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Computation
Special Issues
Signal Processing and Machine Learning in Data Science
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Signal Processing and Machine Learning in Data Science

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 4158

Special Issue Editors

Dr. Maria Trigka

E-Mail Website
Guest Editor
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece
Interests: 5G; 6G; artificial intelligence; deep learning; image processing; IoT; machine learning; MIMO; mmWave; signal processing; wireless communications
Special Issues, Collections and Topics in MDPI journals
Dr. Elias Dritsas

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, School of Engineering, University of Patras, 26504 Patras, Greece
Interests: artificial intelligence; big data; data analysis; databases; data mining; data structures; machine learning; privacy; security; trust
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data science is a field of study that focuses on the extraction of valuable information from noisy data, and incorporates various disciplines, such as data engineering, data preprocessing, visualization, predictive analytics, data mining, machine learning and statistics. In recent years, there has been rapidly growing interest in the mathematical and theoretical aspects of data science. This manifests in deterministic and non-deterministic models (i.e., probabilistic and a family of probabilistic known as statistical) in order to provide performance guarantee, robustness, reusable and interpretable results.

The digital transformation of information systems has made feasible the effective use of data science techniques such as artificial intelligence (AI) and machine learning (ML) for various applications. In addition, the use of sensor technology and AI/ML will inevitably lead to more objective and improved performance, lower cost and more effective system management overall.

The aim of this Special Issue is to provide original high-quality innovative ideas and research solutions (for both theoretical and practical challenges) for data analysis and modelling with the aid of artificial intelligence and machine learning in various domains and applications.

Dr. Maria Trigka
Dr. Elias Dritsas
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. Computation 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 1800 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

  • data science
  • data mining
  • artificial intelligence
  • machine learning
  • statistics
  • predictive modeling
  • monitoring
  • data analytics

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Published Papers (2 papers)

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28 pages, 3882 KiB  
Article
Short-Term Wind Speed Prediction via Sample Entropy: A Hybridisation Approach against Gradient Disappearance and Explosion
by Khathutshelo Steven Sivhugwana and Edmore Ranganai
Computation 2024, 12(8), 163; https://doi.org/10.3390/computation12080163 - 12 Aug 2024
Viewed by 871
Abstract
High-variant wind speeds cause aberrations in wind power systems and compromise the effective operation of wind farms. A single model cannot capture the inherent wind speed randomness and complexity. In the proposed hybrid strategy, wavelet transform (WT) is used for data decomposition, sample [...] Read more.
High-variant wind speeds cause aberrations in wind power systems and compromise the effective operation of wind farms. A single model cannot capture the inherent wind speed randomness and complexity. In the proposed hybrid strategy, wavelet transform (WT) is used for data decomposition, sample entropy (SampEn) for subseries complexity evaluation, neural network autoregression (NNAR) for deterministic subseries prediction, long short-term memory network (LSTM) for complex subseries prediction, and gradient boosting machine (GBM) for prediction reconciliation. The proposed WT-NNAR-LSTM-GBM approach predicts minutely averaged wind speed data collected at Southern African Universities Radiometric Network (SAURAN) stations: Council for Scientific and Industrial Research (CSIR), Richtersveld (RVD), Venda, and the Namibian University of Science and Technology (NUST). For comparison purposes, in WT-NNAR-LSTM-GBM, LSTM and NNAR are respectively replaced with a k-nearest neighbour (KNN) to form the corresponding hybrids: WT-NNAR-KNN-GBM and WT-KNN-LSTM-GBM. We assessed WT-NNAR-LSTM-GBM’s efficacy against NNAR, LSTM, WT-NNAR-KNN-GBM, and WT-KNN-LSTM-GBM as well as the naïve model. The comparative study found that the WT-NNAR-LSTM-GBM model was the most accurate, sharpest, and robust based on mean absolute error, median absolute deviation, and residual analysis. The study results suggest using short-term forecasts to optimise wind power production, enhance grid operations in real-time, and open the door to further algorithmic enhancements. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning in Data Science)
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15 pages, 611 KiB  
Article
Predicting the Occurrence of Metabolic Syndrome Using Machine Learning Models
by Maria Trigka and Elias Dritsas
Computation 2023, 11(9), 170; https://doi.org/10.3390/computation11090170 - 3 Sep 2023
Cited by 6 | Viewed by 2333
Abstract
The term metabolic syndrome describes the clinical coexistence of pathological disorders that can lead to the development of cardiovascular disease and diabetes in the long term, which is why it is now considered an initial stage of the above clinical entities. Metabolic syndrome [...] Read more.
The term metabolic syndrome describes the clinical coexistence of pathological disorders that can lead to the development of cardiovascular disease and diabetes in the long term, which is why it is now considered an initial stage of the above clinical entities. Metabolic syndrome (MetSyn) is closely associated with increased body weight, obesity, and a sedentary lifestyle. The necessity of prevention and early diagnosis is imperative. In this research article, we experiment with various supervised machine learning (ML) models to predict the risk of developing MetSyn. In addition, the predictive ability and accuracy of the models using the synthetic minority oversampling technique (SMOTE) are illustrated. The evaluation of the ML models highlights the superiority of the stacking ensemble algorithm compared to other algorithms, achieving an accuracy of 89.35%; precision, recall, and F1 score values of 0.898; and an area under the curve (AUC) value of 0.965 using the SMOTE with 10-fold cross-validation. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning in Data Science)
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