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Applied Sciences
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Novel Applications of Machine Learning and Bayesian Optimization
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Novel Applications of Machine Learning and Bayesian Optimization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4000

Special Issue Editors

Dr. Glenn Hawe

E-Mail Website
Guest Editor
School of Computing, Ulster University, Belfast, UK
Interests: Bayesian optimization; Gaussian processes; applications of machine learning
Dr. Aidan Meade

E-Mail Website
Guest Editor
School of Physics and Clinical and Optometric Sciences, Technological University Dublin, Dublin 8, Ireland
Interests: data science; machine learning and artificial intelligence; prognostic and diagnostic technologies for oncology; hyperspectral imaging

Special Issue Information

Dear Colleagues,

Machine learning and Bayesian optimization have found wide-ranging applications in applied sciences, revolutionizing the way in which we make data-driven decisions and find optimal solutions to complex problems. In the chemical and molecular sciences, machine learning has been used to power data-driven force-fields and to accelerate the discovery of novel materials. In applied biosciences, machine learning has been used to assist humans in image processing, disease diagnosis and predicting patient outcomes. In the environmental and Earth sciences, machine learning has been used to predict probabilities of earthquakes and automate the detection of litter. When data are expensive or scarce, Bayesian optimization has been used to design experiments, optimize parameters and explore trade-offs, and has a long history in engineering design.

This Special Issue will publish high-quality, original research papers advancing the state of the art in the application of machine learning and/or Bayesian optimization.

Dr. Glenn Hawe
Dr. Aidan Meade
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. Applied Sciences 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
  • deep learning
  • Bayesian optimization
  • applied sciences
  • healthcare
  • materials science
  • environmental science
  • predictive modelling
  • data-driven design
  • anomaly detection
  • classification
  • regression

Published Papers (5 papers)

