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Academic Articles & Research Papers
Showing 20 of 423 academic articles
Alexander Craik, Yongtian He, Jose Luis Contreras-Vidal • Journal of Neural Engineering
(2019)
OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analyti...
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DOI: https://doi.org/10.1088/1741-2552/ab0ab5
Shervin Minaee, Yuri Y. Boykov, Fatih Porikli, Antonio Plaza et al. • IEEE Transactions on Pattern Analysis and Machine Intelligence
(2021)
Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, amon...
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Open Access
DOI: https://doi.org/10.1109/tpami.2021.3059968
Swaraja K., Akshitha V., Pranav K., Vyshnavi B. et al. • E3S Web of Conferences
(2021)
Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision s...
DOAJ
Open Access (DOAJ)
DOI: 10.1051/e3sconf/202130901069
Jae Joon Hwang, Yun‐Hoa Jung, Bong‐Hae Cho, Min-Suk Heo • Imaging Science in Dentistry
(2019)
PURPOSE: Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about d...
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DOI: https://doi.org/10.5624/isd.2019.49.1.1
PMC: 6444007
Usman Manzo Gidado, Haruna Chiroma, Nahla Aljojo, Saidu Abubakar et al. • IEEE Access
(2020)
Steering angle prediction is critical in the control of Autonomous Vehicles (AVs) and has attracted the attention of researchers, manufacturers, and insurance companies in the automotive industry. Different Deep Learning (DL) architectures ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2020.3017883
Imma Lory Aprea, Gianni Bosi, Gabriele Sbaiz, Salvatore Scognamiglio • Mathematics
(2025)
In this paper, we tackle a bi-objective optimization problem in which we aim to maximize the portfolio diversification and, at the same time, minimize the portfolio volatility, where the ESG (Environmental, Social, and Governance) informati...
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Open Access (DOAJ)
DOI: 10.3390/math13203308
Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni et al. • Frontiers in Cardiovascular Medicine
(2020)
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalit...
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DOI: https://doi.org/10.3389/fcvm.2020.00025
PMC: 7066212
Samane Esmaelzade Kalkhoran, Seyyed Saeed Ghannadpour • Scientific Reports
(2026)
Abstract Porphyry Cu deposits in the Urmia–Dokhtar magmatic belt constitute important exploration targets, yet the heterogeneous and multi-scale nature of geological, geochemical, and remote sensing data introduces substantial uncertainty i...
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Open Access (DOAJ)
DOI: 10.1038/s41598-026-61085-6
A. Geiss, S. J. Silva, S. J. Silva, J. C. Hardin et al. • Geoscientific Model Development
(2022)
Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrat...
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Open Access (DOAJ)
DOI: 10.5194/gmd-15-6677-2022
Zhihao Wang, Jian Chen, Steven C. H. Hoi • IEEE Transactions on Pattern Analysis and Machine Intelligence
(2020)
Image Super-Resolution (SR) is an important class of image processing techniqueso enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning ...
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Open Access
DOI: https://doi.org/10.1109/tpami.2020.2982166
Jing Liu, Xuesong Hai, Keqin Li • Future Internet
(2023)
Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/fi16010006
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter • Frontiers in Environmental Science
(2016)
The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...
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DOI: https://doi.org/10.3389/fenvs.2015.00080
Longqian Huang, Ruichen Luo, Xü Liu, Xiang Hao • Light Science & Applications
(2022)
The goal of spectral imaging is to capture the spectral signature of a target. Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes. In contrast, computational sp...
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DOI: https://doi.org/10.1038/s41377-022-00743-6
PMC: 8927154
Bryan Y. Lim, Stefan Zohren • Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
(2021)
Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-hori...
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Open Access
DOI: https://doi.org/10.1098/rsta.2020.0209
Saleh Albahli • Future Internet
(2019)
Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict thes...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/fi11120246
Hao Shi, Guo Cao, Youqiang Zhang, Zixian Ge et al. • Remote Sensing
(2022)
Deep learning (DL) has recently been a core ingredient in modern computer vision tasks, triggering a wave of revolutions in various fields. The hyperspectral image (HSI) classification task is no exception. A wide range of DL-based methods ...
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Open Access (DOAJ)
DOI: 10.3390/rs14174235
Lei Zhou, Chu Zhang, Fei Liu, Zhengjun Qiu et al. • Comprehensive Reviews in Food Science and Food Safety
(2019)
Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food sci...
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DOI: https://doi.org/10.1111/1541-4337.12492
Cyprien Alexandre, Rodolphe Devillers, David Mouillot, Raphael Seguin et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
(2024)
Detecting and tracking ships remotely is now required in a wide range of contexts, from military security to illegal immigration control, as well as the management of fisheries and marine protected areas. Among the available methods, radar ...
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Open Access (DOAJ)
DOI: 10.1109/JSTARS.2024.3437187
Muhammad Attique Khan, Usama Shafiq, Ameer Hamza, Anwar M. Mirza et al. • BMC Medical Informatics and Decision Making
(2025)
Abstract Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imba...
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Open Access (DOAJ)
DOI: 10.1186/s12911-025-02966-0
Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami, Fatema Khatun et al. • IET Image Processing
(2024)
Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory”...
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Open Access (DOAJ)
DOI: 10.1049/ipr2.13287