3,562 research outputs found
Global ECG Classification by Self-Operational Neural Networks with Feature Injection
Objective: Global (inter-patient) ECG classification for arrhythmia detection
over Electrocardiogram (ECG) signal is a challenging task for both humans and
machines. The main reason is the significant variations of both normal and
arrhythmic ECG patterns among patients. Automating this process with utmost
accuracy is, therefore, highly desirable due to the advent of wearable ECG
sensors. However, even with numerous deep learning approaches proposed
recently, there is still a notable gap in the performance of global and
patient-specific ECG classification performances. This study proposes a novel
approach to narrow this gap and propose a real-time solution with shallow and
compact 1D Self-Organized Operational Neural Networks (Self-ONNs). Methods: In
this study, we propose a novel approach for inter-patient ECG classification
using a compact 1D Self-ONN by exploiting morphological and timing information
in heart cycles. We used 1D Self-ONN layers to automatically learn
morphological representations from ECG data, enabling us to capture the shape
of the ECG waveform around the R peaks. We further inject temporal features
based on RR interval for timing characterization. The classification layers can
thus benefit from both temporal and learned features for the final arrhythmia
classification. Results: Using the MIT-BIH arrhythmia benchmark database, the
proposed method achieves the highest classification performance ever achieved,
i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N)
segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the
supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10%
recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs)
An automated artificial neural network system for land use/land cover classification from Landsat TM imagery. Remote Sens
Abstract: This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning algorithm which incorporated Simulated Annealing (SA). The ability of our automated ANN system to perform Land-Use/Land-Cover (LU/LC) classifications of a Landsat Thematic Mapper (TM) image was tested using a supervised MLP network, an unsupervised SOM network, and a combination of SOM with SA network. Our case study demonstrated that the ANN classification system fulfilled the tasks of network training pattern creation, network training, and network generalization. The results from the three networks were assessed via a comparison with reference data derived from the high spatial resolution Digital Colour Infrared (CIR) Digital Orthophoto Quarter Quad (DOQQ) data. The supervised MLP network obtained the most accurate classification accuracy as compared to the twoRemote Sens. 2009, 1 244 unsupervised SOM networks. Additionally, the classification performance of the refine
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations
It is challenging but important to predict the collaborations between different entities which in
academia, for example, would enable finding evaluating trends of scientific research collaboration and the
provision of decision support for policy formulation and incentive measures. In this paper, we propose an
attention-based Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) model to predict
the collaborations between different research affiliations, which takes both the influence of research articles
and time (year) relationships into consideration. The experimental results show that the proposed model
outperforms the competitive Support Vector Machine (SVM), CNN and LSTM methods. It significantly
improves the prediction precision by a minimum of 3.23 percent points and up to 10.80 percent points
when compared with the mentioned competitive methods, while in terms of the F1-score, the performance
is improved by 13.48, 4.85 and 4.24 percent points, respectively.This work was supported in part by the Humanities and Social Science Research Project of the Ministry of Education in China under
Grant 17YJCZH262 and Grant 18YJAZH136, in part by the National Natural Science Foundation of China under Grant 61303167,
Grant 61702306, Grant 61433012, Grant U1435215, and Grant 71772107, in part by the Natural Science Foundation of Shandong
Province under Grant ZR2018BF013 and Grant ZR2017BF015, in part by the Innovative Research Foundation of Qingdao under
Grant 18-2-2-41-jch, in part by the Key Project of Industrial Transformation and Upgrading in China under Grant TC170A5SW, and in part
by the Scientific Research Foundation of SDUST for Innovative Team under Grant 2015TDJH102
Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better
generalization performance. Currently, deep learning models with multilayer
processing architecture is showing better performance as compared to the
shallow or traditional classification models. Deep ensemble learning models
combine the advantages of both the deep learning models as well as the ensemble
learning such that the final model has better generalization performance. This
paper reviews the state-of-art deep ensemble models and hence serves as an
extensive summary for the researchers. The ensemble models are broadly
categorised into ensemble models like bagging, boosting and stacking, negative
correlation based deep ensemble models, explicit/implicit ensembles,
homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised,
semi-supervised, reinforcement learning and online/incremental, multilabel
based deep ensemble models. Application of deep ensemble models in different
domains is also briefly discussed. Finally, we conclude this paper with some
future recommendations and research directions
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa
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