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Deep learning improves antimicrobial peptide recognition

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...

OPENALEX cc-by-nc DOI: https://doi.org/10.1093/bioinformatics/bty179 PMC: 6084614

Credit card fraud detection in the era of disruptive technologies: A systematic review

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

Deep Learning in Microscopy Image Analysis: A Survey

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...

OPENALEX DOI: https://doi.org/10.1109/tnnls.2017.2766168

Review of deep learning for photoacoustic imaging

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...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.pacs.2020.100215 PMC: 7779783

Deep Learning

• 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...

OPENALEX DOI: https://doi.org/10.1515/9783110670905

Computer‐aided diagnosis in the era of deep learning

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 ...

OPENALEX Open Access DOI: https://doi.org/10.1002/mp.13764 PMC: 7293164

HRCTCov19-a high-resolution chest CT scan image dataset for COVID-19 diagnosis and differentiation

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

A review of deep learning in image recognition

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...

OPENALEX DOI: https://doi.org/10.1109/caipt.2017.8320684

Deep Learning and Its Application to LHC Physics

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 ...

OPENALEX Open Access DOI: https://doi.org/10.1146/annurev-nucl-101917-021019

Deep Learning: The Good, the Bad, and the Ugly

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...

OPENALEX DOI: https://doi.org/10.1146/annurev-vision-091718-014951

Deep Learning for Population Genetic Inference

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...

OPENALEX cc-by DOI: https://doi.org/10.1371/journal.pcbi.1004845 PMC: 4809617

Machine learning and deep learning applications in microbiome research

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...

OPENALEX cc-by DOI: https://doi.org/10.1038/s43705-022-00182-9 PMC: 9723725

Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization

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

Comprehensive framework of machine learning and deep learning architectures with metaheuristic optimization for high-fidelity prediction of nanofluid specific heat capacity

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...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-28268-z

Heart Disease Detection using ML and ES (Smart Wearable Health Monitoring System)

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

A Review on Conventional Machine Learning vs Deep Learning

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 ...

OPENALEX DOI: https://doi.org/10.1109/gucon.2018.8675097

HDLTex: Hierarchical Deep Learning for Text Classification

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...

OPENALEX Open Access DOI: https://doi.org/10.1109/icmla.2017.0-134