[1]
|
PCDM and PCDM4MP: New Pairwise Correlation-Based Data Mining Tools for Parallel Processing of Large Tabular Datasets
|
|
Mathematics,
2022 |
|
|
[2]
|
A deep feature fusion network for fetal state assessment
|
|
Frontiers in Physiology,
2022 |
|
|
[3]
|
Machine Learning Techniques for Identifying Fetal Risk During Pregnancy
|
|
International Journal of Image and …,
2022 |
|
|
[4]
|
Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach
|
|
Sustainability,
2022 |
|
|
[5]
|
A Multi-Technique Approach to Exploring the Main Influences of Information Exchange Monitoring Tolerance
|
|
Electronics,
2022 |
|
|
[6]
|
MEM and MEM4PP: New Tools Supporting the Parallel Generation of Critical Metrics in the Evaluation of Statistical Models
|
|
Axioms,
2022 |
|
|
[7]
|
Investigating association relationship between fetal heart rate parameters from cardiotocography employing multi-objective evolutionary algorithms
|
|
International Journal of Information …,
2022 |
|
|
[8]
|
Deep Learning Classification of Fetal Cardiotocography Data with Differential Privacy
|
|
2022 International Conference on …,
2022 |
|
|
[9]
|
Classification Tree of Breast Cancer Data with Mode Value for Missing Data Replacement
|
|
… of the 7th International Conference on …,
2022 |
|
|
[10]
|
Enhanced Classification Performance of Cardiotocogram Data for Fetal State Anticipation Using Evolutionary Feature Reduction Techniques
|
|
Handbook of Artificial …,
2021 |
|
|
[11]
|
A Novel Hybrid Model For Automated Analysis Of Cardiotocograms Using Machine Learning Algorithms
|
|
International Journal of Intelligent Systems and …,
2021 |
|
|
[12]
|
A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning
|
|
… Conference on Data Mining and Big …,
2021 |
|
|
[13]
|
Effective techniques for intelligent cardiotocography interpretation using XGB-RF feature selection and stacking fusion
|
|
2021 IEEE …,
2021 |
|
|
[14]
|
Towards Making More Reliable Cardiotocogram Data Prediction with Limited Expert Knowledge: Exploiting Unlabeled Data with Semi-supervised Boosting Method
|
|
… Conference on Data Mining and Big …,
2021 |
|
|
[15]
|
Multi-objective ant lion optimization based feature retrieval methodology for investigation of fetal wellbeing
|
|
2021 Third international …,
2021 |
|
|
[16]
|
Multi-Classification of Fetal Health Status Using Extreme Learning Machine
|
|
2021 |
|
|
[17]
|
Comprehensive Study of Fetal Monitoring Methods for Detection of Fetal Compromise
|
|
2021 |
|
|
[18]
|
REKOMENDASI KESEHATAN JANIN DENGAN PENERAPAN ALGORITMA C5. 0 MENGGUNAKAN CLASSIFYING CARDIOTOCOGRAPHY DATASET
|
|
2021 |
|
|
[19]
|
A method for medical data analysis using the LogNNet for clinical decision support systems and edge computing in healthcare
|
|
Sensors,
2021 |
|
|
[20]
|
Analyzing uncertainty in cardiotocogram data for the prediction of fetal risks based on machine learning techniques using rough set
|
|
2021 |
|
|
[21]
|
Intelligent classification of antepartum cardiotocography model based on deep forest
|
|
2021 |
|
|
[22]
|
A Shallow 1-D Convolution Neural Network for Fetal State Assessment Based on Cardiotocogram
|
|
2021 |
|
|
[23]
|
Prediction of Defect Hotspots for Highway Maintenance Management: a Multi-Asset Machine Learning Approach
|
|
2020 |
|
|
[24]
|
Predicting Ayurveda-Based Constituent Balancing in Human Body Using Machine Learning Methods
|
|
2020 |
|
|
[25]
|
Decision Tree Method Using for Fetal State Classification from Cardiotography Data
|
|
2020 |
|
|
[26]
|
Intelligent Antenatal Fetal Monitoring Model Based on Adaptive Neuro-Fuzzy Inference System Through
|
|
2020 |
|
|
[27]
|
Automatic Classification of Antepartum Cardiotocography Using Fuzzy Clustering and Adaptive Neuro-Fuzzy Inference System
|
|
2020 |
|
|
[28]
|
Classification and Feature Selection Approaches for Cardiotocography by Machine Learning Techniques
|
|
2020 |
|
|
[29]
|
Fetal Health Status Classification Using MOGA-CD Based Feature Selection Approach
|
|
2020 |
|
|
[30]
|
Intelligent Antenatal Fetal Monitoring Model Based on Adaptive Neuro-Fuzzy Inference System Through Cardiotocography
|
|
2020 |
|
|
[31]
|
Cardiotocogram Data Classification Using Random Forest Based Machine Learning Algorithm
|
|
2019 |
|
|
[32]
|
Exploring Fetal Health Status Using an Association Based Classification Approach
|
|
2019 |
|
|
[33]
|
The Application of Machine Learning Models in Fetal State Auto-Classification Based on Cardiotocograms
|
|
2019 |
|
|
[34]
|
Cardiotocography Class Status Prediction Using Machine Learning Techniques
|
|
2019 |
|
|
[35]
|
A K-means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis
|
|
2019 |
|
|
[36]
|
SEM-based study for interpretability of intelligent prenatal fetal monitoring models
|
|
2019 |
|
|
[37]
|
Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth …
|
|
2019 |
|
|
[38]
|
Classification Of Cardiotocographies Using Naive Bayes Classifier
|
|
International Scientific and Vocational Studies Journal,
2019 |
|
|
[39]
|
FETAL HEART RATE CLASSIFICATION AND COMPARATIVE ANALYSIS USING CARDIOTOCOGRAPHY DATA AND KNOWN CLASSIFIERS
|
|
2019 |
|
|
[40]
|
Imbalanced Cardiotocography Multi-classification for Antenatal Fetal Monitoring Using Weighted Random Forest
|
|
2019 |
|
|
[41]
|
Prediction of Fetal Distress Using Linear and Non-linear Features of CTG Signals
|
|
2019 |
|
|
[42]
|
An Integrated Firefly Algorithm with K-Nearest Neighbor for Cardiotocography Classification
|
|
2019 |
|
|
[43]
|
Comparative Performance Exploration and Prediction of Fibrosis, Malign Lymph, Metastases, Normal Lymphogram Using Machine Learning Method
|
|
2019 |
|
|
[44]
|
Cardiotocography Class Status Prediction Using Machine Learning Techniques.
