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Bioengineering
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Advances of Artificial Intelligence for Sustainable Engineering and Medical Applications:...
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Advances of Artificial Intelligence for Sustainable Engineering and Medical Applications: Machine Learning and Optimization

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 5974

Special Issue Editor

Dr. Erfan Babaee Tirkolaee

E-Mail Website
Guest Editor
Department of Industrial Engineering, Istinye University, Istanbul 34396, Turkey
Interests: supply chain management; waste management; sustainable development; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing amount of data being collected in complex systems as well as the convergence of various areas of expertise in the engineering domain are leading Artificial Intelligence (AI) and data analytics-related studies towards novel fronts in sustainable engineering and medical applications. This orientation also effectuates an inimitable opportunity and expected assurance to solve different critical and complex problems in a wide range of AI applications such as manufacturing, transportation, medicine and healthcare systems. Nevertheless, such a pledge resolutely depends on the extent to which scholars can discover helpful patterns, detect informative mechanisms underlying the segmented and diverse data sets, as well as transform this knowledge into expert and intelligent decision-making models. AI tools have been recently studied and implemented as propitious techniques for the application and development of expert and intelligent decision-making systems in the engineering and medical fields to achieve sustainable development. For example, they improve preventive care and quality of life, provide more precise diagnoses and treatment plans. AI-based systems are typically able to learn from data and evolve based on real-time fluctuations by taking into account the incontrovertible uncertainty of engineering data and processes. Accordingly, many efforts have been devoted so far to utilizing different techniques comprising inter alia, optimization, mathematical modelling, Machine Learning (ML), neural networks, human–machine interface, informatics, and statistical inference. Emergency situations such as the recent pandemic or natural disasters further enlighten the significance of predictive data analytics as part of mathematics and computer science along with ML and optimization methods in dealing with the challenges in healthcare systems. The main mission of this Special Issue is explicitly to explore and advance the latest achievements of AI, ML, computational intelligence, neuroscience, automation and robotics, operational research, mathematics, statistics/simulation, and data science for sustainable engineering applications. It offers a scientific venue catered for scholars who are interested in making a quick exchange of their ideas and innovative research findings that leverage the topics covered in the call. Particularly, novel interdisciplinary approaches in optimization, computer science, and engineering and healthcare applications as well as strong conceptual foundations in newly evolving topics are welcomed

Dr. Erfan Babaee Tirkolaee
Guest Editor

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. Bioengineering 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 2700 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

  • artificial intelligence
  • machine learning
  • optimization
  • statistics
  • simulation
  • fuzzy logic
  • data science and analytics
  • medicine
  • engineering application

Published Papers (4 papers)

