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Academic Articles & Research Papers
Showing 20 of 74 academic articles
Michał Wawer, Jarosław A. Chudziak, Ewa Niewiadomska-Szynkiewicz • Applied Sciences
(2024)
Traditional technical analysis methods face limitations in accurately predicting trends in today’s complex financial markets. Meanwhile, existing AI-driven approaches, while powerful in processing large datasets, often lack interpretability...
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Open Access (DOAJ)
DOI: 10.3390/app142411897
Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T. Dudley, Alberto Lavelli et al. • JMIR Medical Informatics
(2019)
BACKGROUND: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the seconda...
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DOI: https://doi.org/10.2196/12239
PMC: 6528438
Julia El Zini, Mariette Awad • ACM Computing Surveys
(2022)
Despite their success, deep networks are used as black-box models with outputs that are not easily explainable during the learning and the prediction phases. This lack of interpretability is significantly limiting the adoption of such model...
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Open Access
DOI: https://doi.org/10.1145/3529755
Arezo Shakeri, Mina Farmanbar • Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
(2025)
Abstract INTRODUCTION Alzheimer's disease (AD) prevalence is increasing, with no current cure. Natural language processing (NLP) offers the potential for non‐invasive diagnostics, social burden assessment, and research advancements in AD. M...
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Open Access (DOAJ)
DOI: 10.1002/dad2.70082
Priyal Singhal, Praneet Saurabh, Mayank Sharma, Jyoti Badge et al. • Discover Artificial Intelligence
(2026)
Abstract Air pollution constitutes one of the most pressing environmental and public health crises of the twenty-first century, disproportionately affecting rapidly urbanising nations such as India. Accurate prediction of the Air Quality In...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44163-026-02131-0
Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann et al. • IEEE Transactions on Computational Biology and Bioinformatics
(2018)
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categori...
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Open Access
DOI: https://doi.org/10.1109/tcbb.2018.2849968
PMC: 6388621
Sumaya Mustafa, Mariwan Hama Saeed • Journal of Electrical Systems and Information Technology
(2025)
Abstract Artificial intelligence (AI) models have demonstrated significant success in classifying various types of text. However, the complex nature of these models often complicates the interpretability of their classifications. To address...
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Open Access (DOAJ)
DOI: 10.1186/s43067-025-00273-2
Aditi S. Krishnapriyan, Joseph Montoya, Maciej Haranczyk, Jens Hummelshøj et al. • Scientific Reports
(2021)
Abstract Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an ...
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Open Access (DOAJ)
DOI: 10.1038/s41598-021-88027-8
Maria Frasca, Davide La Torre, Gabriella Pravettoni, Ilaria Cutica • Discover Artificial Intelligence
(2024)
Abstract This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box alg...
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Open Access (DOAJ)
DOI: 10.1007/s44163-024-00114-7
Hang Li • National Science Review
(2017)
Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). Recently, deep ...
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DOI: https://doi.org/10.1093/nsr/nwx110
Shahrzad Gholami, Derek E. Lee, Caleb Robinson, Monica L. Bond et al. • Scientific Reports
(2026)
Abstract Accurate wildlife re-identification is critical for a wide range of ecological studies, including density estimation via capture–recapture, demographic analyses, and behavioral research. We pr esent GIRAFFE (Generalized Image-based...
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Open Access (DOAJ)
DOI: 10.1038/s41598-026-54115-w
Sharmarke A. Gabayre, Sergey Savel’ev, Varuna De Silva, Mindula Illeperuma et al. • Scientific Reports
(2026)
Abstract We present a Wilson–Cowan reservoir computer (WC–RC) that treats a retinotopic excitatory–inhibitory neural field as a structured reservoir. Travelling waves and bounded oscillations provide an interpretable spatiotemporal basis, w...
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Open Access (DOAJ)
DOI: 10.1038/s41598-026-49359-5
Eda Nur Urtekin, Emre Dandıl • Journal of Agricultural Sciences
(2026)
Sustainable food production increasingly relies on intelligent automation technologies that optimize resource usage while maintaining crop health. This study presents a deep learning-driven computer vision and web based monitoring system de...
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Open Access (DOAJ)
DOI: 10.15832/ankutbd.1817471
Zeynep Akata, Andreas Geiger, Torsten Sattler • International Journal of Computer Vision
(2021)
This special issue covers a wide range of topics from the area of Computer Vision, Pattern Recognition, and Machine Learning.This breadth of scope is reflected by the papers included in this special issue, which touch topics including geome...
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DOI: https://doi.org/10.1007/s11263-021-01522-3
Jiajun Guo, Qingge Ji, Guangwei Shan • IET Image Processing
(2024)
Abstract Current skeleton‐based action recognition methods usually assume the input skeleton is complete and noise‐free. However, it is inevitable that the captured skeletons are incomplete due to occlusions or noisy due to changes in the e...
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Open Access (DOAJ)
DOI: 10.1049/ipr2.12944
Bethany Lusch, J. Nathan Kutz, Steven L. Brunton • Nature Communications
(2018)
Identifying coordinate transformations that make strongly nonlinear dynamics approximately linear has the potential to enable nonlinear prediction, estimation, and control using linear theory. The Koopman operator is a leading data-driven e...
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DOI: https://doi.org/10.1038/s41467-018-07210-0
PMC: 6251871
Jianjing Zhang, Robert X. Gao • Chinese Journal of Mechanical Engineering
(2021)
Abstract Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production b...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s10033-021-00587-y
Syarifah Muliana, Taufiq Taufiq, Munirul Ula • Journal of Applied Informatics and Computing
(2026)
Customer churn is a major challenge in the insurance industry because it directly affects customer retention, business sustainability, and company profitability. Early identification of customers at risk of churn is therefore essential for ...
DOAJ
Open Access (DOAJ)
DOI: 10.30871/jaic.v10i3.12864
Dimitrios Georgiou, Sotiris Kotsiantis, Fotini Sereti • Mathematics
(2026)
Topological data analysis (TDA) has evolved into a flexible and robust paradigm for obtaining qualitative, geometry-inspired insights from high-dimensional, noisy, and complex data. Grounded in algebraic topology, geometry, statistics, and ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/math14122205
Mengnan Du, Fan Yang, Na Zou, Xia Hu • IEEE Intelligent Systems
(2020)
Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. We provide a review covering recent progresses to ta...
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Open Access
DOI: https://doi.org/10.1109/mis.2020.3000681