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Applied Sciences
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Novel Applications of Machine Learning and Bayesian Optimization, 2nd Edition
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Novel Applications of Machine Learning and Bayesian Optimization, 2nd Edition

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 April 2026 | Viewed by 8148

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
Special Issues, Collections and Topics in MDPI journals
Dr. Aidan D. Meade

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

Special Issue Information

Dear Colleagues,

Machine learning and Bayesian optimization are transforming applied sciences by enabling data-driven discovery, prediction, and decision-making. For example, in the chemical and molecular sciences, machine learning accelerates the discovery of novel materials and powers data-driven force fields. In biomedical and clinical sciences, it supports image analysis, disease diagnosis, and predictions of patient outcomes. In the environmental and Earth sciences, machine learning is used to forecast earthquake probabilities and automate the detection of environmental hazards such as litter. Large language models are opening further avenues, from automating hypothesis generation, to generating code for computational experiments. When data are scarce or expensive to obtain, Bayesian optimization plays a crucial role in experimental design, parameter tuning, and exploring 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 D. 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 250 words) can be sent to the Editorial Office for assessment.

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
  • large language models
  • Bayesian optimization
  • applied sciences
  • healthcare
  • materials science
  • environmental science
  • predictive modelling
  • data-driven design
  • anomaly detection
  • classification
  • regression

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Related Special Issue

Published Papers (6 papers)