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Research

18 pages, 1303 KiB  
Article
Leveraging Interpretable Feature Representations for Advanced Differential Diagnosis in Computational Medicine
by Genghong Zhao, Wen Cheng, Wei Cai, Xia Zhang and Jiren Liu
Bioengineering 2024, 11(1), 29; https://doi.org/10.3390/bioengineering11010029 - 26 Dec 2023
Viewed by 982
Abstract
Diagnostic errors represent a critical issue in clinical diagnosis and treatment. In China, the rate of misdiagnosis in clinical diagnostics is approximately 27.8%. By comparison, in the United States, which boasts the most developed medical resources globally, the average rate of misdiagnosis is [...] Read more.
Diagnostic errors represent a critical issue in clinical diagnosis and treatment. In China, the rate of misdiagnosis in clinical diagnostics is approximately 27.8%. By comparison, in the United States, which boasts the most developed medical resources globally, the average rate of misdiagnosis is estimated to be 11.1%. It is estimated that annually, approximately 795,000 Americans die or suffer permanent disabilities due to diagnostic errors, a significant portion of which can be attributed to physicians’ failure to make accurate clinical diagnoses based on patients’ clinical presentations. Differential diagnosis, as an indispensable step in the clinical diagnostic process, plays a crucial role. Accurately excluding differential diagnoses that are similar to the patient’s clinical manifestations is key to ensuring correct diagnosis and treatment. Most current research focuses on assigning accurate diagnoses for specific diseases, but studies providing reasonable differential diagnostic assistance to physicians are scarce. This study introduces a novel solution specifically designed for this scenario, employing machine learning techniques distinct from conventional approaches. We develop a differential diagnosis recommendation computation method for clinical evidence-based medicine, based on interpretable representations and a visualized computational workflow. This method allows for the utilization of historical data in modeling and recommends differential diagnoses to be considered alongside the primary diagnosis for clinicians. This is achieved by inputting the patient’s clinical manifestations and presenting the analysis results through an intuitive visualization. It can assist less experienced doctors and those in areas with limited medical resources during the clinical diagnostic process. Researchers discuss the effective experimental results obtained from a subset of general medical records collected at Shengjing Hospital under the premise of ensuring data quality, security, and privacy. This discussion highlights the importance of addressing these issues for successful implementation of data-driven differential diagnosis recommendations in clinical practice. This study is of significant value to researchers and practitioners seeking to improve the efficiency and accuracy of differential diagnoses in clinical diagnostics using data analysis. Full article
(This article belongs to the Special Issue Advances of Artificial Intelligence for Sustainable Engineering and Medical Applications: Machine Learning and Optimization)
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14 pages, 3062 KiB  
Article
Research on Mental Workload of Deep-Sea Oceanauts Driving Operation Tasks from EEG Data
by Xiaoguang Liu, Lu Shi, Cong Ye, Yangyang Li and Jing Wang
Bioengineering 2023, 10(9), 1027; https://doi.org/10.3390/bioengineering10091027 - 31 Aug 2023
Viewed by 787
Abstract
A person’s present mental state is closely associated with the frequency and temporal domain features of spontaneous electroencephalogram (EEG) impulses, which directly reflect neurophysiological signals of brain activity. EEG signals are employed in this study to measure the mental workload of drivers while [...] Read more.
A person’s present mental state is closely associated with the frequency and temporal domain features of spontaneous electroencephalogram (EEG) impulses, which directly reflect neurophysiological signals of brain activity. EEG signals are employed in this study to measure the mental workload of drivers while they are operating a vehicle. A technique based on the quantum genetic algorithm (QGA) is suggested for improving the kernel function parameters of the multi-class support vector machine (MSVM). The performance of the algorithm based on the quantum genetic algorithm is found to be superior to that of other ways when other methods and the quantum genetic algorithm are evaluated for the parameter optimization of kernel function via simulation. A multi-classification support vector machine based on the quantum genetic algorithm (QGA-MSVM) is applied to identify the mental workload of oceanauts through the collection and feature extraction of EEG signals during driving simulation operation experiments in a sea basin area, a seamount area, and a hydrothermal area. Even with a limited data set, QGA-MSVM is able to accurately identify the cognitive burden experienced by ocean sailors, with an overall accuracy of 91.8%. Full article
(This article belongs to the Special Issue Advances of Artificial Intelligence for Sustainable Engineering and Medical Applications: Machine Learning and Optimization)
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25 pages, 5186 KiB  
Article
A Computer Method for Pronation-Supination Assessment in Parkinson’s Disease Based on Latent Space Representations of Biomechanical Indicators
by Luis Pastor Sánchez-Fernández, Alejandro Garza-Rodríguez, Luis Alejandro Sánchez-Pérez and Juan Manuel Martínez-Hernández
Bioengineering 2023, 10(5), 588; https://doi.org/10.3390/bioengineering10050588 - 13 May 2023
Viewed by 1222
Abstract
One problem in the quantitative assessment of biomechanical impairments in Parkinson’s disease patients is the need for scalable and adaptable computing systems. This work presents a computational method that can be used for motor evaluations of pronation-supination hand movements, as described in item [...] Read more.
One problem in the quantitative assessment of biomechanical impairments in Parkinson’s disease patients is the need for scalable and adaptable computing systems. This work presents a computational method that can be used for motor evaluations of pronation-supination hand movements, as described in item 3.6 of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The presented method can quickly adapt to new expert knowledge and includes new features that use a self-supervised training approach. The work uses wearable sensors for biomechanical measurements. We tested a machine-learning model on a dataset of 228 records with 20 indicators from 57 PD patients and eight healthy control subjects. The test dataset’s experimental results show that the method’s precision rates for the pronation and supination classification task achieved up to 89% accuracy, and the F1-scores were higher than 88% in most categories. The scores present a root mean squared error of 0.28 when compared to expert clinician scores. The paper provides detailed results for pronation-supination hand movement evaluations using a new analysis method when compared to the other methods mentioned in the literature. Furthermore, the proposal consists of a scalable and adaptable model that includes expert knowledge and affectations not covered in the MDS-UPDRS for a more in-depth evaluation. Full article
(This article belongs to the Special Issue Advances of Artificial Intelligence for Sustainable Engineering and Medical Applications: Machine Learning and Optimization)
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16 pages, 2544 KiB  
Article
Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network
by Abbas Bagherian Kasgari, Sadaf Safavi, Mohammadjavad Nouri, Jun Hou, Nazanin Tataei Sarshar and Ramin Ranjbarzadeh
Bioengineering 2023, 10(4), 495; https://doi.org/10.3390/bioengineering10040495 - 20 Apr 2023
Cited by 20 | Viewed by 1906
Abstract
In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and [...] Read more.
In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and their corresponding contexts. To overcome this issue, we propose a deep learning model based on an attention mechanism in this study. The suggested technique employs an attention mechanism that focuses on the pattern’s friendship, which is responsible for concentrating on the relevant features related to individual users. To compute context-aware similarities among diverse users, our model employs six features of each user as inputs, including user ID, hour, month, day, minute, and second of visiting time, which explore the influences of both spatial and temporal features for the users. In addition, we incorporate geographical information into our attention mechanism by creating an eccentricity score. Specifically, we map the trajectory of each user to a shape, such as a circle, triangle, or rectangle, each of which has a different eccentricity value. This attention-based mechanism is evaluated on two widely used datasets, and experimental outcomes prove a noteworthy improvement of our model over the state-of-the-art strategies for POI recommendation. Full article
(This article belongs to the Special Issue Advances of Artificial Intelligence for Sustainable Engineering and Medical Applications: Machine Learning and Optimization)
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