Home »
Academic Articles & Research Papers
Showing 20 of 506 academic articles
Qing Huang, Xiaoyan Zhang, Anqi Jin, Menghui Lei et al. • Journal of Marine Science and Engineering
(2025)
Many studies have used various time-frequency feature extraction methods to convert ship-radiated noise into three-dimensional (3D) data suitable for computer vision (CV) models, which have shown good results in public datasets. However, tr...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/jmse13030599
Zhiwen Xiong, Yuanchun Li • Scientific Reports
(2026)
Abstract Salient object detection (SOD) plays a crucial role in computer vision by mimicking human visual attention to identify and segment the most prominent objects in a scene. The advent of depth sensing technologies has propelled RGB-D ...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-026-56237-7
Dakang Guo, Jiatao Wang, Zeyuan Lin, Youfu Liu et al. • Poultry Science
(2026)
In the stereoscopic cage-rearing system, monitoring the individual egg production of laying ducks is essential for identifying low-yield individuals and optimizing breeding management. To overcome the high deployment cost of multi-sensor mo...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.psj.2026.106479
Qianxia Jiang, Xiayu Summer Chen, Dev Patel, Keith Brazendale et al. • JMIR Pediatrics and Parenting
(2026)
Abstract
BackgroundAI-enabled chatbots and related conversational systems can facilitate human–computer interaction through natural language, personalization, and automated support. In pediatric health promotion, these tools hav...
DOAJ
Open Access (DOAJ)
DOI: 10.2196/98889
Faisal Mehmood, Shabir Ahmad, Taeg Keun Whangbo • Mathematics
(2023)
Deep learning is a sub-branch of artificial intelligence that acquires knowledge by training a neural network. It has many applications in the field of banking, automobile industry, agriculture, and healthcare industry. Deep learning has pl...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/math11061360
Juhyeon Kim, Emin Orhan, Kijung Yoon, Xaq Pitkow • IEEE Access
(2022)
Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this complexity, typically down to a scalar activation function of a...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2022.3178951
Mohaiminul Islam, Guorong Chen, Shangzhu Jin • American Journal of Neural Networks and Applications
(2019)
Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in ...
OPENALEX
cc-by
DOI: https://doi.org/10.11648/j.ajnna.20190501.12
Frank R. Burden, David Alan Winkler • Methods in molecular biology
(2008)
Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts ...
OPENALEX
DOI: https://doi.org/10.1007/978-1-60327-101-1_3
Itay Hubara, Daniel Soudry, Yaniv, Ran El • arXiv (Cornell University)
(2016)
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time and when computing the parameters' gradient at train-time. We conduct two sets of experiments, each based on a...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1602.02505
Rui Zhang, Ranran Zhou, Zuting Zhong, Haifeng Qi et al. • Sensors
(2024)
Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their high weight compression rate. However, multi-class classification of ECG signals based on bCN...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/s24227207
Holger Schwenk, Yoshua Bengio • Neural Computation
(2000)
Boosting is a general method for improving the performance of learning algorithms. A recently proposed boosting algorithm, AdaBoost, has been applied with great success to several benchmark machine learning problems using mainly decision tr...
OPENALEX
DOI: https://doi.org/10.1162/089976600300015178
Ye Yuan, Yongtong Zhu, Jiaqi Wang, Ruoshi Li et al. • Frontiers in Neuroscience
(2023)
IntroductionSpiking neural networks (SNNs), inspired by biological neural networks, have received a surge of interest due to its temporal encoding. Biological neural networks are driven by multiple plasticities, including spike timing-depen...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fnins.2023.1224752
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness et al. • Proceedings of the National Academy of Sciences
(2017)
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitabl...
OPENALEX
Open Access
DOI: https://doi.org/10.1073/pnas.1611835114
PMC: 5380101
Uihoon Jung, Miseong Kim, Jaewon Jang, Jin‐Hyuk Bae et al. • Advanced Science
(2024)
Abstract With increasing demand for wearable electronics capable of computing huge data, flexible neuromorphic systems mimicking brain functions have been receiving much attention. Despite considerable efforts in developing practical neural...
DOAJ
Open Access (DOAJ)
DOI: 10.1002/advs.202307494
XIONG Zhongmin, ZENG Qi, LU Peng, WANG Zhenhua, ZHENG Zongsheng • Jisuanji gongcheng
(2023)
Logical reasoning is the ability to perceive patterns and connections between visual elements. Endowing computers with human-like reasoning ability is a critical area of research；state-of-the-art deep neural networks have achieved superhuma...
DOAJ
Open Access (DOAJ)
DOI: 10.19678/j.issn.1000-3428.0064591
Andrej Krenker, Janez Bešter, Andrej Kos • InTech eBooks
(2011)
An Artificial Neural Network (ANN) is a mathematical model that tries to simulate the structure and functionalities of biological neural networks. Basic building block of every artificial neural network is artificial neuron, that is, a simp...
OPENALEX
cc-by-nc-sa
DOI: https://doi.org/10.5772/15751
Xin Yao • Proceedings of the IEEE
(1999)
Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations betwee...
OPENALEX
DOI: https://doi.org/10.1109/5.784219
Hojjat Adeli, Xiaomo Jiang • CRC Press eBooks
(2008)
An artificial neural network (ANN) is a functional abstraction of the biologic neural structures of the central nervous system (Aleksander and Morton 1993; Rudomin et al. 1993; Arbib 1995; Anderson 1995). It is essentially a simple mathemat...
OPENALEX
DOI: https://doi.org/10.1201/9781482281767-9
Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger • arXiv (Cornell University)
(2017)
Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike thos...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1706.04599
Zuo-cheng Yang, Bo-wen Xue, Xin-yu Song, Chuan-dong Yin et al. • Frontiers in Neuroscience
(2024)
ObjectivesThis study aimed to elucidate the influences of 1p/19q co-deletion on structural connectivity alterations in patients with dominant hemisphere insular diffuse gliomas.MethodsWe incorporated 32 cases of left insular gliomas and 20 ...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fnins.2024.1283518