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Deep learning in nano-photonics: inverse design and beyond

Peter R. Wiecha, Arnaud Arbouet, Christian Girard, Otto L. Muskens • Photonics Research (2021)

Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nano-structures. Many of the recent works on machine-learning inverse design are highly specific, and the...

OPENALEX Open Access DOI: https://doi.org/10.1364/prj.415960

Deep Learning in Image Cytometry: A Review

Anindya Gupta, Philip John Harrison, Håkan Wieslander, Nicolas Pielawski et al. • Cytometry Part A (2018)

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and ho...

OPENALEX cc-by-nc DOI: https://doi.org/10.1002/cyto.a.23701 PMC: 6590257

Deep learning in computational mechanics: a review

Leon Herrmann, Stefan Kollmannsberger • Computational Mechanics (2024)

Abstract The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies w...

OPENALEX cc-by DOI: https://doi.org/10.1007/s00466-023-02434-4

On optimization methods for deep learning

Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow et al. • International Conference on Machine Learning (2011)

The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are difficult to tune and parallelize. These problems make it challenging to dev...

OPENALEX

Deep Learning

Dulani Meedeniya • Chapman & Hall/CRC eBooks (2023)

This book focuses on deep learning (DL), which is an important aspect of data science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising....

OPENALEX public-domain DOI: https://doi.org/10.1201/9781003390824

A Preliminary Study on the Auto-Segmentation of Targets and Organs at Risk in Pediatric Total Marrow and Lymphoid Irradiation

Zhihua Xie BS, Jinfeng Liu PhD, Lixia Hou MS, Haoran Feng MS et al. • Technology in Cancer Research & Treatment (2026)

Introduction Leukemia is one of the most prevalent cancers in children. The use of total marrow and lymphoid irradiation (TMLI) via helical tomotherapy (TOMO) as a conditioning regimen prior to bone marrow transplant (BMT) has been widely a...

DOAJ Open Access (DOAJ) DOI: 10.1177/15330338261426261

Identifying urban villages using graph neural network and transfer learning in Southern Jiangsu, China

Zhenkang Wang, Nan Xia, Jiale Liang, Jiechen Wang • Geo-spatial Information Science (2026)

China’s rapid urbanization has led to the emergence of urban villages (UVs), whose governance is crucial for urban quality and built environment management. However, conventional identification methods rely on high-resolution remote sensing...

DOAJ Open Access (DOAJ) DOI: 10.1080/10095020.2026.2711549

Face recognition with Bayesian convolutional networks for robust surveillance systems

Umara Zafar, Mubeen Ghafoor, Tehseen Zia, Ghufran Ahmed et al. • EURASIP Journal on Image and Video Processing (2019)

Abstract Recognition of facial images is one of the most challenging research issues in surveillance systems due to different problems including varying pose, expression, illumination, and resolution. The robustness of recognition method st...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13640-019-0406-y

A Space-Time Partial Differential Equation Based Physics-Guided Neural Network for Sea Surface Temperature Prediction

Taikang Yuan, Junxing Zhu, Wuxin Wang, Jingze Lu et al. • Remote Sensing (2023)

Sea surface temperature (SST) prediction has attracted increasing attention, due to its crucial role in understanding the Earth’s climate and ocean system. Existing SST prediction methods are typically based on either physics-based numerica...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs15143498

Detection of short-term peaks in VLF/LF signals around the time of gamma-ray burst detections using deep learning

Sarnovský M., Nina A., Popović L.Č., Srećković V. et al. • Serbian Astronomical Journal (2026)

Gamma-ray bursts (GRBs) are recognized as the most energetic and luminous astrophysical phenomena whose high-energy photons can ionize atmospheric particles and thus affect electrical conductivity. Consequently, electromagnetic waves ...

DOAJ Open Access (DOAJ) DOI: 10.2298/SAJ2612107S

Understanding the role of autoencoders for stiff dynamical systems using information theory

Vijayamanikandan Vijayarangan, Harshavardhana A. Uranakara, Francisco E. Hernández–Pérez, Hong G. Im • Energy and AI (2025)

Using information theory, this study provides insights into how the construction of latent space of autoencoder (AE) using deep neural network (DNN) training finds a smooth (non-stiff) low-dimensional manifold in the stiff dynamical system....

DOAJ Open Access (DOAJ) DOI: 10.1016/j.egyai.2025.100567

Providing a framework for designing electronic and virtual learning environments based on the principles and criteria of cognitive flexibility

Z. Karami • Fanāvarī-i āmūzish (2023)

Background and Objectives: In order to solve the problem of education in complex and unstructured domains, Spiro and his colleagues proposed the theory of cognitive flexibility, in which they advocated multiple forms of pedagogical models, ...

DOAJ Open Access (DOAJ) DOI: 10.22061/tej.2023.9686.2885

A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence

Ali Raza, Mohammad Rustom Al Nasar, Essam Said Hanandeh, Raed Abu Zitar et al. • Technologies (2023)

Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily li...

DOAJ Open Access (DOAJ) DOI: 10.3390/technologies11020055

A Review of Deep Learning Applications in Intrusion Detection Systems: Overcoming Challenges in Spatiotemporal Feature Extraction and Data Imbalance

Ya Zhang, Ravie Chandren Muniyandi, Faizan Qamar • Applied Sciences (2025)

In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. T...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15031552

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron

Milad Mozafari, Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini et al. • Frontiers in Neuroscience (2019)

Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient. Unlike the non-spiking counterparts, most of...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnins.2019.00625

A Comprehensive Survey on Graph Anomaly Detection With Deep Learning

Xiaoxiao Ma, Jia Xin Wu, Shan Xue, Jian Yu Yang et al. • IEEE Transactions on Knowledge and Data Engineering (2021)

Anomalies are rare observations (e.g., data records or events) that deviate significantly from the others in the sample. Over the past few decades, research on anomaly mining has received increasing interests due to the implications of thes...

OPENALEX Open Access DOI: https://doi.org/10.1109/tkde.2021.3118815

Fundamentals of Deep Learning

P. V. Chandrika, Sandeep Kelkar • Advances in medical diagnosis, treatment, and care (AMDTC) book series (2024)

This chapter deals with the basic understanding of the deep learning model. It starts with a view of artificial intelligence, machine learning, and deep learning. It also represents how these three concepts are related to each other. The en...

OPENALEX DOI: https://doi.org/10.4018/979-8-3693-1281-0.ch001