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Electronics
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Machine Learning in Data Analytics and Prediction
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Machine Learning in Data Analytics and Prediction

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

Deadline for manuscript submissions: 15 November 2024 | Viewed by 1418

Special Issue Editors

Dr. Rogério Luís De Carvalho Costa

E-Mail Website
Guest Editor
Computer Science and Communication Research Centre (CIIC), Polytechnic of Leiria, 2411-901 Leiria, Portugal
Interests: big data; machine learning; cybersecurity and privacy; data integration and quality; data analytics; forecasting
Prof. Dr. Leonel Filipe Simões Santos

E-Mail Website
Guest Editor
Computer Science Engineering Department at Superior School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal
Interests: cybersecurity; information and networks security; Internet of Things; intrusion detection systems; computer forensics
Prof. Dr. Carlos Rabadão

E-Mail Website
Guest Editor
Computer Science Engineering Department, Superior School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal
Interests: information and networks security; information security management systems; security incident response systems for Industry 4.0; next generation networks and services; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue, "Machine Learning in Data Analytics and Prediction", which aims to allow researchers and practitioners from different research areas to share their experiences in developing state-of-the-art machine learning-based analytics and prediction solutions through new methods, novel architectures and systems, and real-world applications that could benefit from the proposed solutions. Researchers are invited to submit research works describing innovative methods, algorithms, and platforms covering any facets of machine learning in data analytics and prediction. Application papers detailing industrial implementations, design, and deployment experience reports on how machine learning can solve relevant practical problems related to analytics and prediction are also welcome. 

We welcome technical, experimental, and methodological manuscripts, as well as contributions to applied data science, that address any topics on advances in machine learning-based analytics and prediction methods, systems, and applications, including data and integration, deep neural networks, explainable AI, computational intelligence, and concept drift management, in fields like management, business, engineering, computer science, and physical, social, and life sciences.

Application scenarios of interest include, but are not limited to:

  • Cybersecurity and privacy maintenance.
  • Management and marketing.
  • Economics, finance, and accounting.
  • Life sciences and healthcare.
  • Internet of Things (IoT).
  • Energy and Industry 4.0.
  • Business and societal challenges.
  • Environmental sustainability.

Dr. Rogério Luís De Carvalho Costa
Prof. Dr. Leonel Filipe Simões Santos
Prof. Dr. Carlos Rabadão
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. 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

  • machine learning
  • big data
  • data analytics
  • forecasting
  • knowledge extration

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Published Papers (1 paper)

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Research

40 pages, 29439 KiB  
Article
A Multivariate Time Series Prediction Method Based on Convolution-Residual Gated Recurrent Neural Network and Double-Layer Attention
by Chuxin Cao, Jianhong Huang, Man Wu, Zhizhe Lin and Yan Sun
Electronics 2024, 13(14), 2834; https://doi.org/10.3390/electronics13142834 - 18 Jul 2024
Viewed by 743
Abstract
In multivariate and multistep time series prediction research, we often face the problems of insufficient spatial feature extraction and insufficient time-dependent mining of historical series data, which also brings great challenges to multivariate time series analysis and prediction. Inspired by the attention mechanism [...] Read more.
In multivariate and multistep time series prediction research, we often face the problems of insufficient spatial feature extraction and insufficient time-dependent mining of historical series data, which also brings great challenges to multivariate time series analysis and prediction. Inspired by the attention mechanism and residual module, this study proposes a multivariate time series prediction method based on a convolutional-residual gated recurrent hybrid model (CNN-DA-RGRU) with a two-layer attention mechanism to solve the multivariate time series prediction problem in these two stages. Specifically, the convolution module of the proposed model is used to extract the relational features among the sequences, and the two-layer attention mechanism can pay more attention to the relevant variables and give them higher weights to eliminate the irrelevant features, while the residual gated loop module is used to extract the time-varying features of the sequences, in which the residual block is used to achieve the direct connectivity to enhance the expressive power of the model, to solve the gradient explosion and vanishing scenarios, and to facilitate gradient propagation. Experiments were conducted on two public datasets using the proposed model to determine the model hyperparameters, and ablation experiments were conducted to verify the effectiveness of the model; by comparing it with several models, the proposed model was found to achieve good results in multivariate time series-forecasting tasks. Full article
(This article belongs to the Special Issue Machine Learning in Data Analytics and Prediction)
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