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Artificial Intelligence Methods for Environmental Sciences or Health Applications

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6385

Special Issue Editors


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Guest Editor
Graduate Program in Electrical Engineer (PPGEE), Universidade Tecnológica Federal do Paraná (UTFPR), Ponta Grossa 81217-220, PR, Brazil
Interests: artificial intelligence; neural networks; genetic algorithm; echo state networks; extreme learning machines; bio-inspired computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Graduate Program in Urban Environmental Sustainability, Graduate Program in Mechanical Engineering, Federal University of Technology-Paraná (UTFPR), Dr. Washington Subtil Chueire St., 330, Jardim Carvalho, Ponta Grossa 84017-220, PR, Brazil
Interests: numerical modeling; health risks; air pollution; atmospheric dispersion; life cycle impact assessment
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Graduate Program in Electrical Engineering, Graduate Program in Computer Sciences, Federal University of Technology-Paraná (UTFPR), Dr. Washington Subtil Chueire St., 330, Jardim Carvalho, Ponta Grossa 84017-220, PR, Brazil
Interests: instrumentation; electronic instrumentation; biomedical instrumentation; electronic engineering; electronics circuit design; automotive experimental physics

Special Issue Information

Dear Colleagues,

In recent times, artificial intelligence methods stood out in many fields. The prominent representatives of this class are Artificial Neural Networks (deep and shallow approaches), Fuzzy Systems, and nature-inspired metaheuristics (Swarm Intelligence, Evolutionary algorithms, and physical models).

Among the different research areas, the application of AI has stood out in human health, correlating from wearable devices to environmental sciences. The ability of AI approaches to map nonlinear patterns or optimize systems can bring these sub-areas of research closer together to solve real-world problems.

In this regard, this Special Issue aims to encourage both academic and industrial researchers to present their latest findings concerning the previously cited aspects, which can significantly contribute to the achievement of new methods to develop processes or devices to improve the usage of such systems.

The authors should provide a comprehensive and scientifically sound overview of the most recent research and methodological approaches. Both experimental and methodological contributions, as well as systematic reviews, are welcome.

The Editors of this Special Issue welcome submissions that address, but are not limited to, the following issues:

  • Machine learning;
  • Artificial neural networks;
  • Fuzzy systems;
  • Nature-inspired metaheuristics;
  • Convolutional neural networks;
  • Deep learning;
  • Feature selection;
  • Clustering;
  • Classification;
  • Signal processing;
  • Reinforced learning;
  • Supervised/unsupervised learning;
  • Swarm intelligence;
  • Evolutionary algorithms;
  • Time series forecasting (such as pollutant series);
  • Pollution impact on human health;
  • Health wearable devices;
  • Embedded systems for healthcare;
  • Pattern recognition;
  • Human activity recognition.

Prof. Dr. Hugo Valadares Siqueira
Prof. Dr. Yara de Souza Tadano
Prof. Dr. Sergio Luiz Stevan, Jr.
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. International Journal of Environmental Research and Public Health 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 2500 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
  • artificial neural networks
  • signal processing
  • pattern recognition
  • embedded systems
  • health wearable devices
  • pollution
  • time series forecasting

Published Papers (3 papers)

