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Academic Articles & Research Papers
Showing 20 of 34 academic articles
Didit Adytia, Dede Tarwidi, Deni Saepudin, Semeidi Husrin et al. • MethodsX
(2024)
The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsuna...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.mex.2024.102791
LIANG Yulong,QU Dan,QIU Zeyu • Jisuanji gongcheng
(2018)
The performance of speaker aware training method based on i-vector is poor because of using MFCC which has the relative poor robustness as the input feature for the extraction of the i-vector.To solve this problem,an improved i-vector based...
DOAJ
Open Access (DOAJ)
DOI: 10.19678/j.issn.1000-3428.0046946
Xiaoting Zhong, Brian J. Gallagher, Shusen Liu, Bhavya Kailkhura et al. • npj Computational Materials
(2022)
Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explaina...
OPENALEX
cc-by
DOI: https://doi.org/10.1038/s41524-022-00884-7
Wenjie Tian, Lili Zhang, Tao Yu, Dong Yao et al. • Atmosphere
(2024)
CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they of...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/atmos15080985
Albert Whata, Charles Chimedza • IEEE Access
(2022)
Logistic regression (LR) is a popular method that is used for estimating causal effects in observational studies using propensity scores. We examine the use of deep learning models such as the deep neural network (DNN), PropensityNet (PN), ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2021.3140189
Sean McCormick, Adrien Richez, Violet Mwaffo, Donald H. Costello • IEEE Access
(2024)
The next generation of United States Navy uncrewed aerial systems (UASs) is expected to operate in global positioning system and radio frequency-denied maritime environments. In these challenging conditions, these UASs must accurately ident...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2024.3483014
Fenglei Fan, Jinjun Xiong, Mengzhou Li, Ge Wang • IEEE Transactions on Radiation and Plasma Medical Sciences
(2021)
Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become ...
OPENALEX
Open Access
DOI: https://doi.org/10.1109/trpms.2021.3066428
PMC: 9105427
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Cohen, Jonathan et al. • arXiv (Cornell University)
(2014)
We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels mu...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1410.0759
Zhongtian Dong, Marçal Comajoan Cara, Gopal Ramesh Dahale, Roy T. Forestano et al. • Axioms
(2024)
This paper presents a comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENNs) and Deep Ne...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/axioms13030188
Wenpeng Yin, Katharina Kann, Mo Yu, Hinrich Schütze • arXiv (Cornell University)
(2017)
Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle var...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1702.01923
Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li • ACM Transactions on Intelligent Systems and Technology
(2020)
With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulne...
OPENALEX
Open Access
DOI: https://doi.org/10.1145/3374217
Ma Lina • Discover Artificial Intelligence
(2025)
Abstract The current evaluation of English teaching quality in colleges and universities faces the problems of information uncertainty and fuzziness. Traditional evaluation methods cannot accurately reflect the complex teaching effects, mai...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44163-025-00481-9
Hang Li • National Science Review
(2017)
Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). Recently, deep ...
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DOI: https://doi.org/10.1093/nsr/nwx110
Vijeta Sharma, Manjari Gupta, Ajai Kumar, Deepti Mishra • IEEE Access
(2021)
Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are presen...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2021.3118541
Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Guido Masera et al. • IEEE Access
(2020)
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2020.3039858
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
Vijayamanikandan Vijayarangan, Harshavardhana A. Uranakara, Francisco E. Hernández–Pérez, Hong G. Im • Energy and AI
(2025)
Using information theory, this study provides insights into how the construction of latent space of autoencoder (AE) using deep neural network (DNN) training finds a smooth (non-stiff) low-dimensional manifold in the stiff dynamical system....
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.egyai.2025.100567
Lucas Prado Osco, José Marcato, Ana Paula Marques Ramos, Lúcio André de Castro Jorge et al. • International Journal of Applied Earth Observation and Geoinformation
(2021)
Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, ...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.jag.2021.102456
Boris Galitsky • Entropy
(2023)
It is well known that deep learning (DNN) has strong limitations due to a lack of explainability and weak defense against possible adversarial attacks. These attacks would be a concern for autonomous teams producing a state of high entropy ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/e25111496
Nasim Shakouri Mahmoudabadi, Charles V. Camp, Afaq Ahmad • Infrastructures
(2026)
Fiber-reinforced polymer (FRP) composites are widely used to strengthen reinforced concrete (RC) columns due to their high strength, durability, and ease of installation. Accurate prediction of the axial capacity of CFRP-strengthened concre...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/infrastructures11050151