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Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function

Georgios Petmezas, Grigorios-Aris Cheimariotis, Leandros Stefanopoulos, Bruno Rocha et al. • Sensors (2022)

Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the ef...

DOAJ Open Access (DOAJ) DOI: 10.3390/s22031232

Deep Learning: A Review

Rocio Vargas, Amir Hosein Mosavi, Ramon Ruiz • Preprints.org (2018)

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level i...

OPENALEX cc-by DOI: https://doi.org/10.20944/preprints201810.0218.v1

Deep Learning in Proteomics

Bo Wen, Wen‐Feng Zeng, Yuxing Liao, Zhiao Shi et al. • PROTEOMICS (2020)

Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS)...

OPENALEX cc-by DOI: https://doi.org/10.1002/pmic.201900335 PMC: 7757195

Review of Deep Learning for Language Modeling

WANG Sili, ZHANG Ling, YANG Heng, LIU Wei • Nongye tushu qingbao xuebao (2023)

[Purpose/Significance] Deep learning for language modeling is one of the major methods and advanced technologies to enhance language intelligence of machines at present, which has become an indispensable important technical means for automa...

DOAJ Open Access (DOAJ) DOI: 10.13998/j.cnki.issn1002-1248.23-0251

Machine learning for advanced energy materials

Liu Yun, Oladapo Christopher Esan, Zhefei Pan, Liang An • Energy and AI (2021)

The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low su...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.egyai.2021.100049

A cascaded classification approach using transfer learning and feature engineering for improved breast cancer classification

Chokri Ferkous, Ouissal Fadel, Abderrahmane Kefali, Hayet-Farida Merouani • IJAIN (International Journal of Advances in Intelligent Informatics) (2026)

The primary objective of this study is to design a cascaded classification framework that integrates deep-learning representations with handcrafted and clinical features to enhance the reliability and accuracy of breast cancer detection in ...

DOAJ Open Access (DOAJ) DOI: 10.26555/ijain.v12i1.1670

Machine Learning in Orthopedics: A Literature Review

Federico Cabitza, Angela Locoro, Giuseppe Banfi • Frontiers in Bioengineering and Biotechnology (2018)

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic probl...

OPENALEX cc-by DOI: https://doi.org/10.3389/fbioe.2018.00075 PMC: 6030383

Bayesian machine learning-based method for prediction of slope failure time

Jie Zhang, Zipeng Wang, Jinzheng Hu, Shihao Xiao et al. • Journal of Rock Mechanics and Geotechnical Engineering (2022)

The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time (SFT). The observational and model uncertainties could lead the predicted SFT calculated from the phenomen...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.jrmge.2021.09.010

WEKA: a machine learning workbench

Geoffrey Holmes, A. Donkin, Ian H. Witten • Australian and New Zealand Intelligent Information Systems Conference (ANZIIS) (2002)

WEKA is a workbench for machine learning that is intended to aid in the application of machine learning techniques to a variety of real-world problems, in particular, those arising from agricultural and horticultural domains. Unlike other m...

OPENALEX DOI: https://doi.org/10.1109/anziis.1994.396988

Using machine learning to predict persecutory beliefs based on aetiological models of delusions identified in a systematic literature search

Saskia Denecke, Felix Strakeljahn, Antonia Bott, Tania M. Lincoln • Communications Psychology (2025)

Abstract Aetiological models of delusions propose a broad range of predictors. The extent to which these predictors explain variance in persecutory beliefs across the continuum requires systematic investigation. As part of a previous review...

DOAJ Open Access (DOAJ) DOI: 10.1038/s44271-025-00311-9

Machine learning in pain research

Jörn Lötsch, Alfred Ultsch • Pain (2017)

1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1097/j.pain.0000000000001118 PMC: 5895117

Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis

Lin Wang, Jiaming Su, Zhongjie Liu, Shaowei Ding et al. • BioData Mining (2024)

Abstract Background Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-st...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13040-024-00369-x

A Sensor-to-API Data Infrastructure for Building Digital Twins: Harmonizing Multi-Rate IoT Energy and Indoor Data Streams for Proactive Analytics

J. W. Njoroge, M. Koeva, M. Farnaghi, D. Petrova-Antonova et al. • ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2026)

Digital twins are increasingly used in smart buildings to monitor energy use. This requires reliable sensor data, geospatial information, and semantically rich 3D building geometry. However, building sensor data often has inconsistent sampl...

DOAJ Open Access (DOAJ) DOI: 10.5194/isprs-annals-XII-4-W1-2026-251-2026

Machine Learning in Virtual Screening

James L. Melville, Edmund K. Burke, Jonathan D. Hirst • Combinatorial Chemistry & High Throughput Screening (2009)

In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a partic...

OPENALEX DOI: https://doi.org/10.2174/138620709788167980

The challenge of studying perovskite solar cells’ stability with machine learning

Paolo Graniero, Paolo Graniero, Mark Khenkin, Hans Köbler et al. • Frontiers in Energy Research (2023)

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technolog...

DOAJ Open Access (DOAJ) DOI: 10.3389/fenrg.2023.1118654

Machine learning in polymer informatics

Wuxin Sha, Yan Li, Shun Tang, Jie Tian et al. • InfoMat (2021)

Abstract Polymers have been widely used in energy storage, construction, medicine, aerospace, and so on. However, the complexity of chemical composition and morphology of polymers has brought challenges to their development. Thanks to the i...

OPENALEX cc-by DOI: https://doi.org/10.1002/inf2.12167

Fatigue Monitoring Through Wearables: A State-of-the-Art Review

Neusa R. Adão Martins, Neusa R. Adão Martins, Simon Annaheim, Christina M. Spengler et al. • Frontiers in Physiology (2021)

The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems...

DOAJ Open Access (DOAJ) DOI: 10.3389/fphys.2021.790292

When more is not always better: exercise dose and children’s cognitive development from an interpretable machine learning perspective

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...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpsyt.2026.1908580

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach

Kai Zhao, Yujia Shi, Hon-Cheong So • Pharmaceutics (2022)

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles fro...

DOAJ Open Access (DOAJ) DOI: 10.3390/pharmaceutics14020234

Trinity: In-Database Near-Data Machine Learning Acceleration Platform for Advanced Data Analytics

Ji-Hoon Kim, Seunghee Han, Kwanghyun Park, Soo-Young Ji et al. • IEEE Access (2024)

The ability to perform machine learning (ML) tasks in a database management system (DBMS) is a new paradigm for conventional database systems as it enables advanced data analytics on top of well-established capabilities of DBMSs. However, t...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2023.3347725