Special Issue "Embedded Artificial Intelligence for Energy and Sustainability Issues"

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E:Engineering and Technology".

Deadline for manuscript submissions: 30 June 2023 | Viewed by 958

Special Issue Editors

Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
Special Issues, Collections and Topics in MDPI journals
Facultad de Ingeniería, Universidad Autónoma de Queretaro, Queretaro 76010, Mexico
Interests: solar energy; power generation; waste heat recovery; control techniques; renewable energy technologies; solar radiation; energy engineering

Special Issue Information

Dear Colleagues,

Artificial intelligence is a technology with the most significant presence in energy and sustainability issues. Integrating this technology into embedded systems can help utilize available resources and develop new technologies focused on improving the environment. This has promoted the development of cutting-edge technologies focused on smart grid projects based on new microsystem architectures, making tasks more efficient for a sustainable society. These systems have been implemented in different areas of industry and research. This Special Issue aims to show innovative findings using artificial intelligence as the main solution in the energy transition of developing technologies with embedded systems for energy and sustainability.

This Special Issue aims to highlight findings on research and developments of embedded artificial intelligence applied to sustainability technologies.

We invite contributions to this Special Issue on topics including but not limited to the following:

Artificial Intelligence techniques focused on sustainability engineering issues:

  • Machine learning;
  • Deep learning;

Optimization of autonomous systems using artificial intelligence;

  • Metaheuristic algorithms;
  • Fuzzy or neural technics;
  • Mixed techniques;

Embedded artificial intelligence;

  • FPGA;
  • DSP;
  • Microcontrollers;

Applications with embedded artificial intelligence;

  • Energy management;
  • Monitoring;
  • Mechatronics;
  • Alternative energy systems;
  • Renewable energy;
  • Information and communication technologies.

Dr. Juvenal Rodriguez-Resendiz
Dr. José Manuel Álvarez-Alvarado
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. Micromachines 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 2000 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.

Dr. Juvenal Rodriguez-Resendiz
Dr. José Manuel Álvarez-Alvarado
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. Micromachines 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 2000 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

Article
A Deep Learning Approach for Predicting Multiple Sclerosis
Micromachines 2023, 14(4), 749; https://doi.org/10.3390/mi14040749 - 29 Mar 2023
Viewed by 615
Abstract
This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning [...] Read more.
This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network. Full article
(This article belongs to the Special Issue Embedded Artificial Intelligence for Energy and Sustainability Issues)
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