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Showing 20 of 987 academic articles
Daniel Veltri, Uday Kamath, Amarda Shehu • Bioinformatics
(2018)
Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by w...
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DOI: https://doi.org/10.1093/bioinformatics/bty179
PMC: 6084614
Sidrah Liaqat, Kia Dashtipour, Adnan Zahid, Khaled Assaleh et al. • Information
(2020)
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocar...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/info11120549
Asma Cherif, Arwa Badhib, Heyfa Ammar, Suhair Alshehri et al. • Journal of King Saud University: Computer and Information Sciences
(2023)
Credit card fraud is becoming a serious and growing problem as a result of the emergence of innovative technologies and communication methods, such as contactless payment. In this article, we present an in-depth review of cutting-edge resea...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.jksuci.2022.11.008
Fuyong Xing, Yuanpu Xie, Hai Su, Fujun Liu et al. • IEEE Transactions on Neural Networks and Learning Systems
(2017)
Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerg...
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DOI: https://doi.org/10.1109/tnnls.2017.2766168
Changchun Yang, Hengrong Lan, Feng Gao, Fei Gao • Photoacoustics
(2020)
Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.pacs.2020.100215
PMC: 7779783
• De Gruyter eBooks
(2020)
This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep...
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DOI: https://doi.org/10.1515/9783110670905
Heang‐Ping Chan, Lubomir M. Hadjiiski, Ravi K. Samala • Medical Physics
(2020)
Computer-aided diagnosis (CAD) has been a major field of research for the past few decades. CAD uses machine learning methods to analyze imaging and/or nonimaging patient data and makes assessment of the patient's condition, which can then ...
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Open Access
DOI: https://doi.org/10.1002/mp.13764
PMC: 7293164
Iraj Abedi, Mahsa Vali, Bentolhoda Otroshi, Maryam Zamanian et al. • BMC Research Notes
(2024)
Abstract Introduction Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the ...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s13104-024-06693-z
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
Myeongsuk Pak, Sang-Hoon Kim • International Conference on Computer Applications and Information Processing Technology (CAIPT)
(2017)
Deep learning has been the core topic in machine learning and convolutional neural network is one of the most prominent approaches. Convolutional neural network has won numerous competitions in recent years. It has outstanding results in im...
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DOI: https://doi.org/10.1109/caipt.2017.8320684
D. Guest, Kyle Stuart Cranmer, Daniel Whiteson • Annual Review of Nuclear and Particle Science
(2018)
Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex ...
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Open Access
DOI: https://doi.org/10.1146/annurev-nucl-101917-021019
Thomas Serre • Annual Review of Vision Science
(2019)
Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains...
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DOI: https://doi.org/10.1146/annurev-vision-091718-014951
Sara Sheehan, Yun S. Song • PLoS Computational Biology
(2016)
Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep...
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DOI: https://doi.org/10.1371/journal.pcbi.1004845
PMC: 4809617
Ricardo Hernández Medina, Svetlana Kutuzova, Knud Nor Nielsen, Joachim Johansen et al. • ISME Communications
(2022)
The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and bio...
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DOI: https://doi.org/10.1038/s43705-022-00182-9
PMC: 9723725
Junjie Ji, Yongzhang Zhou, Qiuming Cheng, Shoujun Jiang et al. • Land
(2023)
Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/land12061125
Priya Mathur, Dheeraj Kumar, Farhan Sheth, Hammad Shaikh et al. • Scientific Reports
(2025)
Abstract Accurately predicting the specific heat capacity of nanofluids is critical for optimizing their performance in engineering and industrial applications. This study explores twelve machine learning and deep learning models using conv...
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Open Access (DOAJ)
DOI: 10.1038/s41598-025-28268-z
Marwan Aly, Mostafa Kamal, Mahmoud Ossman, Kamel El-Sehly et al. • Kafrelsheikh Journal of Information Sciences
(2021)
This paper proposed a smart wearable system for heart disease detection using machine learning and embedded systems. A smart wearable system that able to monitor the heart beat rate condition of patient. The heart beat rate is detected usin...
DOAJ
Open Access (DOAJ)
DOI: 10.21608/kjis.2021.42695.1009
Shaveta Dargan, Munish Kumar, Maruthi Rohit Ayyagari, Gulshan Kumar • Archives of Computational Methods in Engineering
(2019)
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DOI: https://doi.org/10.1007/s11831-019-09344-w
Nitin Kumar Chauhan, Krishna Pal Singh • International Conference on Computing, Power and Communication Technologies (GUCON)
(2018)
In now days, deep learning has become a prominent and emerging research area in computer vision applications. Deep learning permits the multiple layers models for computation to learn representations of data by processing in their original ...
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DOI: https://doi.org/10.1109/gucon.2018.8675097
Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, Kiana Jafari Meimandi et al. • International Conference on Machine Learning and Applications
(2017)
Increasingly large document collections require improved information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an importan...
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Open Access
DOI: https://doi.org/10.1109/icmla.2017.0-134