Neural Networks : the Official Journal of the International Neural Network Society
Overview
Neural Networks is the official journal of the International Neural Network Society, providing a platform for researchers and practitioners to explore the latest advancements in the field of neural networks. This interdisciplinary journal covers topics such as computational neuroscience, machine learning, artificial intelligence, and cognitive science, fostering the exchange of knowledge and ideas among experts worldwide. With a focus on both theoretical and practical aspects, Neural Networks serves as a valuable resource for those interested in understanding and applying neural network models
Details
Details
Abbr.
Neural Netw
Start
1988
End
Continuing
Frequency
Ten no. a year, 1999-
p-ISSN
0893-6080
e-ISSN
1879-2782
Country
United States
Language
English
Metrics
Metrics
h-index / Ranks: 1033
173
SJR / Ranks: 775
2605
CiteScore / Ranks: 671
14.50
JIF / Ranks: 720
7.8
Recent Articles
1.
Zhao L, Wen J, Lu X, Wong W, Long J, Xie W
Neural Netw
. 2025 Mar;
187:107349.
PMID: 40088833
In real-world scenarios, multi-view multi-label learning often encounters the challenge of incomplete training data due to limitations in data collection and unreliable annotation processes. The absence of multi-view features impairs...
2.
Pan X, Jiao C, Yang B, Zhu H, Wu J
Neural Netw
. 2025 Mar;
187:107332.
PMID: 40088832
Land use and land cover (LULC) classification is a popular research area in remote sensing. The information of single-modal data is insufficient for accurate classification, especially in complex scenes, while...
3.
Gao T, Chen D, Zhou M, Wang Y, Zuo Y, Tu W, et al.
Neural Netw
. 2025 Mar;
187:107337.
PMID: 40088831
Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite its significant successes, this approach has long been facing challenges...
4.
Cai Y, Chen G, Qiao Z
Neural Netw
. 2025 Mar;
187:107277.
PMID: 40088830
The universal approximation property is fundamental to the success of neural networks, and has traditionally been achieved by training networks without any constraints on their parameters. However, recent experimental research...
5.
Zhu H, Jia Y, Yan Y, Yang Z
Neural Netw
. 2025 Mar;
187:107341.
PMID: 40086136
Adversarial attacks are significant in uncovering vulnerabilities and assessing the robustness of deep neural networks (DNNs), offering profound insights into their internal mechanisms. Feature-level attacks, a potent approach, craft adversarial...
6.
Xiao J, Wu J, Wang S, Yu Q, Xie H, Wang Y
Neural Netw
. 2025 Mar;
187:107299.
PMID: 40086135
Abnormal behavior detection in surveillance video, as one of the essential functions in the intelligent surveillance system, plays a vital role in anti-terrorism, maintaining stability, and ensuring social security. Aiming...
7.
Ramanarayanan S, G S R, Fahim M, Ram K, Venkatesan R, Sivaprakasam M
Neural Netw
. 2025 Mar;
187:107334.
PMID: 40086134
Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between regions from distant pixel neighborhoods to compute feature representations. Accelerated magnetic resonance image (MRI) reconstruction...
8.
Xue W, Liu Q, Wang Y, Wei Z, Xing X, Xu X
Neural Netw
. 2025 Mar;
187:107348.
PMID: 40086133
Human-object interaction (HOI) detection aims to locate human-object pairs and identify their interaction categories in images. Most existing methods primarily focus on supervised learning, which relies on extensive manual HOI...
9.
Graph Neural Networks with Coarse- and Fine-Grained Division for mitigating label noise and sparsity
Li S, Zhang B, Song J, Ruan G, Wang C, Xie J
Neural Netw
. 2025 Mar;
187:107338.
PMID: 40086132
Graph Neural Networks (GNNs) have gained considerable prominence in semi-supervised learning tasks in processing graph-structured data, primarily owing to their message-passing mechanism, which largely relies on the availability of clean...
10.
Wang S, Zhang D, Belatreche A, Xiao Y, Qing H, Wei W, et al.
Neural Netw
. 2025 Mar;
187:107333.
PMID: 40081275
Deep Neural Networks (DNNs) have been successfully implemented across various signal processing fields, resulting in significant enhancements in performance. However, DNNs generally require substantial computational resources, leading to significant economic...