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Showing 20 of 1,679 academic articles

Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics

F. Cadini, E. Zio, N. Pedroni • Science and Technology of Nuclear Installations (2008)

Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. Their use is becoming increasingly popular in the complex modeling tasks required by diagnostic, safety, and control appli...

DOAJ Open Access (DOAJ) DOI: 10.1155/2008/681890

Neural networks in geophysical applications

Mirko van der Baan, Christian Jutten B • Geophysics (2000)

Abstract Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to fin...

OPENALEX DOI: https://doi.org/10.1190/1.1444797

Building neural networks

David M. Skapura (1995)

Foundations. Motivation. Neural-Network Fundamentals. Single Neuron Computations. Network Computations. Network Simulation. Foundations Summary. Suggested Readings. Bibliography. Paradigms. The Backpropagation Network. The Counterpropagatio...

OPENALEX

Neural network ensembles

Lars Kai Hansen, Peter Salamon • IEEE Transactions on Pattern Analysis and Machine Intelligence (1990)

Several means for improving the performance and training of neural networks for classification are proposed. Crossvalidation is used as a tool for optimizing network parameters and architecture. It is shown that the remaining residual gener...

OPENALEX Open Access DOI: https://doi.org/10.1109/34.58871

Boosting Neural Networks

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

CG-DINO: Improved Network for Small Object Detection in Uncrewed Aerial Vehicle Imagery Based on DINO

Jian Liu, Pei Niu, Yihui Jiang, Zilong Zhao et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2026)

Object detection in uncrewed aerial vehicle (UAV) acquired images remains a challenging task due to extreme scale variation, dense object layouts, and complex backgrounds, etc. Although convolutional neural networks (CNNs), transformers, an...

DOAJ Open Access (DOAJ) DOI: 10.1109/JSTARS.2026.3729713

Dynamic Sign Language Recognition in Bahasa using MediaPipe, Long Short-Term Memory, and Convolutional Neural Network

Ivana Valentina Lemmuela, Mewati Ayub, Oscar Karnalim • Journal of Information Systems Engineering and Business Intelligence (2025)

Background: Communication is important for everyone, including individuals with hearing and speech impairments. For this demographic, sign language is widely used as the primary medium of communication with others who share similar conditio...

DOAJ Open Access (DOAJ) DOI: 10.20473/jisebi.11.1.17-29

Predicting air quality index using artificial neural network: a case study of the Coimbatore region

P. Jagadesh, J. Harshini, D. Arul, Solomon Oyebisi et al. • Discover Civil Engineering (2026)

Abstract This study predicts the Air Quality Index (AQI) in the Coimbatore region using Artificial Neural Networks (ANN) to enhance air quality management and environmental science. The researchers measured six input variables over a two-ye...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44290-026-00423-z

Principles of Artificial Neural Networks

Daniel Graupe • Advanced series in circuits and systems (1997)

The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and compu...

OPENALEX DOI: https://doi.org/10.1142/2982

Modeling of Self-Aligned Selector Based on Ultra-Thin Metal Oxide for Resistive Random-Access Memory (RRAM) Crossbar Arrays

Mikhail Fedotov, Viktor Korotitsky, Sergei Koveshnikov • Nanomaterials (2024)

Resistive random-access memory (RRAM) is a crucial element for next-generation large-scale memory arrays, analogue neuromorphic computing and energy-efficient System-on-Chip applications. For these applications, RRAM elements are arranged i...

DOAJ Open Access (DOAJ) DOI: 10.3390/nano14080668

Logical Reasoning Based on Residual Attention Multi-scale Relation Network

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

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

Digital Local Return Services and Purchase Intention in Cross-Border E-Commerce: A Risk–Trust Perspective

Xianfa Shi, Miao Su, Keun-sik Park • Journal of Theoretical and Applied Electronic Commerce Research (2026)

Cross-border e-commerce offers consumers broader product access, yet uncertainty surrounding returns continues to suppress online purchase decisions. This study conceptualizes digital local return services as a digital assurance mechanism i...

DOAJ Open Access (DOAJ) DOI: 10.3390/jtaer21060165

Brain tumor grading based on Neural Networks and Convolutional Neural Networks

Yuehao Pan, Weimin Huang, Zhiping Lin, Wanzheng Zhu et al. • Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2015)

This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, wit...

OPENALEX DOI: https://doi.org/10.1109/embc.2015.7318458

Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents

Su-Hsin Huang, Joon-Khim Loh, Jinn-Tsong Tsai, Ming-Feng Houg et al. • Chinese Journal of Cancer (2017)

Abstract Background Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast...

DOAJ Open Access (DOAJ) DOI: 10.1186/s40880-017-0192-9