier Based on Wavelet RGB Image Classification
by Jose-Luis Cabra Lopez, Carlos Parra and Gonzalo Forero
Data 2023, 8(6), 97; https://doi.org/10.3390/data8060097 - 29 May 2023
Cited by 1 | Viewed by 1759
Abstract
Human sex recognition with electrocardiogram signals is an emerging area in machine learning, mostly oriented toward neural network approaches. It might be the beginning of a field of heart behavior analysis focused on sex. However, a person’s heartbeat changes during daily activities, which [...] Read more.
Human sex recognition with electrocardiogram signals is an emerging area in machine learning, mostly oriented toward neural network approaches. It might be the beginning of a field of heart behavior analysis focused on sex. However, a person’s heartbeat changes during daily activities, which could compromise the classification. In this paper, with the intention of capturing heartbeat dynamics, we divided the heart rate into different intervals, creating a specialized identification model for each interval. The sexual differentiation for each model was performed with a deep convolutional neural network from images that represented the RGB wavelet transformation of ECG pseudo-orthogonal X, Y, and Z signals, using sufficient samples to train the network. Our database included 202 people, with a female-to-male population ratio of 49.5–50.5% and an observation period of 24 h per person. As our main goal, we looked for periods of time during which the classification rate of sex recognition was higher and the process was faster; in fact, we identified intervals in which only one heartbeat was required. We found that for each heart rate interval, the best accuracy score varied depending on the number of heartbeats collected. Furthermore, our findings indicated that as the heart rate increased, fewer heartbeats were needed for analysis. On average, our proposed model reached an accuracy of 94.82% ± 1.96%. The findings of this investigation provide a heartbeat acquisition procedure for ECG sex recognition systems. In addition, our results encourage future research to include sex as a soft biometric characteristic in person identification scenarios and for cardiology studies, in which the detection of specific male or female anomalies could help autonomous learning machines move toward specialized health applications. Full article
(This article belongs to the Special Issue Signal Processing for Data Mining)
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