Results 41 to 50 of about 693,698 (310)
Classification accuracy obtained using selected handcrafted, radiomic and deep features.
, 2022 Classification accuracy obtained using selected handcrafted, radiomic and deep features.
Jeonghwan Gwak (12459621) +1 more
core +1 more source
Radar HRRP recognition based on CNN
The Journal of Engineering, 2019 In this study, ground target recognition based on one-dimensional convolutional neural network (CNN) is studied by exploiting the targets’ high-resolution range profiles (HRRPs). Contrary to conventional methods which need feature extraction artificially,
Jia Song +4 more
doaj +1 more source
Frontiers in Neuroscience, 2023 IntroductionIn the clinical setting, it becomes increasingly important to detect epileptic seizures automatically since it could significantly reduce the burden for the care of patients suffering from intractable epilepsy.
Wei Zeng +6 more
doaj +1 more source
Deep Feature Factorization for Concept Discovery [PDF]
, 2018 We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep convolutional neural network's learned features, where we detect hierarchical cluster structures in feature space.
Edo Collins +2 more
openaire +2 more sources
Human Facial Age Estimation: Handcrafted Features Versus Deep Features
, 2021 International audienceIn recent times, human facial age estimation topic attracted a lot of attention due to its ability to improve biometrics systems. Recently, several applications that exploit demographic attributes have emerged.
Ouafi, A. +7 more
core +1 more source
Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine [PDF]
International Journal of Mathematical, Engineering and Management Sciences, 2020 The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected
Prabira Kumar Sethy +3 more
doaj +1 more source
Deep Feature Learning for Medical Acoustics
, 2022 The purpose of this paper is to compare different learnable frontends in medical acoustics tasks. A framework has been implemented to classify human respiratory sounds and heartbeats in two categories, i.e. healthy or affected by pathologies. After obtaining two suitable datasets, we proceeded to classify the sounds using two learnable state-of-art ...
Poire A. M. +2 more
openaire +3 more sources
Feature Enhancement with Deep Feature Losses for Speaker Verification [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Speaker Verification still suffers from the challenge of generalization to novel adverse environments. We leverage on the recent advancements made by deep learning based speech enhancement and propose a feature-domain supervised denoising based solution.
Saurabh Kataria 0001 +5 more
openaire +2 more sources
Deep Feature Learning for Graphs
CoRR, 2017 This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs. In particular, DeepGL begins by deriving a set of base features (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive ...
Ryan A. Rossi +2 more
openaire +2 more sources
LOTS about attacking deep features [PDF]
2017 IEEE International Joint Conference on Biometrics (IJCB), 2017 Deep neural networks provide state-of-the-art performance on various tasks and are, therefore, widely used in real world applications. DNNs are becoming frequently utilized in biometrics for extracting deep features, which can be used in recognition systems for enrolling and recognizing new individuals.
Andras Rozsa +2 more
openaire +2 more sources