DeepCQ: Deep multi-task conditional quantification network for estimation of left ventricle parameters
Sign Up to like & getrecommendations! Published in 2020 at "Computer methods and programs in biomedicine"
DOI: 10.1016/j.cmpb.2019.105288
Abstract: BACKGROUND AND OBJECTIVE Automatic cardiac left ventricle (LV) quantification plays an important role in assessing cardiac function. Although many advanced methods have been put forward to quantify related LV parameters, automatic cardiac LV quantification is… read more here.
Keywords: deep multi; quantification; left ventricle; multi task ... See more keywords
Deep Multi-Layer Perceptron based Prediction of Energy Efficiency and Surface Quality for Milling in The Era of Sustainability and Big Data
Sign Up to like & getrecommendations! Published in 2020 at "Procedia Manufacturing"
DOI: 10.1016/j.promfg.2020.10.164
Abstract: Abstract In advanced industries such as aerospace, medical and automotive, high precision machining is increasingly required for many parts made by difficult-to-cut alloys. Machine tool manufacturers respond to this demand by developing more advanced machine… read more here.
Keywords: big data; energy; multi layer; deep multi ... See more keywords
A deep multi-stream model for robust prediction of left ventricular ejection fraction in 2D echocardiography
Sign Up to like & getrecommendations! Published in 2024 at "Scientific Reports"
DOI: 10.1038/s41598-024-52480-y
Abstract: We propose a deep multi-stream model for left ventricular ejection fraction (LVEF) prediction in 2D echocardiographic (2DE) examinations. We use four standard 2DE views as model input, which are automatically selected from the full 2DE… read more here.
Keywords: multi stream; stream model; left ventricular; prediction ... See more keywords
Semi-supervised few-shot bearing fault diagnosis method based on deep multi-view meta-learning
Sign Up to like & getrecommendations! Published in 2025 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/adfe07
Abstract: Few-shot learning (FSL) effectively addresses performance degradation caused by insufficient labeled data in the fault diagnosis of rotating machinery bearings. However, prevailing methods are confined to the contribution of the features from a single view,… read more here.
Keywords: view; method; semi supervised; fault diagnosis ... See more keywords
Deep Multi-Layer Perceptron Classifier for Behavior Analysis to Estimate Parkinson’s Disease Severity Using Smartphones
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2851382
Abstract: Although the preclinical detection of Parkinson’s disease (PD) has been explored, a practical, inexpensive, and overall screening diagnosis has yet to be made available. However, due to the large variability and complexity in progress of… read more here.
Keywords: parkinson; multi layer; disease; deep multi ... See more keywords
Identification of Protein-Nucleotide Binding Residues With Deep Multi-Task and Multi-Scale Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2025.3547386
Abstract: Accurate identification of protein-nucleotide binding residues is essential for protein functional annotation and drug discovery. Advancements in computational methods for predicting binding residues from protein sequences have significantly improved predictive accuracy. However, it remains a… read more here.
Keywords: binding residues; multi task; nucleotide binding; protein nucleotide ... See more keywords
Deep Multi-Stage CSI Acquisition for Reconfigurable Intelligent Surface Aided MIMO Systems
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2021.3063464
Abstract: This letter aims to reduce huge pilot overhead when estimating the reconfigurable intelligent surface (RIS) relayed wireless channel. Motivated by the compelling grasp of deep learning in tackling nonlinear mapping problems, the proposed approach only… read more here.
Keywords: reconfigurable intelligent; deep multi; stage; intelligent surface ... See more keywords
Deep Multi-Label Joint Learning for RNA and DNA-Binding Proteins Prediction
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2022.3150280
Abstract: The recognition of DNA- (DBPs) and RNA-binding proteins (RBPs) is not only conducive to understanding cell function, but also a challenging task. Previous studies have shown that these proteins are usually considered separately due to… read more here.
Keywords: joint learning; binding proteins; deep multi; multi label ... See more keywords
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2020.2972974
Abstract: Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple… read more here.
Keywords: multi modal; detection semantic; deep multi; object detection ... See more keywords
Deep Multi-Task Learning for Spatio-Temporal Incomplete Qualitative Event Forecasting
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2024.3460539
Abstract: Forecasting spatiotemporal social events has significant benefits for society to provide the proper amounts and types of resources to manage catastrophes and any accompanying societal risks. Nevertheless, forecasting event subtypes are far more complex than… read more here.
Keywords: task; event subtypes; underline underline; event ... See more keywords
Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2021 at "Applied Sciences"
DOI: 10.3390/app11041884
Abstract: Dynamic spectrum access (DSA) has been considered as a promising technology to address spectrum scarcity and improve spectrum utilization. Normally, the channels are related to each other. Meanwhile, collisions will be inevitably caused by communicating… read more here.
Keywords: deep multi; reinforcement learning; spectrum; user reinforcement ... See more keywords