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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

On Calibration of Modern Neural Networks

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

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

3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification with Limited Samples

Yuchao Feng, Jianwei Zheng, Mengjie Qin, Cong Bai et al. • Remote Sensing (2021)

Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well know...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs13214407

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

Face recognition with Bayesian convolutional networks for robust surveillance systems

Umara Zafar, Mubeen Ghafoor, Tehseen Zia, Ghufran Ahmed et al. • EURASIP Journal on Image and Video Processing (2019)

Abstract Recognition of facial images is one of the most challenging research issues in surveillance systems due to different problems including varying pose, expression, illumination, and resolution. The robustness of recognition method st...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13640-019-0406-y

Design of a TSR-based project learning strategy for biochemistry undergraduate teaching and research labs: a case study

Camille R. Reaux, Shelby A. Meche, Jordan M. Grider, Soundharya Dhanabal et al. • Frontiers in Education (2025)

Given the exponential growth of biochemical data and deep effect of computational methods on life sciences, there is a need to rethink undergraduate curricula. A project-oriented learning approach based on the Triangular Spatial Relationshi...

DOAJ Open Access (DOAJ) DOI: 10.3389/feduc.2024.1455173

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

Deep Learning--based Text Classification

Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad-Khasmakhi et al. • ACM Computing Surveys (2021)

Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this art...

OPENALEX Open Access DOI: https://doi.org/10.1145/3439726

Hierarchical Multimodal Sleep Staging with Optimized EEG, EOG, and PPG Features for Wearable Applications

Roberto De Fazio, Matteo Paiano, Ramiro Velazquez, Carolina Del-Valle-Soto et al. • Applied Sciences (2026)

Automatic sleep staging is fundamental for diagnosing sleep disorders and enabling long-term sleep monitoring with wearable devices. Although Deep Learning has significantly improved classification performance, balancing accuracy with compu...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16168164

MMSplice: modular modeling improves the predictions of genetic variant effects on splicing

Jun Cheng, Thi Yen Duong Nguyen, Kamil J. Cygan, Muhammed Hasan Çelik et al. • Genome Biology (2019)

Abstract Predicting the effects of genetic variants on splicing is highly relevant for human genetics. We describe the framework MMSplice (modular modeling of splicing) with which we built the winning model of the CAGI5 exon skipping predic...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13059-019-1653-z

Deep learning in medical image registration: a review

Yabo Fu, Yang Lei, Tonghe Wang, Walter John Curran et al. • Physics in Medicine and Biology (2020)

This paper presents a review of deep learning (DL)-based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into se...

OPENALEX Open Access DOI: https://doi.org/10.1088/1361-6560/ab843e PMC: 7759388

Machine Learning and Deep Learning Approaches for CyberSecurity: A Review

Asmaa Hani Halbouni, Teddy Surya Gunawan, Mohamed Hadi Habaebi, Murad Halbouni et al. • IEEE Access (2022)

The rapid evolution and growth of the internet through the last decades led to more concern about cyber-attacks that are continuously increasing and changing. As a result, an effective intrusion detection system was required to protect data...

OPENALEX cc-by DOI: https://doi.org/10.1109/access.2022.3151248

Deep learning improves antimicrobial peptide recognition

Daniel Veltri, Uday Kamath, Amarda Shehu • Bioinformatics (2018)

Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by w...

OPENALEX cc-by-nc DOI: https://doi.org/10.1093/bioinformatics/bty179 PMC: 6084614

Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing

A. I. M. Schwebig, A. I. M. Schwebig, R. Tutsch • Journal of Sensors and Sensor Systems (2020)

Ensuring the highest quality standards at competitive prices is one of the greatest challenges in the manufacture of electronic products. The identification of flaws has the uppermost priority in the field of automotive electronics, particu...

DOAJ Open Access (DOAJ) DOI: 10.5194/jsss-9-167-2020