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Research

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19 pages, 444 KB  
Article
Development of an AI-Based Clinical Decision Support System to Predict and Simulate Exercise-Driven Functional Improvement in Cardiac Rehabilitation
by Arturo Martinez-Rodrigo, Celia Álvarez-Bueno, Araceli Sanchis, Laura Núñez-Martínez, José Manuel Pastor and Susana Priego-Jiménez
Appl. Sci. 2026, 16(3), 1358; https://doi.org/10.3390/app16031358 - 29 Jan 2026
Viewed by 380
Abstract
Cardiac rehabilitation (CR) improves functional capacity and reduces cardiovascular morbidity, yet clinical response remains highly heterogeneous and difficult to stratify using conventional assessment. This study presents a machine-learning framework for the early stratification of CR patients into responders and non-responders based exclusively on [...] Read more.
Cardiac rehabilitation (CR) improves functional capacity and reduces cardiovascular morbidity, yet clinical response remains highly heterogeneous and difficult to stratify using conventional assessment. This study presents a machine-learning framework for the early stratification of CR patients into responders and non-responders based exclusively on pre-intervention baseline characteristics. A total of 122 patients undergoing an 8-week CR program were evaluated using 56 clinical, physiological and metabolic predictors. Multiple classification models were trained under a stratified 10-fold cross-validation scheme. Among them, an SVM-RBF classifier achieved the best performance and retained high discriminative capacity after dimensionality reduction. The final reduced model, based on the ten most informative features identified through convergence between Random Forest and SHAP analyses, preserved >95% of the full-feature performance. The predictors were physiologically coherent, reflecting muscular strength, ventilatory efficiency, chronotropic modulation and metabolic burden. SHAP-based explainability enabled patient-level attribution of improvement likelihood, identifying modifiable variables associated with favorable or limited training response. In parallel, we developed a web-based clinical decision-support prototype that estimates improvement probability and highlights the most influential determinants for each patient, illustrating translational applicability for precision rehabilitation planning. These findings support a transition toward personalized CR strategies guided by explainable AI and baseline phenotyping. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization, 2nd Edition)
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15 pages, 1330 KB  
Article
Enhancing Early Alzheimer’s Disease Detection via Transfer Learning: From Big Structural MRI Datasets to Ethnically Distinct Small Cohorts
by Minjae Lee, Suwon Lee and Hyeon Seo
Appl. Sci. 2026, 16(2), 1004; https://doi.org/10.3390/app16021004 - 19 Jan 2026
Viewed by 338
Abstract
Deep learning-based analysis of brain magnetic resonance imaging (MRI) plays a crucial role in the early diagnosis of Alzheimer’s disease (AD). However, data scarcity and racial bias present significant challenges to the generalization of diagnostic models. Large-scale public datasets, which are predominantly composed [...] Read more.
Deep learning-based analysis of brain magnetic resonance imaging (MRI) plays a crucial role in the early diagnosis of Alzheimer’s disease (AD). However, data scarcity and racial bias present significant challenges to the generalization of diagnostic models. Large-scale public datasets, which are predominantly composed of Caucasian individuals, often lead to performance degradation when applied to other ethnic groups owing to domain shifts. To address these issues, this study proposes a two-stage transfer learning framework. Initially, a 3D ResNet model was pretrained on a large-scale Alzheimer’s disease neuroimaging initiative (ADNI) dataset to learn structural brain features. Subsequently, the pretrained weights were transferred and fine-tuned on a small-scale Korean dataset utilizing only 30% of the data for training. The proposed model achieved superior performance in classifying mild cognitive impairment (MCI), which is crucial for early diagnosis, compared with a model trained from scratch using 70% of the Korean data. Furthermore, it effectively mitigated the significant performance degradation observed when directly applying the pretrained model, demonstrating its ability to resolve the domain-shift issue. This study explored the feasibility of transfer learning to address data scarcity and domain shift issues in AD classification, underscoring its potential for developing AI-based diagnostic systems tailored to specific ethnic populations. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization, 2nd Edition)
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15 pages, 2030 KB  
Article
Automated Classification of Baseball Pitching Phases Using Machine Learning and Artificial Intelligence-Based Posture Estimation
by Shin Osawa, Atsuyuki Inui, Yutaka Mifune, Kohei Yamaura, Tomoya Yoshikawa, Issei Shinohara, Masaya Kusunose, Shuya Tanaka, Shunsaku Takigami, Yutaka Ehara, Daiji Nakabayashi, Takanobu Higashi, Ryota Wakamatsu, Shinya Hayashi, Tomoyuki Matsumoto and Ryosuke Kuroda
Appl. Sci. 2025, 15(22), 12155; https://doi.org/10.3390/app152212155 - 16 Nov 2025
Viewed by 1501
Abstract
High-precision analyses of baseball pitching have traditionally relied on optical motion capture systems, which, despite their accuracy, are complex and impractical for widespread use. Classifying sequential pitching phases, essential for biomechanical evaluation, conventionally requires manual expert labeling, a time-consuming and labor-intensive process. Accurate [...] Read more.
High-precision analyses of baseball pitching have traditionally relied on optical motion capture systems, which, despite their accuracy, are complex and impractical for widespread use. Classifying sequential pitching phases, essential for biomechanical evaluation, conventionally requires manual expert labeling, a time-consuming and labor-intensive process. Accurate identification of phase boundaries is critical because they correspond to key temporal events related to pitching injuries. This study developed and validated a smartphone-based system for automatically classifying the five key pitching phases—wind-up, stride, arm-cocking, arm acceleration, and follow-through—using pose estimation artificial intelligence and machine learning. Slow-motion videos (240 frames per second, 1080p) of 500 healthy right-handed high school pitchers were recorded from the front using a single smartphone. Skeletal landmarks were extracted using MediaPipe, and 33 kinematic features, including joint angles and limb distances, were computed. Expert-annotated phase labels were used to train classification models. Among the models evaluated, Light Gradient Boosting Machine (LightGBM) achieved a classification accuracy of 99.7% and processed each video in a few seconds demonstrating feasibility for on-site analysis. This system enables high-accuracy phase classification directly from video without motion capture, supporting future tools to detect abnormal pitching mechanics, prevent throwing-related injuries, and broaden access to pitching analysis. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization, 2nd Edition)
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26 pages, 29749 KB  
Article
Landslide Susceptibility Mapping Optimization for Improved Risk Assessment Using Multicollinearity Analysis and Machine Learning Technique
by Buddhi Raj Joshi, Netra Prakash Bhandary, Indra Prasad Acharya, Niraj KC and Chakra Bhandari
Appl. Sci. 2025, 15(22), 12152; https://doi.org/10.3390/app152212152 - 16 Nov 2025
Cited by 2 | Viewed by 1358
Abstract
This study integrates geospatial modeling with multi-criteria decision analysis for an improved approach to landslide susceptibility mapping (LSM). This approach addresses key challenges in LSM through sophisticated multicollinearity analysis and machine learning strategies. We compared three machine learning models for weighting, and of [...] Read more.
This study integrates geospatial modeling with multi-criteria decision analysis for an improved approach to landslide susceptibility mapping (LSM). This approach addresses key challenges in LSM through sophisticated multicollinearity analysis and machine learning strategies. We compared three machine learning models for weighting, and of them the Permutation-Weighted model yielded the best prediction results, with an Area Under Curve (AUC) of 95%, an accuracy of 69%, and a recall of 66%. To resolve perfect multicollinearity (r = 1) between land use land cover (LULC) and geological factors, we implemented Principal Component Analysis (PCA). The selected factors demonstrated strong predictive power, with the PCA-derived features exhibiting the best performance, having a Variation Inflation Factor (VIF) of 1.004. Slope appeared as the most influential factor (51.7% contribution), while the Topographic Wetness Index (TWI) was less dominant with only 6.6%. Multiple landslide susceptibility mapping methods yielded consistent results, with 29.8–30.1% of the study area showing moderate susceptibility and 35.2–36.9% in the high to very high susceptibility class. The model also incorporated vulnerability parameters weighted by the United Nations Office for Disaster Risk Reduction (UNDRR) indicators, including farmland, buildings, bare land, water bodies, roads, and amenities to generate hazard, vulnerability, and risk maps. The results were verified through visual comparison with high-resolution Google Earth imagery. The Permutation-Weighted model performed better than others, categorizing 12.4% at high-risk, while Random Forest (RF) categorized 7.2% at high risk. This study makes three key contributions: (1) It establishes the effectiveness of PCA/VIF for variable selection, (2) it provides a comparison of machine learning weighting techniques, and (3) it validates a workflow applicable to data-scarce regions. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization, 2nd Edition)
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25 pages, 2631 KB  
Article
Machine Learning Approaches for Detecting Hate-Driven Violence on Social Media
by Yousef Abuhamda and Pedro García-Teodoro
Appl. Sci. 2025, 15(21), 11323; https://doi.org/10.3390/app152111323 - 22 Oct 2025
Viewed by 1464
Abstract
Cyberbullying and hate-driven behavior on social media have become increasingly prevalent, posing serious psychological and social risks. This study proposes a machine learning-based approach to detect hate-driven content by integrating temporal and behavioral features—such as message frequency, interaction duration, and user activity patterns—alongside [...] Read more.
Cyberbullying and hate-driven behavior on social media have become increasingly prevalent, posing serious psychological and social risks. This study proposes a machine learning-based approach to detect hate-driven content by integrating temporal and behavioral features—such as message frequency, interaction duration, and user activity patterns—alongside traditional text-based features. Furthermore, we extend our evaluation to include recent neural network architectures, namely ALBERT and BiLSTM, enabling a more robust representation of semantic and sequential patterns. Building on our previous research presented at JNIC-2024, we conduct a comparative evaluation of multiple classification algorithms using both existing and engineered datasets. The results show that incorporating non-textual features significantly improves detection accuracy and robustness. This work contributes to the development of intelligent cyberbullying detection systems and highlights the importance of behavioral context in online threat analysis. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization, 2nd Edition)
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Review

