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Showing 20 of 817 academic articles

Binarized Neural Networks

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

A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution–Pooling Method

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

Surrogate Modeling of the Electric Field in the End-Winding Region of Pumped-Storage Generator Stators Based on Deep Neural Networks

Chunxu Qin, Yiran Ma, Huijuan Liang, Zhifan Wang et al. • Modelling (2026)

The end-winding insulation structure of stator windings in pumped-storage generator units is complex, with pronounced electric field concentration under out-of-phase conditions, making them critical concerns in insulation design and conditi...

DOAJ Open Access (DOAJ) DOI: 10.3390/modelling7050190

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

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

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

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

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

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

Monthly Evapotranspiration Modeling in the Absence of Climatic Data Using the Soft Computing Methods in West and Northwest of Iran

Saman Maroufpour, Eisa Maroufpoor, Hadi Sanikhani • مهندسی منابع آب (2017)

Evapotranspiration (ET0), a major component of the hydrologic cycle, is important in water resources development and irrigation planning. The ET0 for west and northwest of Iran was estimated using the FAO Penman-Montieth method (FAO-56) as ...

DOAJ Open Access (DOAJ)

Application of Artificial Neural Networks for Corrosion Behavior of Ni–Zn Electrophosphate Coating on Galvanized Steel and Gene Expression Programming Models

Malihe Zeraati, Hossein Abbasi, Parvin Ghaffarzadeh, Narendra Pal Singh Chauhan et al. • Frontiers in Materials (2022)

Zn–Ni electrophosphate coating is one of the most commonly used materials in industrial applications. The corrosion resistance of this coating is very important in order to achieve the minimum corrosion current of the Zn–Ni electrophosphate...

DOAJ Open Access (DOAJ) DOI: 10.3389/fmats.2022.823155

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

CL-BPUWM: continuous learning with Bayesian parameter updating and weight memory

Yao He, Jing Yang, Shaobo Li, Jianjun Hu et al. • Complex & Intelligent Systems (2024)

Abstract Catastrophic forgetting in neural networks is a common problem, in which neural networks lose information from previous tasks after training on new tasks. Although adopting a regularization method that preferentially retains the pa...

DOAJ Open Access (DOAJ) DOI: 10.1007/s40747-024-01350-1

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

Evaluating artificial intelligence for a focal nodular hyperplasia diagnosis using magnetic resonance imaging: preliminary findings

Mecit Kantarcı, Volkan Kızılgöz, Ramazan Terzi, Ahmet Enes Kılıç et al. • Diagnostic and Interventional Radiology (2025)

PURPOSE: This study aimed to evaluate the effectiveness of artificial intelligence (AI) in diagnosing focal nodular hyperplasia (FNH) of the liver using magnetic resonance imaging (MRI) and compare its performance with that of radiologists....

DOAJ Open Access (DOAJ) DOI: 10.4274/dir.2025.243095