Results 21 to 30 of about 788,437 (264)
Energies, 2023 In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and
Yunlong Han +4 more
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Frontiers in Bioengineering and Biotechnology, 2021 We present a novel and computationally efficient method for the detection of meniscal tears in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional Neural Network (CNN) that operates on complete 3D MRI scans. Our approach detects
Alexander Tack +4 more
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, 2020 We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides.
Agarwal, Alekh +2 more
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Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks
Journal of Imaging, 2020 Multispectral sensors constitute a core Earth observation image technology generating massive high-dimensional observations. To address the communication and storage constraints of remote sensing platforms, lossy data compression becomes necessary, but ...
Michalis Giannopoulos +4 more
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Siamese Visual Tracking With Residual Fusion Learning
IEEE Access, 2022 Multi-stage feature fusion is pretty effective for deep Siamese trackers to promote tracking performance. Unfortunately, conventional fusion approaches, such as weighted average, are so simple that they are inappropriate to combine the features with ...
Xinglong Sun, Guangliang Han, Lihong Guo
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Learning Probabilistic Automata Using Residuals
, 2021 A probabilistic automaton is a non-deterministic finite automaton with probabilities assigned to transitions and states that define a distribution on the set of all strings. In general, there are distributions generated by automata with a non-deterministic structure that cannot be generated by a deterministic one. There exist several methods in machine
Chu, W., Chen, S., Bonsangue, M.M.
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Source Detection With Multi-Label Classification
IEEE Open Journal of Signal Processing, 2023 Radio source detection through conventional algorithms has been unreliable when trying to solve for large number of sources in the presence of low SINR and less number of snapshots.
Jayakrishnan Vijayamohanan +4 more
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Cervical Cancer Diagnosis Using Very Deep Networks Over Different Activation Functions
IEEE Access, 2021 Cancer prevention is mainly achieved by screening the transformation zones. Cervical pre-cancerous stages can be seen in three different types, and all can transform into cancer. Thus, it is crucial to intelligently screen cervical abnormality and have a
Khaled Mabrouk Amer Adweb +2 more
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Cross-media residual correlation learning [PDF]
Applied Informatics, 2017 نظرًا لتقدم الشبكات العصبية العميقة (DNN)، تم توظيف DNN لاسترجاع الوسائط المتقاطعة. يمكن لطرق الاسترجاع عبر الوسائط الحالية القائمة على DNN تحويل التمثيل المنفصل لكل نوع وسائط إلى تمثيل مشترك من خلال القيود بين الوسائط وداخل الوسائط. باستخدام التمثيل المشترك، يمكننا قياس أوجه التشابه بين الحالات غير المتجانسة وإجراء استرجاع عبر الوسائط.
Mingkuan Yuan, Xin Huang, Yuxin Peng
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Going Deeper with Dense Connectedly Convolutional Neural Networks for Multispectral Pansharpening
Remote Sensing, 2019 In recent years, convolutional neural networks (CNNs) have shown promising performance in the field of multispectral (MS) and panchromatic (PAN) image fusion (MS pansharpening).
Dong Wang +4 more
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