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25 pages, 1779 KB  
Review
Machine Learning for Adaptive Accessible User Interfaces: Overview and Applications
by Mihaela Kristić, Ivona Zakarija, Frano Škopljanac-Mačina and Željka Car
Appl. Sci. 2025, 15(23), 12538; https://doi.org/10.3390/app152312538 - 26 Nov 2025
Cited by 1 | Viewed by 2719
Abstract
This paper presents a systematic literature review on the use of machine learning (ML) for developing adaptive accessible user interfaces (AUI) with emphasis on applications in emerging technologies such as augmented and virtual reality (AR/VR). The review, conducted according to the PRISMA 2020 [...] Read more.
This paper presents a systematic literature review on the use of machine learning (ML) for developing adaptive accessible user interfaces (AUI) with emphasis on applications in emerging technologies such as augmented and virtual reality (AR/VR). The review, conducted according to the PRISMA 2020 methodology, included 57 studies published between 2018 and 2025. Among them we identified 24 papers explicitly describing ML-based adaptive interface solutions. Supervised learning was dominant (83% of studies) with only isolated cases of reinforcement, generative AI, and fuzzy–NLP hybrid paradigms. The analysis of all 57 papers included in review revealed that adaptive interfaces dominate current research (65%), while intelligent or hybrid systems remain less explored. Mobile platforms were the most prevalent implementation environment (25%), followed by web-based (19%) and multi-platform systems (11%), with immersive (VR/XR) and IoT contexts still emerging. Among 43 studies addressing accessibility, the most were focused on visual impairments (33%), followed by cognitive and learning disorders (25%). The results of this review can inform the creation of accessibility guidelines in emerging AR and VR applications and support the development of inclusive solutions that benefit people with disabilities, older adults, and the general population. The main contribution of this paper lies in identifying existing gaps in the integration of accessibility and Universal Design principles into ML-based adaptive systems and in proposing a new AUI model that enables user-approved, time-delayed adaptations through machine learning, balancing autonomy, personalization, and user control. Full article
(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization, 2nd Edition)
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