Signal Processing and Machine Learning II

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1528

Special Issue Editors


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Guest Editor
Faculty of Electronic Engineering, University of Nis, 18106 Nis, Serbia
Interests: digital telecommunication; quantization; compression; machine learning; coding
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Guest Editor
BERG Faculty, Technical University of Kosice, 040 01 Kosice, Slovakia
Interests: fractional-order differential equations; fractional calculus; applied mathematics; control theory; mathematical modeling
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Guest Editor
Bioinformatics Platform, Luxembourg Institute of Health, 1445 Strassen, Luxembourg
Interests: speech processing; vocal biomarkers; machine learning; medical image processing
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Guest Editor
Faculty of Natural Science and Mathematics, University of Pristina, 10000 Pristina, Serbia
Interests: statistics; probability; electronics and communication engineering; designing; statistical analysis; digital signal processing
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Special Issue Information

Dear Colleagues,

The problems in signal processing and machine learning fields are similar or related, and the development of modern technology relies on research in these fields. A number of methods and theories have been developed which aim to solve various problems, including in speech and speaker recognition, the classification of signals (image, speech, audio, biomedical signals), emotion recognition and sentiment analysis, signal quality enhancement (filtering and other algorithms), denoising and detection of signals in the presence of noise, and pattern recognition in signals (speech, image, ECG, and other medical signals), with applications in networks, communications, predictive maintenance, as well as business predictions.

One of the goals of signal processing in real time is to reduce the amount of data required to provide a high quality of representation with a reduction in signal. Statistical data processing, statistical signal processing, as well as methods and algorithms which deal with signal reduction all support achievement of this goal.

This Special Issue aims not only to present articles involving the application of methods and algorithms for signal processing and learning but also to promote development in these two fields—both independently and in combination.

This Special Issue will include original research in signal processing, machine learning, and information processing.

Potential topics include, but are not limited to, the following areas of study:

  • Parametric estimation in signal and probability density function models of signal source
  • Methods in speech recognition and text to speech synthesis
  • Speaker identification
  • Emotion recognition, sentiment analysis, and face recognition
  • Conversational agents (chatbots)
  • Natural language processing
  • Adaptive signal processing
  • Signal processing and learning representation
  • Linear and nonlinear regression and data mining
  • Machine learning
  • Signal extraction and quality enhancement using filtering techniques
  • Classification and quantization
  • Estimation of statistical parameters in processing of signals
  • Deep learning methods
  • Quantization in neural networks
  • Learning and quantization
  • Methods of signal compression and learning
  • Autoregressive processing
  • Signals, time series, and prediction
  • Signal processing and machine learning

Prof. Dr. Zoran H. Perić
Dr. Tomas Skovranek
Prof. Dr. Vlado Delić
Dr. Vladimir Despotovic
Dr. Stefan Panic
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. Information 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 (1 paper)

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Research

17 pages, 900 KiB  
Article
Two-Stage Convolutional Neural Network for Classification of Movement Patterns in Tremor Patients
by Patricia Weede, Piotr Dariusz Smietana, Gregor Kuhlenbäumer, Günther Deuschl and Gerhard Schmidt
Information 2024, 15(4), 231; https://doi.org/10.3390/info15040231 - 18 Apr 2024
Viewed by 930
Abstract
Accurate tremor classification is crucial for effective patient management and treatment. However, clinical diagnoses are often hindered by misdiagnoses, necessitating the development of robust technical methods. Here, we present a two-stage convolutional neural network (CNN)-based system for classifying physiological tremor, essential tremor (ET), [...] Read more.
Accurate tremor classification is crucial for effective patient management and treatment. However, clinical diagnoses are often hindered by misdiagnoses, necessitating the development of robust technical methods. Here, we present a two-stage convolutional neural network (CNN)-based system for classifying physiological tremor, essential tremor (ET), and Parkinson’s disease (PD) tremor. Employing acceleration signals from the hands of 408 patients, our system utilizes both medically motivated signal features and (nearly) raw data (by means of spectrograms) as system inputs. Our model employs a hybrid approach of data-based and feature-based methods to leverage the strengths of both while mitigating their weaknesses. By incorporating various data augmentation techniques for model training, we achieved an overall accuracy of 88.12%. This promising approach demonstrates improved accuracy in discriminating between the three tremor types, paving the way for more precise tremor diagnosis and enhanced patient care. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning II)
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