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Academic Articles & Research Papers
Showing 20 of 74 academic articles
Taiyu Zhu, Kezhi Li, Pau Herrero, Pantelis Georgiou • IEEE Journal of Biomedical and Health Informatics
(2020)
Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabetes, digital health has been widely adopted in recent years and generated a huge amount of ...
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DOI: https://doi.org/10.1109/jbhi.2020.3040225
Xinxin Yu, Yimeng Wang, Long Chen, Weihua Li et al. • Journal of Pharmaceutical Analysis
(2025)
Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid m...
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Open Access (DOAJ)
DOI: 10.1016/j.jpha.2025.101317
Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni et al. • Frontiers in Cardiovascular Medicine
(2020)
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalit...
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DOI: https://doi.org/10.3389/fcvm.2020.00025
PMC: 7066212
Samane Esmaelzade Kalkhoran, Seyyed Saeed Ghannadpour • Scientific Reports
(2026)
Abstract Porphyry Cu deposits in the Urmia–Dokhtar magmatic belt constitute important exploration targets, yet the heterogeneous and multi-scale nature of geological, geochemical, and remote sensing data introduces substantial uncertainty i...
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Open Access (DOAJ)
DOI: 10.1038/s41598-026-61085-6
Sai Sri Hemantha Konala, Srinivas Koppu • Frontiers in Medicine
(2026)
IntroductionKidney-related disorders are one of the global health concerns that require timely detection to prevent severe health complications. The use of computed tomography (CT) images for accurate classification of kidney diseases is im...
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Open Access (DOAJ)
DOI: 10.3389/fmed.2026.1893419
Yihan Chen, Xingyu Gu, Zhen Liu, Jia Liang • Remote Sensing
(2022)
Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful a...
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Open Access (DOAJ)
DOI: 10.3390/rs14081877
Shiyao Duan, Jiaojiao Li, Rui Song, Yunsong Li et al. • Remote Sensing
(2023)
Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design. Additionally, models excl...
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Open Access (DOAJ)
DOI: 10.3390/rs15102619
Shiv Singh, Neelu Jyothi Ahuja • Discover Computing
(2026)
Abstract The application of deep learning models in resource-constrained environments requires a trade-off among accuracy, efficiency, and interpretability a trilemma frequently neglected in conventional knowledge distillation (KD) approach...
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Open Access (DOAJ)
DOI: 10.1007/s10791-026-10123-y
José Padarian, Budiman Minasny, Alex B. McBratney • SOIL
(2020)
Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proxi...
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DOI: https://doi.org/10.5194/soil-6-35-2020
Sergio Muñoz, Carlos Á. Iglesias • Big Data and Cognitive Computing
(2024)
The proliferation of fake news threatens the integrity of information ecosystems, creating a pressing need for effective and interpretable detection mechanisms. Recent advances in machine learning, particularly with transformer-based models...
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Open Access (DOAJ)
DOI: 10.3390/bdcc8100129
Min-Hwa Choi, Woongchang Yoon • IEEE Access
(2025)
Port congestion and prolonged ship waiting times pose challenges for global trade and increase operational costs and inefficiencies. In this study, a novel machine learning-based predictive approach was proposed to improve port operations b...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3566429
Van-Ngoc Pham, Huu-Dao Do, Thi Phuong Khue Nguyen, Van-Khanh Tran et al. • Tạp chí Khoa học và Công nghệ
(2026)
This study develops a data-driven framework using Extreme Gradient Boosting (XGBoost) to predict the Indirect Tensile Strength (ITS) of cement-treated clayey soils. Using 180 specimens with five input variables - cement content, curing time...
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Open Access (DOAJ)
DOI: 10.31130/ud-jst.2026.24(7B).582E
ChunYu Meng, FeiFei Zuo, Shuai Liu, YuLiang Ji et al. • Frontiers in Medicine
(2026)
BackgroundThis study aimed to develop an interpretable and clinically applicable machine learning prediction model to assess the risk of adverse outcomes within one year following acute ischaemic stroke, and to establish a corresponding web...
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Open Access (DOAJ)
DOI: 10.3389/fmed.2026.1956859
Pengsheng Hui, Yu Jiang, Jie Wang, Congxiao Wang et al. • Frontiers in Aging Neuroscience
(2024)
BackgroundParkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a cri...
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Open Access (DOAJ)
DOI: 10.3389/fnagi.2024.1354147
Wenbing Zhu, Weiqiang Zhang, Liming Yong • Frontiers in Psychiatry
(2026)
BackgroundExercise intervention has been associated with cognitive development in children. However, current study methods have not clarified the potential nonlinear association between exercise dosage and cognitive ability, and interpretab...
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Open Access (DOAJ)
DOI: 10.3389/fpsyt.2026.1908580
Alberto Gil-Maciá, J. Enrique Sierra-García, Matilde Santos • Machine Learning with Applications
(2026)
Discrete reinforcement learning (RL) offers interpretability advantages compared to continuous reinforcement learning. Neural networks used in continuous RL behave like black boxes that do not show an obvious relationship between the system...
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Open Access (DOAJ)
DOI: 10.1016/j.mlwa.2026.100919
Somporn Sahachaiseree, Takashi Oguchi • IET Intelligent Transport Systems
(2025)
ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximat...
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Open Access (DOAJ)
DOI: 10.1049/itr2.70034
Inese Polaka, Danute Razuka-Ebela, Jin Young Park, Marcis Leja • BioData Mining
(2021)
Abstract Background The amount of available and potentially significant data describing study subjects is ever growing with the introduction and integration of different registries and data banks. The single specific attribute of these data...
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Open Access (DOAJ)
DOI: 10.1186/s13040-021-00271-w
Ifiok Udoidiok, Fuhao Li, Jielun Zhang • Information
(2025)
Machine learning (ML) has become a cornerstone of critical applications, but its vulnerability to data poisoning attacks threatens system reliability and trustworthiness. Prior studies have begun to investigate the impact of data poisoning ...
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Open Access (DOAJ)
DOI: 10.3390/info17010009
B.S. Prashanth, Manoj Kumar, Ariful Hoque, Nasser Al Muraqab et al. • International Review of Economics & Finance
(2026)
The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems b...
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Open Access (DOAJ)
DOI: 10.1016/j.iref.2026.104916