|
|
2019 |
|
|
[45]
|
Modeling fetal morphologic patterns through cardiotocography data: Decision tree-based approach
|
|
2018 |
|
|
[46]
|
Cardiotocography Data Set Classification with Extreme Learning Machine
|
|
International Conference on Advanced Technologies, Computer Engineering and Science,
2018 |
|
|
[47]
|
Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets
|
|
Information Sciences,
2018 |
|
|
[48]
|
Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces
|
|
Computers in Biology and Medicine,
2018 |
|
|
[49]
|
A study of artificial neural network training algorithms for classification of cardiotocography signals
|
|
Bitlis Eren University Journal of Science and Technology,
2017 |
|
|
[50]
|
Modelling Characteristics of Eye Movement Analysis for stress detection–Performance Analysis using Decision tree approach
|
|
International Journal of Engineering and Innovative Technology (IJEIT),
2017 |
|
|
[51]
|
A Bio Inspired Approach for Cardiotocogram Data Classification
|
|
International Journal of Organizational and Collective Intelligence (IJOCI),
2017 |
|
|
[52]
|
Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms
|
|
2017 |
|
|
[53]
|
Modelling Fetal Morphologic Patterns Through Cardiotocography Data: Decision Tree Based Approach
|
|
2017 |
|
|
[54]
|
Decision tree to analyze the cardiotocogram data for fetal distress determination
|
|
2017 |
|
|
[55]
|
Kardiyotokografi işaretlerinin analizi ve makine öğrenmesi teknikleri ile sınıflandırılması
|
|
2017 |
|
|
[56]
|
Prediksi Kelahiran Bayi secara Prematur dengan Menggunakan Algoritma C. 45 Berbasis Particle Swarm Optimization
|
|
2016 |
|
|
[57]
|
Application of Machine Learning Techniques to classify Fetal Hypoxia
|
|
2016 |
|
|
[58]
|
Methods of classification of the cardiotocogram
|
|
2016 |
|
|
[59]
|
Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
|
|
Journal of Computer and Communications,
2016 |
|
|
[60]
|
Classification model for cardiotocographies
|
|
2016 |
|
|
[61]
|
Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks
|
|
Journal of Medical and Biological Engineering,
2016 |
|
|
[62]
|
A Decision Support System for Determination of Fetal Well-Being from Cardiotocogram Data
|
|
2016 |
|
|
[63]
|
KARDİOTOKOGRAM VERİSİNDEN FETAL İYİLİK HALİNİN BELİRLENMESİ İÇİN BİR KARAR DESTEK SİSTEMİ
|
|
Uluda? University Journal of The Faculty of Engineering,
2016 |
|
|
[64]
|
ALGORITMA C4. 5 BERBASIS DECISION TREE UNTUK PREDIKSI KELAHIRAN BAYI PREMATUR
|
|
Konferensi Nasional Ilmu Pengetahuan dan Teknologi (KNIT),
2015 |
|
|
[65]
|
Effectiveness of an Education Program Concerning Cardiotocography on Nurse-Midwife's knowledge in Maternity Hospitals at Baghdad City
|
|
IOSR Journal of Nursing and Health Science (IOSR-JNHS),
2015 |
|
|
[66]
|
Decision Trees Based Classification of Cardiotocograms Using Bagging Approach
|
|
2015 13th International Conference on Frontiers of Information Technology (FIT),
2015 |
|
|
[67]
|
Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal
|
|
2015 |
|
|
[68]
|
Effectiveness of an education program concerning cardiotocography on nurse-midwife's knowledge in maternity hospitals at Baghdad City'
|
|
2015 |
|
|
[69]
|
Geographical Indications in India: Hitherto and Challenges
|
|
Research Journal of Pharmaceutical, Biological and Chemical Sciences,
2014 |
|
|