On the public citation graph

Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data

https://doi.org/10.3390/math8071078
0 signed · 45 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-20518-8_3openalex-snapshot
Unsupervised Learning as a Complement to Convolutional Neural Network Classification in the Analysis of Saccadic Eye Movement in Spino-Cerebellar Ataxia Type 2
inferredcites →https://doi.org/10.1007/978-3-030-45385-5openalex-snapshot
Bioinformatics and Biomedical Engineering
inferredcites →https://doi.org/10.1007/978-3-319-67588-6_6openalex-snapshot
Generational Feature Elimination and Some Other Ranking Feature Selection Methods
inferredcites →https://doi.org/10.1007/s00521-018-03980-2openalex-snapshot
RETRACTED ARTICLE: Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
inferredcites →https://doi.org/10.1007/s00521-018-3709-5openalex-snapshot
Analysis on the potential of an EA–surrogate modelling tandem for deep learning parametrization: an example for cancer classification from medical images
inferredcites →https://doi.org/10.1016/j.cmpb.2019.05.004openalex-snapshot
A new approach for arrhythmia classification using deep coded features and LSTM networks
inferredcites →https://doi.org/10.1016/j.measurement.2019.05.022openalex-snapshot
CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer
inferredcites →https://doi.org/10.1016/j.media.2020.101636openalex-snapshot
Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach
inferredcites →https://doi.org/10.1016/j.neucom.2018.10.105openalex-snapshot
Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks
inferredcites →https://doi.org/10.1016/j.neucom.2019.09.044openalex-snapshot
BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights
inferredcites →https://doi.org/10.1038/s41598-019-42516-zopenalex-snapshot
Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network
inferredcites →https://doi.org/10.1038/s41598-019-47181-wopenalex-snapshot
Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading
inferredcites →https://doi.org/10.1109/tnnls.2019.2899744openalex-snapshot
Augmenting Recurrent Neural Networks Resilience by Dropout
inferredcites →https://doi.org/10.1109/tnsre.2019.2896269openalex-snapshot
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
inferredcites →https://doi.org/10.1371/journal.pone.0220294openalex-snapshot
A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG
inferredcites →https://doi.org/10.1371/journal.pone.0223593openalex-snapshot
Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations
inferredcites →https://doi.org/10.1371/journal.pone.0236401openalex-snapshot
A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis
inferredcites →https://doi.org/10.3389/fbioe.2019.00246openalex-snapshot
Digital Assessment of Stained Breast Tissue Images for Comprehensive Tumor and Microenvironment Analysis
inferredcites →https://doi.org/10.3389/fphy.2019.00103openalex-snapshot
A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
inferredcites →https://doi.org/10.3390/a13030067openalex-snapshot
Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics
inferredcites →https://doi.org/10.3390/e21030305openalex-snapshot
Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest
inferredcites →https://doi.org/10.3390/s20113032openalex-snapshot
Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals
inferredcites →https://doi.org/10.36001/ijphm.2019.v10i4.2612openalex-snapshot
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1706.01242openalex-snapshot
Bayesian LSTMs in medicine
inferredcites →https://doi.org/10.48550/arxiv.1802.09791openalex-snapshot
Bioinformatics and Medicine in the Era of Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1806.03106openalex-snapshot
Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1907.05143openalex-snapshot
Deep Active Learning for Axon-Myelin Segmentation on Histology Data
inferredcites →https://doi.org/10.5220/0008967200002513openalex-snapshot
A Novel Approach for Modelling the Relationship between Blood Pressure and ECG by using Time-series Feature Extraction

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-030-85030-2_1openalex-snapshot
Deep Learning for the Detection of Frames of Interest in Fetal Heart Assessment from First Trimester Ultrasound
inferred← citeshttps://doi.org/10.1016/j.asoc.2024.111551openalex-snapshot
Multi-head attention ResUnet with sequential sliding windows for sea surface height anomaly field forecast: A regional study in North Atlantic Ocean
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.105982openalex-snapshot
Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features
inferred← citeshttps://doi.org/10.1016/j.ibmed.2026.100488openalex-snapshot
Explainable meta-learning framework for maternal risk prediction using clinical feature engineering
inferred← citeshttps://doi.org/10.1016/j.inffus.2021.05.008openalex-snapshot
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112026openalex-snapshot
Forecasting bitcoin: Decomposition aided long short-term memory based time series modeling and its explanation with Shapley values
inferred← citeshttps://doi.org/10.1039/d1ra03428fopenalex-snapshot
Heat induction in two-dimensional graphene–Fe 3 O 4 nanohybrids for magnetic hyperthermia applications with artificial neural network modeling
inferred← citeshttps://doi.org/10.1051/e3sconf/202127902003openalex-snapshot
Increasing the accuracy of signal extraction by correcting the approximating function under conditions of a priori uncertainty
inferred← citeshttps://doi.org/10.1080/19942060.2024.2305318openalex-snapshot
Instantaneous 2D extreme wind speed prediction using the novel Wind Gust Prediction Net based on purely convolutional neural mechanism
inferred← citeshttps://doi.org/10.1109/jstsp.2021.3122886openalex-snapshot
On the Use of Uncertainty in Classifying Aedes Albopictus Mosquitoes
inferred← citeshttps://doi.org/10.1109/synasc54541.2021.00049openalex-snapshot
Semantic Segmentation for Corrosion Detection in Archaeological Artefacts before Restoration
inferred← citeshttps://doi.org/10.3390/axioms12030266openalex-snapshot
Metaheuristic-Based Hyperparameter Tuning for Recurrent Deep Learning: Application to the Prediction of Solar Energy Generation
inferred← citeshttps://doi.org/10.3390/en16031434openalex-snapshot
On the Benefits of Using Metaheuristics in the Hyperparameter Tuning of Deep Learning Models for Energy Load Forecasting
inferred← citeshttps://doi.org/10.3390/math8081374openalex-snapshot
Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks
inferred← citeshttps://doi.org/10.3390/math8101784openalex-snapshot
Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM
inferred← citeshttps://doi.org/10.68159/f249917157openalex-snapshot
Uncertainty Quantification for Postoperative Delirium Prediction: A Position Paper on Why Bayesian Deep Learning Matters for Elderly Surgical Patients
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.