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Academic Articles & Research Papers
Showing 20 of 233 academic articles
Txomin Hermosilla • ELCVIA Electronic Letters on Computer Vision and Image Analysis
(2014)
Urban areas areimportant environments, accounting for approximately half the population of theworld. Cities attract residents partly because they offer ample opportunitiesfor development, which often results in urban sprawl and its complex ...
DOAJ
Open Access (DOAJ)
DOI: 10.5565/rev/elcvia.584
Amol Bhosle, Deepali Godse, Sandip Thite, Kailas Patil et al. • Data in Brief
(2025)
This data paper provides image dataset that includes 8432 high-quality images of Tamarindus indica [1] (tamarind), categorized into six types: Shelled Healthy Single, Shelled Healthy Multiple, Unshelled Healthy Single, Unshelled Healthy Mul...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.dib.2025.111917
Lakshmi Sree Pugalenthi, Chris Garapati, Srivarshini Maddukuri, Fnu Kanwal et al. • Journal of Vascular Diseases
(2025)
Background: Varicose veins (VVs) of the lower limbs, characterized by palpable, dilated, and tortuous veins, affect 2–73% of the global population. Artificial intelligence (AI) offers significant potential to enhance healthcare efficiency a...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/jvd4020019
Jinxin Chen, Luo Liu, Peng Li, Wen Yao et al. • Agriculture
(2025)
The resting postures of piglets are crucial indicators for assessing their health status and environmental comfort. This study proposes a resting posture recognition method for piglets during lactation based on the PPR-YOLO model, aiming to...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/agriculture15030230
Kalab Yigermal Gete, Asnakew Achaw Ayele • BMC Neurology
(2025)
Abstract Background Non-contrast CT (NCCT) is first-line imaging for suspected acute ischemic stroke (AIS) but has limited early sensitivity; deep learning (DL) may improve patient-level detection. Objectives To estimate the diagnostic accu...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s12883-025-04528-3
Yanming Yang, Xin Xia, David Lo, John Grundy • ACM Computing Surveys
(2021)
In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)” and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstr...
OPENALEX
Open Access
DOI: https://doi.org/10.1145/3505243
Phillip M. Cheng, Emmanuel Montagnon, Rikiya Yamashita, Ian Pan et al. • Radiographics
(2021)
Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the...
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DOI: https://doi.org/10.1148/rg.2021200210
Khoa Anh Tran, Olga Kondrashova, Andrew Peter Bradley, Elizabeth D. Williams et al. • Genome Medicine
(2021)
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning acr...
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DOI: https://doi.org/10.1186/s13073-021-00968-x
PMC: 8477474
Marek L. Borowiec, Rebecca B. Dikow, Paul B. Frandsen, Alexander J. McKeeken et al. • Methods in Ecology and Evolution
(2022)
Abstract Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been use...
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DOI: https://doi.org/10.1111/2041-210x.13901
Marco Paracchini, Marco Marcon, Federica Villa, Franco Zappa et al. • Sensors
(2020)
The problem of performing remote biomedical measurements using just a video stream of a subject face is called remote photoplethysmography (rPPG). The aim of this work is to propose a novel method able to perform rPPG using single-photon av...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/s20216102
Raghavendra Chalapathy, Sanjay Chawla • arXiv (Cornell University)
(2019)
Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods i...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1901.03407
Polina Mamoshina, Armando S. Vieira, Eugene Lane, Alex A. Zhavoronkov • Molecular Pharmaceutics
(2016)
Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this ...
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DOI: https://doi.org/10.1021/acs.molpharmaceut.5b00982
Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay • ACM Computing Surveys
(2019)
With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applic...
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Open Access
DOI: https://doi.org/10.1145/3285029
Max Mowbray, Thomas Savage, Chufan Wu, Ziqi Song et al. • Biochemical Engineering Journal
(2021)
The field of machine learning is comprised of techniques, which have proven powerful approaches to knowledge discovery and construction of ‘digital twins’ in the highly dimensional, nonlinear and stochastic domains common to biochemical eng...
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DOI: https://doi.org/10.1016/j.bej.2021.108054
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
Marco Fiorucci, Marina Khoroshiltseva, Massimiliano Pontil, Arianna Traviglia et al. • Pattern Recognition Letters
(2020)
The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the unde...
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DOI: https://doi.org/10.1016/j.patrec.2020.02.017
Yernazar Bolat, Iain Murray, Yifei Ren, Nasim Ferdosian • Sensors
(2025)
The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challeng...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/s25154595
Jafar Ahmad Abed Alzubi, Anand Nayyar, Akshi Kumar • Journal of Physics Conference Series
(2018)
The current SMAC (Social, Mobile, Analytic, Cloud) technology trend paves the way to a future in which intelligent machines, networked processes and big data are brought together. This virtual world has generated vast amount of data which i...
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DOI: https://doi.org/10.1088/1742-6596/1142/1/012012
Aleksanteri Hamalainen, Aku Karhinen, Jesse Miettinen, Zacharias Dahl et al. • IEEE Access
(2025)
Rotating machines are extremely common in many industries, and their maintenance involves substantial costs and labor. Most recent studies aiming to automate fault diagnosis have focused on deep learning, but industry adoption has been slow...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3627885
Sebastian Raschka, Joshua M. Patterson, Corey Nolet • Information
(2020)
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the...
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DOI: https://doi.org/10.3390/info11040193