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Research

28 pages, 2596 KiB  
Article
Optimization of Well Placement in Carbon Capture and Storage (CCS): Bayesian Optimization Framework under Permutation Invariance
by Sofianos Panagiotis Fotias, Ismail Ismail and Vassilis Gaganis
Appl. Sci. 2024, 14(8), 3528; https://doi.org/10.3390/app14083528 - 22 Apr 2024
Viewed by 332
Abstract
Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations being already an established practice for enhanced oil recovery operations. The overarching objective of CCS is to [...] Read more.
Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations being already an established practice for enhanced oil recovery operations. The overarching objective of CCS is to protract the operational viability and sustainability of platforms and oilfields, thereby facilitating a seamless transition towards sustainable practices. This study introduces a comprehensive framework for optimizing well placement in CCS operations, employing a derivative-free method known as Bayesian Optimization. The development plan is tailored for scenarios featuring aquifers devoid of flow boundaries, incorporating production wells tasked with controlling pressure buildup and injection wells dedicated to CO2 sequestration. Notably, the wells operate under group control, signifying predefined injection and production targets and constraints that must be adhered to throughout the project’s lifespan. As a result, the objective function remains invariant under specific permutations of the well locations. Our investigation delves into the efficacy of Bayesian Optimization under the introduced permutation invariance. The results reveal that it demonstrates critical efficiency in handling the optimization task extremely fast. In essence, this study advocates for the efficacy of Bayesian Optimization in the context of optimizing well placement for CCS operations, emphasizing its potential as a preferred methodology for enhancing sustainability in the energy sector. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization)
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31 pages, 7924 KiB  
Article
A Deep Learning Approach to Semantic Segmentation of Steel Microstructures
by Jorge Muñoz-Rodenas, Francisco García-Sevilla, Valentín Miguel-Eguía, Juana Coello-Sobrino and Alberto Martínez-Martínez
Appl. Sci. 2024, 14(6), 2297; https://doi.org/10.3390/app14062297 - 08 Mar 2024
Viewed by 651
Abstract
The utilization of convolutional neural networks (CNNs) for semantic segmentation has proven to be successful in various applications, such as autonomous vehicle environment analysis, medical imaging, and satellite imagery. In this study, we investigate the application of different segmentation networks, including Deeplabv3+, U-Net, [...] Read more.
The utilization of convolutional neural networks (CNNs) for semantic segmentation has proven to be successful in various applications, such as autonomous vehicle environment analysis, medical imaging, and satellite imagery. In this study, we investigate the application of different segmentation networks, including Deeplabv3+, U-Net, and SegNet, each recognized for their effectiveness in semantic segmentation tasks. Additionally, in the case of Deeplabv3+, we leverage the use of pre-trained ResNet50, ResNet18 and MobileNetv2 as feature extractors for a comprehensive analysis of steel microstructures. Our specific focus is on distinguishing perlite and ferrite phases in micrographs of low-carbon steel specimens subjected to annealing heat treatment. The micrographs obtained using an optical microscope are manually segmented. Preprocessing techniques are then applied to create a dataset for building a supervised learning model. In the results section, we discuss in detail the performance of the obtained models and the metrics used. The models achieve a remarkable 95% to 98% accuracy in correctly labeling pixels for each phase. This underscores the effectiveness of our approach in differentiating perlite and ferrite phases within steel microstructures. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization)
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21 pages, 22648 KiB  
Article
3D Ultrasound Mosaic of the Whole Shoulder: A Feasibility Study
by Ahmed Sewify, Maria Antico, Marian Steffens, Jacqueline Roots, Ashish Gupta, Kenneth Cutbush, Peter Pivonka and Davide Fontanarosa
Appl. Sci. 2024, 14(5), 2152; https://doi.org/10.3390/app14052152 - 04 Mar 2024
Viewed by 603
Abstract
A protocol is proposed to acquire a tomographic ultrasound (US) scan of the musculoskeletal (MSK) anatomy in the rotator cuff region. Current clinical US imaging techniques are hindered by occlusions and a narrow field of view and require expert acquisition and interpretation. There [...] Read more.
A protocol is proposed to acquire a tomographic ultrasound (US) scan of the musculoskeletal (MSK) anatomy in the rotator cuff region. Current clinical US imaging techniques are hindered by occlusions and a narrow field of view and require expert acquisition and interpretation. There is limited literature on 3D US image registration of the shoulder or volumetric reconstruction of the full shoulder complex. We believe that a clinically accurate US volume reconstruction of the entire shoulder can aid in pre-operative surgical planning and reduce the complexity of US interpretation. The protocol was used in generating data for deep learning model training to automatically register US mosaics in real-time. An in vivo 3D US tomographic reconstruction of the entire rotator cuff region was produced by registering 53 sequential 3D US volumes acquired by an MSK sonographer. Anatomical surface thicknesses and distances in the US mosaic were compared to their corresponding MRI measurements as the ground truth. The humeral head surface was marginally thicker in the reconstructed US mosaic than its original thickness observed in a single US volume by 0.65 mm. The humeral head diameter and acromiohumeral distance (ACHD) matched with their measured MRI distances with a reconstruction error of 0 mm and 1.2 mm, respectively. Furthermore, the demonstration of 20 relevant MSK structures was independently graded between 1 and 5 by two sonographers, with higher grades indicating poorer demonstration. The average demonstration grade for each anatomy was as follows: bones = 2, muscles = 3, tendons = 3, ligaments = 4–5 and labrum = 4–5. There was a substantial agreement between sonographers (Cohen’s Weighted kappa of 0.71) on the demonstration of the structures, and they both independently deemed the mosaic clinically acceptable for the visualisation of the bony anatomy. Ligaments and the labrum were poorly observed due to anatomy size, location and inaccessibility in a static scan, and artefact build-up from the registration and compounding approaches. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization)
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22 pages, 1304 KiB  
Article
Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways
by Chuanxiang Song, Seong-Yoon Shin and Kwang-Seong Shin
Appl. Sci. 2024, 14(2), 916; https://doi.org/10.3390/app14020916 - 21 Jan 2024
Viewed by 1225
Abstract
This study introduces a novel approach named the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), aimed at personalizing educational pathways through machine learning technology. Our findings reveal that this framework significantly enhances student engagement and learning effectiveness by providing real-time feedback and personalized instructional [...] Read more.
This study introduces a novel approach named the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), aimed at personalizing educational pathways through machine learning technology. Our findings reveal that this framework significantly enhances student engagement and learning effectiveness by providing real-time feedback and personalized instructional content tailored to individual learning needs. This research demonstrates the potential of leveraging advanced technology to create more effective and individualized learning environments, offering educators a new tool to support each student’s learning journey. The study thus contributes to the field by showcasing how personalized education can be optimized using modern technological advancements. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization)
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27 pages, 3440 KiB  
Article
Sparse Representations Optimization with Coupled Bayesian Dictionary and Dictionary Classifier for Efficient Classification
by Muhammad Riaz-ud-din, Salman Abdul Ghafoor and Faisal Shafait
Appl. Sci. 2024, 14(1), 306; https://doi.org/10.3390/app14010306 - 29 Dec 2023
Viewed by 640
Abstract
Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention [...] Read more.
Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention from researchers. These techniques jointly learn two sets of sparse representations, one for the training samples over the dictionary and the other for the corresponding labels over the dictionary classifier. At the prediction stage, the representations of the test samples computed over the learned dictionary do not truly represent the corresponding labels, exposing weakness in the joint learning claim of these techniques. We mitigate this problem and strengthen the joint by learning a set of weights over the dictionary to represent the training data and further optimizing the same weights over the dictionary classifier to represent the labels of the corresponding classes of the training data. Now, at the prediction stage, the representation weights of the test samples computed over the learned dictionary also represent the labels of the corresponding classes of the test samples, resulting in the accurate reconstruction of the labels of the classes by the learned dictionary classifier. Overall, a reduction in the size of the Bayesian model’s parameters also improves training time. We analytically and nonparametrically derived the posterior conditional probabilities of the model from the overall joint probability of the model using Bayes’ theorem. We used the Gibbs sampler to solve the joint probability of the model using the derived conditional probabilities, which also supports our claim of efficient optimization of the coupled/joint dictionaries and the sparse representation parameters. We demonstrated the effectiveness of our approach through experiments on the standard datasets, i.e., the Extended YaleB and AR face databases for face recognition, Caltech-101 and Fifteen Scene Category databases for categorization, and UCF sports action database for action recognition. We compared the results with the state-of-the-art methods in the area. The classification accuracies, i.e., 93.25%, 89.27%, 94.81%, 98.10%, and 95.00%, of our approach on the datasets have increases of 0.5 to 2% on average. The overall average error margin of the confidence intervals in our approach is 0.24 compared with the second-best approach, JBDC, for which it is 0.34. The AUC–ROC scores of our approach are 0.98 and 0.992, which are better than those of others, i.e., 0.960 and 0.98, respectively. Our approach is also computationally efficient. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization)
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