Reset
Showing 20 of 1,100 academic articles

SPIKING NEURAL NETWORKS

Samanwoy Ghosh‐Dastidar, Hojjat Adeli • International Journal of Neural Systems (2009)

Most current Artificial Neural Network (ANN) models are based on highly simplified brain dynamics. They have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. A...

OPENALEX DOI: https://doi.org/10.1142/s0129065709002002

An Improved Convolutional Neural Network (CNN) for Disease Detection and Diagnosis for Multi-crop Plants

Florence Choong Chiao Mei, Bryan Ng Jan Hong • Journal of Engineering Technology and Applied Physics (2025)

Agriculture is one of the largest sectors that contribute to the economic growth of countries, including Malaysia. However, plant diseases affect the quality of the harvest and impede farmers’ maximum yield output. Therefore, early detectio...

DOAJ Open Access (DOAJ) DOI: 10.33093/jetap.2025.7.1.2

Neural Networks for Chemists

Qingyang Xiao, Kaiyuan Liu, Yuhui Hong, Haixu Tang • ACS in focus (2024)

In recent years, breakthroughs in artificial intelligence (AI) have dramatically transformed many fields. Developments in neural networks primarily drive the advances. These powerful models have demonstrated unprecedented capabilities in le...

OPENALEX DOI: https://doi.org/10.1021/acsinfocus.7e8012

Evaluation of noise reduction methods in digital images: Classical filtering vs adaptive learning-based models

Subbulakshmi M, Mohammad Malik Mubeen S, Mohamed Ali E A • AIP Advances (2026)

Digital images acquired through imaging systems often lose their original fidelity due to various types of noise introduced during acquisition, storage, or transmission, consequently reducing clarity and adversely affecting subsequent image...

DOAJ Open Access (DOAJ) DOI: 10.1063/5.0332993

How structure determines correlations in neuronal networks.

Volker Pernice, Benjamin Staude, Stefano Cardanobile, Stefan Rotter • PLoS Computational Biology (2011)

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, ...

DOAJ Open Access (DOAJ) DOI: 10.1371/journal.pcbi.1002059

Tailoring Mission Effectiveness and Efficiency of a Ground Vehicle Using Exergy-Based Model Predictive Control (MPC)

Robert Jane, Tae Young Kim, Emily Glass, Emilee Mossman et al. • Energies (2021)

To ensure dominance over a multi-domain battlespace, energy and power utilization must be accurately characterized for the dissimilar operational conditions. Using MATLAB/Simulink in combination with multiple neural networks, we created a m...

DOAJ Open Access (DOAJ) DOI: 10.3390/en14196049

Neural networks and their applications

Chris M. Bishop • Review of Scientific Instruments (1994)

Neural networks provide a range of powerful new techniques for solving problems in pattern recognition, data analysis, and control. They have several notable features including high processing speeds and the ability to learn the solution to...

OPENALEX DOI: https://doi.org/10.1063/1.1144830

A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks

Enrique González-Plaza, David García, Jesús-Ignacio Prieto • Inventions (2023)

Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models ...

DOAJ Open Access (DOAJ) DOI: 10.3390/inventions8040088

Deep learning for digital holography: a review

Tianjiao Zeng, Yanmin Zhu, Edmund Y. M. Lam • Optics Express (2021)

Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities ...

OPENALEX Open Access DOI: https://doi.org/10.1364/oe.443367

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

Ji Su Hwang, Seok Gi Kim, Tae Hwan Shin, Yong Eun Jang et al. • Pharmaceutics (2022)

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cance...

DOAJ Open Access (DOAJ) DOI: 10.3390/pharmaceutics14050997

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

Computational Hyperspectral Camera Design Based on Co-Modulation of Diffractive Optical Element and Superposition Fabry-Perot Filter Array

Shiqi Feng, Xuquan Wang, Xiong Dun, Zhanshan Wang et al. • IEEE Photonics Journal (2025)

Encoded computational hyperspectral cameras, propelled by advances in compressed sensing theory, making both miniaturization and real-time hyperspectral imaging feasible. Spectral-encoded or spatial-encoded hyperspectral imaging strategy ha...

DOAJ Open Access (DOAJ) DOI: 10.1109/JPHOT.2025.3624799

On the Origin of Deep Learning

Haohan Wang, Bhiksha Raj • arXiv (Cornell University) (2017)

This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep ...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1702.07800

Deep learning for universal linear embeddings of nonlinear dynamics

Bethany Lusch, J. Nathan Kutz, Steven L. Brunton • Nature Communications (2018)

Identifying coordinate transformations that make strongly nonlinear dynamics approximately linear has the potential to enable nonlinear prediction, estimation, and control using linear theory. The Koopman operator is a leading data-driven e...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41467-018-07210-0 PMC: 6251871

Deep Learning with R

François Chollet, JJ Allaire (2018)

Introduces deep learning systems using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples...

OPENALEX

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia et al. • IEEE Geoscience and Remote Sensing Magazine (2017)

Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in ...

OPENALEX Open Access DOI: https://doi.org/10.1109/mgrs.2017.2762307

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