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Research

10 pages, 1451 KiB  
Article
Word2vec Word Embedding-Based Artificial Intelligence Model in the Triage of Patients with Suspected Diagnosis of Major Ischemic Stroke: A Feasibility Study
by Antonio Desai, Aurora Zumbo, Mauro Giordano, Pierandrea Morandini, Maria Elena Laino, Elena Azzolini, Andrea Fabbri, Simona Marcheselli, Alice Giotta Lucifero, Sabino Luzzi and Antonio Voza
Int. J. Environ. Res. Public Health 2022, 19(22), 15295; https://doi.org/10.3390/ijerph192215295 - 19 Nov 2022
Cited by 6 | Viewed by 2125
Abstract
Background: The possible benefits of using semantic language models in the early diagnosis of major ischemic stroke (MIS) based on artificial intelligence (AI) are still underestimated. The present study strives to assay the feasibility of the word2vec word embedding-based model in decreasing the [...] Read more.
Background: The possible benefits of using semantic language models in the early diagnosis of major ischemic stroke (MIS) based on artificial intelligence (AI) are still underestimated. The present study strives to assay the feasibility of the word2vec word embedding-based model in decreasing the risk of false negatives during the triage of patients with suspected MIS in the emergency department (ED). Methods: The main ICD-9 codes related to MIS were used for the 7-year retrospective data collection of patients managed at the ED with a suspected diagnosis of stroke. The data underwent “tokenization” and “lemmatization”. The word2vec word-embedding algorithm was used for text data vectorization. Results: Out of 648 MIS, the word2vec algorithm successfully identified 83.9% of them, with an area under the curve of 93.1%. Conclusions: Natural language processing (NLP)-based models in triage have the potential to improve the early detection of MIS and to actively support the clinical staff. Full article
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25 pages, 1673 KiB  
Article
Self-Organizing Maps to Multidimensionally Characterize Physical Profiles in Older Adults
by Lorena Parra-Rodríguez, Edward Reyes-Ramírez, José Luis Jiménez-Andrade, Humberto Carrillo-Calvet and Carmen García-Peña
Int. J. Environ. Res. Public Health 2022, 19(19), 12412; https://doi.org/10.3390/ijerph191912412 - 29 Sep 2022
Viewed by 1389
Abstract
The aim of this study is to automatically analyze, characterize and classify physical performance and body composition data of a cohort of Mexican community-dwelling older adults. Self-organizing maps (SOM) were used to identify similar profiles in 562 older adults living in Mexico City [...] Read more.
The aim of this study is to automatically analyze, characterize and classify physical performance and body composition data of a cohort of Mexican community-dwelling older adults. Self-organizing maps (SOM) were used to identify similar profiles in 562 older adults living in Mexico City that participated in this study. Data regarding demographics, geriatric syndromes, comorbidities, physical performance, and body composition were obtained. The sample was divided by sex, and the multidimensional analysis included age, gait speed over height, grip strength over body mass index, one-legged stance, lean appendicular mass percentage, and fat percentage. Using the SOM neural network, seven profile types for older men and women were identified. This analysis provided maps depicting a set of clusters qualitatively characterizing groups of older adults that share similar profiles of body composition and physical performance. The SOM neural network proved to be a useful tool for analyzing multidimensional health care data and facilitating its interpretability. It provided a visual representation of the non-linear relationship between physical performance and body composition variables, as well as the identification of seven characteristic profiles in this cohort. Full article
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26 pages, 2571 KiB  
Article
Exploration of Telemidwifery: An Initiation of Application Menu in Indonesia
by Alyxia Gita Stellata, Fedri Ruluwedrata Rinawan, Gatot Nyarumenteng Adhipurnawan Winarno, Ari Indra Susanti and Wanda Gusdya Purnama
Int. J. Environ. Res. Public Health 2022, 19(17), 10713; https://doi.org/10.3390/ijerph191710713 - 28 Aug 2022
Cited by 6 | Viewed by 2272
Abstract
The midwifery continuity-of-care model improves the quality and safety of midwifery services and is highly dependent on the quality of communication and information. The service uses a semi-automated chatbot-based digital health media service defined with the new term “telemidwifery”. This study aimed to [...] Read more.
The midwifery continuity-of-care model improves the quality and safety of midwifery services and is highly dependent on the quality of communication and information. The service uses a semi-automated chatbot-based digital health media service defined with the new term “telemidwifery”. This study aimed to explore the telemidwifery menu content for village midwives and pregnant women in the Purwakarta Regency, West Java, Indonesia. The qualitative research method was used to explore with focus group discussion (FGD). The data collection technique was purposive sampling. The research subjects were 15 village midwives and 6 multiparous pregnant women. The results of this study involved 15 characteristics of menu content: (1) Naming, (2) Digital Communication, (3) Digital Health Services, (4) Telemidwifery Features, (5) Digital Check Features, (6) Media Services, (7) Attractiveness, (8) Display, (9) Ease of Use, (10) Clarity of Instructions, (11) Use of Language, (12) Substances, (13) Benefits, (14) Appropriateness of Values, and (15) Supporting Components. The content characteristics of this telemidwifery menu were assigned to the ISO 9126 Model standards for usability, functionality, and efficiency. The conclusion is that the 15 themes constitute the characteristic menu content required within the initiation of telemidwifery. Full article
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